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H10 subtype avian influenza viruses (AIV) have been circulating in China for 40 years. H10 AIVs in China have expanded their host range from wild birds to domestic poultry and mammals, even human. Most of the H10 subtype AIVs reported in China were isolate from the southeast part. We isolated an H10N3 AIV, A/Chicken/Liaoning/SY1080/2021 (SY1080), from live poultry market (LPM) in Liaoning Province of the Northeast China. SY1080 replicated efficiently in mice lungs and nasal turbinates without prior adaptation. We systematically compared SY1080 with other H10 subtype isolates in China. Phylogenetic analysis showed that SY1080 and most of the H10 strains belonged to the Eurasian lineage. H10 AIVs in China have formed 63 genotypes. SY1080 as well as the H10N3 strains from human infections belonged to G60 genotype. H10Nx AIV acquired multiple mammalian adaptive and virulence related mutations during circulation and the recent reassortants derived internal genes from chicken H9N2 AIVs. The H10Nx subtypes AIVs posed potential threat to public health. These results suggested we should strengthen the surveillance and evaluation of H10 subtype strains.

Abstract Background Nutritional supplements for sports and exercise (NSSE) can facilitate the exogenous replenishment of the body. This study provides the first extensive overview of NSSE research through bibliometric and visual analyses. Methods We searched the Web of Science Core Collection database for literature related to “NSSE” from 1st January 2000 to 8th March 2024. A total of 1744 articles were included. CiteSpace, VOSviewer, and Bibliometrix R package software were used to analyze the data. Results Research in the NSSE can be divided into steady growth, exponential growth, fluctuating stage, and surge stages. The United States is the most active country in this field. In recent years, the leading countries have been Croatia, Colombia, Slovenia, Chile, Egypt, China, and Thailand. The Australian Institute of Sports is the top research institution in terms of number of publications. Burke, LM from Australia published the most articles. Research in this area has primarily been published in Nutrients in Switzerland. The study population mainly consisted of men, and postmenopausal women were the main focus of the female group. Coronary heart and cardiovascular diseases continue to dominate research. Conclusion Research on the NSSE is developing rapidly, with an annual growth trend. Insulin resistance, sports nutrition, inflammation, alpha-linolenic acid, limb strength performance, female sex, and gut microbiota are the focus of the current research and trends for future research. Future research should focus on improving the scientific training system for athletes and quality of training and life for the general public.

Achieving a high color rendering index (CRI) and luminous stability in single-structured Ce:Y3Al5O12 (Ce:YAG) phosphor ceramics (PCs) is crucial for high-power white light-emitting diodes or laser diodes (LEDs/LDs). However, cyan valleys and insufficient amounts of the red component in the Ce:YAG emission spectra significantly limit their real applications. In this work, a series of Ce,Mn:Y3(Al,Sc)2Al3O12 (Ce,Mn:YSAG) PCs were fabricated by vacuum sintering, and efficient spectral regulation was realized for full-color lighting. The cyan valley was filled by the blueshifted emission peak of Ce3+ via Sc3+ doping. The orange‒red emission at approximately 580 nm was effectively supplemented via Mn2+ doping. In particular, CRI of Ce,Mn:YSAG increased from 56.4 to 85.8, a 52% increase compared with that of Ce:YAG under high-power LED excitation, and the operating temperature was stable at approximately 50 °C for long working time. Moreover, CRI of 80.9 could still be obtained for PC-based white LDs. These results indicated that Ce,Mn:YSAG PC, which has excellent CRI and luminous stability, is an extremely promising color convertor for high-power white LEDs/LDs.

Although the amygdala is associated with cognitive impairment resulting from cerebral small vessel disease, the relationship between alterations in amygdala structure and cerebral small vessel disease (CSVD) remains controversial. Given that the amygdala comprises several subregions, detecting subtle regional changes through total amygdala volume measurement is challenging. This study aimed to identify the patterns of amygdala subregion atrophy in cerebral small vessel disease patients and their relationship with cognitive impairment. A total of 114 participants diagnosed with cerebral small vessel disease and 129 healthy participants, aged 40 to 70, underwent 3 T magnetic resonance imaging scans. The amygdala subregions were automatically segmented using FreeSurfer. In the Propensity Score Matching (PSM)-matched cohort, Lasso regression was employed to identify subregions associated with cerebral small vessel disease, and restricted cubic splines (RCS) were used to explore their nonlinear relationship with cognitive abilities. Subsequently, multivariate linear regression models were used to investigate the impact of amygdala subregion volumes on various cognitive abilities. Compared to healthy controls (HC), the volume of the left cortical nucleus was significantly reduced in cerebral small vessel disease patients. The volume of the left cortical nucleus was significantly negatively correlated with cerebral small vessel disease progression, and atrophy in this region was also identified as an independent risk factor for decreased cognitive control and processing ability. Our findings suggest that patients with cerebral small vessel disease exhibit atrophy in specific amygdala subregions compared to healthy controls, which correlates with poorer cognitive control and processing abilities. These insights may advance our understanding of the pathogenesis of cerebral small vessel disease.

IntroductionAccurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance.MethodsThis study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem.Results and discussionYOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency.

