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Abstract Background The activation of endoplasmic reticulum (ER) stress is an early pathological hallmark of Alzheimer’s disease (AD) brain, but how ER stress contributes to the onset and development of AD remains poorly characterized. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is a non-canonical neurotrophic factor and an ER stress inducible protein. Previous studies reported that MANF is increased in the brains of both pre-symptomatic and symptomatic AD patients, but the consequence of the early rise in MANF protein is unknown. Methods We examined the expression of MANF in the brain of AD mouse models at different pathological stages. Through behavioral, electrophysiological, and neuropathological analyses, we assessed the level of synaptic dysfunctions in the MANF transgenic mouse model which overexpresses MANF in the brain and in wild type (WT) mice with MANF overexpression in the hippocampus. Using proteomic and transcriptomic screening, we identified and validated the molecular mechanism underlying the effects of MANF on synaptic function. Results We found that increased expression of MANF correlates with synapse loss in the hippocampus of AD mice. The ectopic expression of MANF in mice via transgenic or viral approaches causes synapse loss and defects in learning and memory. We also identified that MANF interacts with ELAV like RNA-binding protein 2 (ELAVL2) and affects its binding to RNA transcripts that are involved in synaptic functions. Increasing or decreasing MANF expression in the hippocampus of AD mice exacerbates or ameliorates the behavioral deficits and synaptic pathology, respectively. Conclusions Our study established MANF as a mechanistic link between ER stress and synapse loss in AD and hinted at MANF as a therapeutic target in AD treatment.

Abstract Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs.

IntroductionThe moment quantities associated with the nonlinear Schrödinger equation offer important insights into the evolution dynamics of such dispersive wave partial differential equation (PDE) models. The effective dynamics of the moment quantities are amenable to both analytical and numerical treatments.MethodsIn this paper, we present a data-driven approach associated with the “Sparse Identification of Nonlinear Dynamics” (SINDy) to capture the evolution behaviors of such moment quantities numerically.Results and DiscussionOur method is applied first to some well-known closed systems of ordinary differential equations (ODEs) which describe the evolution dynamics of relevant moment quantities. Our examples are, progressively, of increasing complexity and our findings explore different choices within the SINDy library. We also consider the potential discovery of coordinate transformations that lead to moment system closure. Finally, we extend considerations to settings where a closed analytical form of the moment dynamics is not available.

Traditional methods for algae removal in drinking water treatment, such as coagulation and sedimentation, face challenges due to the negative charge on algae cells’ surfaces, resulting in ineffective removal. Ultrasonic cavitation has shown promise in enhancing coagulation performance by disrupting extracellular polymer structures and improving cyanobacteria removal through various mechanisms like shear force and free radical reactions. However, the short lifespan and limited mass transfer distance of free radicals in conventional ultrasonic treatment lead to high energy consumption, limiting widespread application. To overcome these limitations and enhance energy efficiency, advanced carbon-based materials were developed and tested. Nitrogen-doped functional groups on nanodiamond surfaces were found to boost sonosensitivity by increasing the production of reactive oxygen species at the sonosensitizer-water interface. Utilizing low-power ultrasound (0.12 W/mL) in combination with N-ND treatment for 5 min, removal rates of Microcystis aeruginosa cells in water exceeded 90 %, with enhanced removal of algal organic matters and microcystins in water. Visualization through confocal microscopy highlighted the role of positively charged nitrogen-doped nanodiamonds in aggregating algae cells. The synergy between cell capturing and catalysis of N-ND indicates that efficient mass transfer of free radicals from the sonosensitizer’s surface to the microalgae’s surface is critical for promoting cyanobacteria floc formation. This study underscores the potential of employing a low-intensity ultrasound and N-ND system in effectively improving algae removal in water treatment processes.

Urban Sustainable Development (USD) is crucial for social advancement, and the rise of the Digital Economy (DE) offers potential pathways to achieve it. This study has employed the DPSIR framework and an entropy-based approach to evaluate the DE and USD across 284 Chinese prefecture-level cities from 2011 to 2020. Using the Spatial Durbin Model, the results have revealed that DE significantly enhances USD and has a spatial spillover effect of up to 300 km. The eastern region, as well as the Pearl River Delta and Yangtze River Delta regions, show stronger spillover effects than the central and western regions and the Beijing-Tianjin-Hebei region. These findings have provided insights for policymakers to leverage digital advancements to promote urban sustainability.

Abstract Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.

Abstract Huntington’s disease (HD) is a monogenic neurodegenerative disease, caused by the CAG trinucleotide repeat expansion in exon 1 of the Huntingtin (HTT) gene. The HTT gene encodes a large protein known to interact with many proteins. Huntingtin-associated protein 40 (HAP40) is one that shows high binding affinity with HTT and functions to maintain HTT conformation in vitro. However, the potential role of HAP40 in HD pathogenesis remains unknown. In this study, we found that the expression level of HAP40 is in parallel with HTT but inversely correlates with mutant HTT aggregates in mouse brains. Depletion of endogenous HAP40 in the striatum of HD140Q knock-in (KI) mice leads to enhanced mutant HTT aggregation and neuronal loss. Consistently, overexpression of HAP40 in the striatum of HD140Q KI mice reduced mutant HTT aggregation and ameliorated the behavioral deficits. Mechanistically, HAP40 preferentially binds to mutant HTT and promotes Lysine 48-linked ubiquitination of mutant HTT. Our results revealed that HAP40 is an important regulator of HTT protein homeostasis in vivo and hinted at HAP40 as a therapeutic target in HD treatment.

