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Abstract With the rapid advancement of digital games, serious games have emerged as a significant medium for engaging with cultural heritage. As a primary visual element in cultural heritage, color plays a crucial role in users’ digital learning. However, there is currently a scarcity of serious game designs that specifically focus on cultural heritage colors, indicating potential for improvement in the user experience. To further enhance users’ perception of color and create a more engaging and enriching digital cultural heritage experience, this study proposes a virtual reality serious game (VR SG) design method specifically tailored to cultural heritage colors. Additionally, it explores the potential of VR SG in enhancing the education, entertainment, and dissemination aspects of these colors within cultural heritage. Specifically, we explore how to integrate relevant knowledge of cultural heritage colors into the application process of VR SG and assess users’ learning performance, engagement, and interactive willingness within these VR SG. The study employs the colors of the Dunhuang cave frescoes as a case study for design practice. A total of 30 participants took part in a between-group comparison experiment, utilizing a mixed-methods approach that combined quantitative and qualitative assessments to compare the performance of a VR SG with a VR exhibition game. The results indicate that both the VR SG and the VR exhibition game effectively enhance users’ understanding and retention of knowledge, with VR SG users demonstrating higher levels of task engagement, emotional engagement, willingness to explore, and willingness to share. This study confirms the potential of VR SG to enhance the education, entertainment, and dissemination aspects of cultural heritage colors and discusses the implications of VR SG in supporting digital cultural heritage experiences.

Abstract Objectives This study aims to retrospectively analyze the clinical features of Stanford type A acute aortic dissection (TAAAD) based on Sun’s modified classification, and to investigate whether the Sun’s modified classification can be used to assess the risk of preoperative rupture. Methods Clinical data was collected between January 2018 and June 2019. Data included patient demographics, history of disease, type of dissection according to the Sun’s modified classification, time of onset, biochemical tests, and preoperative rupture. Results A total of 387 patients with TAAAD who met the inclusion criteria of Sun’s modified classification were included. There were more complex types, with 75, 151 and 140 patients in the type A1C, A2C and A3C groups, respectively. The age of the entire group of patients was 51.46 ± 12.65 years and 283 (73.1%) were male. The time from onset to the emergency room was 25.37 ± 30.78 h. There were a few cases of TAAAD combined with stroke, pericardial effusion, pleural effusion, and lower extremity and organ ischemia in the complex type group. The white blood cell count (WBC), neutrophil count (NEC) and blood amylase differed significantly between the groups. Three independent risk factors for preoperative rupture were identified: neutrophil count, blood potassium ion level, and platelet count. Binary logistic regression analysis showed that the Sun’s modified classification could not be used to assess the risk of preoperative rupture in TAAAD. Conclusion TAAAD was classified as the complex type in most patients. WBC, NEC and blood amylase were significantly different between the groups. NEC and serum potassium ion level were independent risk factors for preoperative rupture of TAAAD, while platelet count was its protective factor. More samples are needed to determine whether Sun’s modified classification can be used to evaluate the risk of preoperative rupture.

Abstract Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials, it can achieve optical storage and neuromorphic computing, surpassing the von Neuman architecture-based systems. However, previous research implemented PPC required additional gate voltages and low temperatures, which need additional energy consumption and PPC cannot be achieved across a wide temperature range. Here, we fabricated a simple heterojunctions using zinc(II)-meso-tetraphenyl porphyrin (ZnTPP) and single-walled carbon nanotubes (SWCNTs). By leveraging the strong binding energy at the heterojunction interface and the unique band structure, the heterojunction achieved PPC over an exceptionally wide temperature range (77 K-400 K). Remarkably, it demonstrated nonvolatile storage for up to 2×104 s, without additional gate voltage. The minimum energy consumption for each synaptic event is as low as 6.5 aJ. Furthermore, we successfully demonstrate the feasibility to manufacture a flexible wafer-scale array utilizing this heterojunction. We applied it to autonomous driving under extreme temperatures and achieved as a high impressive accuracy rate as 94.5%. This tunable and stable wide-temperature PPC capability holds promise for ultra-low-power neuromorphic computing.

To investigate the energetic release attributes and damage potential of a specific thermobaric explosive at atmospheric pressure, we had employed a pressure testing system, high-speed camera system, and thermal imager to elucidate the impact of explosive mass on the energetic release characteristics. Our findings underscored that thermobaric explosive can significantly enhance the after-burning effect. We noted a discernible positive relationship among the magnitude of the explosive force, post-detonation combustion effects, and the inherent quality of the given explosive. In addition, the resultant overpressure from the shockwave, positive pressure-area impulse, and the duration of the positive pressure, all markedly exceeded those produced by TNT, exhibiting increases of 38.9 %, 51.48 %, and 7 %, respectively. To quantify their explosive potency, empirical formulae were derived to account for the shock wave parameters of thermobaric explosive. Under equivalent mass conditions, the peak thermal radiation flux per unit area from thermobaric explosive substantially exceeded that of TNT. When the PROBIT (probability unit) model and the overpressure-impulse criteria were utilized to ascertain the damage potential of the respective explosives on human targets and structures, it emerged that TBX imparted superior destructive effects. This analysis underpinned the foundational assessment concerning the blast hazard presented by TBX.

In recent years, wide‐bandgap semiconductor β‐Ga2O3 material has been widely studied because of its excellent properties. Simultaneously, 2D metal oxides (2DMOs) have also become a focus of research owing to their superior stability and unique physical properties arising from quantum confinement effects. Therefore, the exploration of 2D β‐Ga2O3 is expected to reveal its novel electrical properties in electronic applications. However, the synthesis of high‐quality 2D β‐Ga2O3 remains a formidable challenge. Herein, a confined space is constructed to synthesize high‐quality 2D β‐Ga2O3 nanoflakes by enhancing the control of the kinetics of chemical vapor deposition process. In the device results, it is shown that the grown nanoflakes have excellent switching properties and potential artificial synaptic response characteristics. Based on this premise, an artificial recognition system for handwritten numerals is developed, achieving a peak recognition accuracy of approximately 96%. This system holds significant potential for application within an emerging neuromorphic recognition framework tailored for advanced driver‐assistance systems. In this work, a new feasible pathway is provided for the synthesis of 2D non‐layered oxides and the potential of 2D oxides in the field of neuroanalog electronics and recognition is shown, thereby advancing the fields of 2D β‐Ga2O3 electronics and 2DMOs electronics.

