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Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

Abstract The incidence rate of chronic heart failure has increased significantly in China and has a large impact on patients’ lives. The purpose of the study was to validate the Chronic Heart Failure Health-Related Quality of Life Questionnaire (CHFQOLQ-20) cross-culturally and test its reliability and validity in patients with chronic heart failure. This study was conducted by investigating 622 patients with chronic heart failure from August 2023 to January 2024, and the data were tested for validity using SPSS 25.0 and AMOS 23.0. The final psychometric validation of the CHFQOLQ-20 was performed. The Chinese version of the CHFQOLQ-20 had a Cronbach’s α of 0.908, a split-half reliability of 0.870, and a retest reliability of 0.892. An exploratory factor analysis yielded four common factors with a cumulative ANOVA of 59.355%, and the validated factor analysis with one correction resulted in a better-fitting model (χ2/df = 2.020, RMSEA = 0.057, CFI = 0.920, TLI = 0.907). The scale is a reliable assessment tool that can be used in future clinical practice.

Abstract Chromatin spatial organization plays a crucial role in gene regulation. Recently developed and prospering multiplexed DNA FISH technologies enable direct visualization of chromatin conformation in the nucleus. However, incomplete data caused by limited detection efficiency can substantially complicate and impair downstream analysis. Here, we present SnapFISH-IMPUTE that imputes missing values in multiplexed DNA FISH data. Analysis on multiple published datasets shows that the proposed method preserves the distribution of pairwise distances between imaging loci, and the imputed chromatin conformations are indistinguishable from the observed conformations. Additionally, imputation greatly improves downstream analyses such as identifying enhancer-promoter loops and clustering cells into distinct cell types. SnapFISH-IMPUTE is freely available at https://github.com/hyuyu104/SnapFISH-IMPUTE .

Abstract The flexibility and stability of transparent electrodes play a crucial role in the growing popularity of flexible devices, especially in potential wearable electronics. To date, various solution-coating techniques have been developed for fabricating silver nanowire (AgNW) flexible bioelectronics. However, achieving the orderly distributed patterns of AgNW without undesirable aggregations still poses a grand challenge. Here, an approach to realize regular patterned ultrathin AgNW networks on a freestanding electrospun PVDF-TrFE frame by evaporation-induced self-assembly is proposed. The patterning mechanism of evaporating AgNW colloidal suspension is investigated from experimental and theoretical analysis. The influence of evaporation-induced flow inside colloidal freestanding membranes on forming regular square hole-shaped arrays, selective deposition of AgNW, and aligning them along the artificial pinning array are addressed. Owing to the orderly arrangement of AgNW networks, the resultant flexible electrode achieves ultrathin thickness (about 5 μm), high optical transmittance (87.8%), and low sheet resistance (8.4 Ω·sq−1) with a relatively low dosage of AgNW (9 μg·cm−2). The electrode exhibits excellent durability during cyclic bending (50,000 times) and stretching (50% strain). The resistance remains virtually unchanged during 200 days in everyday environments. Furthermore, the excellent conformability and breathability of the flexible transparent electrode attached to the human skin demonstrates its potential application as an e-skin sensor. Our findings reliably urge a simple approach to underscore better outcomes with effective patterns by self-assembly of AgNW for highly conformal wearable electronics.

Abstract Background To analyze the effects and pathways of factors such as psychological capital, family functioning, and sources of meaning in life on the level of self-management in elderly patients with chronic diseases and to provide a basis for the development of relevant nursing interventions in the future. Methods Convenience sampling was used to select elderly patients with chronic diseases who underwent medical checkups and consultations at three community hospitals in Jinzhou city from March 2023 to October 2023, and the self-designed General Information Questionnaire (GIS), Psychological Capital of the Elderly Scale (PCE), Family Functioning Index Questionnaire (APGAR), Sources of Meaning of Life Scale for Older Adults(SMSE), and Self-Management Behavior of Chronic Patients Scale (SMCS) were used. SPSS 26.0 was used for data entry, one-way analysis, Pearson correlation analysis, and multiple linear regression were used to analyze the data, and Amos 17.0 was used to construct the structural equation model. Results A total of 355 elderly patients with chronic diseases were included, and their self-management score was 74.75 ± 12.93, which was moderate. The results of the influencing factor analysis showed that the influencing factors of the self-management level of elderly chronic disease patients were age, years of illness, psychological capital, family functioning, and sources of meaning in life (p

