Chemigenetic Encoding of Fluorescent Dyes Enables High-Fidelity and Wash-Free Imaging of Proteins in Live Cells.

Fluorescent dyes are indispensable chemical tools for protein labeling, yet their utility in live-cell imaging remains constrained by background fluorescence from off-target interactions. A chemigenetic strategy is presented that integrates synthetic dye chemistry with genetically encoded fluorescence to achieve high-fidelity, wash-free imaging of target proteins. By leveraging Förster resonance energy transfer (FRET) between fluorescent proteins (FPs) and Halo-tag dyes, a system is engineered where fluorescence emission depends on FPs donor excitation, ensuring only dyes bound to target protein are fluorescent, while nonspecifically bound dyes remain dark, enhancing the signal-to-noise ratio (SNR).

Association of greenness exposure with serum vitamin D status and effects of ambient particulate matter among pregnant women in early pregnancy.

Adequate vitamin D is essential for the health of both the mother and fetus, and it can be influenced by environmental factors. However, research on the associations between greenness exposure and vitamin D concentrations during pregnancy is limited. This retrospective birth cohort study, conducted from 2014 to 2018, assessed the greenness of residences using the satellite-derived normalised difference vegetation index (NDVI).

QSAN: A Near-Term Achievable Quantum Self-Attention Network.

Self-attention mechanism (SAM) is good at capturing the intrinsic connection between features to dramatically boost the performance of machine learning models. Nevertheless, the capability of SAM is not equipped with many current quantum machine learning (QML) models, thus confining their expansion on massive high-dimensional quantum data. To address the above problems, a quantum SAM (QSAM) consisting of a quantum logic similarity (QLS)-based quantum bit self-attention score matrix (QBSASM) is introduced to augment the data representation of SAM exponentially.

Naphthalimide-derived fluorogenic SNAP probe for real-time monitoring of protein degradation.

We developed the fluorogenic SNAP probe BGAN-8C to monitor protein degradation. It exhibited a 6-fold fluorescence enhancement upon binding with SNAP-tag. After the SNAP protein is degraded, the fluorescence of the released probe is quenched due to aggregation.

Validation of a non-invasive filling pressure (NIFP) device for measuring cardiac pressure and assessing congestion levels in patients with heart failure.

Background: Heart failure (HF) is a high-burden clinical syndrome characterized by intravascular and extravascular congestion, impacting patients' outcomes. Current diagnostic methods for assessing intravascular congestion, including right heart catheterization (RHC), have some limitations. There is a need for accurate, stable, and widely applicable non-invasive measurement methods to improve HF diagnosis and treatment.

Quantum secure direct communication in Internet of Vehicles.

The Internet of Vehicles (IoVs) is one of the most popular techniques among the applications of Internet of Things. The existing IoVs are mainly protected by public key cryptographic systems, which provide identity authentication and information security. Nevertheless, using the proposed Shor's algorithm, the security of all classical cryptographic schemes will be exposed to future quantum computer technologies.

Quantum Few-Shot Image Classification.

Few-shot learning algorithms frequently exhibit suboptimal performance due to the limited availability of labeled data. This article presents a novel quantum few-shot image classification methodology aimed at enhancing the efficacy of few-shot learning algorithms at both the data and parameter levels. Initially, a quantum augmentation image representation technique is introduced, leveraging the local phase of quantum states to support few-shot learning algorithms at the data level.

QKSAN: A Quantum Kernel Self-Attention Network.

The Self-Attention Mechanism (SAM) excels at distilling important information from the interior of data to improve the computational efficiency of models. Nevertheless, many Quantum Machine Learning (QML) models lack the ability to distinguish the intrinsic connections of information like SAM, which limits their effectiveness on massive high-dimensional quantum data. To tackle the above issue, a Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM.

Trends in cardiovascular risk factor prevalence, treatment, and control among US adolescents aged 12 to 19 years, 2001 to March 2020.

Background: Early-life cardiovascular risk factors (CVRFs) are known to be associated with target organ damage during adolescence and premature cardiovascular morbidity and mortality during adulthood. However, contemporary data describing whether the prevalence of CVRFs and treatment and control rates have changed are limited. This study aimed to examine the temporal trends in the prevalence, treatment, and control of CVRFs among US adolescents over the past 2 decades.

Pretrained Quantum-Inspired Deep Neural Network for Natural Language Processing.

Natural language processing (NLP) may face the inexplicable "black-box" problem of parameters and unreasonable modeling for lack of embedding of some characteristics of natural language, while the quantum-inspired models based on quantum theory may provide a potential solution. However, the essential prior knowledge and pretrained text features are often ignored at the early stage of the development of quantum-inspired models. To attacking the above challenges, a pretrained quantum-inspired deep neural network is proposed in this work, which is constructed based on quantum theory for carrying out strong performance and great interpretability in related NLP fields.

Association of high-sensitivity cardiac troponin T with all-cause and cardiovascular mortality in older adults with low lean mass: A 14.6-year longitudinal study.

Background: Current evidence on the association between high-sensitivity cardiac troponin T (hs-cTnT) levels and mortality in elderly sarcopenic patients is limited. This study aimed to investigate the association of serum hs-cTnT concentrations with all-cause and cardiovascular mortality in older adults with low lean mass (LLM) and without baseline cardiovascular disease.

Methods: This prospective cohort study included 369 older adults (representing 3.

Endogenous NO-release multi-responsive hollow mesoporous silica nanoparticles for drug encapsulation and delivery.

Novel multi-responsive drug delivery vehicles (CDs/PNVCL@HMSNs) were prepared by grafting amino-terminated poly (N-vinyl caprolactam) (PNVCL-NH) and amino-rich carbon dots (CDs) on the surface of aldehyde-functionalized HMSNs (HMSNs-CHO) via Schiff base reaction. The CDs were prepared from L-arginine and their surfaces were rich in guanidine. Doxorubicin (DOX) was loaded into the nanoparticles to form drug loaded vehicles (CDs/PNVCL@HMSNs-DOX) and the drug loading efficiency was 58.

Willingness of parents of 9-to-18-year-old females in China to vaccinate their daughters with HPV vaccine.

Objective: To assess knowledge about HPV and HPV vaccine, willingness to have their daughters receive HPV vaccine, and factors associated with knowledge and willingness among parents of females 9 to 18 years of age in China.

Methods: We conducted a cross-sectional survey of parents with daughters 9 to 18 years of age in four provinces of China using a self-administered questionnaire. We used multivariable regression analyses to determine factors associated with willingness vaccinate.

Parameterized Hamiltonian Learning With Quantum Circuit.

Hamiltonian learning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized Hamiltonian learning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for Hamiltonian learning is first created by decomposing unitary operators to excite the system evolution.

Effect of PM exposure on Vitamin D status among pregnant women: A distributed lag analysis.

Background: Serum vitamin D levels are associated with exposure to air pollution, however, the lagged effect of exposure to air pollution remains unknown in pregnant women.

Methods: Pregnant women who delivered at a maternity center in Shanghai, China, from 2015 to 2019 were included in the present study. The concentration of particulate matter 2.

An Approach to Cryptography Based on Continuous-Variable Quantum Neural Network.

An efficient cryptography scheme is proposed based on continuous-variable quantum neural network (CV-QNN), in which a specified CV-QNN model is introduced for designing the quantum cryptography algorithm. It indicates an approach to design a quantum neural cryptosystem which contains the processes of key generation, encryption and decryption. Security analysis demonstrates that our scheme is security.