BackgroundSerum vitamin B12 and α-Klotho are important markers associated with aging. Limited studies have been conducted on the relationship between vitamin B12 and α-Klotho.ObjectivesThis study investigated the relationship between circulating α-Klotho and vitamin B12.MethodsA total of 4,502 American adults with circulating vitamin B12 levels and α-Klotho levels from the National Health and Nutrition Examination Survey (2011–2014) were included. A weighted multiple linear regression model was used to evaluate the correlation between vitamin B12 and α-Klotho levels. To clarify potential non-linearities, smoothed curve fitting and threshold effects analysis were employed.ResultsA statistically significant non-linear relationship was found between vitamin B12 levels and circulating α-Klotho levels after adjusting for potential confounders. We observed an inverted U-shaped relationship between serum vitamin B12 levels and circulating α-Klotho levels. Notably, serum vitamin B12 levels below the threshold (1,020 pg/mL) exhibited a positive correlation with circulating α-Klotho levels (β = 0.14, 95% confidence interval (CI): 0.09–0.18, p

BackgroundPostural instability and gait disorder and cognitive dysfunction are common symptoms of Parkinson’s disease (PD). Scale assessment is frequently used in the clinic to evaluate PD, but this technique is limited by its lack of sensitivity to changes in disease progression and its difficulty in capturing subtle movements and changes in cognitive function. It is currently believed that high-frequency repetitive transcranial magnetic stimulation (rTMS) can improve motor and cognitive dysfunction in patients with PD, though it remains controversial. Therefore, it is imperative to monitor and dynamically identify changes in postural instability and gait disorder, as well as those in cognitive dysfunction, in PD to develop targeted interventions. In this study, we observed the effect of high-frequency rTMS on gait disorders and cognitive functions in patients with PD by comparing data from wearable devices and eye-tracking devices before and after treatment.MethodsA total of 159 patients with PD were included in this study. A GYENNO MATRIX wearable gait analyzer was used to monitor the objective gait data (including the timed up-and-go, narrow-track, and turning tests), the Eyeknow eye-tracking evaluation system was used to monitor the patient’s eye movement cognition data (including the smooth pursuit, pro-saccade, and anti-saccade tests), and gait and cognitive function–related scales, including the Tinetti Balance Scale, Tinetti Gait Scale, Berg Balance Scale, Mini-Mental State Examination, and Montreal Cognitive Assessment (MoCA), were evaluated at the same time before and after high-frequency rTMS treatment.ResultsThe mean step length, mean stride velocity, stride length, and mean step frequency of patients with PD in the timed up-and-go test all increased compared with those before rTMS treatment, whereas the mean stride time and double support decreased. In the narrow-track test, the mean stride velocity increased and the mean stride time decreased. In the turning test, the turning left duration, turning right duration, mean duration, mean number of steps, and average step duration decreased, while the mean angular velocity increased after rTMS treatment. Compared with those before rTMS treatment, the latency period of patients with PD in overlapping saccades decreased, the completion time of overlapping saccades decreased, and the average saccade speed increased. In the anti-saccade test, the completion time decreased and the average saccade speed increased after rTMS treatment. Compared with those before rTMS treatment, the Tinetti Balance Scale, Tinetti Gait Scale, Berg Balance Scale, Mini-Mental State Examination, and MoCA scores increased, and the MoCA sub-items improved in terms of visual–spatial and executive function, language, abstraction, delayed recall, and orientation after rTMS treatment.ConclusionHigh-frequency rTMS may be an effective therapy for improving gait disorders and cognitive functions in patients with PD.

BackgroundThe mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control. ObjectiveThis review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research. MethodsThe scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic. ResultsA total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization’s declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15%), and Twitter was the most used social media app (32/61, 52%) for researchers. Researchers from 33 countries were concerned about mpox infodemic–related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic. ConclusionsThis comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future.

Background: Pediatric pneumonia presents a significant global health challenge, particularly in low- and middle-income countries. This study aimed to investigate the incidence of pneumonia in preschool children in Urumqi and its association with indoor environmental factors. Methods: This case-control study collected data from December 2018 to December 2019 on 1522 preschool children in Urumqi (779 boys and 743 girls) who were diagnosed with pneumonia by a physician. A control group of children who had never had pneumonia was matched in a 1:1 ratio based on gender, age, and ethnicity. Using questionnaires, data were collected on children's general characteristics, passive smoking, types of housing, flooring materials, and indoor dampness, analyzing potential factors associated with the incidence of pediatric pneumonia. Results: Multivariate analysis revealed that cesarean birth (odds ratio [OR] = 1.27; 95 % confidence interval [95%CI] = 1.08–1.48), being an only child (OR = 1.32; 95%CI = 1.13–1.55), antibiotic treatment during the first year of life (OR = 2.51; 95%CI = 1.98–3.19), passive smoking during the mother's pregnancy (OR = 1.62; 95%CI = 1.24–2.13), living in multi-family apartment housing (OR = 1.64; 95%CI = 1.28–2.10) and other types of housing (OR = 1.47; 95%CI = 1.09–1.99), laminate flooring (OR = 1.31; 95%CI = 1.01–1.72), and tile/stone/cement flooring flooring (OR = 1.31; 95%CI = 1.06–1.61), and dampness in dwelling (during first year of mother's pregnancy) (OR = 1.30; 95%CI = 1.04–1.63) were risk factors for pediatric pneumonia. The use of fresh air filtration systems in children's residences (OR = 0.66; 95%CI = 0.50–0.86) was identified as a protective factor. Conclusion: This study underscores the importance of indoor environmental factors in the prevention of pediatric pneumonia. Public health strategies should consider these factors to reduce the incidence of pneumonia in children. Future research needs to be conducted over a broader geographical range and consider a more comprehensive range of factors influencing pediatric pneumonia.

As a crucial component of the oil transportation system, the oil terminal (or oil tank farm) may often pose some serious threats and damage to the ecological environment and production safety for the oil leakage. An effective emergency management strategy is particularly significant, involving the accurate assessment of leakage time, oil migration path, volume distribution, etc. Therefore, the volume of fluid (VOF) and liquid phase leakage model were applied to investigate the migration and distribution characteristic after the accidental hole occurs in the tank wall, and influence factors including height and diameter of the hole were considered in our study. Results show that the lower the leaking hole position is, the faster the horizontal injection rate of the leakage hole, the increase of the accumulation area of the oil on the ground, and the difference in the overall pressure value at the leakage hole is nearly four times. Under different leakage diameters, the oil spill is symmetrical. The larger the leakage diameter, the higher the amount of oil leakage and the initial leakage speed, the maximum speed is 1.5 times the minimum speed, causing harm to safety and environmental pollution. These results can quickly locate the leakage location of the tank and provide valuable references for emergency management of the oil pipeline transportation system.

Gut microbiota has been implicated in the initiation and progression of various diseases; however, the underlying mechanisms remain elusive and effective therapeutic strategies are scarce. In this study, we investigated the role and mechanisms of gut microbiota in TNBS-induced colitis and its associated kidney injury while evaluating the potential of dietary protein as a therapeutic intervention. The intrarectal administration of TNBS induced colitis in mice, concurrently with kidney damage. Interestingly, this effect was absent when TNBS was administered intraperitoneally, indicating a potential role of gut microbiota. Depletion of gut bacteria with antibiotics significantly attenuated the severity of TNBS-induced inflammation, oxidative damage, and tissue injury in the colon and kidneys. Mechanistic investigations using cultured colon epithelial cells and bone-marrow macrophages unveiled that TNBS induced cell oxidation, inflammation and injury, which was amplified by the bacterial component LPS and mitigated by thiol antioxidants. Importantly, in vivo administration of thiol-rich whey protein entirely prevented TNBS-induced colonic and kidney injury. Our findings suggest that gut bacteria significantly contribute to the initiation and progression of colitis and associated kidney injury, potentially through mechanisms involving LPS-induced exaggeration of oxidative cellular damage. Furthermore, our research highlights the potential of dietary thiol antioxidants as preventive and therapeutic interventions.