Abstract The regions of East Asia, as well as South Asia and the Middle East are rich in cultural heritage of grottoes where a large number of Buddhist niches exist. Three-dimensional (3D) semantic modelling enriches 3D geometric models with an understanding of the historical and cultural value of cultural heritage, facilitating interoperability and analysis beyond mere visualization. 3D models with semantic information act as essential digital infrastructure for heritage management, knowledge dissemination and simulation analysis in cultural heritage. However, due to the lack of standardized data model for the grotto domain, it is difficult to exchange information, share knowledge and the advancement of spatial analysis and simulation. In order to fill the above-mentioned gap, this study develops a data model for niches as a CityGML Application Domain Extension (ADE) based on the CityGML 3.0 standard. In this ADE, niche components and their attributes are defined on two semantic levels, i.e., niche structural component and niche component member. A famous niche in China belonging to the World Heritage was selected as a case study to demonstrate the integration of geometries, semantics and attributes, illustrating that the extended ADE module complies with the CityGML 3.0 standard. This study provides novel insights into the 3D semantic modelling of niches as well as expands the applications scope of CityGML standard within the cultural heritage sector.

Abstract Moisture-electric generators (MEGs) has emerged as promising green technology to achieve carbon neutrality in next-generation energy suppliers, especially combined with ecofriendly materials. Hitherto, challenges remain for MEGs as direct power source in practical applications due to low and intermittent electric output. Here we design a green MEG with high direct-current electricity by introducing polyvinyl alcohol-sodium alginate-based supramolecular hydrogel as active material. A single unit can generate an improved power density of ca. 0.11 mW cm−2, a milliamp-scale short-circuit current density of ca. 1.31 mA cm−2 and an open-circuit voltage of ca. 1.30 V. Such excellent electricity is mainly attributed to enhanced moisture absorption and remained water gradient to initiate ample ions transport within hydrogel by theoretical calculation and experiments. Notably, an enlarged current of ca. 65 mA is achieved by a parallel-integrated MEG bank. The scalable MEGs can directly power many commercial electronics in real-life scenarios, such as charging smart watch, illuminating a household bulb, driving a digital clock for one month. This work provides new insight into constructing green, high-performance and scalable energy source for Internet-of-Things and wearable applications.

In recent years, the deep learning-based semantic segmentation for point clouds has demonstrated remarkable capabilities in processing 3D urban scenes for applications such as three-dimensional reconstruction, semantic modeling, and augmented reality. However, research on grottoes scenes is very limited. It is currently unclear how existing neural architectures for point cloud semantic segmentation perform in grotto scenes, and how to effectively incorporate the unique characteristics of grotto scenes to enhance the performance of deep neural networks. This study proposed a method for point cloud semantic segmentation of grotto scenes, combining knowledge with deep learning approaches. The method adopted knowledge to guide the creation of benchmark datasets, the design of a neural network called GSS-Net, and the correction of segmentation errors in the results of deep learning. The results show that the proposed method outperforms four existing mainstream models without the correction of segmentation results. Moreover, a set of ablation studies verified the effectiveness of each proposed module. This method not only improves the accuracy of point cloud semantic segmentation in grotto scenes but also enhances the interpretability of network designs. It provides new insights into the application of knowledge-guided deep learning models in grotto scenes.

Abstract Background Relevant evidence suggests that angiogenic factors contribute significantly to fibril matrix reconstruction following physical injuries to tendon ligaments. Vascular endothelial growth factor A (VEGFA), with its potent angiogenic effect, has been studied extensively, and its functional polymorphisms, including rs699947, rs1570360, and rs2010963, have been the focus of numerous investigations. Some scholars have explored the association between gene polymorphisms in the VEGFA and the risk of tendon ligament injury, but the findings are not entirely consistent. Objectives The purpose of this study was to investigate the association between rs699947, rs1570360, and rs2010963 gene polymorphisms in VEGFA and the risk of tendon and ligament injuries. Methods After including articles about the association of VEGFA rs699947, rs1570360, and rs2010963 polymorphisms with tendon and ligament injuries according to the search strategy, we assessed their quality and conducted meta-analyses to examine the link between these polymorphisms and the risk of tendon and ligament injuries using odds ratios and 95% confidence intervals. Results Of 86 related articles, six were included in the meta-analysis. Some of these suggest an association between VEGFA rs2010963 and the risk of tendon and ligament injury in the population, with the specific C allele being one of the adverse factors for knee injury. Some studies suggest that VEGFA rs699947 and VEGFA rs1570360 single-nucleotide polymorphisms are associated with anterior cruciate ligament rupture. The risk of non-contact anterior cruciate ligament rupture is nearly doubled in individuals with the rs699947 CC genotype compared to the control group. Our analysis did not find any significant relationship between VEGFA gene polymorphisms (rs699947, rs1570360, and rs2010963) and the chance of tendon and ligament injury without consideration of race. However, the European population reveals that the CC genotype of VEGFA rs699947 can result in a greater risk of tendon and ligament injury, whereas the AG genotype for rs1570360 provides some protection. Additionally, rs2010963 was significantly associated with tendon and ligament injury; individuals with the C allele and the CC genotype had higher risk. False-positive report probability confirmed the high credibility of our results. Conclusion Overall, this study found no significant association between VEGFA rs699947, rs1570360, and rs2010963 polymorphisms and the risk of tendon ligament injury. However, in subgroup analysis, some genotypes of VEGFA rs699947, rs1570360, and rs2010963 were found to increase the risk of tendon ligament injury in European populations.