IntroductionAccording to career construction theory, middle school students are in a critical phase of growth and exploration that requires self-reflection on their interests, values, and aspirations. Career adaptability is a key indicator of career development for middle school students and a crucial ability for achieving career success. Research indicates that many Chinese middle school students face significant hurdles in their career development, including a lack of motivation, limited self-awareness, and unclear career trajectories.ObjectiveTo address these challenges, it is imperative to explore the factors influencing career adaptability, with a particular focus on the role of parental and teacher autonomy support within the framework of self-determination theory. This study aims to explore the correlation between parental autonomy support (PAS), teacher autonomy support (TAS), core self-evaluations (CSE), and career adaptability (CA) among middle school students.MethodologyThe longitudinal data for this study were collected from two middle schools in the Anhui province. Middle school students were recruited as research participants through a cluster sampling method. A total of 482 students were surveyed in three stages during a 1-year period, and a cross-lag model was employed to analyze the data.Results(1) From T1 to T2, T1PAS predicted T2CS, T1CSE predicted T2CA, and T1CA predicted T2TA; (2) from T2 to T3, T2PAS predicted T3CS, T2CSE predicted T3CA, and T2TAS predicted T3PAS; (3) TAS did not predict CSE and CA over time; (4) T2CSE mediated the relationship between T1PAS and T3CA.DiscussionThese findings suggest that autonomy support has a consistently positive influence on the career development of early adolescents. Valuing children’s autonomy is beneficial for fostering positive self-evaluations and shaping their career trajectories.ConclusionAutonomy support plays a pivotal role in enhancing middle school students’ career adaptability and promoting career development by strengthening self-evaluations. Additionally, the effect of parental autonomy support is more stable than that of teacher autonomy support.

Abstract The demand for miniaturized and integrated multifunctional devices drives the progression of high‐performance infrared photodetectors for diverse applications, including remote sensing, air defense, and communications, among others. Nonetheless, infrared photodetectors that rely solely on single low‐dimensional materials often face challenges due to the limited absorption cross‐section and suboptimal carrier mobility, which can impair sensitivity and prolong response times. Here, through experimental validation is demonstrated, precise control over energy band alignment in a type‐II van der Waals heterojunction, comprising vertically stacked 2D Ta2NiSe5 and the topological insulator Bi2Se3, where the configuration enables polarization‐sensitive, wide‐spectral‐range photodetection. Experimental evaluations at room temperature reveal that the device exhibits a self‐powered responsivity of 0.48 A·W−1, a specific directivity of 3.8 × 1011 cm·Hz1/2·W−1, a response time of 151 µs, and a polarization ratio of 2.83. The stable and rapid photoresponse of the device underpins the utility in infrared‐coded communication and dual‐channel imaging, showing the substantial potential of the detector. These findings articulate a systematic approach to developing miniaturized, multifunctional room‐temperature infrared detectors with superior performance metrics and enhanced capabilities for multi‐information acquisition.

Efficient, non-destructive cabbage harvesting is crucial for preserving its flavor and quality. Current cabbage harvesting mainly relies on mechanized automatic picking methods. However, a notable deficiency in most existing cabbage harvesting devices is the absence of a root posture recognition system to promptly adjust the root posture, consequently impacting the accuracy of root cutting during harvesting. To address this issue, this study introduces a cabbage root posture recognition method that combines deep learning with traditional image processing algorithms. Preliminary detection of the main root Region of Interest (ROI) areas of the cabbage is carried out through the YOLOv5s deep learning model. Subsequently, traditional image processing methods, the Graham algorithm, and the method of calculating the minimum circumscribed rectangle are employed to specifically detect the inclination angle of cabbage roots. This approach effectively addresses the difficulty in calculating the inclination angle of roots caused by occlusion from outer leaves. The results demonstrate that the precision and recall of this method are 98.7 % and 98.6 %, respectively, with an average absolute error of 0.80° and an average relative error of 1.34 % in posture. Using this method as a reference for mechanical harvesting can effectively mitigate cabbage damage rates.

Sensor data fusion is increasingly crucial in the field of autonomous driving. In sensor fusion research, LiDAR and camera have become prevalent topics. However, accurate data calibration from different modalities is essential for effective fusion. Current calibration methods often depend on specific targets or manual intervention, which are time-consuming and have limited generalization capabilities. To address these issues, we introduce MSANet: LiDAR-Camera Online Calibration with Multi-Scale Fusion and Attention Mechanisms, an end-to-end deep learn-based online calibration network for inferring 6-degree of freedom (DOF) rigid body transformations between 2D images and 3D point clouds. By fusing multi-scale features, we obtain feature representations that contain a lot of detail and rich semantic information. The attention module is used to carry out feature correlation among different modes to complete feature matching. Rather than acquiring the precise parameters directly, MSANet online corrects deviations, aligning the initial calibration with the ground truth. We conducted extensive experiments on the KITTI datasets, demonstrating that our method performs well across various scenarios, the average error of translation prediction especially improves the accuracy by 2.03 cm compared with the best results in the comparison method.