Inpainting images that have sizable missing blocks presents a considerable challenge in terms of preserving visual consistency and attaining a convincing result. In this study, the multi-scale generative adversarial network with multi-head external attention for image inpainting (denoted as MGDAN) is proposed. First, an adaptive weight style loss is designed into the generator of multi-scale generative adversarial networks (GAN) to guide the inpainting of the style and structure in the image inpainting, which improves the inpainting effect of image contour and emoticon. Second, the Adaptive Mix model is introduced to address the imbalance in generator and discriminator training by reducing the distance between the difficult and easy samples. This approach is intended to improve the overall performance of the network and drive high-quality image generation. Third, the generator and local discriminator in the image inpainting process are enhanced by the introduction of a multi-head external attention mechanism. This addition aims to capture the long-distance and multi-level dependent relationships between different areas of the image. It is proved beneficial for the generating clear geometric contours in the restored images and improving the overall global consistency of the inpainted results. By employing specific experimental methods, we find the optimal number of heads. The effectiveness of the proposed approach also is demonstrated through the extensive experiments conducted on public datasets.

In the burgeoning remote sensing image data era, the swift and precise retrieval of images from extensive databases has emerged as a critical challenge. This need is particularly pronounced in applications, such as environmental and disaster monitoring, resource investigation, and ground target monitoring, all heavily reliant on remote sensing images. The efficacy of image retrieval hinges significantly on advanced feature extraction methods. However, remote sensing images often suffer from disturbances caused by rich and complex backgrounds. How to extract key regions from remote sensing images, reduce background interference, and improve retrieval accuracy has become a hot research topic. Addressing this challenge, in this article, we propose a region of interest (ROI) guided attention network designed to detect key category regions of targets. This network integrates a class activation map (CAM) module into a deep learning framework for image retrieval. First, the CAM identifies multiple categories corresponding to different remote sensing categories. Second, multiple category features are fed into an ROI-attention module to distinguish the importance of the category. The attention module highlights the category to be detected by suppressing interference from the background. Finally, two branches, the globally extracted image features and the locally extracted features of important categories obtained through the attention module, are integrated to form a comprehensive image representation optimized for retrieving the target object. The efficacy of our proposed method is validated through experiments conducted on diverse datasets, demonstrating an improvement in retrieval accuracy.

Isorhamnetin (ISO) is an active flavonoid compound mainly isolated from the fruits of Hippophae rhamnoides L. and the leaves of Ginkgo biloba L. Previous studies have revealed the antifibrotic action of ISO in the liver and lungs, although its potential protective effects against renal fibrosis and the underlying mechanisms are still poorly understood. Given that many actions of ISO could be similarly attained by hydrogen sulfide (H2S), we speculated that ISO may work through the induction of endogenous H2S. To test the hypothesis, we established the unilateral ureteral obstruction (UUO) renal fibrosis rat model and transforming growth factor-β1(TGF-β1)-induced fibrosis in cultured renal tubular cells. ISO treatment inhibited epithelial–mesenchymal transition (EMT) formation, decreased extracellular matrix (ECM) deposition, and relieved renal fibrosis. Further analysis revealed that ISO stimulated the expression of the H2S-synthesizing enzyme cystathionine lyase (CSE) and cystathionine beta-synthase (CBS), and promoted H2S production in vivo and in vitro. The elevated H2S attenuated oxidative stress and elevated the thiol level. It induced Keap1 sulfhydration, disrupted Keap1-Nrf2 interaction, and promoted the entry of Nrf2 into the nucleus. Finally, we found that circulating H2S mainly derived from the liver, and not the kidney. Collectively, our study revealed that ISO alleviated renal fibrosis by inducing endogenous H2S and regulating Keap1-Nrf2 interaction through sulfhydration of Keap1. Endogenous H2S could be an important mediator underlying the pharmacological actions of ISO. Due to the multifunctional properties of H2S, the H2S-inducing nature of ISO could be exploited to treat various diseases.

In light of the growing demand for green and low-carbon development, the advancement of low-carbon agriculture in alignment with China’s specific national circumstances is imminent. Given this urgency, the accounting of non-CO2 greenhouse gas (GHG) emissions in China’s agricultural system is still in the process of continuous research and improvement. Therefore, in this paper, we present an account of agricultural non-CO2 GHG emissions in Southwest China from 1995 to 2021, based on the carbon emission coefficient method. Furthermore, we explore the extent of the influence of the drivers and the relationship with economic development, utilizing the Stochastic Impact of Regression of Population, Affluence, and Technology (STIRPAT) model and the Tapio model. We observe a general trend of increasing and then decreasing non-CO2 GHG emissions from agriculture in the Southwest region, with a pattern of higher in the center and lower in the east and west. Economic, demographic, structural, and technological levels show different degrees of impact in different provinces, favoring the development of targeted agricultural planning policies in each region. For the majority of the study period, there was a weak or strong decoupling between economic growth and GHG emissions. Finally, recommendations are made to promote low-carbon agricultural development in Southwest China, providing a database and policy support to clarify the GHG contribution of the agricultural system.

Due to the significant impact of occupant behavior uncertainty on building performance, robustness indicators are crucial for assessing and predicting building energy consumption. This study evaluates the robustness of building performance under occupant behavior uncertainty using various robustness indicators such as Maximax, Maximin, Hurwicz, and Laplace. Benchmark values (0.635) and coefficients of variation (0.544) from statistics were introduced to quantify the relative performance of each indicator and the relative dispersion of the data, allowing for fair comparisons across different magnitudes of indicators. “Variance”, “Starr’s Domain”, “Kurtosis”, and “Maximin” were identified as key indicators for assessing the robustness of energy consumption and load data. Based on these indicators and statistical principles, assuming the data follows a normal distribution, energy consumption and loads were predicted, showing that the optimized outcomes demonstrate good robustness.

In the railway system environment, the interconnection of a vast array of intelligent sensing devices has brought about revolutionary changes in the management and monitoring of railway transportation. However, this also poses challenges to the communication service quality within the railway Internet of Things (IoT). Through collective intelligence and collaboration, the nodes within the railway IoT can not only share data and information but also work synergistically to enhance the overall intelligence level and improve decision-making quality of the network. Therefore, this paper proposes a reconnection mechanism based on the computation of node game-theoretic benefits and optimizes this process with the concept of swarm intelligence collaboration. Initially, the game-theoretic benefit values of the nodes in the railway IoT network are calculated. Subsequently, based on the weight priority of the edges, the two edges with the larger weights are selected, and connections are established between nodes with similar game-theoretic benefit values to enhance the network’s robustness. This approach enables rapid networking and efficient communication transmission within the railway IoT, providing robust assurance for the safe and stable operation of the railway.

