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Abstract The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh–Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. Two different kinds of arrhythmia characterized by major anomalies of parameters a and $$\varepsilon$$ in the FHN model were simulated, and the synthetic ECG signals were obtained respectively. A multi-objective optimization method based on non-dominated sorting was incorporated into the crayfish optimization algorithm to optimize the key parameters in VMD, then a variational mode decomposition technique for ECG signal processing based on a multi-objective crayfish optimization algorithm (MOCOA-VMD) was proposed, wherein the spectral kurtosis and KL divergence were determined as the indicators for decomposition. The Pareto optimal front was generated by MOCOA and the intrinsic mode functions of VMD with the best combination of K and $$\alpha$$ were obtained. A deep attention model based on MOCOA-VMD was constructed for ECG signal classification. The ablation study was implemented to verify the effectiveness of the proposed signal decomposition method and deep attention modules. The performance of the model based on MOCOA-VMD achieves the best accuracy of 94.35%, much higher than the model constructed by modules of EEMD, VMD and CNN. Moreover, Bayesian optimization was carried out to fine-tune the hyperparameters batch size, learning rate, epochs, and momentum. After TPE optimization, the deep model’s performance achieved a maximum accuracy of 95.91%. The MIT-BIH arrhythmia database was further utilized for model validation, ascertaining its robustness and generalizability. The proposed deep attention modeling and classification strategy can help in arrhythmia signal processing and may offer inspiration for other signal processing fields as well.

Land surface temperature (LST) is a crucial parameter for representing the earth's surface energy balance. Thermal infrared remote sensing is the primary method for rapidly retrieving LST over large areas. The Chinese Atmospheric Environment Monitoring Satellite (DQ-1) is equipped with the wide swath imager (WSI), which includes three thermal infrared bands capable of providing global LST retrieval. This article introduces a nonlinear hybrid algorithm that combines the split-window (SW) algorithm and the temperature and emissivity separation (TES) algorithm, and the accuracies of the three algorithms, including hybrid, SW and TES algorithm are analyzed. The results demonstrated that the root mean square errors of LST for SW, TES, and hybrid algorithm are approximately 2.11, 1.78, and 1.64 K, with mean absolute errors (of 1.72, 1.40, and 1.21 K using in situ measurements from the SURFRAD sites. Cross-validation with moderate-resolution imaging spectroradiometer (MODIS) LST products showed that the hybrid algorithm outperforms the SW and TES algorithms in retrieving LST, achieving reductions in LST error of 0.43 and 0.16 K at the Qinghai Lake site, and 0.67 and 0.06 K at the Dunhuang site, respectively. In summary, this study demonstrates that the nonlinear hybrid algorithm can accurately estimate LST from DQ1/WSI data.

Abstract Temporomandibular disorders (TMD) intelligent diagnosis promises to elevate clinical efficiency and facilitate timely TMD management for patients. However, development of TMD intelligent diagnostic tools with high accuracy and sensitivity presents challenges, particularly in sensing minute deformations and ensuring rapid self-recovery. Here we report a biocompatible hydrogel electronic sensor with instantaneous self-recovery (within 2.1 s) and ultra-low detection limit (0.005% strain). It could efficiently diagnose disc displacement with reduction (DDwR) with satisfactory accuracy of 90.00%, and also had a clear indication of the typical clinical manifestations of DDwR and the timing of temporomandibular joint (TMJ) clicking, with a sensitivity of up to 100% in human compared to the diagnostic criteria for TMD (DC/TMD). Furthermore, a predictive model based on waveform features achieved 84.4% accuracy and 86% sensitivity, reducing dependence on physicians. In summary, the hydrogel sensor is expected to become a radiation-free, non-invasive, practical and effective tool for future TMD diagnosis.

Abstract Synthetic aperture radar (SAR) is crucial for military reconnaissance and remote sensing, but image quality can be affected by various factors, impacting target detection performance. Thus, pre-evaluation of SAR image quality is essential to filter out poor-quality images, optimize resource allocation, and enhance detection accuracy and efficiency. This paper proposes a comprehensive SAR image quality evaluation method combining objective and subjective approaches. Specifically, the processes encompassing the generation of a series of disturbed SAR images on the SAR ship detection dataset (SSDD), the calculation of various objective quality indicators for those images, and the assignment of a subjective quality label to each image through subjective evaluation. Based on the dataset constructed by the above evaluation methods, the IHHO-XGBoost model was developed. This model uses an improved harris hawk optimization (IHHO) algorithm to optimize extreme gradient boosting (XGBoost) hyperparameters. The IHHO algorithm effectively alleviates the problem of getting trapped in local optima by improving the escape energy calculation strategy and integrating the average difference evolution mechanism while maintaining the diversity of the population, showing significant advantages over the traditional HHO algorithm. Comparative experiments demonstrate the model’s superiority in SAR image quality evaluation. This study validates the scientificity and practicability of the proposed method, offering new tools for SAR image quality research.

Abstract Background Dyslipidemia is prominently associated with adverse outcomes in patients with coronary artery disease (CAD). The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is a novel comprehensive lipid index. However, limited evidence exists on the relationship of the NHHR with the risk of adverse outcomes in patients with CAD. This study aimed to explore the associations between the NHHR and adverse outcomes and identify the optimal NHHR ranges linked to the lowest adverse outcome risk in patients with CAD undergoing percutaneous coronary intervention (PCI). Methods Among 2253 patients with CAD undergoing PCI, 2251 with available total cholesterol and HDL-C levels were analyzed. Furthermore, all patients were classified into quintiles based on the NHHR. The primary outcome was the incidence of MACCEs, comprising cardiac mortality, acute myocardial infarction, stroke, and repeat revascularization. Multivariable logistic regression analysis was used to assess the relationship between the NHHR and MACCEs. Moreover, restricted cubic spline (RCS) analysis was performed to quantify nonlinearity. Lastly, the consistency between these associations was confirmed by conducting subgroup and interaction analyses. Results A total of 270 patients experienced MACCEs over a median follow-up of 29.8 months (interquartile range, 25.6–34 months). After adjustment for confounding variables, the adjusted ORs (95% CIs) of the patients in quintiles 2, 3, 4, and 5 were 0.79 (0.52–1.20), 0.64 (0.42–0.99), 1.00 (0.67–1.48), and 1.17 (0.74–1.64), respectively (reference group: quintile 1). Additionally, RCS analysis demonstrated a U-shaped relationship between the NHHR and MACCEs, with an inflection point at an NHHR of 3.119 using a two-piecewise regression model. This relationship was consistent across the various subgroups, while significant interactions were not observed in these associations.The ORs and 95% CIs to the left and right of the inflection point were 0.734 (0.551–0.978) and 1.231 (1.038–1.460), respectively. Conclusions This study reveals a U-shaped association between baseline NHHR and MACCE incidence in patients with CAD undergoing PCI.

