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Tripterygium wilfordii Hook F (TWHF) is a traditional Chinese medicine widely used in the treatment of systemic lupus erythematosus (SLE), with triptolide (TP) as its main active ingredient. However, its side effects also induced by TP, especially hepatotoxicity and reproductive toxicity, largely limit its application in a subset of patients. Monoclonal antibodies (mAbs) developed for the treatment of SLE that deplete B cells by targeting B cell-expressing antigens, such as CD19, have failed in clinical trials, partly due to their poor efficacy in consuming B cells. Here, we report the development of a rationally designed antibody‒drug conjugate (ADC), CD19 mAb-TP conjugate, to alleviate the side effects of TWHF and simultaneously improve the therapeutic efficacy of CD19 mAb. The CD19 mAb-TP conjugate, which was named ADC-TP, selectively depleted B cell subsets both in vitro and in vivo and effectively alleviated disease symptoms in mouse lupus models with enhanced therapeutic efficacy than CD19 mAb and fewer side effects than TP. Our present study proposes a CD19 mAb‒TP conjugate strategy to mitigate the toxicity of TWHF while also enhancing the therapeutical efficacy of CD19 mAbs for the treatment of SLE, providing a feasible method for improving the current agents used for treating SLE.

Abstract Excessive exercise is an etiological factor of intervertebral disc degeneration (IVDD). Engineered extracellular vesicles (EVs) exhibit excellent therapeutic potential for disease-modifying treatments. Herein, we fabricate an exercise self-powered triboelectric-responsive microneedle (MN) assay with the sustainable release of optogenetically engineered EVs for IVDD repair. Mechanically, exercise promotes cytosolic DNA sensing-mediated inflammatory activation in senescent nucleus pulposus (NP) cells (the master cell population for IVD homeostasis maintenance), which accelerates IVDD. TREX1 serves as a crucial nuclease, and disassembly of TRAM1-TREX1 complex disrupts the subcellular localization of TREX1, triggering TREX1-dependent genomic DNA damage during NP cell senescence. Optogenetically engineered EVs deliver TRAM1 protein into senescent NP cells, which effectively reconstructs the elimination function of TREX1. Triboelectric nanogenerator (TENG) harvests mechanical energy and triggers the controllable release of engineered EVs. Notably, an optogenetically engineered EV-based targeting treatment strategy is used for the treatment of IVDD, showing promising clinical potential for the treatment of degeneration-associated disorders.

ObjectiveThe study aims to determine the correlation between cerebral small vessel diseases (SVDs) and nonarteritic anterior ischemic optic neuropathy (NAION). Additionally, investigate whether NAION raises the risk of an increased total cerebral small vessel disease score (CSVD score) compared to control group without ocular conditions.Methods101 controls without any retinal illness and 61 individuals with NAION were enrolled for this retrospective case control study. Ophthalmic examinations and brain magnetic resonance imaging (MRI) scans were performed on all participants. Data on demographics and clinical characteristics were obtained from hospital medical records. We evaluated and compared the distribution of SVDs and rated the total CSVD score based on SVD indications observed on MRI scans.ResultsSVDs were more frequently in NAION individuals than in control group (82%, p

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Construction workers often suffer from musculoskeletal disorders due to awkward postures and weight-carrying in hot environments. Previous studies have focused on posture, neglecting weight-carrying or the environment. This study develops an automated real-time version of the RULA-based posture assessment method, identifying a worker’s posture and load weight using an RGB-D Microsoft Kinect sensing camera. The method includes ambient workplace temperature and is tested on a small-scale bricklaying and reinforcement task. Laboratory tests show the method improves assessment efficiency significantly. This is the first of its kind to fully automate the assessment of construction worker WMSD propensity, providing a continuous warning system to replace manual supervision.

Electromagnetic pollution prevention and military stealth technology requirements for wave absorbing materials gradually increased, therefore, the research and development of absorbing materials with the merits of low reflection loss, wide effective absorption bandwidth, and lightweight is of great significance. In this work, Air@Co@Co7Fe3@Fe3O4 hollow spherical particles have been successfully synthesized via a simple chemical plating method combined with a post heat treatment. It reveals that the Air@Co@Co7Fe3@Fe3O4 particles with a loading of 50 wt % exhibits an optimum reflection loss (RL) value of −20.77 dB at 15.52 GHz and broad effective absorption bandwidth (EAB, RL ≤ −10 dB) of 4.56 GHz with a thickness of 1.4 mm. Compared to Air@Co hollow spherical particles, Air@Co@Co7Fe3@Fe3O4 particles with natural resonance and eddy current loss, as well as the interlayer interfacial polarization mechanism of the multilayer structure, exhibit superior electromagnetic wave(EMW) absorption capabilities. The Air@Co@Co7Fe3@Fe3O4 hollow spherical particles could be a new highly efficient absorbing material for electromagnetic waves.

Background: Retinal health indices were suggested to be related to psychopathological symptoms, such as depressive symptoms. However, large-scale studies using optical coherence tomography (OCT) are missing to investigate the associations among them. Methods: In the META-KLS cohort study, 1456 participants (mean age [standard deviation]= 54.6 [11.91] years; n= 680 [47.8%] women) completed the Patient Health Questionnaire (PHQ-9) to measure depressive symptoms and underwent OCT. Poor-quality OCTs and multivariate outliers were excluded, and principal component analysis was performed to obtain retinal health indices separately for macular (n= 930) and optic nerve head (ONH) thicknesses (n= 800). Linear regressions were run controlling for covariates. Exploratory interaction models were run with demographic, lifestyle and health markers. Results: Although there were no direct significant associations between the retinal indices and depressive symptoms (macular: B= −0.05, 95% CI= [-0.15, 0.05], p= 0.32; ONH index: B= -0.02, 95% CI= [-0.11, 0.08], p= 0.71), their associations were moderated by demographic and health factors, e.g., C-reactive protein (CRP) (macular index: B= −0.03, 95% CI= [-0.05, −0.01], p= 0.002; ONH index: B= 0.05, 95% CI= [0.02, 0.08], p= 0.002). Limitations: Study was cross-sectional and there were no functional assessments of vision. Conclusions: In a large cohort, we observed associations between retinal indices and self-reported depressive symptoms depending on demographic and health factors, notably CRP. Following up, the study will investigate the prospective prediction of retinal health on depressive symptoms, especially in persons who may have chronic inflammation.