Hypercrosslinked polymers (HCPs) constructed by the Friedel–Crafts alkylation reaction of aromatic compounds have emerged as a new class of porous materials with unique merit. Herein, a HCP named HCP-TPB was coated onto a capillary column through in situ synthesis. The prepared column exhibited a nonpolar nature, and the column efficiency for n-dodecane was 3003 plates m−1. Moreover, the relative standard deviations of retention time and peak area for six replicate injections of the C3–C6 were lower than 0.1% and 1.5%, respectively. The results of this study showed that it is very promising to utilize HCPs as stationary phases for the separation of C3–C6.

This paper presents a robust point cloud registration method based on a multi-scale covariance matrix descriptor and an accurate transformation estimation. Compared with state-of-the-art feature descriptors, such as FPH, 3DSC, spin image, etc., our proposed multi-scale covariance matrix descriptor is superior for dealing with registration problems in a higher noise environment since the mean operation in generating the covariance matrix can filter out most of the noise-damaged samples or outliers and also make itself robust to noise. Compared with transformation estimation, such as feature matching, clustering, ICP, RANSAC, etc., our transformation estimation is able to find a better optimal transformation between a pair of point clouds since our transformation estimation is a multi-level point cloud transformation estimator including feature matching, coarse transformation estimation based on clustering, and a fine transformation estimation based on ICP. Experiment findings reveal that our proposed feature descriptor and transformation estimation outperforms state-of-the-art feature descriptors and transformation estimation, and registration effectiveness based on our registration framework of point cloud is extremely successful in the Stanford 3D Scanning Repository, the SpaceTime dataset, and the Kinect dataset, where the Stanford 3D Scanning Repository is known for its comprehensive collection of high-quality 3D scans, and the SpaceTime dataset and the Kinect dataset are captured by a SpaceTime Stereo scanner and a low-cost Microsoft Kinect scanner, respectively.

One new genus (Ocrognethoda gen. nov.) and three new species of webspinners (Ocrognethoda olivea gen. et sp. nov., Gnethoda lata sp. nov. and Parasorellembia hamata sp. nov.) are described from the Upper Cretaceous of Myanmar amber. Ocrognethoda olivea gen. et sp. nov. and Gnethoda lata sp. nov. are attributed to the family Clothodidae due to their simplified and symmetrical male terminalia, in which the tenth tergum is undivided. Parasorellembia hamata sp. nov. is classified in the family Scelembiidae by a specialized abdominal apex: fused cerci, a broad right hemitergite of the tenth abdominal tergite, and ocelli presence. Moreover, based on the discovery of new genus and species, the male terminalia of Clothodidae and Sorellembiinae are briefly discussed.

In the concurrent optimization of topology and fiber orientation, the design of smooth fiber helps to maintain the stability of numerical calculation and the compatibility of the manufacturing process. However, the improvement of fiber continuity is often accompanied by a significant decrease in the overall structural stiffness. Aiming at this problem, this paper proposes a topology optimization method for anisotropic materials with smooth fiber orientation. This method improves the smoothness of fiber orientation and reduces stiffness loss by introducing a fiber angle constraint strategy and adaptive filtering technology. The fiber angle constraint strategy integrates the created angle constraint function into the Method of Moving Asymptotes (MMA) to complete the strong constraint of the angle. This strategy quantifies the continuity of the fiber and effectively improves the continuity of the fiber. At the same time, the application of adaptive filtering technology can adjust a reasonable fiber angle distribution on the basis of smoothing fibers, thereby enhancing the stiffness of the overall structure. In addition, this paper shows the complete optimization process and MATLAB code implementation and verifies the effectiveness of the method through a series of numerical examples, that is, on the basis of improving fiber continuity, the stiffness of the whole structure is guaranteed, and then the effective balance between the two is realized.

Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.

In recent years, as more and more fossil species of berothids from Myanmar have been reported, the species and morphological diversity of Berothidae continues to increase. Herein, one new species of Berothidae, Aggregataberotha paucipunctata sp. nov., and one new genus, Sejunctaberotha gen. nov., with three new species (Sejunctaberotha sphaerica gen. et sp. nov., Sejunctaberotha tenuis gen. et sp. nov. and Sejunctaberotha transversa gen. et sp. nov.) are described from mid-Cretaceous Myanmar amber. A. paucipunctata sp. nov. is assigned to Aggregataberotha Wang, Huang & Wang, 2022, based on the characteristics of the similar female terminalia and wing venation, but can be different from A. punctate regarding the pale pterostigma and a few detailed features of wing venation. Additionally, representatives of Sejunctaberotha gen. nov. are remarkably different from the representatives of the other genera within Berothidae in the configuration of wing venation. For example, Sejunctaberotha gen. nov. has simple subcostal veinlets, obviously free Sc and RA at the apex present both in fore- and hindwings, a single ra-rp crossvein connecting the RA with RP3, a single rp-m crossvein locating before the origin of the MP, a simple CuP and no gradate veins. Interestingly, in one of the specimens of Sejunctaberotha gen. nov., a pair of spherical bulges was found at the end of the antennae. The new genus Sejunctaberotha gen. nov. suggests that Berothidae had a higher potential diversification during the Mesozoic Era.