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 During space flight, prolonged weightlessness stress exerts a range of detrimental impacts on the physiology and psychology of astronauts. These manifestations encompass depressive symptoms, anxiety, and impairments in both short-term memory and motor functions, albeit the precise underlying mechanisms remain elusive. Recent studies have revealed that hindlimb unloading (HU) animal models, which simulate space weightlessness, exhibited a disorder in memory and motor function associated with endogenous formaldehyde (FA) accumulation in the hippocampus and cerebellum, disruption of brain extracellular space (ECS), and blockage of interstitial fluid (ISF) drainage. Notably, the impairment of the blood-brain barrier (BBB) caused by space weightlessness elicits the infiltration of albumin and hemoglobin from the blood vessels into the brain ECS. However, excessive FA has the potential to form cross-links between these two proteins and amyloid-beta (Aβ), thereby obstructing ECS and inducing neuron death. Moreover, FA can inhibit N-methyl-D-aspartate (NMDA) currents by crosslinking NR1 and NR2B subunits, thus impairing memory. Additionally, FA has the ability to modulate the levels of certain microRNAs (miRNAs) such as miRNA-29b, which can affect the expression of aquaporin-4 (AQP4) so as to regulate ECS structure and ISF drainage. Especially, the accumulation of FA may inactivate the ataxia telangiectasia-mutated (ATM) protein kinase by forming cross-linking, a process that is associated with ataxia. Hence, this review presents that weightlessness stress-derived FA may potentially serve as a crucial catalyst in the deterioration of memory and motor abilities in the context of microgravity.

Abstract Background There are few studies on the safety of sub-maximal exercise testing of aerobic exercise in apparently healthy Chinese populations. The purpose of this study was to explore the frequency of exercise electrocardiography (ECG) abnormalities and the corresponding exercise intensities, as well as the associated influencing factors, during a symptom-limited stepwise incremental cardiopulmonary exercise test (CPET) in an apparently healthy Chinese population. Methods A cross-sectional study was done in four communities, one urban and one rural in the North (Beijing) and in the South (Hezhou, Guangxi) of China from 1 January 2017 to 31 December 2018, respectively. Total of 1642 participants was recruited, 918 were eligible and completed demographic indicators, routine blood indicators, physical activity status, symptom-limited CPET and exercise ECG were included in the analysis. Results Of the exercise ECG outcomes, 10 (1.1%) were positive and occurred at exercise intensities ≥ 62.50% heart rate reserve (HRR); 44 (4.8%) were equivocal and 864 (94.1%) were normal. Individuals with Cardiovascular Disease Risk Factor (CVDRF) = 3–4 were 2.6 times more likely to have a equivocal and abnormal exercise ECG than those with CVDRF = 0–2. Exercise ECGs of individuals with CVDRF = 5–7 were 5.4 times more likely to be positive and abnormal than exercise ECGs of individuals with CVDRF = 0–2. Conclusions The exercise intensity of 62.5% HRR can be used as a safe upper limit for safe participation in exercise in apparently healthy Chinese population; the greater the number of CVDRFs, the greater the likelihood of cardiovascular risk during exercise.

BackgroundThe COVID-19 pandemic, as well as associated prevention and control measures, have impacted the entire healthcare system, including the use patterns of medicine. However, the impact of COVID-19 on the use of antibiotics has not yet been extensively evaluated in China. This study aimed to evaluate the impact of the pandemic on the consumption and expenditure of antibiotics in public healthcare institutions in Shaanxi Province, China.MethodsWe used longitudinal data from the Shaanxi provincial drug procurement database in this study. An interrupted time-series analysis was performed to evaluate the effects of COVID-19 on consumption and expenditures of antibiotics in public healthcare institutions in Shaanxi from 1 January 2017 to 31 December 2020. Antibiotic consumption was expressed as the number of defined daily doses (DDDs) per 1,000 residents per day (DIDs), based on the population of Shaanxi Province at the end of each year from the National Bureau of Statistics. The pre-pandemic period was from January 2017 to January 2020, and the post-pandemic period was from February 2020 to December 2020.ResultsA declining trend in antibiotic consumption was observed immediately following the COVID-19 pandemic (β2 = −4.099; p

PurposeTo explore the controversial relationship between uric acid (UA) levels and bone mineral density (BMD) in patients with type 2 diabetes mellitus (T2DM).Patients and methodsThe PubMed, Embase, and Cochrane Library databases were searched using keywords and related words. Study quality was evaluated using the Newcastle-Ottawa Scale. Studies retrieved in the literature search were systematically screened to extract information and data based on predefined inclusion and exclusion criteria. RevMan version 5.3 and Stata Release 13.0 were used for statistical analysis. Results are expressed as mean difference (MD) and corresponding 95% confidence interval (CI). Heterogeneity was evaluated using the I2 and Q tests.ResultsThis meta-analysis included 10 studies comprising 5,717 patients with T2DM. Study quality ranged from moderate to high. Results of comparative analyses were as follows: normal BMD versus (vs.) osteoporosis (OP) in females, MD −13.83 μmol/L (95% CI −41.69 to 14.03); I2 = 7%; P=0.30); normal BMD vs. osteopenia in females, MD −12.41 μmol/L (95% CI −37.81 to 12.99; I2 = 0%; P=0.92); normal BMD vs. abnormal BMD (osteopenia/OP), MD −23.82 μmol/L (95% CI −33.50 to −14.13; I2 = 0%; P=0.44); and osteopenia vs. OP, MD −22.35 μmol/L (95% CI −29.55 to −15.15; I2 = 5%; P=0.39). No publication bias was observed.ConclusionCompared with normal BMD, abnormal BMD (osteopenia/OP) was associated with lower UA levels. Compared with osteopenia, OP also showed lower UA.Systematic review registration:

Head and neck squamous cell carcinoma (HNSCC) presents significant challenges for its treatment, especially for the treatment aimed at preserving maxillofacial function due to its complex anatomical structure, resistance to radiotherapy and chemotherapy, and low intertumoral immunogenicity. In this study, we introduce a novel approach employing manganese-based immunotherapy (Mn-IT) for the effective treatment of HNSCC. Firstly, we used a tumor-immune cell co-culture system to demonstrate that Mn2+ enhances immune cytotoxicity in HNSCC via cGAS-STING activation. Subsequently, we used an orthotopic transplant tumor model to validate that Mn-IT exerts antitumor effects and immune activation in HNSCC. Subsequently, to enhance efficacy and reduce the immune-related side effects of Mn-IT, we utilized the biocompatibility and immunomodulatory potential of inosine to develop a supramolecular hydrogel, guanosine-phenylboronic-tannic acid-phenylboronic-inosine (G-TA-I), through a green and straightforward method. The G-TA-I hydrogel is characterized by its dynamic borate ester-based structure, facilitating localized, sustained release of therapeutics. This hydrogel not only exhibited acceptable biocompatibility and biodegradability but also was able to load and sustain the release of Mn2+ and PD-1 antibody in situ. Finally, we adopted a range of mouse tumor models to reveal that G-TA-I supramolecular hydrogel drug delivery system loaded with Mn2+ and PD-1 antibody could efficiently activate local immunity, effectively combating the occurrence and progression of HNSCC. These findings indicate that the Mn-αPD-1-loaded in situ sustained-release G-TA-I hydrogel represents a novel, safe, and effective cancer therapeutic drug delivery system that can maximize the efficacy of immunotherapy and reduce the side effects of immunotherapy. This supramolecular hydrogel drug delivery system provides a basis for new therapeutic strategies for tumor types such as head and neck tumors that require maximal preservation of the structure and function of the diseased site.