Abstract As immovable stone cultural heritage is kept in the open air, they are more susceptible to damage, and damage detection is very important for the protection and restoration of cultural heritage. This is especially true for gold-overlaid stone cultural heritage, which is usually more complicated than ordinary stone carvings. However, the detection of cultural heritage damages is mainly based on expert visual inspection, which is often subjective, time-consuming, and laborious. This paper uses the Mask R-CNN algorithm to rapidly and accurately detect the gold foil shedding of stone cultural heritage through two-dimensional images. The research data are from the high-precision images of the Dazu Thousand-Hand Bodhisattva Statue (World Heritage, UNESCO) in Chongqing, China. After cleaning and augmentation, 1900 images are input into Mask R-CNN model for training. Finally, the average precision value (AP) for detecting gold foil shedding is found to be 0.967. In order to test the performance of the model, the new images that do not participate in the training period are used, and it is found that the model can still accurately detect the gold foil shedding even if there are interference factors. This is the first attempt to detect the damages of gold-overlaid stone cultural heritage based on a deep learning algorithm, and it has achieved good results.

Abstract The integration of 3D geometric models with semantic information significantly improves the applicability and comprehensibility of cultural heritage. The semantic 3D modeling of Chinese grottoes poses challenges for individuals without expertise in cultural heritage due to gaps in domain knowledge and discrepancies in understanding. However, the existing domain ontology and knowledge graph provide an insufficient representation of the knowledge of Chinese grottoes. To overcome these obstacles, we propose a knowledge graph representation method to provide explicit knowledge for participants at different stages of semantic 3D modeling of Chinese grottoes, which includes schema layer construction and data layer construction. On the schema layer, we design a domain ontology named ChgOnto (Chinese Grottoes Ontology) that consists of four high-level concept classes: spatial object, informational object, digital device, and temporal object. Among the classes in the ChgOnto, the components (e.g., cliff wall, cave roof, cliff wall footing), elements (e.g., primary Buddha statue, pedestal, decoration), the properties (e.g., length, width, depth) of caves and niches in Chinese grottoes as well as the spatial relationships between them are all precisely defined. ChgOnto also reuse the classes from the renowned CIDOC CRM ontology in the cultural heritage field and GeoSPARQL in the geospatial domain, facilitating integration between the two subjects. Considering the schema layer as the conceptual data model, the data layer extracts knowledge from unstructured text through natural language processing tools to instantiate the abstract classes and fill the properties of the schema layer. Finally, the knowledge required for semantic 3D modeling of Chinese grottoes is expressed in the data layer by a knowledge graph in a fixed expression form. Dazu Rock Carvings, a World Heritage site in China, is selected as a case study to validate the practicality and effectiveness of the proposed method. The results reveal that our method offers a robust knowledge-sharing platform for the semantic 3D modeling of Chinese grottoes and demonstrates excellent scalability. The method proposed in this paper can also serve as an informative reference for other types of cultural heritage.

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.

Abstract Background The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. Methods The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. Results The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. Conclusions The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes.

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids. Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources. However, there are challenges in building models through centralized shared data due to data privacy concerns and industry competition. Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally. In this paper, we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model. We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach. Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy, and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

Abstract Background The success of cephalometric analysis depends on the accurate detection of cephalometric landmarks on scanned lateral cephalograms. However, manual cephalometric analysis is time-consuming and can cause inter- and intra-observer variability. The purpose of this study was to automatically detect cephalometric landmarks on scanned lateral cephalograms with low contrast and resolution using an attention-based stacked regression network (Ceph-Net). Methods The main body of Ceph-Net compromised stacked fully convolutional networks (FCN) which progressively refined the detection of cephalometric landmarks on each FCN. By embedding dual attention and multi-path convolution modules in Ceph-Net, the network learned local and global context and semantic relationships between cephalometric landmarks. Additionally, the intermediate deep supervision in each FCN further boosted the training stability and the detection performance of cephalometric landmarks. Results Ceph-Net showed a superior detection performance in mean radial error and successful detection rate, including accuracy improvements in cephalometric landmark detection located in low-contrast soft tissues compared with other detection networks. Moreover, Ceph-Net presented superior detection performance on the test dataset split by age from 8 to 16 years old. Conclusions Ceph-Net demonstrated an automatic and superior detection of cephalometric landmarks by successfully learning local and global context and semantic relationships between cephalometric landmarks in scanned lateral cephalograms with low contrast and resolutions.

Abstract The purpose of this study was to automatically classify the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) using a distance-aware network in cone-beam CT (CBCT) images. We developed a network consisting of cascaded stages of segmentation and classification for the buccal-lingual relationship between the M3 and the MC. The M3 and the MC were simultaneously segmented using Dense121 U-Net in the segmentation stage, and their buccal-lingual relationship was automatically classified using a 3D distance-aware network with the multichannel inputs of the original CBCT image and the signed distance map (SDM) generated from the segmentation in the classification stage. The Dense121 U-Net achieved the highest average precision of 0.87, 0.96, and 0.94 in the segmentation of the M3, the MC, and both together, respectively. The 3D distance-aware classification network of the Dense121 U-Net with the input of both the CBCT image and the SDM showed the highest performance of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve, each of which had a value of 1.00. The SDM generated from the segmentation mask significantly contributed to increasing the accuracy of the classification network. The proposed distance-aware network demonstrated high accuracy in the automatic classification of the 3D positional relationship between the M3 and the MC by learning anatomical and geometrical information from the CBCT images.