In maternity rooms, breastfeeding chairs are crucial pieces of equipment that greatly influence the breastfeeding experience. However, an abundance of data indicates that there are still issues with breastfeeding chairs, including inadequate support for breastfeeding, poor body type adaptability, and failure to adequately analyse the requirements of mothers and baby users. This study used an innovative research approach to breastfeeding chair design from the standpoint of user requirements to improve breastfeeding willingness and user experience. We propose a user requirement assessment design approach that integrates the Kano model, analytic hierarchy process (AHP), and quality function development (QFD) based on the requirements of mother and baby groups. This approach was intended to build a user experience evaluation model for mother- and baby-friendly products. Following this approach to breastfeeding chair design, a fuzzy comprehensive evaluation (FCE) was used to assess the chair. Compared to the original breastfeeding chairs, the designs of breastfeeding chairs that met important requirements for mothers and infants, such as safety, hygiene, and breastfeeding support, resulted in an approximately 23 % increase in user satisfaction. This effectively improved the user experience of both mothers and infants. This approach is centred on the basic requirements of mothers and babies. It evaluates the essential requirements that impact the breastfeeding experiences of mothers and babies and provides multifaceted data regarding the attributes of the different requirements of mothers and babies. This results in theoretical research references for ensuing user-driven design products that cater to the requirements of mothers and their infants and play a pivotal role in formulating design guidelines for mother- and baby-friendly products.

The Kangdian region is an important U metallogenic belt in southwestern China and has global significance for investigations of Neoproterozoic U metallogenesis. In this study, we reviewed the findings of previous studies and determined that U mineralization is associated with high-grade metamorphic, Na-rich volcano-sedimentary, and low-grade metasedimentary ore-bearing rocks. Based on geochronology data, U mineralization in the Kangdian region occurred at 830–700 Ma. Neoproterozoic U mineralization in the Kangdian region has been attributed to the breakup of the Rodinia supercontinent. The mineralization is linked to magmatism that included a low degree of partial melting of U-rich basement in an extensional setting. Favorable Neoproterozoic geological and mineralization events in the Kangdian region produced U mineralization, which suggests that this region may contain U deposits. Therefore, studying Neoproterozoic U mineralization in the Kangdian region is important for enhancing the current understanding of the evolution of Rodinia with respect to global events and associated mineralization.

Abstract Background Postural instability and gait disorder dominant (PIGD) is one of the most common disabling symptoms of Parkinson's disease (PD), which seriously affects patients’ quality of life. Therefore, it is essential to identify PIGD and develop targeted interventions to reduce the risk of PIGD in PD patients. Aim: This study aimed to investigate the gait characteristics of PD patients based on wearable devices and to establish a predictive model for their related influencing factors. Methods The retrospective medical records of patients from January 2020 to September 2023 were collected, including 159 patients with PD (divided into PIGD [n = 73] and non‐PIGD [n = 86] groups) and 200 healthy patients (as the healthy control group). Information from social demographic data, a blood test, scale scores, gait analysis based on wearable devices, white matter lesions, and the Fazekas scale was extracted and analyzed. Results Compared with the healthy control group, the mean step length, mean rate, mean angular velocity, and step length were lower in the PD group, while the mean steps were higher in the turning test. The incidence of PIGD was 46% in PD patients, and PD patients with the non‐tremor onset mode were more likely to develop PIGD than those with the tremor onset mode. Compared to the non‐PIGD group, the PIGD group showed more serious gait problems in different experimental tasks and had a higher Hoehn and Yahr (H‐Y) stage, Hamilton Anxiety Scale (HAMA) score, Hamilton Depression Scale score, periventricular white matter (PVWM) score, deep white matter score, and Fazekas scale score, but they had lower hemoglobin levels, D‐dimer levels, Tinetti Balance scores, Tinetti Gait scores, Berg Balance Scale scores, and Mini‐Mental State Examination (MMSE) scores. Logistic regression analysis showed that the MMSE score was negatively correlated with the occurrence of PIGD, while the HAMA score, H‐Y stage, PVWM score, and non‐tremor form of onset were positively correlated with the occurrence of PIGD Conclusion The incidence of gait disorder in PD patients is higher than that in the normal population. Moreover, cognitive dysfunction, anxiety state, H‐Y stage, PVWM score, and the non‐tremor mode of onset can be considered independent risk factors for PIGD.

IntroductionThe cooperation among members of microbial communities based on the exchange of public goods such as 20 protein amino acids (AAs) has attracted widespread attention. However, little is known about how AAs availability affects interactions among members of complex microbial communities and the structure and function of a community.MethodsTo investigate this question, trace amounts of AAs combinations with different synthetic costs (low-cost, medium-cost, high-cost, and all 20 AAs) were supplemented separately to acetate-degrading thermophilic methanogenic reactors, and the differences in microbial community structure and co-occurring networks of main members were compared to a control reactor without AA supplementation.ResultsThe structure of the microbial community and the interaction of community members were influenced by AAs supplementation and the AAs with different synthetic costs had different impacts. The number of nodes, links, positive links, and the average degree of nodes in the co-occurrence network of the microbial communities with AAs supplementation was significantly lower than that of the control without AAs supplementation, especially for all 20 AAs supplementation followed by the medium- and high-cost AAs supplementation. The average proportion of positive interactions of microbial members in the systems supplemented with low-cost, medium-cost, high-cost, all AAs, and the control group were 0.42, 0.38, 0.15, 0.4, and 0.45, respectively. In addition, the ecological functions of community members possibly changed with the supplementation of different cost AAs.DiscussionThese findings highlight the effects of AAs availability on the interactions among members of complex microbial communities, as well as on community function.

Abstract In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Abstract Purpose To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. Methods A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). Results The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. Conclusions A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.

Abstract Background and purpose Renal cell carcinoma (RCC) is a heterogeneous group of cancers. The collagen fiber content in the tumor microenvironment of renal cancer has an important role in tumor progression and prognosis. A radiomics model was developed from dual-energy CT iodine maps to assess collagen fiber content in the tumor microenvironment of ccRCC. Methods A total of 87 patients with ccRCC admitted to our hospital were included in this retrospective study. Among them, 59 cases contained large amounts of collagen fibers and 28 cases contained a small amount of collagen fibers. We established a radiomics model using preoperative dual-energy CT scan Iodine map (IV) imaging to distinguish patients with multiple collagen fibers from those with few collagen fibers in the tumor microenvironment of ccRCC. We extracted features from dual-energy CT Iodine map images to evaluate the effects of six classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT). The effects of the models built based on the dynamic and venous phases are also compared. Model performance was evaluated using quintuple cross-validation and area under the receiver operating characteristic curve (AUC). In addition, a clinical model was developed to assess the clinical factors affecting collagen fiber content. Results Compared to KNN, SVM, and LR classifiers, RF, DT, and XGBoost classifiers trained with higher AUC values, with training sets of 0.997, 1.0, and 1.0, respectively. In the validation set, the highest AUC was found in the SVM classifier with a size of 0.722. In the comparative test of the active and intravenous phase models, the SVM classifier had the best effect with its validation set AUC of 0.698 and 0.741. In addition, there was a statistically significant effect of patient age and maximum tumor diameter on the collagen fiber content in the tumor microenvironment of kidney cancer. Conclusion Radionics features based on preoperative dual-energy CT IV can be used to predict the amount of collagen fibers in the tumor microenvironment of renal cancer. This study better informs clinical prognosis and patient management. Iodograms may add additional value to dual-energy CTs.