Abstract Background Diabetic patients’ oral health concerns are a reality for every diabetic patient in China. The attitudes of diabetic patients toward early identification of oral literacy influence oral literacy in diabetes patients. Diabetes patients' oral health literacy is critical for providing focused education and therapies. However, no instrument exists to measure oral health literacy in Chinese diabetic patients. In this study, the English version of the oral health literacy among people living with diabetes (OHLD) scale was cross-culturally validated to provide a reliable tool for assessing the oral health literacy of diabetic patients in China. Objective The oral literacy among people living with diabetes (OHLD) scale was Chineseized and its reliability and validity tested, and the OHLD scale was revised to test the reliability and validity of the Chinese version of the OHLD scale and to provide a tool for assessing the oral health literacy of diabetic patients in China. Methods A modified version of the Brislin translation model was used, cross-cultural adaptation was performed through expert consultation and pre-survey, and expert opinion was used to assess content validity to form the Chinese version of the Oral Health Literacy Scale for Diabetic Patients, which was administered to 420 diabetic patients in two tertiary hospitals in Liaoning Province from March to August 2023. The reliability of the scale was tested. SPSS 25.0 and AMOS 23.0 were used to analyze the data. Results The Chinese version of the OHLD scale consisted of three dimensions and 10 entries. Structure of the validity analysis: three factors were extracted from the exploratory factors with a cumulative variance contribution of 79.794%; Content validity results:An item’s content validity index (I-CVI) was 0.857 to 1 at the entry level, and the content validity index of the scale (S-CVI) was 0.928 at the scale level. The results of the reliability analyses were: the Cronbach's alpha coefficient for the total scale was 0.908; the Cronbach's alpha coefficients for the factors and dimensions were 0.853 to 0.922; the split-half reliability was 0.827; and the test–retest reliability was 0.848. The results of the validation factor analysis showed that (χ2/df) was 1.430, the root mean square of the error of approximation (RMSEA) was 0.045, the comparative fit index (CFI) was 0.989, and the Tucker Lewis index (TLI) was 0.985, which showed that the model had an overall good fit. Conclusion The Chinese version of the OHLD scale has good reliability and validity and can be used as a valid tool for assessing diabetes mellitus patients in China.

In this work, we present the novel application of SiNx stress-engineering techniques for the suppression of short-channel effects in AlGaN/GaN high-electron-mobility transistors (HEMTs), accompanied by a comprehensive analysis of the underlying mechanisms. The compressive stress SiNx passivation significantly enhances the barrier height at the heterojunction beneath the gate, maintaining it above the quasi-Fermi level even as Vds rises to 20 V. As a result, in GaN devices with a gate length of 160 nm, the devices with compressive stress SiNx passivation exhibit significantly lower drain-induced barrier lowering (DIBL) factors of 2.25 mV/V, 2.56 mV/V, 4.71 mV/V, and 3.84 mV/V corresponding to drain bias voltages of 5 V, 10 V, 15 V, and 20 V, respectively. Furthermore, as Vds increases, there is an insignificant degradation in transconductance, subthreshold swing, leakage current, or output conductance. In contrast, the devices with stress-free passivation show relatively higher DIBL factors (greater than 20 mV/V) and substantial degradation in pinch-off performance and output characteristics. These results demonstrate that the SiNx stress-engineering technique is an attractive technique to facilitate high-performance and high-reliability GaN-based HEMTs for radio frequency (RF) electronics applications.