Abstract Background Osteoarthritis (OA) is caused by a complex set of pathophysiological factors. The genetic factors involved in the occurrence and progress of the disease have been widely discussed by scholars. It was found that growth differentiation factor 5 (GDF5) gene polymorphisms may be linked to OA susceptibility, which has been controversial and needs to be further confirmed by an updated meta-analysis. Objectives We examined the association between GDF5 rs143383 single nucleotide polymorphism (SNP) and OA susceptibility. Methods All relevant articles that met the criteria are retrieved and included, and the search deadline is June 2022. The allele frequencies and different genotype frequencies of GDF5 rs143383 loci in each study were extracted and statistically analyzed by R4.1.3 software, and the different genetic models were analyzed based on their odds ratio (OR) and 95% confidence interval (CI). Results The meta-analysis explained that GDF5 rs143383 SNP was crucial correlated with OA in all patients with OA of knee, hip and hand. The codominant gene model in the whole crowd (OR = 1.17, 95% CI 1.07–1.27, P

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.

With the adoption of a novel system architecture characterized by “physical distribution and logical unity” in new-type power systems， the support system for power grid scheduling is reconstructed. Under the new architecture， the human-machine cloud terminal （hereinafter referred to as “cloud terminal”） can realize local and remote undifferentiated browsing. However， the cloud terminal is also faced with new security challenges. First， the new architecture and the human-machine access process are analyzed， and the security problems are pointed out. Then， a unified security authentication method is proposed based on hardware fingerprint to authenticate human-machine cloud terminal is proposed. By use of digital certificates for power scheduling and biometric identification technology， multi-factor user identity authentication is performed. Startup authentication and service invocation verification are carried out， and encrypted transmission of service communication data is made possible to ensure service startup and access security. The service communication data is encrypted and transmitted to ensure the security of service startup and access. Finally， the performance of service authentication and encryption is tested， and the authentication and encryption strategies for different service request sizes are proposed. The test results show that the whole link security of human-machine access is guaranteed through the authentication of the cloud terminal， user identity， service， and communication data encryption.

Abstract The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method. In detection, five landmarks on CBCT images were automatically detected using a volumetric regression network; in classification, the CBCT images were automatically classified as to the five surgical approaches using a 3D distance-guided network. The mean MRE for landmark detection was 0.87 mm, and SDR for 2 mm or lower, 95.47%. The mean accuracy, sensitivity, specificity, and AUC for classification by the SinusC-Net were 0.97, 0.92, 0.98, and 0.95, respectively. The deep learning model using 3D distance-guidance demonstrated accurate detection of 3D anatomical landmarks, and automatic and accurate classification of surgical approaches for sinus floor augmentation in implant placement at the maxillary posterior edentulous region.

The energy ecological footprint measures the environmental impact of energy consumption and is an important indicator for assessing sustainable development. This study introduced the concept of embodied carbon and developed a new energy ecological footprint accounting model. The Spatial Durbin Model was used to analyze the spillover effects of various factors influencing the energy ecological footprint. The quantile regression method was applied to examine the phased impact of each influencing factor on spatio-temporal evolution of the energy ecological footprint. A nested matrix was constructed to classify the quantile response types of each influencing factor and the spatio-temporal transition types. Additionally, a governance model for regional energy ecological footprint reduction was proposed. The results showed that: First, the spatio-temporal distribution of the energy ecological footprint of China's 29 provinces was not completely random while the energy ecological footprint of each province had significant spatial correlation characteristics. Moreover, the change of energy ecological footprint affected by its neighboring provinces. Second, industrial structure, environmental regulation, and urbanization level had a critical effect on the spatio-temporal evolution of China's energy ecological footprint. Furthermore, each factor will affect the change of the energy ecological footprint in the surrounding areas through the spatial transmission mechanism of the influencing factors, and there was a significant spatio-temporal spillover effect. Third, there were driving and constraint modes of spatio-temporal transition in the process of spatial agglomeration of energy ecological footprint in each province. Quantile regression model can well explain the driving mechanism of each driving factor on the spatio-temporal transition of energy ecological footprint, and there was a strong nesting between the quantile of driving factors at different response stages and the types of spatio-temporal transition of energy ecological footprint. These findings can serve as a basis for decision-making and help formulate targeted policies, considering the “common but differentiated” approach, to reduce China's energy ecological footprint at the provincial level.

Science and technology innovation is the key driving force to support and lead industrial transformation and upgrading. This topic explores the relationship between digital technology application and industrial economic model innovation in the context of emerging technology development. Taking the annual report text of an enterprise as a research sample, the improved LDA theme model is applied to extract the themes of the annual report text and analyze the characteristics and trends of the digital technology application of the sample enterprise. After measuring the degree of digitization of the enterprise, the conceptual model of its relationship with industrial economic model innovation is constructed, relevant hypotheses are put forward, and correlation and regression analyses are applied to validate the role of the application of digital technology in promoting the innovation of the industrial economic model. The use of digital technology in the sample enterprises during the research interval has increased by about 18 times, dominated by the use of artificial intelligence technology. The application of digital technology has a significant positive effect on the industrial economic model’s innovation (p < 0.01), with a regression coefficient value of 0.508. The analysis demonstrates that enterprises can use emerging technologies to connect all links from production to sales, revolutionize the logic of value creation in the existing business model, and promote the innovation of the industrial economic model.