Road segmentation is an important task in the field of semantic segmentation, and the Deeplabv3+ algorithm, which is commonly used for road segmentation, has shortcomings, such as numerous parameters and a tendency to lose detailed information. Therefore, this paper proposes DCN-Deeplabv3+, an improved road segmentation algorithm with dual attention modules based on the Deeplabv3+ network, aiming to reduce the model parameters and computation while improving the segmentation accuracy. (1) MobileNetV2 is used as the backbone network to reduce model parameters and memory consumption. (2) DenseASPP+SP is used for multi-scale information fusion to obtain a larger sensory field for improved model performance. (3) The deep learning model’s understanding of the spatial structure of the input data is enhanced by using CA (coordinate attention) to improve the model’s performance in dealing with spatial structure-related tasks. (4) The neural attention mechanism (NAM) is applied to better focus on key regions in the image, thereby improving the accuracy of target detection. The experimental results show that mIoU and mPA are improved by 1.20% and 2.30% on the PASCAL VOC 2012 dataset, mIoU and mPA are improved by 3.15% and 3.90% on the Cityscapes dataset, respectively. It can be concluded that the method proposed in this paper outperforms the baseline method and has excellent segmentation accuracy on roads.

Abstract Background Plant U-box (PUB) E3 ubiquitin ligases have vital effects on various biological processes. Therefore, a comprehensive and systematic identification of the members of the U-box gene family in potato will help to understand the evolution and function of U-box E3 ubiquitin ligases in plants. Results This work identified altogether 74 PUBs in the potato (StPUBs) and examined their gene structures, chromosomal distributions, and conserved motifs. There were seventy-four StPUB genes on ten chromosomes with diverse densities. As revealed by phylogenetic analysis on PUBs within potato, Arabidopsis, tomato (Solanum lycopersicum), cabbage (Brassica oleracea), rice (Oryza sativa), and corn (Zea mays), were clustered into eight subclasses (C1-C8). According to synteny analysis, there were 40 orthologous StPUB genes to Arabidopsis, 58 to tomato, 28 to cabbage, 7 to rice, and 8 to corn. In addition, RNA-seq data downloaded from PGSC were utilized to reveal StPUBs’ abiotic stress responses and tissue-specific expression in the doubled-monoploid potato (DM). Inaddition, we performed RNA-seq on the ‘Atlantic’ (drought-sensitive cultivar, DS) and the ‘Qingshu NO.9’ (drought-tolerant cultivar, DT) in early flowering, full-blooming, along with flower-falling stages to detect genes that might be involved in response to drought stress. Finally, quantitative real-time PCR (qPCR) was carried out to analyze three candidate genes for their expression levels within 100 mM NaCl- and 10% PEG 6000 (w/v)-treated potato plantlets for a 24-h period. Furthermore, we analyzed the drought tolerance of StPUB25 transgenic plants and found that overexpression of StPUB25 significantly increased peroxidase (POD) activity, reduced ROS (reactive oxygen species) and MDA (malondialdehyde) accumulation compared with wild-type (WT) plants, and enhancing drought tolerance of the transgenic plants. Conclusion In this study, three candidate genes related to drought tolerance in potato were excavated, and the function of StPUB25 under drought stress was verified. These results should provide valuable information to understand the potato StPUB gene family and investigate the molecular mechanisms of StPUBs regulating potato drought tolerance.

Abstract Background The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition. Methods We included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P

Low-light image enhancement (LLIE) improves lighting to obtain natural normal-light images from images captured under poor illumination. However, existing LLIE methods do not effectively utilize positional and frequency domain image information. To address this limitation, we proposed an end-to-end low-light image enhancement network called HPCDNet. HPCDNet uniquely integrates a hybrid positional coding technique into the self-attention mechanism by appending hybrid positional codes to the query and key, which better retains spatial positional information in the image. The hybrid positional coding can adaptively emphasize important local structures to improve modeling of spatial dependencies within low-light images. Meanwhile, frequency domain image information lost under low-light is recovered via discrete wavelet and cosine transforms. The resulting two frequency domain feature types are weighted and merged using a dual-attention module. More effective use of frequency domain information enhances the network's ability to recreate details, improving visual quality of enhanced low-light images. Experiments demonstrated that our approach can heighten visibility, contrast and color properties of low-light images while better preserving details and textures than previous techniques.

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management.

Every country in the world, except for African nations, faces significant challenges due to the increasing older population, with China being particularly affected. This issue is more pronounced in rural areas compared to urban centers. To better understand consumer attitudes and willingness to pay (WTP) for age-friendly retrofitting and to identify industry development shortcomings, this study designed a retrofitting scenario and organized a questionnaire survey to collect WTP and its influencing factors from respondents in the Wuhu area of Anhui Province, China. This study determined the retrofit cost to be CNY 12,224.4 and found that over 80% of respondents intended to pursue age-friendly retrofitting. The analysis results indicated that respondents’ education level, perceived psychological benefits, and perceived social benefits were positively correlated with their WTP. Additionally, education level, monthly personal income, and choice of retirement area positively influenced retrofitting budgets, whereas age bracket, employment status, and perceived situational risk negatively influenced them. The study’s findings will assist consumers in making informed retrofitting decisions and support the government in formulating appropriate policies to enhance the quality of rural residential environments and improve the living standards of the elderly.

Smart healthcare management is one of the frontier research directions. It is vital to achieve life assistance through data science, innovative technology, and wearable devices for residents who age at home. This paper analyzes 42 aids of intelligent communities in residents’ physical, psychological, and medical aspects to understand the demand factors for health-assisted public products and services. The results show that: (1) the importance of intelligent assistance technology is significant; (2) the demand for medical monitoring is the highest, especially for an emergency button, fall detection, sleep drop detection, electronic fence, and medication management; (3) remote nursing and telemedicine has the most significant direct and indirect effect on the need for elderly assistance respectively; and (4) health monitoring affects the need for aging assistance by affecting emergency assistance and psychological assistance. Finally, this paper proposes construction strategies for the space design and the elderly care system of communities.

Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data descriptors, file system organization, and metadata. In this study, we present an approach to the integration of multiple brain MRI datasets with a focus on homogenization of their organization and preprocessing for ML. We use our own fusion example (approximately 84,000 images from 54,000 subjects, 12 studies, and 88 individual scanners) to illustrate and discuss the issues faced by study fusion efforts, and we examine key decisions necessary during dataset homogenization, presenting in detail a database structure flexible enough to accommodate multiple observational MRI datasets. We believe our approach can provide a basis for future similarly-minded biomedical ML projects.

Abstract Kawasaki disease (KD), described as “mucocutaneous lymph node syndrome”, affects infants and toddlers. Patients with KD suffer from an inflammatory cascade leading to vasculitis with a predilection for coronary arteries. While the symptoms and pathogenesis of KD have received more and more attention, the precise mechanisms are still debated. Researches show that endothelial dysfunction process in KD leads to arterial damage and affect clinical outcome. In this study, we constructed a Candida albicans water soluble fraction (CAWS)-induced KD murine model and penetrated investigating the mechanisms behind endothelial dysfunction. CAWS-induced mice presented remarkably elevated vascular endothelial cell growth factor (VEGF) levels. Abundant expression of VEGF was documented in all vessels that showed edema from acute KD. It has been reported that Platelet-derived growth factor (PDGF) co-expression normalizes VEGF-induced aberrant angiogenesis. Hyperexpression of PDGFRβ was induced in the thickened medial layer and vascular endothelium of KD mice. Masitinib (Mas) is an oral tyrosine kinase inhibitor of numerous targets, which can selectively target PDGFR signaling. We set out to explore whether Mas could regulate coronary pathology in KD. Mas administration significantly reduced the VEGF-induced endothelial cells migration. NOX4 was activated in vascular endothelial cells to produce more ROS. Mitochondrial dysregulated fission and mitophagy caused by DRP-1 overexpression precipitated the arterial endothelial cells injury. Here, mitophagy seemed to work as the driving force of DRP-1/Bak/BNIP3-dependent endothelial cells apoptosis. In summary, how mitophagy is regulated by DRP-1 under pathologic status is critical and complex, which may contribute to the development of specific therapeutic interventions in cardiovascular diseases patients, for example Masatinib, the inhibitor of PDGFRβ. Facts and questions Kawasaki disease causing systemic vasculitis, affects infants and toddlers. Coronary artery injury remains the major causes of morbidity and mortality. DRP-1 overexpression induces DRP-1/Bak/BNIP3-dependent endothelial cells apoptosis. PDGFRβ was high-expressed in the thickened medial layer of CAWS-induced KD mice. Inhibition of PDGFRβ signaling alleviates arterial endothelial cells injury.

Nowadays, with the approaching carbon neutrality deadlines and the implementation of zero-net carbon emission policies, the research and development of flame retardants are increasingly influenced by stringent regulations and laws. In this context, natural inorganic materials have garnered significant attention as promising flame retardants to enhance the fire resistance of polymer composites. These materials offer unique advantages, such as being environmentally friendly, cost-effective, and producing no carbon emissions during preparation. Consequently, in recent years, there has been a rapid increase in research on natural mineral flame retardants. This review systematically introduces the research progress on natural minerals as flame retardants in epoxy resin, highlighting their ability to provide exceptional fire resistance. Additionally, we categorize the various chemical modification approaches for natural minerals and explore different various natural mineral-based flame retardants. Furthermore, we elucidate the flame retardant mechanisms behind both natural and modified mineral flame retardant systems. Beyond summarizing and concluding the current state of research, we also project future research directions and identify challenges in the development of natural mineral flame retardants.

In recent years, global trade volume has been increasing, and marine transportation plays a significant role here. In marine transportation, the choice of transportation route has been widely discussed. Minimizing fuel consumption, minimizing voyage time, and maximizing voyage security are concerns of the International Maritime Organization (IMO) regarding Maritime Autonomous Surface Ships (MASS). These goals are contradictory and have not yet been effectively resolved. This paper describes the ship path-planning problem as a multi-objective optimization problem that considers fuel consumption, voyage time, and voyage security. The model considers wind and waves as marine environmental factors. Furthermore, this paper uses an improved Whale Optimization Algorithm to solve multi-objective problems. At the same time, it is compared to three advanced algorithms. Through seven three-objective test functions, the performance of the algorithm is tested and applied in path planning. The results indicate that the algorithm can effectively balance the fuel consumption, voyage time, and voyage security of the ship, offering reasonable paths.

This research investigates how age expectation (AE) influences construction worker safety behavior (CWSB) by utilizing self-validation theory (SVT). Using structural equation modeling (SEM) and data from 391 Chinese construction workers, we found that components of AE, such as physical health expectation (PHE), mental health expectation (MHE), and cognitive function expectation (CFE), positively impact CWSB. Safety value (SV), including safety risk perception (SRP) and safety attitude (SA), mediates these effects. The study provides theoretical insights and practical guidance for enhancing CWSB through interventions on AE, supporting sustainable growth and workforce quality in the construction industry.

The development and provision of soil spectral library (SSL) could facilitate the application of near infrared (NIR) spectroscopy for economical, accurate, and efficient determination of soil organic matter (SOM). In this work, the performances of partial least squares regression (PLSR) and convolutional neural network (CNN) combined with the datasets of Zhejiang provincial SSL (ZSSL) and the feature subset (FS) were compared for the prediction of SOM at the target field. The FS dataset was chosen from ZSSL based on similarity to the spectral characteristics of the target samples. The results showed that compared with modelling using ZSSL, modelling using FS can greatly improve the prediction accuracy of the PLSR model, but the impact on the performance of the CNN model was limited. The method of mean squared Euclidean distance (MSD) was an effective way for determining the optimal spiking sample size for the PLSR model only using the spectral data of the spiking subset and the prediction set. The PLSR model combined with the FS dataset and the spiking subset determined by MSD achieved the optimal prediction results among all developed models, which is an accurate and easy-to-implement solution for the SOM determination based on ZSSL.