A new genus and new species of the euthemistid, Kidaneuthemis ningchengensis gen. et sp. nov., is described from the Middle Jurassic of Inner Mongolia, China. It can be assigned to the Euthemistidae by the several long intercalary veins between RP1 and IR1, IR1 and RP2, as well as between RP2 and IR2 and between IR2 and RP3/4. Kidaneuthemis ningchengensis gen. et sp. nov. distinguishes from the other two genera of this family by the presence of not less than eight intercalary veins between MP and wing margin; about three rows of cells in the distal part between MA and MP; the base of IR2 is two cells distal to that of RP3/4. In addition, a revision of the family Euthemistidae has been proposed.

Cerebral small vessel disease (CSVD) is a universal neurological disorder in older adults that occurs in connection with cognitive dysfunction and is a chief risk factor for dementia and stroke. While whole-brain voxelwise structural and functional abnormalities in CSVD have been heavily explored, the degree of structure-function coupling abnormality possible in patients with different CSVD burdens remains largely unknown. This study included 53 patients with severe CSVD burden (CSVD-s), 108 patients with mild CSVD burden (CSVD-m) and 76 healthy controls. A voxelwise coupling metric of low frequency fluctuations (ALFF) and voxel-based morphometry (VBM) was used to research the important differences in whole-brain structure-function coupling among groups. The correlations between ALFF/VBM decoupling and cognitive parameters in CSVD patients were then investigated. We found that compared with healthy controls, CSVD-s patients presented notably decreased ALFF/VBM coupling in the bilateral caudate nuclei and increased coupling in the right inferior temporal gyrus (ITG). In addition, compared with the CSVD-m group, the CSVD-s group demonstrated significantly decreased coupling in the bilateral caudate nuclei, right putamen and inferior frontal gyrus (IFG) and increased coupling in the left middle frontal gyrus and medial superior frontal gyrus. Notably, the ALFF/VBM decoupling values in the caudate, IFG and ITG not only showed significant correlations with attention and executive functions in CSVD patients but also prominently distinguished CSVD-s patients from CSVD-m patients and healthy controls in receiver operating characteristic curve research. Our discoveries demonstrated that decreased ALFF/VBM coupling in the basal ganglia and increased coupling in the frontotemporal lobes were connected with more severe burden and worse cognitive decline in CSVD patients. ALFF/VBM coupling might serve as a novel effective neuroimaging biomarker of CSVD burden and provide new insights into the pathophysiological mechanisms of the clinical development of CSVD.

Cerebral small vessel disease (CSVD) is a common chronic and progressive disease that can lead to mental and cognitive impairment. Damage to brain structure and function may play an important role in the neuropsychiatric disorders of patients with CSVD. Increasing evidence suggests that functional changes are accompanied by structural changes in corresponding brain regions. Thus, normal structure–function coupling is essential for optimal brain performance, and disrupted structure–function coupling can be found in many neurological and psychiatric disorders. To date, most studies on patients with CSVD have focused on separate structures or functions, including reductions in white matter volume and blood flow, which lead to cognitive dysfunction. However, there are few studies on brain structure–function coupling in patients with CSVD. In recent years, with the rapid development of multilevel (voxel-wise, neurovascular, regional level, and network level) brain structure–functional coupling analysis methods based on multimodal magnetic resonance imaging (MRI), new evidence has been provided to reveal the correlation between brain function and structural abnormalities and cognitive impairment. Therefore, studying brain structure–function coupling has a potential significance in the exploration and elucidation of the neurobiological mechanism of cognitive impairment in patients with CSVD. This article mainly describes the currently popular brain structure–function coupling analysis technology based on multimodal MRI and the important research progress of these coupling technologies on CSVD and cognitive impairment to provide a perspective for the study of the pathogenesis and early diagnosis of CSVD.

The H6 subtype of avian influenza viruses (AIVs) has emerged as one of the predominant subtypes in both wild and domestic avian species. Currently, H6 AIVs have acquired the ability to infect a wide range of mammals, though the related molecular mechanisms have yet to be fully investigated. In this study, a wild bird-origin H6N2 AIV was isolated from the East Asian–Australasian migratory flyway region located in Liaoning Province. This H6N2 virus initially expressed limited replication in mice. However, after one passage in mice, the virus acquired two mutations, PB2 E627K and HA A110V. The mutant displayed enhanced replication both in vitro and in vivo, proving lethal to mice. But the mutant retained the α-2, 3-linked sialic acid binding property and failed to transmit in guinea pigs. We explored the molecular mechanisms underlying the pathogenicity difference between the wild type and the mutant. Our findings revealed that PB2 E627K dramatically enhanced the polymerase activity of the H6N2 virus, while the HA A110V mutation decreased the pH of HA activation. This study demonstrated that the H6N2 subtype wild bird-origin AIV easily acquired the mammalian adaptation. The monitoring and evaluation of H6 wild bird-origin AIV should be strengthened.