The physical stability of solid‒liquid fuel is a factor that needs to be considered for fuel product practicability for storage and transportation. To determine the Influence of liquid bridge force on physical stability, two detection devices were designed. The laws obtained from microscopic experiments are used to verify the physical stability of fuel under different component ratios. The liquid bridge force is found to increase with the droplet volume. Multiliquid bridges above one critical saturation can generate significant resultant forces compared to single-liquid bridges of the same volume. There exist four states of solid‒liquid mixed fuel with increasing liquid saturation rate. The liquid bridge force between the solid and liquid plays a dominant role in the physical stability of the first three states. There may be two stages involved in the stratification process for state 4 fuel, and the liquid viscosity is another factor that cannot be ignored. In the process of selecting a fuel ratio, a larger liquid bridge force between the components can be obtained by properly improving the wetting effect so that the fuel shows better physical stability.

A promising process with the concept of ‘green, efficient and economic’ was carried out to produce a typical AISI D2 tool steel through curved continuous casting (C3). This study aims to demonstrate the segregation and eutectic carbides homogeneity in the C3 billets, which are the key points. The center porosity and center segregation are 2.0 and 1.0 respectively which are according to the standards GB/T 1979–2001 and YB/T 153–2015, and no shrinkage defects are found in the macrostructure. The segregation zones of C and Cr existed in the core and distributed dispersedly. Furthermore, the carbides showed three morphologies such as rod-like, coarse bulk-like and tortuous, where the rod-like carbides formed at the positions of edge to 3/4, the coarse bulk-like carbides emanated at 1/2 and core positions, and the tortuous carbides appeared somewhere in between. The morphologies of carbides are presumably caused by the inhomogeneous diffusion of C and Cr during the solidification. The maximum size of carbides increased and the area ratio of carbides decreased gradually at the positions of the edge to the core in the billets. The carbides evolved during high-temperature annealing, and it exhibited best dissolution which has less amount segregated at grain boundary with smaller size according to the results of experiments and theoretical calculation.

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model’s practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model’s structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting.

Naturally occurring granular uranium particles are typically fine and are often found as accessory minerals. However, reports of megacrystalline uraninite are rare. The discovery of megacrystalline uraninite is a significant achievement in uranium prospecting and mineralogy in the Kangdian region and China. Our team’s research and review of previous studies have led to a systematic summary of the formation age, genetic types, relationship with migmatization, and metallogenic dynamic background of megacrystalline uraninite in the Kangdian region. The key findings are as follows: (1) the formation age of megacrystalline uraninite is Neoproterozoic (790–770 Ma); (2) migmatization preceded uranium mineralization; (3) the formation of megacrystalline uraninite is linked to high-temperature, low-pressure metamorphism caused by partial melting; (4) and the formation of megacrystalline uraninite may be associated with the Rodinia rifting event. This review aims to enhance our understanding of uranium mineralization during the Neoproterozoic in China and worldwide.

ObjectiveTo examine the prevalence and influencing factors of physician-diagnosed allergic rhinitis and parent-reported rhinitis-related symptoms among preschool children in Urumqi city, Xinjiang Uygur Autonomous Region for providing evidence to the prevention and control of the illness in the children. MethodsA self-administered survey was conducted among 10 000 children aged 2 – 7 years in 60 kindergartens selected with stratified random cluster sampling in Urumqi city during August – December 2019. A questionnaire was designed based on the International Study of Asthma and Allergies in Childhood (ISAAC) and Dampness in Buildings and Health (DBH) and the questionnaire was answered by the children's parents or guardians. The data collected were analyzed using SPSS 26.0. ResultsOf the 8 153 preschool children with valid information, 7.6% were reported having been diagnosed as allergic rhinitis and 27.4% ever having experienced rhinitis-related symptoms. The results of unconditional multivariate logistic regression analysis showed that the children with following characteristics were more likely to have allergic rhinitis: at elder age (5 – 7 year vs. 2 years: odds ratio[OR] = 1.36, 95% confidence interval [95%CI]: 1.04 – 1.80), being only child (OR = 1.24, 95%CI: 1.04 – 1.48), being born by cesarean section (versus vaginal delivery: OR = 1.21, 95%CI: 1.02 – 1.44), paternal history of rhinitis (OR = 2.50, 95% CI: 2.10 – 2.98), maternal history of rhinitis (OR = 3.29, 95%CI: 2.76 – 3.91), paternal history of asthma (OR = 1.95, 95%CI: 1.02 – 3.74), with larger living space ( ≥ 75 m2 vs. < 75 m2: OR = 1.25, 95%CI: 1.01 – 1.56), maternal exposure to new furniture at home during pregnancy (OR = 1.79, 95%CI:1.28 – 2.51), and having indoor decoration at home at the age of 0 – 1 year (OR = 1. 82, 95%CI: 1.19 – 2.79) ; while the children being girl (OR = 0.79, 95%CI: 0.67 – 0.94) and living in a house with lime/cement as wallcovering (versus wallpaper: OR = 0.39, 95%CI: 0.16 – 0.98) were less likely to have allergic rhinitis; the results also revealed significant influencing factors of rhinitis-related symptoms as following: being only child (OR = 1.22, 95%CI: 1.10 – 1.36), being born by cesarean section (versus vaginal delivery: OR = 1.14, 95%CI: 1.02 – 1.26), paternal history of rhinitis (OR = 2.16, 95% CI: 1.93 – 2.42), maternal history of rhinitis (OR = 2.38, 95%CI: 2.13 – 2.67), paternal history of asthma (OR = 2.22, 95%CI: 1.34 – 3.69), living in a house with the floor covered by composite material (versus wood: OR = 1.53, 95%CI: 1.28 – 1.84) or tile/stone/cement (versus wood: OR = 1.28, 95%CI: 1.11 – 1.49 ), maternal exposure to new furniture at home during pregnancy (OR = 1.28, 95%CI: 1.03 – 1.60), with beddings being aired in sunny day frequently (OR = 1.42, 95%CI: 1.18 – 1.71), and with indoor cultivation of flowering plant at home at the age of 0 – 1 year (OR = 1.27, 95%CI: 1.11 – 1.46) and protective factors including being girl (OR = 0.87, 95%CI: 0.78 – 0.96), longer duration of exclusive breastfeeding (≥ 6 months vs. < 6 months: OR = 0.86, 95%CI: 0.77 – 0.96), and living in a house with material other than water based paint/emulsion paint/oily paint/board/lime/cement/seaweed mud as wallcovering (versus wallpaper: OR = 0.43, 95%CI: 0.29 – 0.64). ConclusionAmong preschool children in Urumqi city, the prevalence of allergic rhinitis and rhinitis-related symptoms were relatively high and mainly influenced by genetic factors and residential indoor environment.