Abstract Background There is a prevailing view that humans’ capacity to use language to characterize sensations like odors or tastes is poor, providing an unreliable source of information. Methods Here, we developed a machine learning method based on Natural Language Processing (NLP) using Large Language Models (LLM) to predict COVID-19 diagnosis solely based on text descriptions of acute changes in chemosensation, i.e., smell, taste and chemesthesis, caused by the disease. The dataset of more than 1500 subjects was obtained from survey responses early in the COVID-19 pandemic, in Spring 2020. Results When predicting COVID-19 diagnosis, our NLP model performs comparably (AUC ROC ~ 0.65) to models based on self-reported changes in function collected via quantitative rating scales. Further, our NLP model could attribute importance of words when performing the prediction; sentiment and descriptive words such as “smell”, “taste”, “sense”, had strong contributions to the predictions. In addition, adjectives describing specific tastes or smells such as “salty”, “sweet”, “spicy”, and “sour” also contributed considerably to predictions. Conclusions Our results show that the description of perceptual symptoms caused by a viral infection can be used to fine-tune an LLM model to correctly predict and interpret the diagnostic status of a subject. In the future, similar models may have utility for patient verbatims from online health portals or electronic health records.

Animal experiments have played a significant role in advancing scientific knowledge and enhancing people's quality of life. In order to better understand the opinions and knowledge of veterinary students in the domain of laboratory animal welfare and to explore and advance the teaching methods used in animal ethics education, a questionnaire was designed and used to conduct a survey among undergraduate and postgraduate students majoring in veterinary medicine. The survey encompassed various topics, such as students' level of knowledge about animal welfare, their perspectives on laboratory animals, their proficiency with animal experiments, and their opinions on teaching methods and content. The respondents were a total of 150 undergraduate students and 148 graduate students. The survey results indicated that most students expressed a strong sense of responsibility for the safeguarding of the welfare of experimental animals. However, there were a few students who lacked compassion for animals. Additionally, there was a general lack of basic theoretical knowledge of animal ethics and an inadequate grasp of experimental techniques among current students. Furthermore, most of the participants expressed a strong sense of responsibility to advocate for animal welfare. Although a substantial number of students were unaware of the existence of agencies for the supervision of work involving laboratory animals, they supported teaching and supervision in the domain of animal welfare and were open to various teaching methods and topics of content. In conclusion, targeted training and education regarding laboratory animal welfare and ethics should be conducted in the future to address the specific needs of students. This study provides a foundation for future animal welfare education and will help to improve the professional skills and humanistic qualities of veterinary students.

We present the generation of high-repetition-rate strong-field terahertz (THz) pulses from a thin 4-N,N-dimethylamino-4’-N’-methyl-stilbazolium 2,4,6-trimethylbenzenesulfonate (DSTMS) organic crystal pumped by an ytterbium-doped yttrium aluminum garnet laser. The generated THz pulse energy reaches 932.8 nJ at 1 kHz repetition rate, with a conversion efficiency of 0.19% and a peak electric field of 819 kV/cm. At a repetition rate of 10 kHz, it is able to maintain a peak electric field of 236 kV/cm and an average THz power of 0.77 mW. The high-repetition-rate, strong-field THz source provides a convenient tool for the study of THz matter manipulation and THz spectroscopy.

Diamond tools are widely used in grinding, wire sawing and other fields. The characteristics of abrasive particles on the surface are an important factor affecting the machining results and tool performance. To process abrasive grain images with complex background information, this paper proposed an abrasive grain segmentation method based on K-means clustering and convex hull detection, which combines binarization, morphological processing, main contour extraction and other related operations to achieve abrasive grain extraction. Finally, three related indicators, including abrasive grain contour area accuracy ηCAA, abrasive grain position error θPE, and abrasive grain quantity recall rate σQR, were proposed to evaluate the segmentation effect. The results show that the average contour area precision is 98.30%, the average position error is only 2.93%, and the average number recall rate is 95.91%, which proves the accuracy of the method.

Abstract A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

As a successor to LiFePO4, the research interest in LiMn1−yFeyPO4 has been sustained due to its higher working voltage and safety features. However, its further application is limited by the low compaction density caused by uncontrolled particle size. In this study, the high-quality LiMn0.69Co0.01Fe0.3PO4 (LMFP) materials were prepared using the freeze-drying method to process the LMFP precursor synthesized through a solvothermal crystallization method followed by a calcination process at different temperatures (400–550 °C). The results demonstrate that the obtained particles exhibit a spheroidal shape with a low specific surface area after secondary crystallization calcination at 700 °C. The compaction density increased from 1.96 g/cm3 for LMFP precursor (LMFP-M1) to 2.18, 2.27, 2.34, and 2.43 g/cm3 for samples calcined at 400, 450, 500 and 550 °C, respectively, achieving a maximum increase of 24%. The full cell constructed with the high-compaction-density material calcined at 500 °C displayed discharge capacities of 144.1, 143.8, and 142.6 mAh/g at 0.5, 1, and 3 C rates, respectively, with a retention rate of 99% at 3 C rate. After undergoing charging and discharging cycles at a rate of 1 C for up to 800 cycles, the capacity retention rate was found to be 90%, indicating an expected full cell life span exceeding 2500 cycles.

Abstract The sluggish kinetics of sulfur conversions have long been hindering the implementation of fast and efficient sulfur electrochemistry in lithium–sulfur (Li–S) batteries. In this regard, herein the unique chromium boride (CrB) is developed via a well‐confined mild‐temperature thermal reaction to serve as an advanced sulfur electrocatalyst. Its interstitial‐alloy nature features excellent conductivity, while the nano‐lamination architecture affords abundant active sites for host‐guest interactions. More importantly, the CrB nanocatalyst demonstrates a dual sulphophilicity with simultaneous Cr─S and B─S bondage for establishing strong interactions with the intermediate polysulfides. As a result, significant stabilization and promotion of sulfur redox behavior can be achieved, enabling an excellent Li–S cell cyclability with a minimum capacity fading rate of 0.0176% per cycle over 2000 cycles and a favorable rate capability up to 7 C. Additionally, a high areal capacity of 5.2 mAh cm−2, and decent cycling and rate performances are still attainable under high sulfur loading and low electrolyte dosage. This work offers a facile approach and instructive insights into metal boride sulfur electrocatalyst, holding a good promise for pursuing high‐efficiency sulfur electrochemistry and high‐performance Li–S batteries.