IntroductionThe effects of preeclampsia superimposed on chronic hypertension (CHTN-PE) on the structure and function of the human brain are mostly unknown. The purpose of this study was to examine altered gray matter volume (GMV) and its correlation with cognitive function in pregnant healthy women, healthy non-pregnant individuals, and CHTN-PE patients.MethodsTwenty-five CHTN-PE patients, thirty-five pregnant healthy controls (PHC) and thirty-five non-pregnant healthy controls (NPHC) were included in this study and underwent cognitive assessment testing. A voxel-based morphometry (VBM) approach was applied to investigate variations in brain GMV among the three groups. Pearson’s correlations between mean GMV and the Stroop color-word test (SCWT) scores were calculated.ResultsCompared with the NPHC group, the PHC and CHTN-PE groups showed significantly decreased GMV in a cluster of the right middle temporal gyrus (MTG), and the GMV decrease was more significant in the CHTN-PE group. There were significant differences in the Montreal Cognitive Assessment (MoCA) and Stroop word scores among the three groups. Notably, the mean GMV values in the right MTG cluster were not only significantly negatively correlated with Stroop word and Stroop color scores but also significantly distinguished CHTN-PE patients from the NPHC and PHC groups in receiver operating characteristic curve analysis.DiscussionPregnancy may cause a decrease in local GMV in the right MTG, and the GMV decrease is more significant in CHTN-PE patients. The right MTG affects multiple cognitive functions, and combined with the SCWT scores, it may explain the decline in speech motor function and cognitive flexibility in CHTN-PE patients.

IntroductionEmerging evidence suggested widespread decreased gray matter volume (GMV) and tau hyperphosphorylation were associated with type 2 diabetes mellitus (T2DM). Insulin resistance is one of the mechanisms of neuron degeneration in T2DM; it can decrease the activity of protein kinase B and increase the activity of glycogen synthesis kinase-3β, thus promoting the hyperphosphorylation of tau protein and finally leading to neuronal degeneration. However, the association between GMV and serum tau protein phosphorylated at threonine 181 (P-tau-181) in T2DM patients lacks neuroimaging evidence. We aimed to investigate the difference in brain GMV between T2DM patients with different glycated hemoglobin A1c (HbA1c) levels and healthy control (HC) subjects and the correlation between serum P-tau-181 and GMV in T2DM patients.MethodsClinical parameters, biochemical indicators, and MRI data were collected for 41 T2DM patients with high glycosylated hemoglobin level (HGL), 17 T2DM patients with normal glycosylated hemoglobin level (NGL), and 42 HC subjects. Voxel-based morphometry (VBM) method was applied to investigate GMV differences among groups, and multiple regression analysis was used to examine the correlation between serum P-tau-181 and GMV.ResultsCompared with HC subjects, the T2DM patients with HGL or NGL all showed significantly decreased GMV. Briefly, the GMV decreased in T2DM patients with HGL was mainly in the bilateral parahippocampal gyrus (PHG), right middle temporal gyrus (MTG), temporal pole (TPOmid), hippocampus (HIP), and left lingual gyrus. The GMV reduction in T2DM patients with NGL was in the right superior temporal gyrus (STG), and there was no significant difference in GMV between the two diabetic groups. The GMV values of bilateral PHG, right MTG, TPOmid, HIP, and STG can significantly (p

Abstract Exploring the photoelectric performance of emerging materials represented by graphene, black phosphorus and transition metal dichalcogenides is attracting enormous research interest for a wide range of electronic and photonic applications. The realization of low-power consuming photodetectors with high sensitivity and fast photoresponse in the terahertz band remains one of the profound challenges in optoelectronics. In this study, a material-EuSn2As2 has been successfully implemented to realize highly sensitive terahertz photodetectors. The non-equilibrium dynamics in a two-dimensional plane allow an optionally switching between different styles of direct photon-conversions: the analogous photoconductive and photovoltaic modes spontaneously supported by the intrinsic electronic system. The prototype devices exhibited excellent sensitivity of 0.2–1.6 A/W (0.3–2.4 kV/W) from 0.02 to 0.30 THz at room temperature, corresponding to a noise-equivalent power

This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs.

Methylmalonic acidemia (MMA) is a genetic disease of abnormal organic acid metabolism, which is one of the important factors affecting the survival rate and quality of life of newborns or infants. Early detection and diagnosis are particularly important. The diagnosis of MMA mainly depends on clinical symptoms, newborn screening, biochemical detection, gene sequencing and neuroimaging diagnosis. The accumulation of methylmalonic acid and other metabolites in the body of patients causes brain tissue damage, which can manifest as various degrees of intellectual disability and severe neurological dysfunction. Neuroimaging examination has important clinical significance in the diagnosis and prognosis of MMA. This review mainly reviews the etiology, pathogenesis, and nervous system development, especially the neuroimaging features of MMA.