ABSTRACT Background Vitamin D is the focus of extensive medical research globally. Recent studies have investigated the correlation between serum 25‐hydroxyvitamin D (25(OH)D) and common inflammatory markers. However, few studies have incorporated novel inflammatory markers such as the platelet‐to‐lymphocyte ratio (PLR), platelet‐to‐high density lipoprotein cholesterol ratio (PHR), systemic inflammatory index (SII), neutrophil‐to‐lymphocyte ratio (NLR), systemic inflammatory response (SIRI), and neutrophil‐to‐high‐density lipoprotein cholesterol ratio (NHR). This study investigated these correlations among adults in the USA. Methods We ultimately included a total of 5308 participants from the National Health and Nutrition Examination Survey (NHANES) database spanning 2007 to 2018. A multivariable linear regression model assessed the links between serum 25(OH)D and these novel inflammatory markers, with subgroup analyses for hypertension and diabetes. To further explore the relationship between the two, we applied smooth curve fittings and generalized additive models. Upon detecting nonlinear relationships, we used a recursive algorithm to pinpoint the inflection point. Results In our multivariate linear regression model, serum 25(OH)D concentrations were negatively correlated with NHR (β = −0.003, 95% CI: −0.005 to −0.001), NLR (β = −0.002, 95% CI: −0.003 to 0.000), SII (β = −0.579, 95% CI: −0.954 to −0.205), PHR (β = −0.171, 95% CI: −0.249 to −0.093), and PLR (β = −0.096, 95% CI: −0.051 to −0.040) among adults in the USA. Nevertheless, no significant association was found with SIRI (β = −0.001, 95% CI: −0.002 to 0.000). Subgroup analysis by hypertension and diabetes showed that in the hypertensive group, serum 25(OH)D was significantly and negatively associated with NHR, NLR, SII, SIRI, PHR, and PLR. However, no correlation was found in the diabetic group between serum 25(OH)D levels and these inflammatory markers. Conclusions Our research confirms that serum 25(OH)D levels are negatively correlated with several novel inflammatory markers among adults in the USA, suggesting potential directions for further research into vitamin D's role in inflammation.

Oral leukoplakia (OLK) is the most emblematic oral potentially malignant disorder that may precede the diagnosis of oral squamous cell carcinoma (OSCC) and has an overall malignant transformation rate of 9.8 %. Early intervention is crucial to reduce the malignant transformation rate from OLK to OSCC but the lack of effective local pharmaceutical preparations poses a challenge to clinical management. Rapamycin is speculated to prevent OLK from carcinogenesis and its inherent lipophilicity facilitates its penetration into stratum corneum. Nevertheless, hydrophilic hydrogels frequently encounter challenges when attempting to deliver lipophilic drugs. Furthermore, the oral cavity presents a complex environment defined by oral motor functions, saliva secretion cycles, dynamic fluctuations, and protective barriers comprising mucus and lipid layers. Consequently, addressing issues of muco-penetration and muco-adhesion is imperative for developing an effective drug delivery system aiming at delivering rapamycin to target oral potentially malignant disorders.Here, a dual-function hydrogel drug delivery system integrating adhesion and lipophilicity was successfully developed based on polyvinyl alcohol (PVA) and dioleoyl phosphatidylglycerol (DOPG) via dynamic boronic ester bonds. Rheological experiments based on orthogonal design revealed that PVA-DOPG hydrogels exhibited ideal adhesive strength (around 6 kPa) and could adhere to various surfaces in both dry and wet conditions. PVA-DOPG hydrogels also significantly promoted lipophilic molecules’ penetration into stratum corneum (integrated fluorescence density of 6.95 ± 0.52 × 106 and mean fluorescence depth of 0.96 ± 0.07 mm) of ex-vivo porcine buccal mucosa (p

The global maritime trade plays a key role in propagating alien aquatic invasive species, which incurs side effects in terms of environment, human health and economy. The existing biosecurity methods did not take into account the invaded risk as well as the diffusion of invasive species at the same time, which may lead to inadequate bioinvasion control. In addition, the lack of considering the impact of bioinvasion control on shipping also makes their methods cost-ineffective. To solve the problems of the existing methods, we employ the automatic identification system (AIS) data, the ballast water data and the water temperature & salinity data to construct two networks: the species invasion network (SIN) and the global shipping network (GSN). The former is used to analyze the potential of a port in propagating marine invasive species while the latter is employed to evaluate the shipping importance of ports. Based on the analysis of SIN and GSN, two categories of biosecurity triggering mechanisms are proposed. The first category takes into consideration both being bioinvaded and spreading invasive species and the second one concerns the shipping value of each port besides its invasion risk. A lot of case studies have been done to discover the key ports needed to be controlled preferentially under the guide of the proposed biosecurity triggering mechanisms. Finally, our correlation analysis shows that closeness is most highly correlated to the invasion risk.

Abstract Background Glioblastoma is characterized by high aggressiveness, frequent recurrence, and poor prognosis. Histone acetylation-associated genes have been implicated in its occurrence and development, yet their predictive ability in glioblastoma prognosis remains unclear. Results This study constructs a histone acetylation risk model using Cox and LASSO regression analyses to evaluate glioblastoma prognosis. We assessed the model’s prognostic ability with univariate and multivariate Cox regression analyses. Additionally, immune infiltration was evaluated using ESTIMATE and TIMER algorithms, and the SubMAP algorithm was utilized to predict responses to CTLA4 inhibitor. Multiple drug databases were applied to assess drug sensitivity in high- and low-risk groups. Our results indicate that the histone acetylation risk model is independent and reliable in predicting prognosis. Conclusions Low-risk patients showed higher immune activity and longer overall survival, suggesting anti-CTLA4 immunotherapy suitability, while high-risk patients might benefit more from chemotherapy. This model could guide personalized therapy selection for glioblastoma patients.

Abstract Background Programmed cell death is an important mechanism for the development of hepatic ischemia and reperfusion (IR) injury, and multiple novel forms of programmed cell death are involved in the pathological process of hepatic IR. ERRFI1 is involved in the regulation of cell apoptosis in myocardial IR. However, the function of ERRFI1 in hepatic IR injury and its modulation of programmed cell death remain largely unknown. Methods Here, we performed functional and molecular mechanism studies in hepatocyte-specific knockout mice and ERRFI1-silenced hepatocytes to investigate the significance of ERRFI1 in hepatic IR injury. The histological severity of livers, enzyme activities, hepatocyte apoptosis and ferroptosis were determined. Results ERRFI1 expression increased in liver tissues from mice with IR injury and hepatocytes under oxygen-glucose deprivation/reoxygenation (OGD/R) conditions. Hepatocyte-specific ERRFI1 knockout alleviated IR-induced liver injury in mice by reducing cell apoptosis and ferroptosis. ERRFI1 knockdown reduced apoptotic and ferroptotic hepatocytes induced by OGD/R. Mechanistically, ERRFI1 interacted with GRB2 to maintain its stability by hindering its proteasomal degradation. Overexpression of GRB2 abrogated the effects of ERRFI1 silencing on hepatocyte apoptosis and ferroptosis. Conclusions Our results revealed that the ERRFI1-GRB2 interaction and GRB2 stability are essential for ERRFI1-regulated hepatic IR injury, indicating that inhibition of ERRFI1 or blockade of the ERRFI1-GRB2 interaction may be potential therapeutic strategies in response to hepatic IR injury.