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy of the algorithm proposed in this paper has increased by 31.26%, 14.21%, 3.05%, and 1.56%, respectively. In addition, the comparison experiment results of SLAM mapping with the original Real-Time Appearance-Based Mapping (RTAB-MAP) algorithm prove that the loop closure detection algorithm based on deep learning proposed in this paper can enable the system to better build a globally consistent map, including more environmental information.

Abstract While the efficacy of deep brain stimulation (DBS) is well-established in Parkinson’s Disease (PD), the benefit of DBS varies across patients. Using imaging features for outcome prediction offers potential in improving effectiveness, whereas the value of presurgical brain morphometry, derived from the routinely used imaging modality in surgical planning, remains under-explored. This review provides a comprehensive investigation of links between DBS outcomes and brain morphometry features in PD. We systematically searched PubMed and Embase databases and retrieved 793 articles, of which 25 met inclusion criteria and were reviewed in detail. A majority of studies (24/25), including 1253 of 1316 patients, focused on the outcome of DBS targeting the subthalamic nucleus (STN), while five studies included 57 patients receiving globus pallidus internus (GPi) DBS. Accumulated evidence showed that the atrophy of motor cortex and thalamus were associated with poor motor improvement, other structures such as the lateral-occipital cortex and anterior cingulate were also reported to correlated with motor outcome. Regarding non-motor outcomes, decreased volume of the hippocampus was reported to correlate with poor cognitive outcomes. Structures such as the thalamus, nucleus accumbens, and nucleus of basalis of Meynert were also reported to correlate with cognitive functions. Caudal middle frontal cortex was reported to have an impact on postsurgical psychiatric changes. Collectively, the findings of this review emphasize the utility of brain morphometry in outcome prediction of DBS for PD. Future efforts are needed to validate the findings and demonstrate the feasibility of brain morphometry in larger cohorts.

ABSTRACT: H9N2 is currently the main subtype of avian influenza in China. In order to use reverse genetics to rapid preparation of seed strains for vaccine production, and intend to prevent and control the H9N2 subtype epidemic strains of avian influenza virus (AIV). In this study, we successfully rescued 2 H9N2 recombinant viruses based on the representative viruses of Southeast China and confirmed by RT-PCR and sequencing. Genetic stability, pathogenicity, transmissibility, and antigenicity of 2 recombinant viruses were evaluated. Compared to the FZ1, the growth kinetics of H9N2(HA+NA)/PR8 showed no significant difference, H9N2(HA+NA+M+PB1)/PR8 was slightly lower. Our study also confirmed 2 recombinant viruses had good genetic stability after 10 passages but possessed lower pathogenicity than FZ1. Although both recombinant viruses led to seroconversion in all inoculated birds on 14 dpi, they complete loss of viral transmission of the virus to contact birds. In addition, birds were immunized via hypodermic route by inactivated vaccines of H9N2(HA+NA)/PR8, H9N2(HA+NA+M+PB1)/PR8 and wild-type virus with a single dose, and the results showed that the hemagglutination inhibition titers on 21 dpv were 10.5, 9.6, and 10.5 log2, respectively. And recombinant viruses both provided a certain protection against wild-type virus challenge. In conclusion, these data indicated that 2 recombinant viruses will be expected to be used as inactivated vaccines to controlling the spread of H9N2 subtype AIV even have potential application for attenuated viral vaccines, which provides a reference for the prevention and control of influenza virus pandemics.

IntroductionHybrid positron emission tomography/magnetic resonance imaging (PET/MRI) is a novel advanced non-invasive presurgical examination tool for patients with drug-resistant epilepsy (DRE). This study aims to evaluate the utility of PET/MRI in patients with DRE who undergo stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RFTC).MethodsThis retrospective study included 27 patients with DRE who underwent hybrid PET/MRI and SEEG-guided RFTC. Surgery outcome was assessed using a modified Engel classification, 2 years after RFTC. Potential areas of the seizure onset zone (SOZ) were identified on PET/MRI and confirmed by SEEG.ResultsFifteen patients (55%) became seizure-free after SEEG-guided RFTC. Engel class II, III, and IV were achieved in six, two, and four patients, respectively at the 2 years follow-up. MRI was negative in 23 patients and structural abnormalities were found in four patients. Hybrid PET/MRI contributed to the identification of new structural or metabolic lesions in 22 patients. Concordant results between PET/MRI and SEEG were found in 19 patients in the identification of SOZ. Among the patients with multifocal onset, seizure-free status was achieved in 50% (6/12).ConclusionSEEG-guided RFTC is an effective and safe treatment for drug-resistant epilepsy. Hybrid PET/MRI serves as a useful tool for detecting the potential SOZs in MRI-negative patients and guide the implantation of SEEG electrodes. Patients with multifocal epilepsy may also benefit from this palliative treatment.

Staplers are widely used in clinics; however, complications such as bleeding and leakage remain a challenge for surgeons. To tackle this issue, buttress materials are recommended to reinforce the staple line. This Review provides a systematic summary of the characteristics and applications of the buttress materials. First, the physical and chemical properties of synthetic polymer materials and extracellular matrix used for the buttress materials are introduced, as well as their pros and cons in clinical applications. Second, we review the clinical effects of reinforcement mesh in pneumonectomy, sleeve gastrectomy, pancreatectomy, and colorectal resection. Based on the analysis of numerous research data, we believe that buttress materials play a crucial role in increasing staple line strength and reducing the probability of complications, such as bleeding and leakage. However, considering the requirements of bioactivity, degradability, and biosafety, non-crosslinked small intestinal submucosa (SIS) matrix material is the preferred candidate. It has high research and application value, but further studies are required to confirm this. The aim of this Review is to provide comprehensive guidance on the selection of materials for staple line reinforcement.

Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes is of great significance to their production and consumption. In this research, hyperspectral imaging combined with a convolutional neural network with Transformer (CNN-Transformer) was utilized to analyze the SSC and pH of cherry tomatoes. Conventional machine learning and deep learning models were established for the determination of the SSC and pH. The findings demonstrated that CNN-Transformer yielded outstanding results in predicting the SSC, with the coefficient of determination of calibration (R2C), validation (R2V), and prediction (R2P) for the SSC being 0.83, 0.87, and 0.83, respectively. Relatively worse results were obtained for the pH value prediction, with R2C, R2V, and R2P values of 0.74, 0.68, and 0.60, respectively. Furthermore, the visualization of the CNN-Transformer model revealed the wavelength weight distributions, indicating that the 1380–1650 nm range served as the characteristic band for the SSC, while the spectral range at 945–1280 nm was the characteristic band for pH. In conclusion, integrating spectral information features with the attention mechanism of Transformer through a convolutional neural network can enhance the accuracy of predicting the SSC and pH for cherry tomatoes.