Pyroptosis plays a crucial role in bronchopulmonary dysplasia (BPD) and is associated with various lung injury illnesses. However, the function of pyroptosis-related genes (PRGs) in BPD remains poorly understood. The gene expression omnibus (GEO) database was searched for information on genes associated with BPD. Twenty-five BPD-related DE-PRGs were identified, all of which were closely associated with pyroptosis regulation and immunological response. LASSO and SVM-RFE algorithms identified CHMP7, NLRC4, NLRP2, NLRP6, and NLRP9 among the 25 differentially expressed PRGs as marker genes with acceptable diagnostic capabilities. Using these five genes, we also generated a nomogram with excellent predictive power. Annotation enrichment analyses revealed that these five genes may be implicated in BPD and numerous BPD-related pathways. In addition, the ceRNA network showed an intricate regulatory link based on the marker genes. In addition, CIBERSORT-based studies revealed that alterations in the immunological microenvironment of BPD patients may be associated with the marker genes. We constructed a diagnostic nomogram and gave insight into the mechanism of BPD. Its diagnostic value for BPD must be evaluated in further research before it can be used in clinical practice.

PurposeThe purpose of this study was to investigate whether baseline cerebral magnetic resonance imaging (MRI) characteristics could predict therapeutic responsiveness in patients with cobalamin C (cblC) defects.Materials and methodsThe cerebral MRI results of 40 patients with cblC defects were evaluated by a neuroradiologist. Neuropsychological scores and imaging data were collected. Neuropsychological tests were performed before and after standardized treatment.ResultsThirty-eight patients initially underwent neuropsychological testing [developmental quotient (DQ)]. CblC defects with cerebellar atrophy, corpus callosum thinning and ventricular dilation had significantly lower DQs than those without (P < 0.05). Through a multivariate linear stepwise regression equation after univariate analysis, ventricular dilation was the most valuable predictor of lower DQs. Thirty-six patients (94.7%) underwent follow-up neuropsychological testing. The pre- and post-treatment DQ values were not significantly different (Z = −1.611, P = 0.107). The post-treatment DQ classification (normal, moderately low, or extremely low) showed nearly no change compared to the pretreatment DQ classification (k = 0.790, P < 0.001).ConclusionVentricular dilation, cerebral atrophy and corpus callosum thinning are the main MRI abnormalities of cblC defects, and these manifestations are significantly correlated with delayed development in children. MRI findings can be considered an important tool for determining the severity of cblC defects.

BackgroundEctopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separated from the main gland and without vascular or ductal continuity. A case of retroperitoneal ectopic pancreas with adrenal adenoma has never yet been reported.Case PresentationA 54-year-old man presented three masses in the left retroperitoneum, and two of them were resected. The pathologic findings were a retroperitoneal ectopic pancreas with adrenal adenoma.ConclusionWe report an extremely rare case of a retroperitoneal ectopic pancreas and its characterization with dynamic gadolinium-enhanced magnetic resonance imaging (MRI).

Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal and quantitative properties of brain networks. Imaging markers of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relationships of brain regions and remove noise from the feature matrix. A multimodal classification framework is proposed to address this issue using hypergraph latent relation (HLR). A brain functional network with hypergraph structural information is constructed by fMRI data. The feature matrix is obtained through graph theory (GT). The cerebral blood flow (CBF) from ASL is selected as the second modal feature matrix. Then, the adaptive similarity matrix is constructed by learning the latent relation between feature matrices. Latent relation adaptive similarity learning (LRAS) is introduced to multi-task feature learning to construct a multimodal feature selection method based on latent relation (LRMFS). The experimental results show that the best classification accuracy (ACC) reaches 88.67%, at least 2.84% better than the state-of-the-art methods. The proposed framework preserves more valuable information between brain regions and reduces noise among feature matrixes. It provides an essential reference value for ESRDaMCI recognition.

Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders.

To ensure the accurate and reliable transmission of the measured value of a measurement standard device, research was conducted for the process quality control technology of flowmeter verification in terms of big data analysis. It was the first time that the method for checking verification data with the multivariate linear prediction based on the historical data was explored, which could realize the control indicators of linear regression prediction error within ± 0.1℃ for temperature, －0.01-0.015 MPa for pressure and ±5 m3/h for flow, and the real-time check method based on the mean and standard deviation control charts was proposed. Based on the segment of the flow range of standard turbine flowmeter, the historical data was dynamically calculated to determine the warning limit of the mean and the standard deviation dynamic control charts, which could monitor if the deviation of the standard turbine flowmeter exceeds the dynamic warning limit. Here, the control limit was set as 0.29% for the uncertainty of the device. Thereby, the operation status of the measurement standard device can be presented in real time according to the warning limit and the control limit, and the intelligent analysis and processing of the verification data can be realized, which comprehensively improves the quality and efficiency of metrological verification of natural gas.

Abstract Ground source heat pumps (GSHPs) are one of the renewable energy technologies with features of high efficiency, energy saving, economic feasibility and environmental protection. In China, GSHPs have been widely used for building heating and cooling in recent years, and have shown great potential for future energy development. This paper summarizes the classification, development history, and use status of shallow GSHPs. Several typical engineering cases of GSHP technology are also specified and analyzed. Finally, promising development trends and some advanced technologies are illustrated.