Plant anatomy is patterned early during leaf development which suggests studying the spatial–temporal transcriptomes of primordia will help identify critical regulative and functional genes. We successfully isolated the leaf primordia tissues from the C3 grass rice and the C4 grass foxtail millet by laser capture microdissection (LCM) and studied the gene expression throughout leaf developmental stages. Our data analysis uncovered the conserved expression patterns of certain gene clusters both in rice and foxtail millet during leaf development. We revealed genes and transcription factors involved in vein formation, stomatal development, and suberin accumulation. We identified 79 candidate genes associated with functional regulation of C4 anatomy formation. Screening phenotype of the candidate genes revealed that knock-out of a putative polar auxin transport related gene NAL1 resulted significantly reduced veinal space in rice leaf. Our present work provides a foundation for future analyses of genes with novel functions in grasses and their role in leaf development, in particular the role in leaves with a contrasting C3 vs. C4 biosynthetic pathway.

Recently, the issue of near-surface ozone pollution has become a growing concern. To effectively manage and control ozone pollution, emerging deep learning (DL) techniques have been applied for future ozone concentration trend prediction, generating promising outcomes. However, existing studies employ various DL models and rely on diverse datasets to predict ozone concentrations. This leads to a lack of comprehensive evaluations of how the architecture and depth of different DL models influence the predictive accuracy of ozone concentration trends when assessed using a unified dataset. This lack of uniformity in evaluations creates a gap in our understanding of the influence of different neural network architectures and depths on ozone concentration predictions. In this work, we aim to address this research gap by conducting a systematic performance evaluation that benchmarks six prominent DL architectures, each with varying depths, to evaluate their effectiveness for predicting ozone concentrations across diverse geographical regions. Our findings indicate that the best-performing DL model in the nationwide prediction task is the one-layer bidirectional long short-term memory (Bi-LSTM) model, which achieves an R2 of 0.66, an RMSE of 15.32μg⋅m−3, and an MAE of 11.51μg⋅m−3. In contrast, the poorest-performing model in the same prediction task is the one-block transformer-based model, with an R2 of 0.57, an RMSE of 17.34μg⋅m−3, and an MAE of 13.3μg⋅m−3. Furthermore, fully connected networks (FCNs) demonstrate robust and efficient predictive performance across both nationwide and regional prediction tasks. Notably, our study reveals that no single DL model consistently performs well across all prediction tasks, emphasizing the need for tailored approaches that cater to the specific attributes of each region. Additionally, we observe that DL models with more than two hidden layers frequently suffer from overfitting. Particularly for the Bi-LSTM architecture, as the number of hidden layers increases from 1 to 7, we observe a 12% reduction in R2 performance. Our analysis also identifies the most influential meteorological factors among the top-performing DL models, offering insights for feature selection and optimization in model development. This research contributes to a deeper understanding of the design and selection of appropriate DL architectures for predicting the concentrations of ozone and other air pollutants.

Abstract Extracellular vesicles (EVs) had been described as a next‐generation drug delivery system, due to the compelling evidence that they can facilitate the transfer of a variety of biomolecules between cells. The most frequently used strategy for loading protein cargoes is the endogenous engineering of EVs through genetic fusion of the protein of interest (POI) and scaffold proteins with high EV‐sorting ability. However, the lack of scaffold proteins had become a major issue hindering the promotion of this technology. Herein, we proposed novel screening criteria that relax the inclusion requirement of candidate scaffold proteins and eventually identified a new scaffold protein, PLXNA1. The truncated PLXNA1 not only inherits the high EV‐sorting ability of its full‐length counterpart but also allows the fusion expression of POI in both outer surface and luminal areas, individually or simultaneously. In conclusion, our screening criteria expanded the range of potential scaffold proteins. The identified scaffold protein PLXNA1 showed great potential in developing therapeutic EVs.

Leaf anatomical traits are susceptible to environmental changes and can reflect plant adaptation strategies to the environment. Populus euphratica plays a key role in maintaining ecosystem processes and functions in arid zones, but the variations in leaf anatomical traits of Populus euphratica and their drivers at large scales remain unclear. Here, we investigated 10 leaf anatomical traits of Populus euphratica from 12 sampling sites in the arid zone of China, and explored how they were affected by geographic, climatic, and soil factors. Our results showed that these traits differed significantly between sites (P < 0.05), which was mainly determined by climate and soil factors that together explained 41.4 % of the trait variation. The impact of climate factors is the most critical, as they can directly influence the variation in leaf anatomical traits and indirectly affect trait variation by influencing soil factors. Among them, the max temperature of the warmest month (BIO5), soil available phosphorus (SAP) and soil moisture content (SM) had the most critical effect. Besides, temperature was the major influencing factor for the ratio of spongy tissue thickness to leaf thickness (SR), palisade tissue thickness (PT), the ratio of palisade tissue thickness to spongy tissue thickness (PSR), and the ratio of palisade tissue thickness to leaf thickness (CTR), while soil nutrient was the major influencing factor for leaf thickness (LT), lower epidermis thickness (LET), and spongy tissue thickness (ST). Our results provide important insights into the response of dryland forests to climate change, and suggest that climate warming and declining soil quality may have a stronger effect on Populus euphratica.

Wind energy is increasingly vital globally, requiring precise output forecasting for stable, efficient power systems. However, this becomes particularly challenging for newly built wind farms that lack historical data. Statistical models are unsuitable in this context due to their reliance on historical data. While both physical models and data-driven transfer learning methods offer some solutions, they exhibit limitations when applied to newly built wind farms. Physical models require complex parameter tuning and high computational costs, and transfer learning generally necessitate a certain amount of historical data for model transfer. More critically, existing methods fall short in capturing the causal relationships between wind power and meteorological variables, impacting both the accuracy and robustness of the models in this specialized scenario characterized by distribution shifts. To address these challenges, this study introduces an integrated wind power forecasting model named CPTCFS, comprising two core components: Causal Feature Selection (CFS) and CausalPatchTST. The CFS identifies key features with direct causal relationships to wind power output through causal inference, surpassing traditional feature selection methods like PCA and correlation coefficient analysis. CausalPatchTST, integrating a sample weighting mechanism with the advanced Transformer variant model PatchTST, effectively addresses distribution shift issues caused by the lack of historical data in newly built wind farms, ensuring prediction accuracy and robustness in data-scarce environments. In 24-hour prediction tests using hourly data from two Australian wind farm clusters, the CausalPatchTST model with the sample weighting mechanism achieved a significant 13.29% reduction in Root Mean Square Error (RMSE) compared to the PatchTST model without this mechanism. Furthermore, the entire CPTCFS model outperforms existing models on other key accuracy indicators, demonstrating its broad applicability in the wind power forecasting domain and immense potential in other renewable energy prediction areas.