Abstract Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial‐time (NP)‐complete or NP‐hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous‐time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device‐algorithm co‐design method is proposed for fast sub‐optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network‐based Ising machine without second‐harmonic injection locking (SHIL) is devised to solve Max‐cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System‐wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max‐cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

Abstract Curved displays can adjust their shape to accommodate different objects and are used in electronics and decorative lighting. Due to the immutable pixel spacing, existing commercial curved displays are flexible but not compatible with undevelopable surfaces. Inspired by kirigami and auxetic structures, we propose an approach that combines luminescent elements and rotating square tessellations to create a stretchable, arbitrary curve adaptive display. We connect square islands by vertical interconnects to relieve the stress concentration and provide extra deformation patterns. The vertical interconnects are patterned on a flexible printed circuit board (FPCB) using laser cutting and folded up via specially designed molds. Further, the freed-up space by folded interconnects allows the structure to be compressed. A prototype stretchable display is demonstrated that it can maintain electrical performance under biaxial strain and adapt to different Gaussian curvature surfaces, including cylindrical, spherical, saddle and arbitrary surfaces. Theoretical models and finite element calculations are established to describe the tensile behavior of the structures under different boundary conditions and agree with the experimental results. This proposed technology paves a feasible solution of mass production of adaptive curved displays and sets the trend for the next-generation display.

This paper proposes a physical charge-based analytical MOSFET threshold voltage model that explicitly incorporates interface-trapped charges which have been identified as playing a dominant role in defining threshold voltage trends in deep cryogenic temperatures. The model retains standard threshold voltage definition by various charges across the MOSFET capacitor while being analytical in its form, therefore, suitable for cryogenic CMOS VLSI design. Consequently, a model covering each and all above characteristics is proposed for the first time. Excellent fit between the model and measurement data from 180-nm bulk foundry devices is shown from room temperature to 4 K.

This study investigates the DC and RF performance of RF GaN High Electron Mobility Transistors (HEMTs) subjected to surface pretreatments by N2 and N2O plasma. The filling of nitrogen vacancies or the passivation effect introduced by the thin GaON layer result in enhanced DC characteristics and RF performance for devices treated with nitrogen-based plasma. Compared to the untreated device, the device treated with N2 plasma exhibited a significant improvement in performance, i.e., the saturated current increased by approximately 16%, the characteristic frequency (fT) had an increase of 27.6 GHz, the maximum oscillating frequency (fmax) increased by 60.4 GHz. Furthermore, the breakdown voltage had a 10.7% increase, and the dynamic/static on-resistance ratio decreased from 1.34 to 1.18. These results highlight the potential of nitrogen-based plasma treatments in improving the performance of RF GaN HEMTs.

Abstract Stretchable and curved electronic devices are a promising technology trend due to their remarkable advantages. Many approaches have been developed to manufacture stretchable and curved electronics. Here, to allow such electronics to better serve practical applications, ranging from wearable devices to soft robotics, we propose a novel vertical serpentine conductor (VSC) with superior electrical stability to interconnect functional devices through a silicon-based microfabrication process. Conformal vacuum transfer printing (CVTP) technology was developed to transfer the networked platform onto complex curved surfaces to demonstrate feasibility. The mechanical and electrical performance were investigated numerically and experimentally. The VSC interconnected network provides a new approach for stretchable and curved electronics with high stretchability and reliability.

