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The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology. [ABSTRACT FROM AUTHOR]

Aims/Introduction: Serum asprosin is expected to become a screening indicator in early‐stage diabetic heart disease. The relationship between serum asprosin and left ventricular diastolic dysfunction (LVDD) was studied in elderly patients with type 2 diabetes mellitus in the community. Materials and Methods: A total of 252 elderly patients with type 2 diabetes mellitus were recruited from Zhuoma Community Care Station and Chengbei West Street Community Care Service Center in Changzhi City of Shanxi Province from November 2019 to July 2021. Patients were divided into the LVDD group (n = 195) and the non‐LVDD group (n = 57). The t‐test, Mann–Whitney U test, and χ2 test were used to compare indicators between the LVDD group and the non‐LVDD group. Pearson or Spearman correlation analysis was adopted to evaluate the correlation between serum asprosin and other clinical data. Multivariate logistic regression analysis was applied to analyze the influencing factors on LVDD. Results: Compared with patients without LVDD, patients with LVDD had a higher level of low‐density lipoprotein cholesterol (LDL‐C), fasting blood glucose (FPG), and asprosin, but a lower level of early diastolic movement speed (A) to diastolic movement velocity (E) (E/A). Asprosin was positively associated with waist circumference (WC), body mass index (BMI), creatinine, triglycerides (P < 0.05), and negatively associated with E/A and high density lipoprotein cholesterol HDL‐C (P < 0.05). The risk of LVDD increased with elevated asprosin levels after adjustment for age, systolic blood pressure (SBP), BMI, FPG, and LDL‐C. Compared with patients in the lowest tertile of serum asprosin (<275.25 pg/mL), a serum level of asprosin between 275.25–355.08 pg/mL [OR (95% CI) is 2.368 (1.169–4.796), P < 0.05] and asprosin >355.08 pg/mL [OR (95% CI) is 2.549 (1.275–5.095), P < 0.05] patients have a higher risk of left ventricular diastolic dysfunction. Conclusions: Serum asprosin was positively associated with left ventricular diastolic dysfunction, and the risk of LVDD increased significantly with increased serum levels of asprosin. [ABSTRACT FROM AUTHOR]

Background: Sequencing the mitochondrial genome has been increasingly important for the investigation of primary mitochondrial diseases (PMD) and mitochondrial genetics. To overcome the limitations originating from PCR-based mtDNA enrichment, we set out to develop and evaluate a PCR-independent approach in this study, named Pime-Seq (PCR-independent mtDNA enrichment and next generation Sequencing). Results: By using the optimized mtDNA enrichment procedure, the mtDNA reads ratio reached 88.0 ± 7.9% in the sequencing library when applied on human PBMC samples. We found the variants called by Pime-Seq were highly consistent among technical repeats. To evaluate the accuracy and reliability of this method, we compared Pime-Seq with lrPCR based NGS by performing both methods simultaneously on 45 samples, yielding 1677 concordant variants, as well as 146 discordant variants with low-level heteroplasmic fraction, in which Pime-Seq showed higher reliability. Furthermore, we applied Pime-Seq on 4 samples of PMD patients retrospectively, and successfully detected all the pathogenic mtDNA variants. In addition, we performed a prospective study on 192 apparently healthy pregnant women during prenatal screening, in which Pime-Seq identified pathogenic mtDNA variants in 4 samples, providing extra information for better health monitoring in these cases. Conclusions: Pime-Seq can obtain highly enriched mtDNA in a PCR-independent manner for high quality and reliable mtDNA deep-sequencing, which provides us an effective and promising tool for detecting mtDNA variants for both clinical and research purposes. [ABSTRACT FROM AUTHOR]

Virtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and hand‐held scanner (HHS). This approach considers both the global data and the local details of the steel members. Additionally, a precise registration method based on magnetic 3D targets is proposed for multiscale point clouds, which enables the registration accuracy of multisource point clouds to reach submillimeter precision. Subsequently, a novel algorithm for feature point screening is introduced, which utilizes a dichotomous point cloud grid approach to identify and extract a significant quantity of bolt holes effectively. This approach enables fully automated and fast extraction of the point cloud on the cylindrical inner surface of the holes. Finally, the bounding box and Procrustes analysis approach are employed to perform VTA using the point cloud of the cylindrical bolt holes as the assembled features. The accuracy and feasibility of the above method are verified by a down‐scale modeling experiment and project test, which provide technical support for the VTA of large steel truss structures. [ABSTRACT FROM AUTHOR]