Abstract Background With the development of imaging, the etiology of ocular lesions caused by lung cancer was not found only intraocular. Neuro‐ophthalmological imaging has been found to be useful for the diagnosis of meningeal carcinomatosis, although few studies have previously been published. Our study used magnetic resonance imaging (MRI) to determine if there was a the relationship between ocular symptoms and lung cancer metastasis. Methods We carried out a retrospective analysis which included patients with ocular lesions and lung cancer on which orbit MRI was performed together with ocular examination during January 2014 to January 2022. Here, we describe the characteristics of intraocular metastasis and optic nerve sheath lesions. Results A total of 21 lung cancer patients had ocular lesions, of which eight cases had choroidal metastasis; a further eight patients had optic nerve sheath lesions. There was one case (12.5%) of choroidal metastasis with brain or meningeal metastasis, and eight cases (100%) with optic nerve sheath lesions with brain or meningeal metastasis. A total of 75.0% patients with choroidal metastasis did not have any history of known lung cancer, and 25.0% of optic nerve sheath lesions in patients were found before a meningeal metastasis diagnosis. The features of optic nerve sheath lesions included thickening and strengthening of the long segment of the orbital optic nerve sheath with a clear boundary. Conclusions The pathway of choroidal and optic nerve sheath lesions was different. Optic nerve sheath lesions were associated with brain and meningeal metastasis. When lung cancer‐induced meningeal metastasis is suspected, orbital MRI is recommended to assist in the diagnosis.

Abstract There are different characteristics of BC in developing countries and developed countries. We intended to study the factors which influence the survival and prognosis of BC between southern China and the United States. (a) To study the two groups BC patients in southern China from 2001 to 2016 and SEER database from 1975 to 2016. (b) To register, collect and analyze the clinicopathological features and treatment information. Our study found that there are significant differences in tumor size, positive lymph node status and KI-67 between southern China and SEER cohort (P

Remote-sensing visual question answering (RSVQA) aims to provide accurate answers to remote sensing images and their associated questions by leveraging both visual and textual information during the inference process. However, most existing methods ignore the significance of the interaction between visual and language features, which typically adopt simple feature fusion strategies and fail to adequately model cross-modal attention, struggling to capture the complex semantic relationships between questions and images. In this study, we introduce a unified transformer with cross-modal mixture expert (TCMME) model to address the RSVQA problem. Specifically, we utilize the vision transformer (VIT) and BERT to extract visual and language features, respectively. Furthermore, we incorporate cross-modal mixture experts (CMMEs) to facilitate cross-modal representation learning. By leveraging the shared self-attention and cross-modal attention within CMMEs, as well as the modality experts, we effectively capture the intricate interactions between visual and language features and better focus on their complex semantic relationships. Finally, we conduct qualitative and quantitative experiments on two benchmark datasets: RSVQA-LR and RSVQA-HR. The results demonstrate that our proposed method surpasses the current state-of-the-art (SOTA) techniques. Additionally, we perform an extensive analysis to validate the effectiveness of different components in our framework.

The widely-distributed altitude effect of stable isotopes in meteoric water, i.e., the negative correlation between stable hydrogen (or oxygen) isotope compositions and altitude, is the theoretical basis of isotope paleoaltimetry in climate proxies. However, as many recent local observations have indicated, the inverse altitude effect (IAE) in meteoric water does exist, and the regime controlling IAE is still unclear on a global scale. Based on a remote sensing product of the Infrared Atmospheric Sounding Interferometer (IASI), we examined the global frequency of IAE in water vapor isotopes, and the possible influences on isotopes in precipitation and climate proxies. According to the satellite-based δD values in water vapor at 2950 m and 4220 m above sea level, frequent IAEs are observed on a daily scale in North Africa, West and Central Asia, and North America, and IAEs are more likely to occur during the daytime than during the nighttime. We also converted water vapor δD to precipitation δD via equilibrium fractionation and then analyzed the potential presence of IAE in precipitation, which is more associated with climate proxies, and found that the spatial and temporal patterns of water vapor can be transferred to the precipitation. In addition, different thresholds of δD difference were also tested to understand the impact of random errors. The potential uncertainty of the changing isotope and altitude gradient should be considered in paleo-altitude reconstructions.

The development of high-intensity ultrafast laser facilities provides the possibility to create novel physical phenomena and matter states. The timing fluctuation of the laser pulses is crucial for pump–probe experiments, which is one of the vital means to observe the ultrafast dynamics driven by intense laser pulses. In this paper, we demonstrate the timing fluctuation characterization and control of the front end of a 100-PW laser that is composed of a high-contrast optical parametric amplifier (seed) and a 200-TW optical parametric chirped pulse amplifier (preamplifier). By combining the timing jitter measurement with a feedback system, the laser seed and preamplifier are synchronized to the reference with timing fluctuations of 1.82 and 4.48 fs, respectively. The timing system will be a key prerequisite for the stable operation of 100-PW laser facilities and provide the basis for potential pump–probe experiments performed on the laser.

Lithium-sulfur batteries (LSBs) are promising candidates for next-generation high-efficiency energy storage, yet their practical implementation is seriously impeded by the parasitic shuttle effect and sluggish reaction kinetics. Herein, we develop a unique Cu, Co layered double hydroxide (CuCo-LDH) with a hollow and hierarchical structure as an advanced electrocatalyst to tackle these challenges. Combining the compositional, architectural, and chemical advantages, the as-developed CuCo-LDH enables facile charge transfer, fully exposed active interfaces, and strong interactions with polysulfides via metal–sulfur bonding. When employed in the functional separator, a reliable polysulfide barrier can be established against the shuttling behavior, while the excellent catalytic activity realizes fast and efficient sulfur electrochemistry. As a result, the CuCo-LDH-based LSBs achieve a well-restrained capacity decay of 0.049% per cycle over 500 cycles together with a good rate capability up to 5 C. Moreover, a favorable areal capacity of 4.39 mAh cm−2 and decent cyclability are still attainable even under a high sulfur loading of 4.2 mg cm−2 and a low E/S ratio of 6 ml g−1. This work affords a feasible and instructive pathway toward advanced sulfur electrocatalysts as well as high-performance LSBs.