Although the Multiple Streams Framework (MSF) has abundant research, the mechanism of partial couplings to complete coupling remains to be investigated. The study extends the MSF using the perspective of generating effective alternatives in the policy stream to explain the dynamic of the policy process. This article is an empirical study that compares two cases under the same holistic policy issues. We propose the multiple streams framework based on goal-oriented “policy solutions” to explain the process of Chinese rural governance policy output. The results show that: First, the partial coupling (politics and problems) opens the policy window for policy formulation, which drives the policy stream forming. Second, the periodic stability windows are a realistic path to accelerate the policy stream process. Third, policy experimentation constitutes an essential tool for formulating effective options. Further comparing the policy stream in the MSF with the policy stream in this study found clear differences.

Abstract Recently the ever‐increasing demand for wearable electronics has greatly triggered the development of flexible strain sensors. However, it is still challenging to simultaneously achieve high sensitivity, wide working range, and good wearability. Herein, we developed a highly stretchable fiber strain sensor based on wet‐spun porous polyurethane (PU) fiber, and especially a unique conductive network of dual silver (Ag)/carbon black (CB) layers is constructed. Under strain, the rapid crack propagation on the brittle Ag layer brings a large resistance change and thus high sensitivity, while the tunneling‐effect dominated CB layer bridges the separated Ag islands to maintain the integrity of conductive pathways under large strain. By means of the synergistic effect of Ag/CB layers, this composite fiber of Ag/CB@PU presents not only high conductivity of 5139.9 S/m, but also ultrahigh sensitivity with a gauge factor of 2.52 × 106 and a wide working range of up to 200%. Besides that, it is also capable of detecting very tiny strain of 0.1% and working stably for over 8000 cycles. Using mature weaving technology, this fiber strain sensor can be seamlessly integrated into the textile to conformally track different movements of the human body. Together with the facile all‐solution‐based fabrication protocol, this work proposed a new strategy to prepare high‐performance fiber strain sensor, promising the textile‐based wearable applications.

ObjectiveTo investigate the dietary preference and nutritional knowledge needs of the elderly people who dined at meal service sites.MethodsUsing the form of stratified and convenience sampling method with self-designed questionnaire was used， in November 2021， to select 700 elderly people who dine at meal service sites in 7 jurisdictions in Shanghai were selected， and a self-designed questionnaire was used to investigate the basic information.Results91.64% of the elderly surveyed would eat at relatively fixed meal service sites， and the total Dietary Diversity Score （DDS9） was 3.56±1.46. 41.45% of the elderly with diseases preferred unhealthy cooking methods. Only 8.03% of the surveyed seniors said they were unwilling to accept targeted and personalized nutrition tips and reminders. Multivariate logistic regression analysis showed that the probability reaching the “understanding” level of “Food Guide Pagoda for Chinese Residents” and “Four Principles Recommended by the Core Dietary Guidelines for the Elderly” was different in the elderly with different education levels. The willingness of the elderly to expect to receive different nutrition tips and reminders was related to whether they cared about the corresponding contents. There was a statistically significant difference （P

Abstract Background Osteoarthritis of the knee is an irreversible disease that causes great pain, and genetic factors play an important role in its occurrence and development. There have been many studies on the correlation between ADAM12 polymorphisms and genetic susceptibility to osteoarthritis, but the results remain inconclusive. Methods Papers from PubMed, Web of Science, EMbase, Springer, SCOPUS, Google Scholar and other databases were systematically retrieved with a cut-off of January 2022. All case–control studies on ADAM12 rs3740199, rs1871054, rs1044122, and rs1278279 polymorphisms and osteoarthritis were searched. Fixed or random effects models were used for pooled analysis with OR values and 95% confidence intervals (CI), and publication bias was assessed. In addition, the false-positive reporting probability test was used to assess the confidence of a statistically significant association. Results Eleven articles were included, which included 3332 patients with osteoarthritis and 5108 healthy controls. Meta-analysis showed that the rs1871054 polymorphism of ADAM12 was associated with osteoarthritis in dominant, recessive, allelic, and homozygote genetic models [C vs. T: OR = 1.34 95% CI (1.05, 1.71), P

Background Dynamic alterations of the tumor immune microenvironment in esophageal squamous cell carcinoma (ESCC) after different neoadjuvant therapies were understudied.Methods We used mass cytometry with a 42-antibody panel for 6 adjacent normal esophageal mucosa and 26 tumor samples (treatment-naïve, n=12; postneoadjuvant, n=14) from patients with ESCC. Single-cell RNA sequencing of previous studies and bulk RNA sequencing from The Cancer Genome Atlas were analyzed, flow cytometry, immunohistochemistry, and immunofluorescence analyses were performed.Results Poor tumor regression was observed in the neoadjuvant chemotherapy group. Radiotherapy-based regimens enhanced CD8+ T cells but diminished regulatory T cells and promoted the ratio of effector memory to central memory T cells. Immune checkpoint blockade augmented NK cell activation and cytotoxicity by increasing the frequency of CD16+ NK cells. We discovered a novel CCR4+CCR6+ macrophage subset that correlated with the enrichment of corresponding chemokines (CCL3/CCL5/CCL17/CCL20/CCL22). We established a CCR4/CCR6 chemokine-based model that stratified ESCC patients with differential overall survival and responsiveness to neoadjuvant chemoradiotherapy combined with immunotherapy, which was validated in two independent cohorts of esophageal cancer with neoadjuvant treatment.Conclusions This work reveals that neoadjuvant therapy significantly regulates the cellular composition of the tumor immune microenvironment in ESCC and proposes a potential model of CCR4/CCR6 system to predict the benefits from neoadjuvant chemoradiotherapy combined with immunotherapy.