SHA-3, the latest hash standard from NIST, is utilized by numerous cryptographic algorithms to handle sensitive information. Consequently, SHA-3 has become a prime target for side-channel attacks, with numerous studies demonstrating successful breaches in unprotected implementations. Masking, a countermeasure capable of providing theoretical security, has been explored in various studies to protect SHA-3. However, masking for hardware implementations may significantly increase area costs and introduce additional delays, substantially impacting the speed and area of higher-level algorithms. In particular, current low-latency first-order masked SHA-3 hardware implementations require more than four times the area of unprotected implementations. To date, the specific structure of SHA-3 has not been thoroughly analyzed for exploitation in the context of masking design, leading to difficulties in minimizing the associated area costs using existing methods. We bridge this gap by conducting detailed leakage path and data dependency analyses on two-share masked SHA-3 implementations. Based on these analyses, we propose a compact and low-latency first-order SHA-3 masked hardware implementation, requiring only three times the area of unprotected implementations and almost no fresh random number demand. We also present a complete theoretical security proof for the proposed implementation in the glitch+register-transition-robust probing model. Additionally, we conduct leakage detection experiments using PROLEAD, TVLA and VerMI to complement the theoretical evidence. Compared to state-of-theart designs, our implementation achieves a 28% reduction in area consumption. Our design can be integrated into first-order implementations of higher-level cryptographic algorithms, contributing to a reduction in overall area costs.

Electrode wear during electrical discharge machining (EDM) is inevitable, and tool electrodes of different materials exhibit different wear rates. Unlike a single-material tool electrode, for a laminated electrode consisting of various material foils (LE-VMF), the components suffer from significantly different amounts of wear and also influence each other in EDM, however, the form and cause of the wear are not clear. Thus, the LE-VMFs including symmetric and asymmetric LE-VMFs were prepared and used in EDM in this paper. The wear of foils in LE-VMFs and their mutual influence during the EDM process were investigated through experiments. The wear forms of a single foil electrode and a foil of the same material in the LE-VMF were also compared. The experimental results show that copper foil with a lower wear rate had a protective effect on an adjacent brass foil with a larger wear rate in the LE-VMF, while the protective effect of the brass foil in the symmetric LE-VMF was larger than that in the asymmetric LE-VMF. The closer the distance from the copper foil, the stronger was the protection effect and the smaller was the wear. In addition, the working surface of the brass foil in the LE-VMF did not show any concavity-like wear in the middle region of its thickness after EDM, as was the case for the single brass foil electrode, even if the thickness of the brass foil was greater than 150 μm.

Abstract Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69−0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Chip rate estimation for direct-sequence spread spectrum (DSSS) signals plays a crucial role in signal detection and interference identification in non-cooperative wireless communication systems. However, accurate chip rate estimation is difficult in low signal-to-noise ratio (SNR) environments due to the influence of noise and channel fading. To address this problem, an improved chip rate estimation method, based on a time-reassigned multisynchrosqueezing transform energy squeeze, for DSSS signals is proposed for low SNR conditions. In this method, we explain how to choose an appropriate wavelet function and concentrate the wavelet coefficients on the energy ridge by reordering time details. This enhances the time resolution of the signal energy, reduces noise interference, and improves the accuracy of chip rate estimation for DSSS signals. To validate the proposed method, we compared the effects of different information code lengths, spread spectrum code lengths, spread spectrum code sequences, and modulation schemes on chip rate estimation performance with traditional methods. Additionally, we applied the method to real DSSS signal data to demonstrate its feasibility. Simulation examples and measurements demonstrate the reliability of the proposed method in low SNR environments.

Salmonella enteritidis is a major cause of foodborne illness worldwide, and the emergence of ciprofloxacin-resistant strains poses a significant threat to food safety and public health. This study aimed to investigate the prevalence, spread, and mechanisms of ciprofloxacin resistance in S. enteritidis isolates from food and patient samples in Shanghai, China. A total of 1625 S. enteritidis isolates were screened, and 34 (2.1%) exhibited resistance to ciprofloxacin. Pulsed-field gel electrophoresis (PFGE) results suggested that clonal spread might have persisted among these 34 isolates in the local area for several years. Multiple plasmid-mediated quinolone resistance (PMQR) genes, GyrA mutations in the quinolone resistance-determining region (QRDR), and overexpression of RND efflux pumps were identified as potential contributors to ciprofloxacin resistance. PMQR genes oqxAB, qnrA, qnrB, and aac(6’)-Ib-cr as well as GyrA mutations S83Y, S83R, D87Y, D87G, D87N, and S83Y-D87Y were identified. The co-transfer of the PMQR gene oqxAB with the ESBL gene blaCTX-M-14/55 on an IncHI2 plasmid with a size of ~245 kbp was observed through conjugation, highlighting the role of horizontal gene transfer in the dissemination of antibiotic resistance. Sequencing of the oqxAB-bearing plasmid p12519A revealed a 248,746 bp sequence with a typical IncHI2 backbone. A 53,104 bp multidrug resistance region (MRR) was identified, containing two key antibiotic resistance determinants: IS26-oqxR-oqxAB-IS26 and IS26-ΔISEcp1-blaCTX-M-14-IS903B. The findings of this study indicate that ciprofloxacin-resistant S. Enteritidis poses a significant threat to food safety and public health. The persistence of clonal spread and the horizontal transfer of resistance genes highlight the need for enhanced surveillance and control measures to prevent the further spread of antibiotic resistance.

Mobile networks are facing unprecedented challenges due to the traits of large scale, heterogeneity, and high mobility. Fortunately, the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity, wide-spread geographical distribution, and elastic resource sharing. In this paper, we propose a novel mobile networking framework based on fog computing which outperforms others in resilience. Our scheme is constituted of two parts: the personalized customization mobility management (MM) and the market-driven resource management (RM). The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits; the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices. Synergistically, our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network, which has been practically corroborated by numerical experiments.

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.