In this work, the DC performance and RF characteristics of GaN-based high-electron-mobility transistors (HEMTs) using the SiNx stress-engineered technique were systematically investigated. It was observed that a significant reduction in the peak electric field and an increase in the effective barrier thickness in the devices with compressive SiNx passivation contributed to the suppression of Fowler–Nordheim (FN) tunneling. As a result, the gate leakage decreased by more than an order of magnitude, and the breakdown voltage (BV) increased from 44 V to 84 V. Moreover, benefiting from enhanced gate control capability, the devices with compressive stress SiNx passivation showed improved peak transconductance from 315 mS/mm to 366 mS/mm, along with a higher cutoff frequency (ft) and maximum oscillation frequency (fmax) of 21.15 GHz and 35.66 GHz, respectively. Due to its enhanced frequency performance and improved pinch-off characteristics, the power performance of the devices with compressive stress SiNx passivation was markedly superior to that of the devices with stress-free SiNx passivation. These results confirm the substantial potential of the SiNx stress-engineered technique for high-frequency and high-output power applications, which are crucial for future communication systems.

Abstract This work presents an E(3) equivariant graph neural network called HamGNN, which can fit the electronic Hamiltonian matrix of molecules and solids by a complete data-driven method. Unlike invariant models that achieve equivariance approximately through data augmentation, HamGNN employs E(3) equivariant convolutions to construct the Hamiltonian matrix, ensuring strict adherence to all equivariant constraints inherent in the physical system. In contrast to previous models with limited transferability, HamGNN demonstrates exceptional accuracy on various datasets, including QM9 molecular datasets, carbon allotropes, silicon allotropes, SiO2 isomers, and BixSey compounds. The trained HamGNN models exhibit accurate predictions of electronic structures for large crystals beyond the training set, including the Moiré twisted bilayer MoS2 and silicon supercells with dislocation defects, showcasing remarkable transferability and generalization capabilities. The HamGNN model, trained on small systems, can serve as an efficient alternative to density functional theory (DFT) for accurately computing the electronic structures of large systems.

Abstract With the annual increases in the morbidity and mortality rates of tumors, the use of biomarkers for early diagnosis and real-time monitoring of tumor cells is of great importance. Biomarkers used for tumor cell detection in body fluids include circulating tumor cells, nucleic acids, protein markers, and extracellular vesicles. Among them, circulating tumor cells, circulating tumor DNA, and exosomes have high potential for the prediction, diagnosis, and prognosis of tumor diseases due to the large amount of valuable information on tumor characteristics and evolution; in addition, in situ monitoring of telomerase and miRNA in living cells has been the topic of extensive research to understand tumor development in real time. Various techniques, such as enzyme-linked immunosorbent assays, immunoblotting, and mass spectrometry, have been widely used for the detection of these markers. Among them, the detection of tumor cell markers in body fluids based on electrochemical biosensors and fluorescence signal analysis is highly preferred because of its high sensitivity, rapid detection and portable operation. Herein, we summarize recent research progress in the detection of tumor cell biomarkers in body fluids using electrochemical and fluorescence biosensors, outline the current research status of in situ fluorescence monitoring and the analysis of tumor markers in living cells, and discuss the technical challenges for their practical clinical application to provide a reference for the development of new tumor marker detection methods.

Abstract The development of high-performance oxide-based transistors is critical to enable very large-scale integration (VLSI) of monolithic 3-D integrated circuit (IC) in complementary metal oxide semiconductor (CMOS) backend-of-line (BEOL). Atomic layer deposition (ALD) deposited ZnO is an attractive candidate due to its excellent electrical properties, low processing temperature below copper interconnect thermal budget, and conformal sidewall deposition for novel 3D architecture. An optimized ALD deposited ZnO thin-film transistor achieving a record field-effect and intrinsic mobility (µ FE /µ o ) of 85/140 cm2/V·s is presented here. The ZnO TFT was integrated with HfO2 RRAM in a 1 kbit (32 × 32) 1T1R array, demonstrating functionalities in RRAM switching. In order to co-design for future technology requiring high performance BEOL circuitries implementation, a spice-compatible model of the ZnO TFTs was developed. We then present designs of various ZnO TFT-based inverters, and 5-stage ring oscillators through simulations and experiments with working frequency exceeding 10’s of MHz.