Objective: Allergic rhinitis (AR) is a disease associated with impaired quality of life and heredity. This study aimed to investigate the association of allergic rhinitis in preschool children with exposure to indoor environment-related factors early in life. Methods: In August 2019, the authors implemented a study among 2020 preschool children in Urumqi City using a case-control design. The study included parental reports for the occurrence of AR in children, parental history of respiratory disease, and indoor environmental correlates of maternal exposure from 1 year prior to pregnancy until the child's age of 0-1 year. Results: Mode of birth (cesarean section) (OR = 1.31, 95% CI = 1.02∼1.67), father with AR (OR = 2.67, 95% CI = 2.08∼3.44), mother with AR (OR = 3.70, 95% CI = 2.88∼4.74), mother with asthma (OR = 3.11, 95% CI = 1.18∼8.20), and mother with newly purchased furniture in the parents' residence during pregnancy (OR = 1.49, 95% CI = 1.03∼2.14) were risk factors for AR in children. Conclusions: The focus of allergic rhinitis should be on children with a family history of AR and asthma and cesarean delivery. Primary prevention efforts for AR in preschool children are avoiding exposure of children to indoor environmental hazardous factors early in life.

Objective: To investigate the indoor environmental risk factors to provide measures for the prevention and control of otitis in preschool children. Method: In this cross-sectional study, a questionnaire survey was administered to preschool children aged 2–7 years from 60 kindergartens in six districts of Urumqi City in August 2019. Multiple regression was run to predict influence factors for otitis media in preschool children. Result: A total of 8153 valid questionnaires were collected. After adjusting for age, the prevalence of otitis among preschool children in Urumqi was 13.1%. Multivariate logistic regression showed that previous antibiotic treatment, treatment with one to two antibiotics before 1 year of age, presence of walls with aqueous or latex paint, use of carpet floor bedding in rooms, newly decorated homes of mothers before pregnancy, purchase of new furniture for homes of children at 0–1 year of age, and presence of flowering plants in the residence of children at 0–1 years of age were all identified as risk factors for the development of otitis in children. Conclusion: Parents should also pay attention to indoor living environments, and reduce indoor renovation in the homes of children during their growth and development, which can positively improve children's indoor living environment, thus effectively preventing otitis in preschool children.

Abstract Background Pneumonia is a common disease worldwide in preschool children. Despite its large population size, China has had no comprehensive study of the national prevalence, risk factors, and management of pneumonia among preschool children. We therefore investigated the prevalence of pneumonia among preschool children in Chinese seven representative cities, and explore the possible risk factors of pneumonia on children, with a view to calling the world's attention to childhood pneumonia to reduce the prevalence of childhood pneumonia. Methods Two group samples of 63,663 and 52,812 preschool children were recruited from 2011 and 2019 surveys, respectively. Which were derived from the cross-sectional China, Children, Homes, Health (CCHH) study using a multi-stage stratified sampling method. This survey was conducted in kindergartens in seven representative cities. Exclusion criteria were younger than 2 years old or older than 8 years old, non-permanent population, basic information such as gender, date of birth and breast feeding is incomplete. Pneumonia was determined on the basis of parents reported history of clearly diagnosed by the physician. All participants were assessed with a standard questionnaire. Risk factors for pneumonia, and association between pneumonia and other respiratory diseases were examined by multivariable-adjusted analyses done in all participants for whom data on the variables of interest were available. Disease management was evaluated by the parents’ reported history of physician diagnosis, longitudinal comparison of risk factors in 2011 and 2019. Results In 2011 and 2019, 31,277 (16,152 boys and 15,125 girls) and 32,016 (16,621 boys and 15,395 girls) preschool children aged at 2–8 of permanent population completed the questionnaire, respectively, and were thus included in the final analysis. The findings showed that the age-adjusted prevalence of pneumonia in children was 32.7% in 2011 and 26.4% in 2019. In 2011, girls (odds ratio [OR] 0.91, 95%CI [confidence interval]0.87–0.96; p = 0.0002), rural (0.85, 0.73–0.99; p = 0.0387), duration of breastfeeding ≥ 6 months(0.83, 0.79–0.88; p

Objective We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). Methods A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. Results The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. Conclusion DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

Summary: Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here, we tackle the problem focusing on long-range projections from the medial prefrontal cortex (mPFC) to the anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments reveal the behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis reveals strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduces behavioral performance, the proportion of memory neurons, and memory-specific FC within the aAIC, whereas optogenetic excitation enhances all of them. mPFC-aAIC projections also bidirectionally modulate the efficacy of STM-information transfer, measured by the contribution of FC spiking pairs to the memory-coding ability of following neurons. Thus, prefrontal projections modulate insular neurons’ functional connectivity and memory-coding ability to support STM.

Acute pancreatitis (AP) stands out as a primary cause of hospitalization within gastrointestinal ailments, attributed to diverse factors, including Epstein-Barr virus (EBV) infection. Nevertheless, the common miRNAs and genes shared between AP and EBV infection remain unclear. In the present study, four datasets GSE194331, GSE42455, GSE45918 and GSE109220 were selected and downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to screen for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Target genes of overlapping DEMs were predicted, and intersections with overlapping DEGs were used to construct a miRNA-mRNA network. In addition, the enrichment analysis, drug prediction, diagnostic accuracy assessment, competitive endogenous RNA (ceRNA) network construction, transcription factor (TF)-miRNA-mRNA network construction, and immune cell infiltration analysis were also carried out. We found a total of 111 genes and 8 miRNAs shared between AP and EBV infection. A miRNA-mRNA network was constructed, which comprised 5 miRNAs and 10 genes exhibiting robust diagnostic performance. Histone deacetylase (HDAC) inhibitor was identified as a novel therapeutic intervention from drug prediction analysis. The results of immune cell infiltration analysis revealed that a consistent and significant difference could be found on activated B cell in AP and EBV-infected individuals in comparison to the controls. Taken together, our work, for the first time, revealed a miRNA-mRNA network shared between AP and EBV infection, thereby enriching a deeper comprehension of the intricate molecular mechanisms and potential therapeutic targets entwined in these two pathological conditions.

Cognitive dysfunction is the most common and important nonmotor symptom in Parkinson’s disease (PD) and can occur at any stage. However, there is still a lack of effective biomarkers to evaluate the decline in cognitive function and predict the progression of the disease, especially in the early stage. At present, the cognitive scale is widely used to evaluate the cognitive function of patients with PD, but its sensitivity and accuracy are relatively limited, especially in the early identification of mild cognitive impairment. Eye movement tracking is an advanced neurophysiological measurement method that serves as a powerful means to study the relationship between behavior and neural mechanisms. In recent years, eye movement tracking has been found to provide a nonverbal and less cognitive method to measure the disease progress of patients with cognitive impairment. Moreover, there is a good correlation between eye movement tracking and the traditional cognitive assessment scale, indicating that eye movement tracking can be used to evaluate and monitor the cognitive status, disease severity, and disease progression of patients with PD. Compared to the traditional cognitive scale, the eye movement detected by the instrument has better objectivity and repeatability. Existing studies have found that executive dysfunction is one of the most important manifestations of cognitive dysfunction in patients with PD and is related to an increase in the error rate of the saccade, an increase in the disinhibition of the delayed saccade task, and a prolongation of the saccade reaction time. This suggests that eye movement measurement plays an important role in the early diagnosis, progression, and differential diagnosis of PD and may even help to predict the disease progression of patients with PD and cognitive impairment. In this article, we review the correlation between cognitive impairment and eye movement disorder in patients with PD.