Abstract Background The loach (Misgurnus anguillicaudatus), the most widely distributed species of the family Cobitidae, displays a mud-dwelling behavior and intestinal air-breathing, inhabiting the muddy bottom of extensive freshwater habitats. However, lack of high-quality reference genome seriously limits the interpretation of the genetic basis of specialized adaptations of the loach to the adverse environments including but not limited to the extreme water temperature, hypoxic and noxious mud environment. Results This study generated a 1.10-Gb high-quality, chromosome-anchored genome assembly, with a contig N50 of 3.83 Mb. Multiple comparative genomic analyses found that proto-oncogene c-Fos (fos), a regulator of bone development, is positively selected in loach. Knockout of fos (ID: Mis0086400.1) led to severe osteopetrosis and movement difficulties, combined with the comparison results of bone mineral density, supporting the hypothesis that fos is associated with loach mud-dwelling behavior. Based on genomic and transcriptomic analysis, we identified two key elements involved in the intestinal air-breathing of loach: a novel gene (ID: mis0158000.1) and heat shock protein beta-1 (hspb1). The flavin-containing monooxygenase 5 (fmo5) genes, central to xenobiotic metabolism, undergone expansion in loach and were identified as differentially expressed genes in a drug stress trial. A fmo5 −/− (ID: Mis0185930.1) loach displayed liver and intestine injury, indicating the importance of this gene to the adaptation of the loach to the noxious mud. Conclusions Our work provides valuable insights into the genetic basis of biological adaptation to adverse environments.

High-voltage electric field treatment is an emerging approach to improve the cold flowability of crude oil, of which the physical principle is the negative electrorheological effect of crude oil other than electric heating and electric field dehydration. Based on the review of literatures on electrorheology, several mechanisms of negative electrorheological effect were summarized, and the problems in the electric field treatment mechanisms of crude oil, such as particle aggregation into chains, increasing the particle size of wax crystal, and enhancing the polydispersity, were analyzed. Besides, the basis of wax particle interface polarization was explained. Generally, the co-existence of wax particles, resins and asphaltenes is the necessary condition to realize the treatment effect of electric field. When a high-voltage electric field is applied, the charged particles(i.e. the resins and asphaltenes) in the crude oil would aggregate on the surface of wax crystal, which is the interface polarization. During the process, the electrostatic repulsion between wax particles is increased while their attraction is weakened, resulting in improved cold flowability of the crude oil. Therefore, the behavior of resins and asphaltenes in the oil is the key to understand the mechanism of the negative electrorheological effect of crude oil, which mainly includes three processes: the migration of resins and asphaltenes from the bulk phase to wax particles upon the application of electric field,accumulation of resins and asphaltenes on the surface of wax particles, and their diffusion back to the bulk phase after the removal of electric field. In the future, further in-depth and quantitative research should be focused on these three processes to fully reveal the scientific mechanism of the electrorheological effect of crude oil.

Introduction: Fruit diseases have a serious impact on fruit production, causing a significant drop in economic returns from agricultural products. Due to its excellent performance, deep learning is widely used for disease identification and severity diagnosis of crops. This paper focuses on leveraging the high-latitude feature extraction capability of deep convolutional neural networks to improve classification performance.Methods: The proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits. The VGG is used to replace the U-Net backbone to enhance the segmentation performance of the network.Results: Compared to existing networks, the proposed method achieved recognition accuracy of over 95%. In addition, the accuracies of the segmentation models were compared. VGG-U-Net, a network generated by replacing the backbone of U-Net with VGG, is found to have the best segmentation performance with an accuracy of 87.66%. This method is most suitable for diagnosing the severity level of citrus fruit diseases. In the meantime, transfer learning is applied to improve the training cycle of the network model, both in the detection and severity diagnosis phases of the disease.Discussion: The results of the comparison experiments reveal that the proposed method is effective in identifying and diagnosing the severity of citrus fruit diseases identification.

Healing in compromised and complicated bone defects is often prolonged and delayed due to the lack of bioactivity of the fixation device, secondary infections, and associated oxidative stress. Here, we propose amorphous silicon oxynitride (SiONx) as a coating for the fixation devices to improve both bioactivity and bacteriostatic activity and reduce oxidative stress. We aimed to study the effect of increasing the N/O ratio in the SiONx to fine-tune the cellular activity and the antioxidant effect via the NRF2 pathway under oxidative stress conditions. The in vitro studies involved using human mesenchymal stem cells (MSCs) to examine the effect of SiONx coatings on osteogenesis with and without toxic oxidative stress. Additionally, bacterial growth on SiONx surfaces was studied using methicillin-resistant Staphylococcus aureus (MRSA) colonies. NRF2 siRNA transfection was performed on the hMSCs (NRF2-KD) to study the antioxidant response to silicon ions. The SiONx implant surfaces showed a >4-fold decrease in bacterial growth vs. bare titanium as a control. Increasing the N/O ratio in the SiONx implants increased the alkaline phosphatase activity >1.5 times, and the other osteogenic markers (osteocalcin, RUNX2, and Osterix) were increased >2-fold under normal conditions. Increasing the N/O ratio in SiONx enhanced the protective effects and improved cell viability against toxic oxidative stress conditions. There was a significant increase in osteocalcin activity compared to the uncoated group, along with increased antioxidant activity under oxidative stress conditions. In NRF2-KD cells, there was a stunted effect on the upregulation of antioxidant markers by silicon ions, indicating a role for NRF2. In conclusion, the SiONx coatings studied here displayed bacteriostatic properties. These materials promoted osteogenic markers under toxic oxidative stress conditions while also enhancing antioxidant NRF2 activity. These results indicate the potential of SiONx coatings to induce in vivo bone regeneration in a challenging oxidative stress environment.