The health status of aquatic animals is closely related to environmental and gut microbiota. Imbalance in microbiota homeostasis may be an important cause of diseases outbreak in aquaculture. A new disease called ''translucent post-larva disease'' (TPD) or ''bacterial vitrified syndrome'' (BVS) has caused huge economic losses to Penaeus vannamei seed industry of China in recent years. However, no studies have been reported on the relationship between this disease and microbiota homeostasis in gut or environment. In the present study, we obtained a batch of shrimp post-larva (PL) showing translucent diseased features in clinically and histologically. We used high-throughput sequencing to analyze the differences of microbiota between healthy and diseased shrimp PL, and corresponding pond water. The results of alpha diversity analysis showed, the Shannon and Smithwilson indexes in diseased shrimp PL gut were significantly higher (P

Abstract Latent intracranial meningeal metastases (IMM) of lung cancer is difficult to determine, yet it is critical to do so given that it impacts the treatment agent. Studies on this disease are rare, thus necessitating further investigation. As a case study, we will explore the application of optic neuroimaging in IMM. A 62‐year‐old female patient was diagnosed with lung adenocarcinoma, which had progressed to osseous metastasis. During the course of chemotherapy, the patient had bilateral vision loss and paralysis of extraocular muscles. Ophthalmologists ruled out disease of the retina and suspected intracranial metastasis; however, brain‐enhanced magnetic resonance angiography and magnetic resonance venography were normal. Given the patient's severe osteoarthropathy and poor physical condition, she refused to undergo a lumbar puncture examination. Optic neuro‐ophthalmology imaging was ultimately used. Utilizing optical coherence tomography, we found that the basement membrane layer in the papilledema was protruding up towards the vitreous cavity. To assist in visualization, the optic nerve sheath was enhanced with optic magnetic resonance imaging. With these methods, the dural metastasis was identified, the treatment agent was changed for the patient, and she had a successful recovery. Thus, optic neuro‐ophthalmology imaging should be recommended for patients who are in the latent course of dural metastasis, and it could also be used to evaluate therapeutic efficacy.

While most deep learning approaches are developed for single images, in real‐world applications, images are often obtained as a series to inform decision‐making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. Herein, an approach that seamlessly integrates deep learning and traditional machine learning models is presented, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, the multilevel interconnections across joints and damage types into the machine learning model are integrated and the crossregulation map of joint damages in rheumatoid arthritis is revealed. An interactive preprint version of the article can be found at https://doi.org/10.22541/au.165828097.71839600/v1.

BackgroundsProstate cancer (PCa) is the second most common male cancer in the world and based on its high prevalence and overwhelming effect on patients, more precise diagnostic and therapeutic methods are essential research topics. As such, this study aims to evaluate the value of three-dimensional transrectal ultrasound (3D-TRUS) in the detection, diagnosis and biopsy of PCa, and to provide a basis for clinical practice of PCa.MethodsRetrospective analysis and comparison of a total of 401 male patients who underwent prostate TRUS in our hospital from 2019 to 2020 were conducted, with all patients having prostate biopsy. Nomogram was used to estimate the probability of different ultrasound signs in diagnosing prostate cancer. The ROC curve was used to estimate the screening and diagnosis rates of 3D-TRUS, MRI and TRUS for prostate cancer.ResultsA total of 401 patients were randomly divided into two groups according to different methods of prostate ultrasonography, namely the TRUS group (251 patients) and the 3D-TRUS group (150 patients). Of these cases, 111 patients in 3D-TRUS group underwent MRI scan. The nomogram further determined the value of 3D-TRUS for prostate cancer. The ROC AUC of prostate cancer detected by TRUS, MRI and 3D-TRUS was 0.5580, 0.6216 and 0.6267 respectively. Biopsy complications were lower in 3D-TRUS group than TRUS group, which was statistically significant (P

Based on the historical data of a commercial blast furnace (BF), the evaluation and prediction models for the BF comprehensive operating status were established by big data mining methods. Firstly, based on the data resources of the data warehouse of BF ironmaking, clean data were obtained by processing the original data with the problem of null values, outlier data, and blowing-down operations data. Then, the AHP_EWM_TPOSIS evaluation model was built with the combined weight of AHP and EWM and the improved TOPSIS algorithm. Finally, the model evaluation results were verified with the actual production situation, and the comprehensive matching rate reached 94.49%, indicating that the model can accurately judge the comprehensive operating status of BF. The evaluation result was the target parameter for building the BF comprehensive operating status prediction model. The results showed that the stacking model achieved better results than the base models in all indicators. The accuracy index of the deviation between the predicted value and the actual value within ±0.05 reached 94.50%, which meets the practical needs of BF production. The evaluation and prediction models provided timely and accurate furnace condition information to the operators in the BF smelting process, which promoted the long-term stable operation of the BF condition.

Abstract Background To analyze blood flow volume alteration that involved both retrobulbar artery and internal carotid artery (ICA) in patients with non-arteritic anterior ischemic optic neuropathy (NAION) and to assess their relevance for the pathogenesis of NAION. Methods Forty two patients with NAION (unilateral affected) and 42 age-matched controls participated in this study. By head-and-neck computed tomographic angiography (CTA), the diameter of ICA and ophthalmic artery (OA) were measured. By colour Doppler imaging (CDI), the mean blood flow velocity (Vm) and the blood flow volume of ICA and OA were measured or calculated. By optical coherence tomography angiography (OCTA), peripapillary and optic disc vessel density were measured. Data obtained from the affected side of the patients were compared to those of the contralateral healthy side and the control. Results Compared with the controls and the contralateral healthy side of the patients with NAION, the diameter of ICA, the blood flow volume of ICA and OA, the peripapillary and optic disc vessel density in the affected side decreased significantly (p