Glass fiber-reinforced plastics (GFRP) tube-concrete-steel tube double-skin tubular beam (DSTB) is composed of an outer GFRP tube and an inner steel tube, with concrete sandwiched between two tubes. It possesses numerous advantages, including excellent ductility and corrosion resistance, lightweight, and convenient construction. Current research on DSTBs is primarily focused on the GFRP tube with an approximate ±90° fiber angle (the angle between the fiber direction and the longitudinal axis of DSTB). An experimental study on DSTBs with ±45°, ±56°, ±68°, and ±80° fiber angles was conducted under bending load. Experimental results show that the ultimate bearing capacity and deformation ability of DSTBs increases with the increase of fiber angle, the ultimate moment capacity of DSTBs with ±56°, ±68°, and ±80° fiber angles were 2.61%, 6.69%, and 6.74% higher than that of DSTB with ±45° fiber angle, respectively, the deformation ability of DSTBs with ±56°, ±68°, and ±80° fiber angles were 23.03%, 32.04%, and 38.48% higher than that of DSTB with ±45° fiber angle, respectively. However, the cracking strain decreases with the increase of fiber angle.

ABSTRACTCytotoxic T lymphocyte-associated antigen 4 (CTLA-4) is a critical inhibitory checkpoint molecule, and monoclonal antibodies (mAbs) targeting CTLA-4 that restore anti-tumor T cell immunity have achieved clinical success. Here, we report a humanized IgG1 mAb, namely JS007, with high binding affinity to CTLA-4. JS007 shows superior binding affinity and T-cell activating efficiency over ipilimumab. Moreover, it demonstrates substantial in vivo tumor suppression efficacy at low doses. The crystal structure of JS007/CTLA-4 complex (PDB: 8HIT) shows JS007 adopts a heavy-chain-dominant binding mode, and mainly contacts the BC loop, DE loop and FG loop of CTLA-4. Notably, two Tyr residues (VH-Y100 and VL-Y32) from the complementarity-determining region loops insert into the two cavities formed by the residues from the loops of CTLA-4, which may contribute to the stabilization of the binding. Comparative analysis with other anti-CTLA-4 mAbs indicates that the double “wedge-into-hole” binding mode is unique for JS007 and may be responsible for the high-affinity binding to CTLA-4. These findings have provided an important molecular understanding of the high-affinity CTLA-4 blockade mAbs and shed light on future development of agents targeting CTLA-4.

Abstract Background The flowering period is a critical time for the growth of rape plants. Counting rape flower clusters can help farmers to predict the yield information of the corresponding rape fields. However, counting in-field is a time-consuming and labor-intensive task. To address this, we explored a deep learning counting method based on unmanned aircraft vehicle (UAV). The proposed method developed the in-field counting of rape flower clusters as a density estimation problem. It is different from the object detection method of counting the bounding boxes. The crucial step of the density map estimation using deep learning is to train a deep neural network that maps from an input image to the corresponding annotated density map. Results We explored a rape flower cluster counting network series: RapeNet and RapeNet+. A rectangular box labeling-based rape flower clusters dataset (RFRB) and a centroid labeling-based rape flower clusters dataset (RFCP) were used for network model training. To verify the performance of RapeNet series, the paper compares the counting result with the real values of manual annotation. The average accuracy (Acc), relative root mean square error (rrMSE) and $$R^2$$ R 2 of the metrics are up to 0.9062, 12.03 and 0.9635 on the dataset RFRB, and 0.9538, 5.61 and 0.9826 on the dataset RFCP, respectively. The resolution has little influence for the proposed model. In addition, the visualization results have some interpretability. Conclusions Extensive experimental results demonstrate that the RapeNet series outperforms other state-of-the-art counting approaches. The proposed method provides an important technical support for the crop counting statistics of rape flower clusters in field.

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause significant property damage and casualties. Existing geophysical methods are subject to many limitations in the shield-tunnel environment, where the detection space is extremely small, and a variety of advanced detection methods are unable to meet the required detection requirements. Therefore, it is crucial to accurately detect the geological conditions in front of the tunnel face in real time during the tunnel boring process of TBM tunnels. In this paper, a 3D-ERT advanced detection method using source-position electrode excitation is proposed. First, a source-position electrode array integrated into the TBM cutterhead is designed for the shield-tunnel construction environment, which provides data security for the inverse imaging of the anomalous bodies. Secondly, a 3D finite element tunnel model containing high- and low-resistance anomalous bodies is established, and the GREIT reconstruction algorithm is utilized to reconstruct 3D images of the anomalous body in front of the tunnel face. Finally, a physical simulation experiment platform is built, and the effectiveness of the method is verified by laboratory physical modeling experiments with two different anomalous bodies. The results show that the position and shape of the anomalous body in front of the tunnel face can be well reconstructed, and the method provides a new idea for the continuous detection of shield construction tunnels with boring.

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.

A study of the hot salt corrosion behavior of three nickel-based single-crystal superalloys at 900 °C was conducted. We discovered that the corrosion layer on each alloy was distinctly enriched with Mo, Ni, S, and O, primarily comprising sulfides and oxides. Notably, variations in oxygen distribution across the alloys revealed that the elemental composition plays a pivotal role in their corrosion resistance. These insights not only advance our understanding of the mechanisms driving thermal corrosion in nickel-based single-crystal superalloys but also lay the groundwork for designing alloys with enhanced durability tailored to high-temperature applications. This research marks a significant step toward the optimal design and utilization of superalloys in sectors demanding exceptional material stability under thermal stress.

Chinese spelling errors are commonplace in our daily lives, which might be caused by input methods, optical character recognition, or speech recognition. Due to Chinese characters’ phonetic and visual similarities, the Chinese spelling check (CSC) is a very challenging task. However, the existing CSC solutions cannot achieve good spelling check performance since they often fail to fully extract the contextual information and Pinyin information. In this paper, we propose a novel CSC framework based on multi-label annotation (MLSL-Spell), consisting of two basic phases: spelling detection and correction. In the spelling detection phase, MLSL-Spell uses the fusion vectors of both character-based pre-trained context vectors and Pinyin vectors and adopts the sequence labeling method to explicitly label the type of misspelled characters. In the spelling correction phase, MLSL-Spell uses Masked Language Mode (MLM) model to generate candidate characters, then performs corresponding screenings according to the error types, and finally screens out the correct characters through the XGBoost classifier. Experiments show that the MLSL-Spell model outperforms the benchmark model. On SIGHAN 2013 dataset, the spelling detection F1 score of MLSL-Spell is 18.3% higher than that of the pointer network (PN) model, and the spelling correction F1 score is 10.9% higher. On SIGHAN 2015 dataset, the spelling detection F1 score of MLSL-Spell is 11% higher than that of Bert and 15.7% higher than that of the PN model. And the spelling correction F1 of MLSL-Spell score is 6.8% higher than that of PN model.