Abstract Multiplexed DNA fluorescence in situ hybridization (FISH) imaging technologies have been developed to map the folding of chromatin fibers at tens of nanometers and up to several kilobases in resolution in single cells. However, computational methods to reliably identify chromatin loops from such imaging datasets are still lacking. Here we present a Single-Nucleus Analysis Pipeline for multiplexed DNA FISH (SnapFISH), to process the multiplexed DNA FISH data and identify chromatin loops. SnapFISH can identify known chromatin loops from mouse embryonic stem cells with high sensitivity and accuracy. In addition, SnapFISH obtains comparable results of chromatin loops across datasets generated from diverse imaging technologies. SnapFISH is freely available at https://github.com/HuMingLab/SnapFISH .

Abstract Doped‐metal oxide‐based memristors, with the potential for improved switching performance and capability for multi‐bit information storage, are attractive candidates in the implementation of artificial neural network (ANN) hardware systems. However, performance and process considerations such as switching behavior and complementary‐metal‐oxide‐semiconductor (CMOS) process compatibility remain a challenge. This study shows that amorphous Zr‐doped HfO2 (HZO) memristors fabricated via a co‐sputtering approach improve the switching performance by providing a controllable knob to modulate defects in the switching layer. At the same time, it satisfies the CMOS process compatibility requirements for industry adoption. HZO memristors with optimized stoichiometry exhibit 30% reduced switching voltages and 50% faster switching as compared to control HfO2 memristors. Concurrently, this study shows that high linearity analog states tuning is achievable via a programming scheme that utilizes voltage pulses with increasing amplitudes. This study further shows via simulation evaluation that HZO memristors implemented in a self‐organizing‐map (SOM) network for Fashion MNIST database classification, achieve an accuracy of 92% with short training cycles. The results thus pave a potential pathway for further development of CMOS process compatible HZO memristors for use in future storage and computing applications.

Abstract Thermoelectric devices (TEDs) show great potential for waste heat energy recycling and sensing. However, existing TEDs cannot be self‐adapted to the complex quadratic surface, leading to significant heat loss and restricting their working scenario. Here, surface‐conformable origami‐TEDs (o‐TEGs) are developed through programmable crease‐designed origami substrates and the screen‐printing TE legs. Compared with “π” structured TEDs, the origami design (with heat conductive materials) changed the heat‐transferring direction of the laminated TE legs, resulting in an enhancement in enlarging ΔT/THot and Vout by 5.02 and 3.51 times. Four o‐TEDs with different creases designs are fabricated to verify the heat recycling ability on plane and central quadratic surfaces. Demonstrating a high Vout density (up to 0.98 −2at ΔT of 50 K) and good surface conformability, o‐TEDs are further used in thermal touch panels attached to multiple surfaces, allowing information to be wirelessly transferred on a remote display via finger‐writing.

Chromosomal fusion is a significant form of structural variation, but research into algorithms for its identification has been limited. Most existing methods rely on synteny analysis, which necessitates manual annotations and always involves inefficient sequence alignments. In this paper, we present a novel alignment-free algorithm for chromosomal fusion recognition. Our method transforms the problem into a series of assignment problems using natural vectors and efficiently solves them with the Kuhn-Munkres algorithm. When applied to the human/gorilla and swamp buffalo/river buffalo datasets, our algorithm successfully and efficiently identifies chromosomal fusion events. Notably, our approach offers several advantages, including higher processing speeds by eliminating time-consuming alignments and removing the need for manual annotations. By an alignment-free perspective, our algorithm initially considers entire chromosomes instead of fragments to identify chromosomal structural variations, offering substantial potential to advance research in this field.

Background: The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. Methods: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared. Results: The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921. Conclusion: In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.