On July 15, 2005, the 29th General Assembly of the World Heritage List was held in South Africa. The Historic Centre of Macao was inscribed on the World Heritage List by Criteria C(ii)(iii)(iv)(vi) of the Cultural Heritage Selection Criteria. However, the residential buildings located in the Historic Centre are mostly distributed in the buffer zone and are less studied. Most of the buildings in the Historic Centre are more than 40 years old, some even older, and many of them are facing architectural deterioration, lack of proper maintenance, and the phenomenon of unauthorized building structures. This paper will summarize the history of the development of residential buildings in Macao and provide recommendations for the renewal of residential buildings in the Historic District.

Building Information Modelling(BIM) is the foundation of digital transformation in architecture, engineering, and construction(AEC)industry. BIM is the holistic process of creating and managing information for a built asset. A traditional CAD and 3D modelling technology is widely used in university’s architecture and landscape courses teaching today. Every single detail of traditional course education does not match the internationally market and BIM technology which is more common and popular today. Using BIM technology in university to teach architecture and landscape design will make changes to design education, and will improve the education practice in Environmental Design system. This article will introduce more detail about BIM technology, and how it changes and affects traditional architecture & landscape teaching, how this technology merged with professions, and how this technology brings comprehensive learning ways and new insight for students in their future career life.

Over the last decade, event prediction has drawn attention from both academic and industry communities, resulting in a substantial volume of scientific papers published in a wide range of journals by scholars from different countries and disciplines. However, thus far, a comprehensive and systematic survey of recent literature has been lacking to quantitatively capture the research progress as well as emerging trends in the event prediction field. Aiming at addressing this gap, we employed CiteSpace software to analyze and visualize data retrieved from the Web of Science (WoS) database, including authors, documents, research institutions, and keywords, based on which the author co-citation network, document co-citation network, collaborative institution network, and keyword co-occurrence network were constructed. Through analyzing the aforementioned networks, we identified areas of active research, influential literature, collaborations at the national level, interdisciplinary patterns, and emerging trends by identifying the central nodes and the nodes with strong citation bursts. It reveals that sensor data has been widely used for predicting weather events and meteorological events (e.g., monitoring sea surface temperature and weather sensor data for predicting El Nino). The real-time and multivariable monitoring features of sensor data enable it to be a reliable source for predicting multiple types of events. Our work offers not only a comprehensive survey of the existing studies but also insights into the development trends within the event prediction field. These findings will assist researchers in conducting further research in this area and draw a large readership among academia and industrial communities who are engaged in event prediction research.

Abstract The purpose of this study was to propose a continuity-aware contextual network (Canal-Net) for the automatic and robust 3D segmentation of the mandibular canal (MC) with high consistent accuracy throughout the entire MC volume in cone-beam CT (CBCT) images. The Canal-Net was designed based on a 3D U-Net with bidirectional convolutional long short-term memory (ConvLSTM) under a multi-task learning framework. Specifically, the Canal-Net learned the 3D anatomical context information of the MC by incorporating spatio-temporal features from ConvLSTM, and also the structural continuity of the overall MC volume under a multi-task learning framework using multi-planar projection losses complementally. The Canal-Net showed higher segmentation accuracies in 2D and 3D performance metrics (p

Abstract Background Neoadjuvant chemoradiation followed by esophagectomy has been established as the first-line treatment for locally advanced esophageal cancer. Postoperative enteral nutrition has been widely used to improve perioperative outcomes. However, whether to implement preoperative nutritional intervention during neoadjuvant therapy is yet to be verified by prospective studies. Methods POINT trial is a multicenter, open-labeled, randomized controlled trial. A total of 244 patients with surgically resectable esophageal cancer are randomly assigned to nutritional therapy group (arm A) or control group (arm B) with a 2:1 ratio. Both groups receive neoadjuvant chemotherapy with concurrent radiotherapy based on the CROSS regimen followed by minimally invasive esophagectomy. The primary endpoint is the rate of nutrition and immune-related complications after surgery. Secondary endpoints include completion rate of neoadjuvant chemoradiation and related adverse events, rate of pathological complete response, perioperative outcomes, nutritional status, overall survival, progression-free survival and quality of life. Discussion This trial aims to verify whether immunonutrition during neoadjuvant chemoradiation can reduce the rate of complications and improve perioperative outcomes. Frequent communication and monitoring are essential for a multicenter investigator-initiated trial. Trial registration: ClinicalTrials.gov: NCT04513418. The trial was prospectively registered on 14 August 2020, https://www.clinicaltrials.gov/ct2/show/NCT04513418 .

The Brassica napus (B. napus) LOR (Lurp-One-Related) gene family is a little-known gene family characterized by a conserved LOR domain in the proteins. Limited research in Arabidopsis showed that LOR family members played important roles in Hyaloperonospora parasitica (Hpa) defense. Nevertheless, there is a paucity of research investigating the role of the LOR gene family towards their responses to abiotic stresses and hormone treatments. This study encompassed a comprehensive survey of 56 LOR genes in B. napus, which is a prominent oilseed crop that holds substantial economic significance in China, Europe, and North America. Additionally, the study evaluated the expression profiles of these genes in response to salinity and ABA stress. Phylogenetic analysis showed that 56 BnLORs could be divided into 3 subgroups (8 clades) with uneven distribution on 19 chromosomes. 37 out of 56 BnLOR members have experienced segmental duplication and 5 of them have undergone tandem repeats events with strong evidence of purifying selection. Cis-regulatory elements (CREs) analysis indicated that BnLORs involved in process such as light response, hormone response, low temperature response, heat stress response, and dehydration response. The expression pattern of BnLOR family members revealed tissue specificity. RNA-Seq and qRT-PCR were used to validate BnLOR gene expression under temperature, salinity and ABA stress, revealing that most BnLORs showed inducibility. This study enhanced our comprehension of the B. napus LOR gene family and could provide valuable information for identifying and selecting genes for stress resistant breeding.