In order to realize the intelligent distribution function of natural gas distribution stations in the construction of intelligent pipeline networks, load forecasting should be carried out for users, especially the urban gas users who are greatly affected by the environmental factors, so as to provide a basis for the distribution branch to forecast and adjust the distribution control strategy with the artificial intelligence algorithm. An intelligent forecasting model based on a multilayered BP neural network was established, load forecasting was conducted for a gas user in West-to-East Gas Pipeline Network, and the results of comparison, with the weather parameters such as temperature, wind speed and direction, and the historical load value as the input, indicated that the simulation results with the forecasting method had a relative error of no more than ±8% with the actual load value, capable of accurately forecasting the 72 h short-term load of the urban gas users. In addition, the factors affecting the accuracy of load forecasting were further studied, and it was found that the change of temperature at about 1 ℃ would lead to the change of demand of about 5%-6% in winter. When the temperature exceeds 14 ℃, the user load will decrease linearly with the rise of temperature, and it will not be affected when the temperature exceeds 18 ℃. Variations in wind speed and rainfall, on the other hand, have less than 5% impact on user's demand. Accurate short-term user load forecasting is the basis for the efficient, reasonable and economic operation of the natural gas supply system by the pipeline or pipeline network companies, which can effectively ensure the economy and reasonability of the gas supply scheme, as well as the safety and efficiency of operation and scheduling.

The increasing interest in two-dimensional materials with unique crystal structures and novel band characteristics has provided numerous new strategies and paradigms in the field of photodetection. However, as the demand for wide-spectrum detection increases, the size of integrated systems and the limitations of mission modules pose significant challenges to existing devices. In this paper, we present a van der Waals heterostructure photodetector based on Ta2NiSe5/WSe2, leveraging the inherent characteristics of heterostructures. Our results demonstrate that this detector exhibits excellent broad-spectrum detection ability from the visible to the infrared bands at room temperature, achieving an extremely high on/off ratio, without the need for an external bias voltage. Furthermore, compared to a pure material detector, it exhibits a fast response and low dark currents (~3.6 pA), with rise and fall times of 278 μs and 283 μs for the response rate, respectively. Our findings provide a promising method for wide-spectrum detection and enrich the diversity of room-temperature photoelectric detection.

Speech recognition has progressed tremendously in the area of artificial intelligence (AI). However, the performance of the real-time offline Chinese speech recognition neural network accelerator for edge AI needs to be improved. This paper proposes a configurable convolutional neural network accelerator based on a lightweight speech recognition model, which can dramatically reduce hardware resource consumption while guaranteeing an acceptable error rate. For convolutional layers, the weights are binarized to reduce the number of model parameters and improve computational and storage efficiency. A multichannel shared computation (MCSC) architecture is proposed to maximize the reuse of weight and feature map data. The binary weight-sharing processing engine (PE) is designed to avoid limiting the number of multipliers. A custom instruction set is established according to the variable length of voice input to configure parameters for adapting to different network structures. Finally, the ping-pong storage method is used when the feature map is an input. We implemented this accelerator on Xilinx ZYNQ XC7Z035 under the working frequency of 150 MHz. The processing time for 2.24 s and 8 s of speech was 69.8 ms and 189.51 ms, respectively, and the convolution performance reached 35.66 GOPS/W. Compared with other computing platforms, accelerators perform better in terms of energy efficiency, power consumption and hardware resource consumption.

A new genus and species of Berothidae is described from the Late Cretaceous (Cenomanian) Myanmar amber. Ansoberotha jiewenae gen. et sp. nov. can be easily distinguished from other berothid genera by the long antenna, the scape with ca. 100 flagellomeres, the forewing with four ra-rp, MPand CuA are pectinately branched, and the hind wing with one oblique cua-cup between CuA stem and the distal branch of CuP.

Swine influenza virus (SIV) is an important zoonosis pathogen. The 2009 pandemic of H1N1 influenza A virus (2009/H1N1) highlighted the importance of the role of pigs as intermediate hosts. Liaoning province, located in northeastern China, has become one of the largest pig-farming areas since 2016. However, the epidemiology and evolutionary properties of SIVs in Liaoning are largely unknown. We performed systematic epidemiological and genetic dynamics surveillance of SIVs in Liaoning province during 2020. In total, 33,195 pig nasal swabs were collected, with an SIV detection rate of 2%. Our analysis revealed that multiple subtypes of SIVs are co-circulating in the pig population in Liaoning, including H1N1, H1N2 and H3N2 SIVs. Furthermore, 24 H1N1 SIVs were confirmed to belong to the EA H1N1 lineage and divided into two genotypes. The two genotypes were both triple reassortant, and the predominant one with polymerase, nucleoprotein (NP), and matrix protein (M) genes originating from 2009/H1N1; hemagglutinin (HA) and neuraminidase (NA) genes originating from EA H1N1; and the nonstructural protein (NS) gene originating from triple reassortant H1N2 (TR H1N2) was detected in Liaoning for the first time. According to our evolutionary analysis, the EA H1N1 virus in Liaoning will undergo further genome variation.

A new genus and species of Sisyridae, Stictosisyra pennyi gen. et sp. n., is described from the Late Cretaceous (earliest Cenomanian/late Albian) Myanmar amber. It can be easily distinguished from other sisyrids genera by the configuration of wing venation such as forewing with four ra-rp crossveins, M forked distal to the separation of RP1, CuA pectinate and CuP simple; hind wing 1r-m long and sinuous. Besides, the newly documented spongillaflies bore distinct, irregularly distributed spots on the forewings.