Abstract Background Apatinib (YN968D1) is the first small-molecule-targeting drug with anti-tumor activity created in China for the treatment of advanced gastric cancer (GC) and hepatocellular carcinoma (HCC). It showed significant variation in the efficacy for treating cancers, including advanced non-squamous non-small-cell lung cancer (NSCLC). Whether its efficacy could be optimized by subgrouping patients with certain genetic variation remains elusive. Methods Here, we firstly used kinase screening to identify any possible target of apatinib against 138 kinases. The effects of apatinib on proliferation rates, cell cycle, cell apoptosis, and cell migration on cancer cell lines were analyzed; the in vitro potential pathways of apatinib on cancer cell lines were screened. The effect of apatinib on mouse cancer models in vivo was also analyzed. Results Based on HCC364 cells with BRAF V600E mutation, we have shown that apatinib could inhibit their growth, migration, cell cycle, and induce their apoptosis. Based on mice with transplanted HCC364 cells, we have also shown that apatinib could inhibit the tumor growth. Based on immunohistochemistry, we have demonstrated that apatinib could suppress the phosphorylation of mitogen-activated protein kinase/extracellular signal-regulated kinase and extracellular regulated protein kinases. This may account at least part of the apatinib’s inhibitory effect on HCC364 cancer cells. Conclusions BRAF V600E protein kinase is a target of apatinib by kinase screening. We have demonstrated that apatinib can effectively inhibit tumor cells with BRAF V600E mutation by in vitro and in vivo experiments. Our results have demonstrated that targeting BRAF V600E mutation, apatinib appears to be effective and safe for treating NSCLC and possibly other cancers with the same mutation.

Background The media play an important role in health promotion and disease prevention, while at the same time, a variety of mixed health messages in the media are beginning to pose new challenges to them. However, there is a lack of media health literacy (MHL) assessment tools in China. Therefore, the purpose of this study was to translate the Media Health Literacy (MeHLit) questionnaire into Chinese and to assess its psychometric properties. Methods This cross-sectional study was conducted from October to December 2022, and a methodological study of the translation and validation of the MeHLit questionnaire was conducted. Results As a result of an extensive translation and cultural adaptation process, the final MeHLit questionnaire was developed, which includes five dimensions and 21 items. Cronbach's α value of the questionnaire was 0.859, and Cronbach's α value of the dimensions ranged from 0.776 to 0.911, which is fairly good. As a result, the test–retest reliability coefficient and the split-half reliability coefficient of the questionnaire are both equal to 0.907. Its content validity index was 0.946, suggesting a reasonable level of content validity. Through exploratory factor analysis, a five-factor structure was identified based on the eigenvalues, total variance explained, and scree plot. As a result of the validation factor analysis, all recommended fit indicators were appropriate. Conclusion The Chinese version of the MeHLit questionnaire has been successfully introduced in China. It has shown good psychometric properties among the Chinese public and can be used as a tool to evaluate MHL in health screening.

Abstract Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In‐memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first‐order dynamics. Here, a second‐order memristor is experimentally demonstrated using yttria‐stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second‐order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL‐based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second‐order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high‐efficiency, compact footprint, and hardware‐encoded plasticity.

Abstract Wearable strain sensors have been widely used for human activity monitoring. Most reported strain sensors have mainly focused on material engineering, high stretchability and large gauge factors. Few works have focused on strain sensor’s robustness and reliability, including low hysteresis, good long-term stability, good electrode material stability, and low coupling effects under multi-input signals, which are the factors that limit practical strain sensor applications. To develop a high-performance strain sensor, we propose a flexible capacitive sensor structure with three-dimensional (3D) interdigital electrodes fabricated by vertically aligned carbon nanotubes. Compared with a traditional resistive strain sensor and a capacitive strain sensor with vertical sandwich electrodes, a strain sensor with horizontal parallel interdigital electrodes can benefit from low cross talk in terms of the normal force and improve substrate transparency. Additionally, embedding 3D electrodes into the substrate improves ultrahigh robustness with a low-pressure coupling effect under normal force. Moreover, compared with other reported works, the electrode variation under strain is small (less than 1.6%), which means that the perturbation of inert properties on device performance is small. Finally, the fabricated strain sensor achieves an ultralow hysteresis (0.35%), excellent pressure-insensitive performance (less than 0.8%), fast response (60 ms), good long-term stability, and good transparency. As an application example, a flexible strain sensor was successfully demonstrated as a wearable device for the precise monitoring of different types of human activities, including bending of the finger, knee, elbow, wrist, and neck with large strain signals and small strain signals generated by a mouth-opening activity. This excellent performance indicates that the flexible strain sensor is a promising candidate for human motion detection, soft robotics, and medical care.