BackgroundMyopia is one of the most common eye diseases in school-aged children. Multifocal lens (MFL) is one of the interventions that has being widely applied to control the progress of myopia. However, the treatment effects of MFLs in school-aged children require to be systematically evaluated.MethodsA systematic analysis on qualified randomized controlled trials (RCTs) in which MFLs were prescribed as the intervention and single-vision lenses (SVLs) as the control was conducted. The treatment effects referring to the mean differences in spherical equivalent refraction (SER) and axial length (AL) between MFLs and SVLs groups were analyzed.ResultsWith annual visit (3-years follow-up), the weighted mean differences (WMDs) in SER between MFLs and SVLs were 0.29 D (95% CI, 0.21 ∼ 0.37, p < 0.00001), 0.46 D (95% CI, 0.32 ∼ 0.60, p < 0.00001), and 0.64 D (95% CI, 0.40 ∼ 0.88, p < 0.00001) at the first, second, and third year; in AL were −0.12 mm (95% CI, −0.14 ∼−0.11, p < 0.00001), −0.19 mm (95% CI, −0.22 ∼−0.16, p < 0.00001), and −0.26 mm (95% CI, −0.31 ∼−0.21, p < 0.00001) at the first, second, and third year. With 6-months interval trials (2-years follow-up), the WMDs in SER from MFLs were 0.14 D (95% CI, 0.08 ∼ 0.20, p < 0.0001), 0.19 D (95% CI, 0.11 ∼ 0.28, p < 0.0001), 0.24 D (95% CI, 0.16 ∼ 0.33, p < 0.0001), 0.31 D (95% CI, 0.18 ∼ 0.44, p < 0.0001) and in AL from MFLs were −0.08 mm (95% CI, −0.09 ∼−0.07, p < 0.00001), −0.10 mm (95% CI, −0.12 ∼−0.09, p < 0.00001), −0.14 mm (95% CI, −0.17 ∼−0.11, p < 0.00001), and −0.18 mm (95% CI, −0.22 ∼−0.14, p < 0.00001) slower comparing with SVLs at follow up of 6, 12, 18, and 24 months, respectively.ConclusionThe treatment effects of MFLs to slow down the myopic progress are positive in both 6-months and annual-visit trials and which could be sustained till 36 months. While a slight weaker treatment effect was observed after the first visit in 6-months visit, a slight rebound was observed at the following visit points. Furthermore, the treatment effects in annual visit are more profound than 6-months visit at almost all stages especially in SER. Our analysis encourages the MFLs users to maintain a long-term treatment with annual visit.

The prerequisite for flexible magnetic electronic devices is the knowledge of the preparation technology of flexible magnetic films and the evolution of the film properties under strain. In this work, CoFeB amorphous ferromagnetic films with stripe domains were prepared on flexible polyimide (PI) substrates by oblique sputtering. The results show that oblique sputtering induces the formation of columnar crystal structure in CoFeB films, which increases the perpendicular magnetic anisotropy of the films, thus leading to the appearance of stripe magnetic domain structures. On this basis, the CoFeB films with stripe domains were processed on a microscopic scale to investigate the size effect and strain regulation on the microscopic domain structure of the magnetic films. The characterization of the magnetic domain structure shows that the stripe domain contrast is reduced by the striped structure prepared by lithography. The triangular, circular and ring patterns deflect the alignment of the stripe domain to different degrees. The experimental results show that the deflection of the stripe domains is caused by the anisotropy of the shapes produced by the different patterns and that the size of the microstructure needs to be close to the period of the stripe domains for the size effect to be significant. In addition, the strain-induced magnetoelastic anisotropy effectively rotates the orientation of the stripe domains, and the variation in domain contrast demonstrates that tensile/compressive strains vary the magnitude of the out-of-plane stray field of the film. Our results provide some insight into the modulation of the physical properties of flexible magnetic films.

K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing. Therefore, a novel fault diagnosis method of rolling bearing using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based three-layer wavelet packet decomposition approach is developed to efficiently preprocess the running-state monitoring data to obtain eigenvectors, which are stored in Hadoop Distributed File System (HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis is proposed to improve the diagnosis accuracy. ACO algorithm is adopted to obtain the global optimal initial clustering centers of K-Means from all eigenvectors, and the K-Means clustering algorithm based on weighted Euclidean distance is used to perform clustering analysis on all eigenvectors to obtain a rolling bearing fault diagnosis model. Thirdly, the efficient parallelization of ACO-K-Means clustering algorithm is implemented on a Spark platform, which can make full use of the computing resources of a cluster to efficiently process large-scale rolling bearing datasets in parallel. Extensive experiments are conducted to verify the effectiveness of the proposed fault diagnosis method. Experimental results show that the proposed method can not only achieve good fault diagnosis accuracy but also provide high model training efficiency and fault diagnosis efficiency in a big data environment.

Facing the massive rolling bearing vibration data, how to improve the training efficiency, diagnosis efficiency, and diagnosis accuracy of the rolling bearing fault diagnosis model is a challenge. Considering that the Spark-GPU platform provides powerful distributed parallel computing capabilities and back propagation neural network (BPNN) optimized by quantum particle swarm optimization (QPSO) algorithm has the characteristics of low computational complexity and high diagnosis accuracy, a rolling bearing fault diagnosis method based on parallel QPSO-BPNN under Spark-GPU platform is proposed. First, the distributed parallelization of QPSO-BPNN model based on Spark-GPU platform is realized, which can improve the training efficiency and diagnosis efficiency of rolling bearing fault diagnosis model in the big data environment. Second, in order to improve the convergence speed of fault diagnosis model, a parameter update strategy suitable for the distributed parallel training of QPSO-BPNN model is designed. At each iteration during training, the local parameters of each worker node are collected to the master node, and the global parameters are updated according to the weights and synchronized to each worker node. Third, a combination strategy of multiple QPSO-BPNN models based on ensemble learning is proposed. The weighted voting method is adopted to combine the output results of different QPSO-BPNN models to obtain the best fault diagnosis result of a sample, which can improve the fault diagnosis accuracy to a certain extent. Experimental results show that the proposed method can quickly perform model training and fault diagnosis for large-scale rolling bearing vibration data, and the fault diagnosis accuracy reaches 98.73%.

Li and Guan present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. The algorithm, which won an open competition, enables fast and accurate delineation of sleep arousal events and would be useful in the scoring process in clinical studies.