A new species of Hemerobiidae, Wesmaelius makarkini Yang, Pang & Ren, sp. n. is described from the Lower Miocene, Garang Formation of Zeku County, Qinghai Province (northeastern Tibetan Plateau), China. The species is assigned to the widely distributed extant genus Wesmaelius Krüger (Hemerobiinae). The species represents the first named fossil of this family from China, which sheds light on the historical distribution of Wesmaelius and early divergences within Hemerobiinae.

This study presents a distributed integrated energy system driven by deep and shallow geothermal energy based on forward and reverse cycle for flexible generation of cold, heat and electricity in different scenarios. By adjusting the strategy, the system can meet the demand of heat-electricity in winter, cool-electricity in summer and electricity in transition seasons. The thermodynamic analysis shows that the thermal efficiency of the integrated energy system in the heating and power generation mode is 16% higher than that in the cooling and power generation mode or the single power generation mode. Meanwhile, the annual heat-obtaining quantity of the system is reduced by 11% compared with that of the independent power generation system, which effectively alleviates the imbalance of the temperature field of the shallow geothermal reservoir. In terms of net power generation, the integrated energy system can generate approximately 31% more electricity than the conventional independent cooling and heating system under the same cooling and heating capacity. An integrated system not only realizes the comprehensive supply of cold and thermal ower by using clean geothermal efficiency, but also solves the temperature imbalance caused by the attenuation of a shallow geothermal temperature field. It provides a feasible way for carbon emission reduction to realize sustainable and efficient utilization of geothermal energy.

A well-developed recurrent veinlet is found in the forewing of two species of Nymphidae from the Middle Jurassic locality of Daohugou (Inner Mongolia, China), Liminympha makarkini Ren & Engel and Daonymphes bisulca gen. et sp. n. This is the first record of this trait in the clade comprised of the superfamilies Myrmeleontoidea and Chrysopoidea. We interpret the recurrent veinlet in these species as a remnant of the condition present more basally in the psychopsoid + ithonoid + chrysopoid + myrmeleontoid clade (i.e., as a plesiomorphy). Other venational character states of D. bisulca of interest include the configuration of subcosta anterior (ScA), which is very similar to that of extant Nymphidae. We consider the short ScA terminating on ScP to be an autapomorphy of Neuroptera.

The ground state structures, HOMO and LUMO energy levels, band gaps ΔH-L, ionization potentials (IP), and electron affinities (EA) of three types of copolymer P1 and its derivatives P2, P3, and PBDT-BTA were investigated by using density functional theory (DFT) with B3LYP and 6-31G (d) basis set. On the base of optimized structures of ground states, their absorption spectra were obtained by using TD-DFT//Cam-B3LYP/6-31 G (d). Research shows that with the increasing conjugated units, HOMO energy levels increased, LUMO energy levels decreased, and band gaps decreased gradually. Moreover, their ionization potentials decreased and electron affinities increased along with the increase of conjugated chains, and absorption spectra red-shifted. In addition, the side chain has a significant effect on the properties of ground and excited states. In order to investigate the influence of conjugated units and side chain on the charge transport, their hole and electron reorganization energies were calculated, and the results indicated that Pb have a good hole transport capability. Considering the practical application, the HOMO and LUMO energy levels, band gaps, and absorption spectra under external electric field were studied, and the results proved that the external electric field has an effect on the optical and electronic properties.

Background: Direct stimulation of the afferent nerve endings in the venous endothelium is one explanation of propofol injection pain. Previous studies found that ondansetron can also block sodium channels. This effect is similar to that of lidocaine., Objective: The primary outcome was the efficacy of ondansetron compared to lidocaine and placebo for the reduction of propofol injection pain., Method: This trial was conducted in 240 patients, American Society of Anesthesiologists classification I-III and aged between 18-65 years old, undergoing elective surgery, and having a 20-gauge intravenous catheter at the hand dorsum. Each group of 80 patients received 8 mg. of ondansetron in the O Group, 40 mg. of lidocaine in the L Group and normal saline in the C Group. The study medications were blindly administered to the patients through a 20-gauge intravenous catheter placed on the hand dorsum, and then 1 min later, the small dose of propofol (50 mg.) was infused via the syringe pump at a rate of 600 ml/hr. for 30 s. Following that, the syringe pump of propofol was temporarily stopped, and the patients were asked to rate their pain at the injection site., Result: The incidence of pain was lowest in the L group (66.2%) compared with the O (82.5%) and the C groups (85.0%) (P < 0.01). The median pain score in the L, O, and C groups were 2 (0-4), 4 (2-5), and 4.5 (2-6), respectively (P < 0.01). The incidences of no pain, mild, moderate, and severe pain were also significantly different in the L group (33.8%, 37.5%, 21.2%, and 7.5%, respectively) compared with those in the O group (17.5%, 31.2%, 31.2%, and 20.0%, respectively) and the C groups (15.0%, 22.5%, 40.0%, and 22.5%, respectively) (P < 0.01)., Conclusion: Pretreatment with intravenous lidocaine, rather than ondansetron, can reduce the incidence and intensity of propofol-induced pain., (© 2022. The Author(s).)