Background Active and effective coping is contributive to the prevention of disease recurrence and delay the development of complications. It has been reported that more than 50% of young and middle-aged patients with acute myocardial infarction (AMI) adopt a negative response to the disease, which negatively influences their prognosis and quality of life. But the underlying personal and sociocultural factors associated with the choice of disease coping styles in this population are still unclear. Self-regulation theory has been extensively used in studies of behaviors, comprehensive intervention, and health promotion in patients with stroke, chronic heart failure, or chronic obstructive pulmonary disease. Currently, there is a lack of research on disease coping style and its influencing factors in young and middle-aged patients with first AMI using the framework of this theory. Objective To explore disease coping styles in young and middle-aged patients with first AMI using the four-component framework of self-regulation theory. Methods Purposive sampling was used to recruit young and middle-aged patients with first AMI from the First Affiliated Hospital of Jinan University from January to June 2021. They were invited to attend an individual, semi-structured interview guided using an outline determined based on our research team members and experts' consensuses on the analysis results of a relevant pre-interview. The interview was conducted till data saturation, and the interview results were analyzed using Colaizzi's phenomenological methodology. Results Altogether, the study included 15 cases. Two themes arose from the results of interview with them: (1) the coexistence of positive coping (adjustment of mentality, emotional control, positive attitude towards AMI, self-motivation, lifestyle change, exploring a new way to achieve psychological balance) and negative coping (tolerance, avoidance, reluctant acceptance, submission, concealment) ; (2) influencing factors of coping styles (social support, prognosis estimation, behavioral benefit acquisition, and the powder of role models) . Conclusion Both positive and negative methods for coping with first AMI were found in the young and middle-aged patient population, and the negative coping cannot be overlooked. To improve the physical and mental recovery of these patients via reducing patients' negative coping styles and increasing their positive coping styles, medical workers should guide the patients and their families to make full use of the social support system, provide them with individualized health education and education on benefits of healthy behaviors through multiple ways, and set a good example of leadership, motivation and supervision.

The piezoelectric thin film composed of single-crystal lithium niobate (LiNbO3) exhibits a remarkably high electromechanical coupling coefficient and minimal intrinsic losses, making it an optimal material for fabricating bulk acoustic wave resonators. However, contemporary first-order antisymmetric (A1) Lamb mode resonators based on LiNbO3 thin films face specific challenges, such as inadequate mechanical stability, limited power capacity, and the presence of multiple spurious modes, which restrict their applicability in a broader context. In this paper, we present an innovative design for A1 Lamb mode resonators that incorporates a support-pillar structure. Integration of support pillars enables the dissipation of spurious wave energy to the substrate, effectively mitigating unwanted spurious modes. Additionally, this novel approach involves anchoring the piezoelectric thin film to a supportive framework, consequently enhancing mechanical stability while simultaneously improving the heat dissipation capabilities of the core.