Abstract Surface engineering is effective for developing materials with novel properties, multifunctionality, and smart features that can enable their use in emerging applications. However, surface engineering of carbon nanotubes (CNTs) to add color properties and functionalities has not been well established. Herein, a new surface engineering strategy is developed to achieve rainbow‐colored CNTs with high chroma, high brightness, and strong color travel for visual hydrogen sensing. This approach involved constructing a bilayer structure of W and WO3 on CNTs (CNT/W/WO3) and a trilayer structure of W, WO3, and Pd on CNTs (CNT/W/WO3/Pd) with tunable thicknesses. The resulting CNT/W/WO3 composite film exhibits a wide range of visible colors, including yellow, orange, magenta, violet, blue, cyan, and green, owing to strong thin‐film interference. This coloring method outperforms other structural coloring methods in both brightness and chroma. The smart CNT/W/WO3/Pd films with porous characteristics quickly and precisely detect the hydrogen leakage site. Furthermore, the smart CNT/W/WO3/Pd films allow a concentration as low as 0.6% H2/air to be detected by the naked eye in 58 s, offering a very practical and safe approach for the detection and localization of leaks in onboard hydrogen tanks.

Industrial green development is an important part of the global sustainable development goals, and it is the only way for China to achieve the ''dual carbon goals''. This paper aims to explore the methods and indicators for the assessment of the level of industrial green development, and emphasizes its applicability in the ''city area'' scale. According to the characteristics of industrial green development, an evaluation system including 4 first-level indicators of economic benefits and structure, technological innovation and investment, environmental impact and governance, resource consumption and reuse, and a total of 16 s-level indicators were established, each indicator was determined using the entropy value theory The weight value is based on the conditions of 15 sub-provincial cities across the country to determine the standard value of each indicator. Through the set pair analysis of the five-element connection number and partial connection number, the comprehensive connection degree and the full partial connection number are obtained, so as to evaluate the level of industrial green development in Chengdu static evaluation and dynamic analysis. The results show that the constructed index system and evaluation method are applicable to the evaluation of industrial green development at the ''city'' scale. The current status of Chengdu's industrial green development is at a qualified level, and the industrial process will have a more active and green development trend in the future. The government management department should formulate appropriate management policies and strengthen comprehensive evaluation and research, so as to take the green industrial development process of Chengdu to a new level.

Abstract To investigate the prevalence and indoor environmental influencing factors of wheeze and asthma among preschool children in Urumqi, Xinjiang, China to provide a strong basis for prevention and control. In August 2019, a cross-sectional study involving 8153 preschool children was conducted in 60 kindergartens in Urumqi. The ALLHOME-2 questionnaire was used for childhood wheeze and asthma survey, and the dampness in buildings and health (DBH) questionnaire was used for the childhood home dwelling and living environment survey. Multivariate unconditional logistic regression was then used to analyze the potential influencing factors of childhood asthma and wheeze. The prevalence of wheeze and asthma in children was 4.7% and 2.0%, respectively. Multivariate unconditional logistic regression results suggested that ethnicity other than the Han Chinese (odds ratio (OR) 1.39, 95% confidence interval (CI) 1.05–1.84), caesarean section (OR 1.24, 95% CI 1.00–1.53), family history of asthma (OR 5.00, 95% CI 3.36–7.44), carpet or floor bedding at home (OR 1.40, 95% CI 1.05–1.87), purchasing new furniture in the mother’s residence during pregnancy (OR 1.58, 95% CI 1.06–2.36), pet keeping in the residence at aged 0–1 year (OR 1.55, 95% CI 1.13–2.13), passive smoking by child in the current residence (OR 1.35, 95% CI 1.01–1.80), and having mould or hygroma in the child's residence at aged 0–1 year (OR 1.72, 95% CI 1.12–2.64) were risk factors for wheeze. In addition, Girls (OR 0.73, 95% CI 0.59–0.90) was a protective factor for wheeze. Caesarean section (OR 1.46, 95% CI 1.06–2.00), family history of asthma (OR 7.06, 95% CI 4.33–11.53), carpet or floor bedding at home (OR 2.20, 95% CI 1.50–3.23), and pet keeping in the residence at aged 0–1 year (OR 1.64, 95% CI 1.04–1.83) were risk factors for asthma, whereas Girls (OR 0.58, 95% CI 0.42–0.80) was a protective factor for asthma. This survey indicates that the purchase of new furniture, the placement of carpet or floor bedding in the child's residence, the pets keeping, room dampness or moldy phenomena, and passive smoking may all contribute to an elevated risk of wheeze or asthma in children.

Deep learning techniques continue to be used in various applications in recent years. However, when it is difficult to obtain adequate training samples, the performance of the depth model will degrade. Although few-shot learning and data enhancement techniques can relieve this dilemma, the diversity of real data is too large to simulate. To tackle this challenge, we study a novel method, Data Augmentation Scheme For Few-Shot Object Detection (DA-FSOD), to improve the efficiency of model training on visual tasks. Specifically, to expand data augmentation space, we build a data augmentation operation pool (DAOP) based on several common-applied image process operations. Then we propose a novel data augmentation scheme, the series and parallel connection scheme, which superimposes the effects of different operations to generate diverse variants. To further explore and utilize the deep feature information, we leverage the semantic information of input image in model and propose imposed semantic data augmentation which augments training set semantically via deep features of augmented variants. The proposed method successfully enhanced the model performance. We validated our approach using extensive experiments on the domain of few-shot object detection. The results showed remarkable gains compared to state-of-the-art methods.

Objective To learn the production efficient of platelet components among prefecture-level blood stations in China, to provide supporting data for those blood stations to optimize the production mode of platelet components and continuously improve production efficiency and supply capacity. Methods The data from 2017 to 2020 was obtained from 24 prefecture-level blood stations who were the members of the practice comparison network for blood institutes in China. The collection units of apheresis platelets, the number of dual-collections of apheresis platelets and plasma, the average apheresis units of one platelet apheresis procedure, the discarded rate of apheresis platelets, the amount of expired apheresis platelets and the amount of apheresis platelets issued were collected. For concentrated platelets, the prepared amount of platelet concentrates and the amount of expired platelet concentrates were collected; both the quantity of qualified and issued concentrated platelets were submitted for statistical analysis.The total output and efficiency of platelet components were calculated based on the collected data. Results The average annual growth rate of apheresis platelets collection in 24 prefecture-level blood stations was 12.23%, accounting for 99.80% of the total platelet output; the average collection unit of one platelets apheresis procedure was 1.75; from 2019 to 2020, only 5 blood stations performed dual-collection of platelet and plasma during one apheresis procedure; the discarded rate of apheresis platelets was 0.28%, of which 0.007% was due to expiration. A total of 1 621.2 therapeutic units of concentrated platelets were prepared, and 13.03% of them was discarded due to the expiration. The production efficiency of platelet components was 97.56%, of which the production efficiency of apheresis platelets was 97.61% and the production efficiency of concentrated platelets was 74.43%. Conclusion There are large regional differences in the supply capacity of platelet components in prefecture-level blood stations. Apheresis platelets are the main resource of platelet components product, and the collection capacity is increasing over the years with the characteristics of high production efficiency and low expiration scrapping rate. However, the preparation of concentrated platelets are still limited with relatively low production and high expiration discarded rate.