Effective preservation of high-frequency information such as edges and textures remains an open challenge in image denoising, with existing methods susceptible to noisy interference and poor consistency with human visual quality. To address this limitation, we propose a novel enhanced edge information network for image denoising via feature multi-modulation attention, namely ${\text {M}}^{2}$ A ${\text {E}}^{2}$ Net, for complex denoising tasks on both real and synthetic images. This method divides the conventional maximum likelihood estimation framework into three sub-problems solved collaboratively: (i) image reconstruction, (ii) gradient estimation, and (iii) detail refinement. These objectives are implemented through the integration of two sub-networks: Reconstruction and gradient estimation networks. First, we propose an end-to-end gradient loss function imposed on gradient estimation network to acquire noise-free gradient maps, which guides ${\text {M}}^{2}$ A ${\text {E}}^{2}$ Net to focus on regions abundant with rich edges and contours. Furthermore, to achieve effective feature representation of noisy images synchronously and dynamically in the reconstruction network, we propose a novel feature multi-modulation attention module to adaptively integrate gradient images obtained by the gradient estimation network into input images as an edge prior information. The Laplacian of Gaussian sharpening loss function is also proposed to enhance the preservation of edges and textures, as demonstrated by higher SSIM metric and perceptual quality of denoised images. Our approach leverages the advantages of deep learning while benefiting from classical filter-based methods. Experimental results demonstrate the promising performance of ${\text {M}}^{2}$ A ${\text {E}}^{2}$ Net in terms of PSNR and SSIM metrics and better visual quality of denoised images. The code is available at https://github.com/Wangzt1121/M2AE2Net.

Abstract Accurate prediction of photovoltaic power generation is a critical technical problem for utilizing solar energy. Aiming at the problem that the model parameters are difficult to obtain in applying photovoltaic power prediction methods, this paper has used long‐term monitoring data of output power, various meteorological data, and solar irradiation intensity of photovoltaic modules. This paper establishes the functional relationship between the output power of photovoltaic modules and the irradiation intensity through Pearson correlation analysis. By deducing the distribution relationship of irradiation intensity, the prediction model of irradiation intensity based on peak sunshine hours and sunshine duration is constructed and based on 340 sites across the country 64 years peak sunshine hours and sunshine duration query database. In this work, the theoretical value of the prediction model on sunny days is close to the measured value (R2 = 0.918–0.985). The solar radiation intensity on rainy days is weak, and the prediction accuracy is low (R2 = 0.838–0.930). The relative errors between the sunshine duration and the peak sunshine hours in the database are less than 4.55% and 4.79%, respectively, under sunny conditions in each quarter, indicating that the accuracy of the database meets the actual needs.

The chemistry of the pore fluid within clayey sediments frequently changes in various processes. However, the impacts of pore fluid chemistry have not been well included in the hydraulic permeability model, and the physical bases behind the salinity sensitivity of the hydraulic permeability remains elusive. In this study, a theoretical model for the hydraulic permeability of clayey sediments is proposed, and impacts of the pore fluid chemistry are quantitatively considered by introducing electrokinetic flow theory. Available experimental data were used to verify the theoretical model, and the verified model was further applied as a sensitivity analysis tool to explore more deeply how hydraulic permeability depends on pore fluid chemistry under different conditions. Coupling effects of pore water desalination and the effective stress enhancement on the hydraulic permeability of marine sediments surrounding a depressurization wellbore during hydrate production are discussed. Results and discussion show that the hydraulic permeability reduction is significant only when the electric double layer thickness is comparable to the characteristic pore size, and the reduction becomes more obvious when the ion mobility of the saline solution is smaller and the surface dielectric potential of clay minerals is lower. During gas hydrate production in the ocean, the salinity sensitivity of the hydraulic permeability could become either stronger and weaker, depending on whether the original characteristic pore size of marine sediments is relatively large or small.

Perovskite solar cells (PSCs) are gaining prominence in the photovoltaic industry due to their exceptional photoelectric performance and low manufacturing costs, achieving a significant power conversion efficiency of 26.4%, which closely rivals that of silicon solar cells. Despite substantial advancements, the effective area of high-efficiency PSCs is typically limited to about 0.1 cm2 in laboratory settings, with efficiency decreasing as the area increases. The limitation poses a major obstacle to commercialization, as large-area, high-quality perovskite films are crucial for commercial applications. This paper reviews current techniques for producing large-area perovskites, focusing on slot-die coating, a method that has attracted attention for its revolutionary potential in PSC manufacturing. Slot-die coating allows for precise control over film thickness and is compatible with roll-to-roll systems, making it suitable for large-scale applications. The paper systematically outlines the characteristics of slot-die coating, along with its advantages and disadvantages in commercial applications, suggests corresponding optimization strategies, and discusses future development directions to enhance the scalability and efficiency of PSCs, paving the way for broader commercial deployment.

Abstract Oryza coarctata (2n = 4X = 48, KKLL) is an allotetraploid, undomesticated relative of rice and the only species in the genus Oryza with tolerance to high salinity and submergence. Therefore, it contains important stress and tolerance genes/factors for rice. The initial draft genome published was limited by data and technical restrictions, leading to an incomplete and highly fragmented assembly. This study reports a new, highly contiguous chromosome-level genome assembly and annotation of O. coarctata. PacBio high-quality HiFi reads generated 460 contigs with a total length of 573.4 Mb and an N50 of 23.1 Mb, which were assembled into scaffolds with Hi-C data, anchoring 96.99% of the assembly onto 24 chromosomes. The genome assembly comprises 45,571 genes, and repetitive content contributes 25.5% of the genome. This study provides the novel identification of the KK and LL genome types of the genus Oryza, leading to valuable insights into rice genome evolution. The chromosome-level genome assembly of O. coarctata is a valuable resource for rice research and molecular breeding.

Abstract Quorum‐sensing (QS) is an essential mechanism of microbial self‐regulation. In this work, the presence of QS in Acetobacter pasteurianus was demonstrated, and N‐acyl‐homoserine lactones N‐C4‐HSL and N‐C8‐HSL were identified as the signal molecules. The exogenous addition of N‐C4‐HSL, N‐C8‐HSL, N‐C10‐HSL, and N‐C12‐HSL result in the reduction in biomass, and lead to 11.56%, 11.91%, 7.94%, and 4.69% decrease in acetic acid production, respectively, when compared with the control. A hypothetical N‐acylhomoserine lactonase (AHL lactonase) 0046 was overexpressed in A. pasteurianus CGMCC 3089, and the recombinant strain exhibites a 17.38% and 12.75% increase in the biomass and the acetic acid production rate, respectively. Those results prove the negative regulation of QS on acetic acid fermentation, and overexpression of 0046 protein alleviates this negative effect.

Background and Purpose Intravenous tenecteplase (TNK) efficacy has not been well demonstrated in acute ischemic stroke (AIS) beyond 4.5 hours after onset. This study aimed to determine the effect of intravenous TNK for AIS within 4.5 to 24 hours of onset. Methods In this pilot trial, eligible AIS patients with diffusion-weighted imaging (DWI)-fluid attenuated inversion recovery (FLAIR) mismatch were randomly allocated to intravenous TNK (0.25 mg/kg) or standard care within 4.5–24 hours of onset. The primary endpoint was excellent functional outcome at 90 days (modified Rankin Scale [mRS] score of 0–1). The primary safety endpoint was symptomatic intracranial hemorrhage (sICH). Results Of the randomly assigned 80 patients, the primary endpoint occurred in 52.5% (21/40) of TNK group and 50.0% (20/40) of control group, with no significant difference (unadjusted odds ratio, 1.11; 95% confidence interval 0.46–2.66; P=0.82). More early neurological improvement occurred in TNK group than in control group (11 vs. 3, P=0.03), but no significant differences were found in other secondary endpoints, such as mRS 0–2 at 90 days, shift analysis of mRS at 90 days, and change in National Institutes of Health Stroke Scale score at 24 hours and 7 days. There were no cases of sICH in this trial; however, asymptomatic intracranial hemorrhage occurred in 3 of the 40 patients (7.5%) in the TNK group. Conclusion This phase 2, randomized, multicenter study suggests that intravenous TNK within 4.5–24 hours of onset may be safe and feasible in AIS patients with a DWI-FLAIR mismatch.