Abstract Background The effect of depressive symptoms on hip fracture (HF) and falls among the Chinese population is unclear. This study aims to examine the prospective association between depressive symptoms and HF as well as fall accidents in a nationally representative Chinese population. Methods We used data from 10,596 participants in the Chinese Health and Retirement Longitudinal Study (from 2011 to 2018) who were aged ≥45 years and had no HFs at baseline. Depressive symptoms were assessed using the 10-item version of the Center for Epidemiological Studied Depression scale (cutoff for distinguishing high versus low at ≥12). Logistic regression analyses adjusted for demographic characteristics, lifestyle factors and physical comorbidities were performed. Results For the analysis of baseline depressive symptoms and HF, 399 (3.8%) participants reported HF accidents in the following 7-year period. Individuals with elevated depressive symptoms at baseline experienced a markedly higher HF risk (odds ratio [OR] = 1.33, 95% confidence interval [CI] = 1.06–1.67) than those without elevated depressive symptoms, after adjusting for a wide range of potential confounders. For the analysis of baseline depressive symptoms and falls, 3974 (37.5%) experienced fall accidents during the follow-up. The presence of elevated depressive symptoms was independently associated with an increased risk of fall events (OR = 1.21, 95% CI = 1.10–1.33). These associations were consistent across multiple characteristics. Conclusions In conclusion, elevated depressive symptoms were associated with an increased risk of HF and falls, which may have considerable clinical and preventive implications.

Background Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) have proved to have a certain significance in predicting the severity of pancreatitis, however, at present, there are few relevant studies on the diagnostic and predictive value of NLR and PLR for liver injury in biliary acute pancreatitis (BAP) . Objective To explore the correlation between NLR and PLR in the severity of BAP and the concurrent acute liver injury (ALI) . Methods A total of 142 patients with BAP admitted to Emergency Department of the Second Affiliated Hospital of Nanchang University from March 2019 to March 2021 were selected and divided into mild (MAP) /moderately (MSAP) group (n=98) and severe (SAP) group (n=44) according to Atlanta classification. According to whether the liver function is damaged or not, they were divided into ALI group (n=92) and non-ALI group (n=50) . The ALI group was further divided into hepatocyte type liver injury subgroup (n=1) , bile duct type liver injury subgroup (n=16) and mixed type liver injury subgroup (n=75) . The general condition and clinical data of patients were collected, and the predictive value of NLR and PLR on the severity of BAP and concurrent ALI was explored by the ROC curve and binary Logistic regression analysis. Results The NLR and PLR in MAP/MSAP group were lower than those in SAP group (P0.05) . The area under the ROC curve of NLR, PLR and their joint prediction of SAP was 0.809, 0.667, 0.809, respectively. The area under the ROC curve of NLR, PLR and their joint prediction of ALI in BAP was 0.774, 0.767, 0.806, respectively. The area under the ROC curve of NLR, PLR and their joint prediction of the occurrence of cholangiocytic liver injury in BAP was 0.813, 0.742, 0.861, respectively. The area under ROC curve of NLR, PLR and their joint prediction of mixed liver injury in BAP was 0.763, 0.770 and 0.794 respectively. The results of binary Logistic regression analysis showed that elevated NLR was a risk factor for SAP〔OR=1.184, 95%CI (1.102, 1.271) , P

Abstract Autonomous one‐shot on‐the‐fly learning copes with the high privacy, small dataset, and in‐stream data at the edge. Implementing such learning on digital hardware suffers from the well‐known von‐Neumann and scaling bottlenecks. The optical neural networks featuring large parallelism, low latency, and high efficiency offer a promising solution. However, ex‐situ training of conventional optical networks, where optical path configuration and deep learning model optimization are separated, incurs hardware, energy and time overheads, and defeats the advantages in edge learning. Here, we introduced a bio‐inspired material‐algorithm co‐design to construct a hydrogel‐based optical Willshaw model (HOWM), manifesting Hebbian‐rule‐based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto‐chemical reactions. We first employed the HOWM as an all optical in‐sensor AI processor for one‐shot pattern classification, association and denoising. We then leveraged HOWM to function as a ternary content addressable memory (TCAM) of an optical memory augmented neural network (MANN) for one‐shot learning the Omniglot dataset. The HOWM empowered one‐shot on‐the‐fly edge learning leads to 1000× boost of energy efficiency and 10× boost of speed, which paves the way for the next‐generation autonomous, efficient, and affordable smart edge systems.