Transcription factor IIB (TFIIB) is a general transcription factor for RNA polymerase II, exerting its influence across various biological contexts. In the majority of eukaryotes, TFIIB typically has two homologs, serving as general transcription factors for RNA polymerase I and III. In plants, however, the TFIIB-related protein family has expanded greatly, with 14 and 9 members in Arabidopsis and rice, respectively. BRP5/pollen-expressed transcription factor 2 (PTF2) proteins belong to a subfamily of TFIIB-related proteins found only in plants and algae. The prior analysis of an Arabidopsis atbrp5 mutant, characterized by a T-DNA insertion at the 5′ untranslated region, demonstrated the essential role of BRP5/PTF2 during the process of pollen germination and embryogenesis in Arabidopsis. Using a rice transformation system based on CRISPR/Cas9 technology, we have generated transgenic rice plants containing loss-of-function frameshift mutations in the BRP5/PTF2 gene. Unlike in the Arabidopsis atbrp5 mutant, the brp5/ptf2 frameshift mutations were not transmitted to progeny in rice, indicating an essential role of BRP5/PTF2 in both male and female gamete development or viability. The silencing of rice BRP5/PTF2 expression through RNA interference (RNAi) had little effect on vegetative growth and panicle formation but strongly affected pollen development and grain formation. Genetic analysis revealed that strong RNAi silencing of rice BRP5/PTF2 was still transmissible to progeny almost exclusively through female gametes, as found in the Arabidopsis atbrp5 knockdown mutant. Thus, reduced rice BRP5/PTF2 expression impacted pollen preferentially by interfering with male gamete development or viability. Drawing upon these findings, we posit that BRP5/PTF2 assumes a distinct and imperative function in the realm of plant sexual reproduction.

Objectives: Functional connectivity triggered by naturalistic stimuli (e.g., movie clips), coupled with machine learning techniques provide great insight in exploring brain functions such as fluid intelligence. However, functional connectivity is multi-layered while traditional machine learning is based on individual model, which is not only limited in performance, but also fails to extract multi-dimensional and multi-layered information from the brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method, namely weighted ensemble model and network analysis, which combines machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into an auto-encoder parallelly for automatic feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression models were stacked and fused automatically with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed method achieved the best performance with a 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient; this model outperformed other state-of-the-art methods. It is also worth noting that the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method outperforms the state-of-the-art reports, also is able to effectively capture the biological patterns of functional connectivity during a naturalistic movie state for potential clinical explorations.

As the fourth generation nuclear power technology, high temperature gas-cooled reactor (HTGR) has good prospects due to its inherent safety and high temperature. Two-phase flow stability is one of the most important thermal hydraulic issues for steam generators. For the steam generator of HTR-PM (high temperature gas-cooled reactor-pebble module), the size of the inner tube diameter is uniform. While for steam generator of HTR-10 (10 MW high temperature gas-cooled reactor), the inner tube diameter of single-phase preheating region is smaller than that of two-phase and superheated regions. A model with variable tube diameter will be established to predict the stability boundary of HTR-PM and HTR-10 steam generators. The heat transfer tube of the steam generator is divided into three regions, and they are single-phase preheating region, two-phase flow region and superheated region. Based on the existing frequency domain theoretical model, a new frequency domain theoretical model with different inner tube diameters for the single-phase preheating region and two-phase region was established for analyzing two-phase flow stability. System stability was determined by the Nyquist curve of the open transfer function. The prediction of the new proposed model is in good agreement with the engineering test results of HTR-10 (variable tube diameter) and HTR-PM (uniform tube diameter) once through steam generators. Then, the effects of friction factor difference between helical tube and inclined straight tube, the effects of helical diameter difference, and the effects of different tube diameters for single-phase preheating and two-phase regions on two-phase flow stability were analyzed. For systems with high inlet resistance coefficients, when other conditions are the same, the friction factor of an inclined straight tube is smaller than that of a helical tube, so the system with an inclined straight tube (the inclined angle is the same as the helical angle of the helical tube) is more stable than the system with a helical tube. When other conditions are the same, the friction factor decreases with the increase of helical diameter, so increasing the helical diameter can slightly improve the stability of the system when the value of inlet resistance coefficients is large. Both the difference between helical tube and inclined straight tube and the effect of helical diameter can be attributed to the effect of friction factor on stability boundary. If friction factor changes significantly, stability boundary will be affected more significantly. Compared with the design of uniform inner tube diameter, the system will become slightly stable when the inner diameter of single-phase preheating regions is smaller than that of two-phase and superheated regions. The main reason is that decreasing tube inner diameter will increase the frictional pressure drop of the single-phase preheating regions.

Abstract Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (extracted using magnetometer and gradiometer sensors). AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers and gradiometers. In this preliminary study, it was found that the markers derived from gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the total 10 regions of interest, left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification. Among the four types of markers proposed in this work, the spatial marker developed using wavelet coefficients provided the best recognition performance for the three-way classification. Overall, the proposed approach provides promising results for the potential of AD/MCI/HC three-way classification utilizing the bimodal MEG data.