Trunk canker poses a major threat to the production of Chinese hickory tree (Carya cathayensis Sarg.), which is primarily determined by Botryosphaeriaceae. In our previous work, we identified Botryosphaeria dothidea as the predominant pathogen of this disease. However, it is still unclear about corresponding gene families and mechanisms associated with B. dothidea's pathogenicity on Chinese hickory tree. Here, we present a comparative analysis of high-quality genome assemblies of Botryosphaeria dothidea and other isolated pathogens, showing highly syntenic relationships between B. dothidea and its closely related species and the conservative evolution of the Botryosphaeriaceae family. Higher GC contents were found in the genomes of B. dothidea and three other isolated pathogens (Botryshaeria cortices, Botryshaeria fabicerciana, and Botryshaeria qingyuanensis) compared to Macrophomina phaseolina, Neofusicoccum parvum, Diplodia corticola, and Lasiodiplodia theobromae. An investigation of genes specific to or expanded in B. dothidea revealed that one secreted glucanase, one orsellinic acid biosynthesis enzyme, and two MFS transporters positively regulated B. dothidea's pathogenicity. We also observed an overrepresentation of viral integrase like gene and heterokaryon incompatibility proteins in the B. dothidea's genome. In addition, we observed one LRR-domain-containing protein and two Sec-domain-containing proteins (Sec_1 and Sec_7) that underwent positive selection. This study will help to understand B. dothidea's pathogenicity and potential influence on the infection of Chinese hickory, which will help in the development of disease control and ensure the security of Chinese hickory production. [ABSTRACT FROM AUTHOR]

Bottom water coning is the main reason to reduce the recovery of horizontal bottom water reservoir. By water coning, we mean the oil-water interface changes from a horizontal state to a mound-shaped cone and breaks through to the wellbore. Autonomous inflow control device (AICD) is an important instrument maintain normal production after bottom water coning, however, the resistance increasing ability of the swirl type AICD is insufficient at present, which seriously affects the water control effect. Aiming this problem, this paper designs a multi-stage resistance-increasing and composite type AICD. The separation mechanism of oil-water two phases in this structure, the resistance form of oil-water single phase and the resistance-increasing principle of water phase are analyzed. Establishing the dual-phase multi-stage separation and resistance-increasing model, and verified by measuring the throttling pressure drop and oil-water volume fraction of the AICD, it is found that the composite type AICD has the effect of ICD and AICD at the same time, which can balance the production rate of each well section at the initial stage of production, delay the occurrence of bottom water coning. In the middle and later stages of production, water-blocking can be effectively increased to achieve water control and stable production. After structural sensitivity analysis, the influence law of various structural parameters on the water control performance of composite AICD was obtained. The simulation calculation results show that, compared with the existing swirl type AICD, composite AICD has higher sensitivity to moisture content, the water phase throttling pressure drop is increased by 4.5 times on average. The composite AICD is suitable for the entire stage of horizontal well production. [ABSTRACT FROM AUTHOR]

To improve the scan efficiency of thoracic aorta vessel wall imaging using a self-gating (SG)-based motion correction scheme. A slab-selective variable-flip-angle 3D turbo spin-echo (SPACE) sequence was modified to acquire SG signals and imaging data. Cartesian sampling with a tiny golden-step spiral profile ordering was used to obtain the imaging data during the systolic period, and then the image data were subsequently corrected based on the SG signals and binned to different respiratory cycles. Finally, respiratory artifacts were estimated from image-based registration of 3D undersampled respiratory bins that were reconstructed with L1 iterative self-consistent parallel imaging reconstruction (SPIRiT). This method was evaluated in 11 healthy volunteers and compared against conventional diaphragmatic navigator-gated acquisition to assess the feasibility of the proposed framework. Results showed that the proposed method achieved image quality comparable to that of conventional diaphragmatic navigator-gated acquisition with an average scan time of 4 min. The sharpness of the vessel wall and the definition of the liver boundary were in good agreement with the navigator-gated acquisition, which took approximately above 8.5 min depend on the respiratory rate. Further valuation of this technique in patients will be conducted to determine its clinical use. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