The oncogenesis and development of glioblastoma multiforme have been linked to glycosylation modifications, which are common post-translational protein modifications. Abnormal glycosyltransferase development leads to irregular glycosylation patterns, which hold clinical significance for GB prognosis. By utilizing both single-cell and bulk data, we developed a scoring system to assess glycosylation levels in GB. Moreover, a glycosylation-based signature was created to predict GB outcomes and therapy responsiveness. The study led to the development of an glyco-model incorporating nine key genes. This risk assessment tool effectively stratified GB patients into two distinct groups. Extensive validation through ROC analysis, RMST, and Kaplan-Meier (KM) survival analysis emphasized the model’s robust predictive capabilities. Additionally, a nomogram was constructed to predict survival rates at specific time intervals. The research revealed substantial disparities in immune cell infiltration between low-risk and high-risk groups, characterized by differences in immune cell abundance and elevated immune scores. Notably, the glyco-model predicted diverse responses to immune checkpoint inhibitors and drug therapies, with high-risk groups exhibiting a preference for immune checkpoint inhibitors and demonstrated superior responses to drug treatments. Furthermore, the study identified two potential drug targets and utilized Connectivity Map analysis to pinpoint promising therapeutic agents. Clofarabine and YM155 were identified as potent candidates for the treatment of high-risk GB. Our well-crafted glyco-model effectively discriminates patients by calculating the risk score, accurately predicting GB outcomes, and significantly enhancing prognostic assessment while identifying novel immunotherapeutic and chemotherapeutic strategies for GB treatment.

Friction plays a key role in the assessment of the safety and stability of mechanical systems (such as superconducting magnet quench explosion, aerospace vehicle bearing wear, etc.). Due to the closeness of the interfaces in engineering structures and the randomness of the contact surfaces, existing methods for measuring static friction force are unable to measure it at the contact interfaces of engineering structures under service conditions. In this paper, a new method for measuring the static friction force at the interface based on electrical signals is proposed. This method enables the measurement of the static friction force at interfaces of complex engineering structures under service conditions solely through electrical signals. The results indicate that the contact resistance gradually decreases with the increase in tangential load during the static friction stage until a monotonic behavior of macroscopic sliding occurs. The evolution of contact resistance is linked to the evolution of the real contact area, and this monotonic behavior can be explained as the deformation form of contact points. The accuracy of the proposed electrical measurement method is verified by comparison with experimental results (with an error of less than 9%). The indirect measurement method of friction force proposed in this paper can effectively measure the static friction force at the interfaces of engineering structures under service conditions, and it is expected to be applied to the detection of friction performance at engineering structure interfaces in extreme service environments.

Abstract In recent years, key generation schemes based on the reciprocity of wireless channels have gained widespread attention in the field of wireless physical layer security. However, due to the openness of wireless channels, eavesdroppers may obtain partial legitimate keys by eavesdropping on the wireless channel. To address this issue, this paper proposes a wireless channel coding scheme that induces an “avalanche” of errors for eavesdroppers, which could maximize eavesdropper's bit error rate. The design of this scheme is based on the significant differences between the eavesdropping channel and the legitimate channel, and the average bit key disagreement ratio of the keys generated by the legitimate channel is around 8%. The scheme selects polar codes with strong error correction capabilities and chooses a coding rate matching the error level of the legitimate channel, forcing the eavesdropping channel's error rate into an “avalanche” state, where the error rate approaches 50%. This paper presents the structure of the encoder and determines the coding rate through simulation. Simulation results demonstrate that this scheme can significantly degrade the error rate of the eavesdropping channel while ensuring the prevention of eavesdroppers from obtaining secret information transmitted through the legitimate channel.

Summary: The deciduous tree Idesia polycarpa can provide premium edible oil with high polyunsaturated fatty acid contents. Here, we generate its high-quality reference genome, which is ∼1.21 Gb, comprising 21 pseudochromosomes and 42,086 protein-coding genes. Phylogenetic and genomic synteny analyses show that it diverged with Populus trichocarpa about 16.28 million years ago. Notably, most fatty acid biosynthesis genes are not only increased in number in its genome but are also highly expressed in the fruits. Moreover, we identify, through genome-wide association analysis and RNA sequencing, the I. polycarpa SUGAR TRANSPORTER 5 (IpSTP5) gene as a positive regulator of high oil accumulation in the fruits. Silencing of IpSTP5 by virus-induced gene silencing causes a significant reduction of oil content in the fruits, suggesting it has the potential to be used as a molecular marker to breed the high-oil-content cultivars. Our results collectively lay the foundation for breeding the elite cultivars of I. polycarpa.

PurposeThis study aims to investigate the psychological status of patients with Hepple V osteochondral lesions of the talus (OLT) and evaluate the effect of autologous osteoperiosteal transplantation (AOPT) on their psychological well-being.MethodsFifty patients with Hepple V OLT who underwent AOPT at the Comprehensive Foot and Ankle Surgery Ward of Xi’an Honghui Hospital from November 2021 to May 2023 were included in this study. The patients were divided into two groups based on the presence or absence of preoperative symptoms of anxiety/depression. Group A comprised patients with preoperative symptoms, while Group B included patients without such symptoms. Preoperative and final follow-up assessments included the Hospital Anxiety and Depression Scale for evaluating anxiety and depression, the visual analogue scale for pain assessment, and the American Orthopaedic Foot and Ankle Society scores for assessing ankle and hindfoot function.ResultsAmong the 50 Hepple V OLT patients who obtained complete follow-up, twenty-four had preoperative symptoms of anxiety/depression, with an incidence rate of up to 48%. Patients in Groups A and B showed significant improvement in all evaluation indexes after AOPT compared to the preoperative period, but the overall prognosis of Group A was poorer than that of Group B.ConclusionAOPT can effectively improve patients’ pain, functional activities, and psychological status, and there is a significant correlation between patients’ preoperative psychological status and prognosis.

Masking, an effective countermeasure against side-channel attacks, is commonly applied in modern cryptographic implementations. Considering cryptographic algorithms that utilize both Boolean and arithmetic masking, the conversion algorithm between arithmetic masking and Boolean masking is required. Conventional high-order arithmetic masking to Boolean masking conversion algorithms based on Boolean circuits suffer from performance overhead, especially in terms of hardware implementation. In this work, we analyze high latency for the conversion and propose an improved high-order A2B conversion algorithm. For the conversion of 16-bit variables, the hardware latency can be reduced by 47% in the best scenario. For the case study of second-order 32-bit conversion, the implementation results show that the improved scheme reduces the clock cycle latency by 42% in hardware and achieves a 30% speed performance improvement in software. Theoretically, a security proof of arbitrary order is provided for the proposed high-order A2B conversion. Experimental validations are performed to verify the second-order DPA resistance of second-order implementation. The Test Vector Leakage Assessment does not observe side-channel leakage for hardware and software implementations.

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of “Cuproptosis,” a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the “CP-score,” was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor’s immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

In an experiment performed on the Shenguang-III prototype laser facility, collective Thomson scattering (TS) is used to study the spatial growth of stimulated Brillouin scattering (SBS) in a gas-filled hohlraum by detecting the SBS-driven ion acoustic wave. High-quality time-resolved SBS and TS spectra are obtained simultaneously in the experiment, and these are analyzed by a steady-state code based on the ray-tracing model. The analysis indicates that ion–ion collisions may play an important role in suppressing SBS growth in the Au plasma; as a result, the SBS excited in the filled gas region is dominant. In the early phase of the laser pulse, SBS originates primarily from the high-density plasma at the edges of the interaction beam channel, which is piled up by the heating of the interaction beam. Throughout the duration of the laser pulse, the presence of the TS probe beam might mitigate SBS by perturbing the density distribution around the region overlapping with the interaction beam.

Abstract Background Hypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers. Methods A Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM. Results We identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively. Conclusions We elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.

This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).

Efficient, low-cost photocatalysts with mild synthesis conditions and stable photocatalytic behavior have always been the focus in the field of photocatalysis. This study proves that non-quantum-dot Cs2PbI2Cl2-based materials, created by a simple method, can be successfully employed as new high-efficient photocatalysts. The results demonstrate that two-dimensional Cs2PbI2Cl2 perovskite can achieve over three times higher photocatalytic performance compared to three-dimensional CsPbBr3 perovskite. Moreover, the photocatalytic performance of Cs2PbI2Cl2 can be further improved by constructing a heterojunction structure, such as Cs2PbI2Cl2/CsPbBr3. Cs2PbI2Cl2 can connect well with CsPbBr3 through a simple method, resulting in tight bonding at the interface and efficient carrier transfer. Cs2PbI2Cl2/CsPbBr3 exhibits notable 5-fold and 10-fold improvements in photocatalytic performance and rate compared to CsPbBr3. Additionally, Cs2PbI2Cl2/CsPbBr3 demonstrates superb stable catalytic performance, with nearly no decrease in photocatalytic performance after 7 months (RH = 20% ± 10, T = 25 °C ± 5). This study also reveals that the photocatalytic process based on Cs2PbI2Cl2/CsPbBr3 can directly oxidize organic matter using holes, without relying on the generation of intermediate reactive oxygen species from water or oxygen (such as ·OH or ·O2−), showcasing further potential for achieving high photocatalytic efficiency and selectivity in anhydrous/anaerobic catalytic reactions and treating recalcitrant pollutants.

Diaphorina citri Kuwayama (D. citri) is one of the major pests in the citrus industry, which spreads Citrus Huanglongbing disease. It has developed resistance to chemical insecticides. Therefore, searching for greener solutions for pest management is critically important. The main aim of this study was to evaluate the repellent and insecticidal efficacy of essential oils (EOs) from four species of Myrtaceae plants: Psidium guajava (PG), Eucalyptus robusta (ER), Eucalyptus tereticornis (ET), and Baeckea frutescens (BF) against D. citri and to analyze their chemical compositions. GC-MS analysis was performed, and the results indicated that the EOs of PG, ER, ET, and BF were rich in terpenoids, ketones, esters, and alcohol compounds. The repellent rate of all four EOs showed that it decreased with exposure time but increased with the concentration of EOs from 80.50% to 100.00% after treating D. citri for 6 h with four EOs at 100% concentration and decreased to 67.71% to 85.49% after 24 h of exposure. Among the compounds from the EOs tested, eucalyptol had the strongest repellent activity, with a 24 h repellency rate of 100%. The contact toxicity bioassay results showed that all EOs have insecticidal toxicity to D. citri; the LC50 for nymphs was 36.47–93.15 mL/L, and for adults, it was 60.72–111.00 mL/L. These results show that when PG is used as the reference material, the ER, ET, and BF EOs have strong biological activity against D. citri, which provides a scientific basis for the further development of plant-derived agrochemicals.

Accumulating evidence has highlighted a strong association between gut microbiota and the occurrence, development, prevention, and treatment of atopic dermatitis (AD). The regulation of gut microbial dysbiosis by oral traditional Chinese medicine (TCM) has garnered significant attention. In the treatment of AD, the TCM formula Qingre-Qushi Recipe (QRQS) has demonstrated clinical efficacy. However, both the therapeutic mechanisms of QRQS and its impact on gut microbiota remain unclear. Thus, our study aimed to assess the efficacy of QRQS and evaluate its influence on the composition and diversity of gut microbiota in AD animal models. First, we investigated the therapeutic effect of QRQS on AD using two animal models: filaggrin-deficient mice (Flaky tail, ft/ft) and MC903-induced AD-like mice. Subsequently, we explored its influence on the composition and diversity of gut microbiota. Our results demonstrated that QRQS treatment ameliorated the symptoms in both ft/ft mice and MC903-induced AD-like mice. It also reduced the levels of serum IgE and pro-inflammatory cytokines, including IL-1β, IL-4, IL-5, IL-9, IL-13, IL-17A, and TNF-α. Furthermore, QRQS remarkably regulated gut microbiota diversity by increasing Lactobacillaceae and decreasing Bacteroidales. The inflammatory factors in peripheral serum of ft/ft mice showed a close correlation with gut microbiota, as determined using the Spearman correlation coefficient. Additionally, PICRUSt analysis revealed an enrichment in ascorbate and aldarate metabolism, fatty acid metabolism and biosynthesis, and propanoate metabolism in the QRQS group compared to the ft/ft group. Finally, we identified liquiritin as the primary active ingredient of QRQS using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). Our findings revealed that QRQS improved AD-like symptoms and alleviated skin inflammation in ft/ft and MC903-induced mice. This suggests that modulating the gut microbiota may help elucidate its anti-inflammation activation mechanism, highlighting a new therapeutic strategy that targets the intestinal flora to prevent and treat AD.

Summary: The differentiation of embryonic stem cells (ESCs) begins with the transition from the naive to the primed state. The formative state was recently established as a critical intermediate between the two states. Here, we demonstrate the role of the histone chaperone FACT in regulating the naive-to-formative transition. We found that the Q265K mutation in the FACT subunit SSRP1 increased the binding of FACT to histone H3-H4, impaired nucleosome disassembly in vitro, and reduced the turnover of FACT on chromatin in vivo. Strikingly, mouse ESCs harboring this mutation showed elevated naive-to-formative transition. Mechanistically, the SSRP1-Q265K mutation enriched FACT at the enhancers of formative-specific genes to increase targeted gene expression. Together, these findings suggest that the turnover of FACT on chromatin is crucial for regulating the enhancers of formative-specific genes, thereby mediating the naive-to-formative transition. This study highlights the significance of FACT in fine-tuning cell fate transition during early development.