High‐throughput satellite (HTS) is an ideal way to realize cross‐regional massive, multifaceted digital exchange services, and it requires a signal processing module that can be massively multiplexed and has high flexibility. Due to the limitations of the frequency characteristics, microwave integrated circuits are difficult to meet this requirement. One solution to this problem is photonic integrated circuits (PICs). However, full‐size PIC satellite payloads containing main optoelectronic components are extremely challenging to implement on monolithic or hybrid integrated platforms. Here, the study demonstrates a hybrid integrated on‐chip microwave‐photonic satellite repeater with large‐scale multiplexing potential and high flexibility. This is a demonstration of a hybrid integration of a InP/Si3N4 external cavity laser, arrayed InP modulators, and semiconductor optical amplifiers (SOAs), as well as multifunctional Si3N4 signal processors, to fulfill a 1 × 4 Ka‐band repeater module with on‐chip arrayed frequency down‐conversion and outstanding narrowband photonic channelization. When combined with the full‐chip photonic RF repeater, broadband, highly integrated, and cost‐effective communications satellite payloads will become realizable more quickly in the near future. [ABSTRACT FROM AUTHOR]

Over the past three decades, considerable efforts have been expended on understanding the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in leukemia, following the identification of the JAK2V617F mutation in myeloproliferative neoplasms (MPNs). The aim of this review is to summarize the latest progress in our understanding of the involvement of the JAK/STAT signaling pathway in the development of leukemia. We also attempt to provide insights into the current use of JAK/STAT inhibitors in leukemia therapy and explore pertinent clinical trials in this field. [ABSTRACT FROM AUTHOR]

The unique characteristics, such as high magnetic moment, high Curie temperature, large magnetic crystalline anisotropy, and low cost, make the τ-phase MnAl a promising candidate as the market alternatives which could fill the gap between the rare earth magnets and ferrite magnets. Because at low temperatures the metal will become more brittle, the cryomilling technology may allow the τ-phase MnAl to be broken quickly and introduce few crystal defects, and then obtain better permanent magnetic properties. In this study, the cryomilling technology was used to grind the τ-phase MnAl, and the structural and magnetic properties of the obtained powder sample were investigated. It was found that compared to room-temperature ball milling, the morphology of cryomilled powders is granular and the powder agglomeration is dramatically suppressed at a low temperature, and as a result the larger particles tend to be broken down into smaller ones rather than being agglomerated to larger thin layers. The grain size D reduced continuously from 145 nm to 36 nm when the sample was milled for 120 minutes. The coercivity increased with increasing the cryo-milling time, while the saturation magnetization decreased. The maximum coercivity of up to 4.9 kOe was obtained by cryo-milling for 100 minutes. By the neutron diffraction analysis, it was confirmed that the decrease of the saturation magnetization with increasing milling time is mainly due to the migration of Mn atoms from 1a site to the 1d site and the decrease of Mn atomic magnetic moment. [ABSTRACT FROM AUTHOR]

Oriented by the demands on high-speed and large-load picking up/handling in textile, electronics and other industries, a novel parallel manipulator (PM) achieving SCARA motion was designed. First, the mobility of the PM was obtained by dual space theory. Thereafter, the kinematic model was deduced in detail, and a dynamics model was developed using the Lagrangian formulation. The validity of the above models was verified by ADAMS software. Furthermore, the singularity existing in the workspace was analyzed. Subsequently, a homogeneous Jacobian matrix was constructed by the characteristic length factor, based on which dexterity index was defined. Aiming at the optimal performance in the singularity-free task workspace, a dimensional synthesis model incorporating kinematics and dynamics performance is proposed, which is efficiently tackled by differential evolution (DE) algorithm. The obtained result provides a sound foundation for the development of an engineering prototype. [ABSTRACT FROM AUTHOR]

Users can interact with the advertisements and share their impressions through the review system on short video applications. However, spammers may post false or malicious comments to mislead normal users due to profit-driven reasons, damaging the community's positive atmosphere. In this paper, we introduce a new challenge of spammer detection on short video applications, where the multi-modal information of videos and reviews plays a more critical role than the spam relation graph. Then we propose SPAM-3 , a novel baseline to detect SPAM reviews with M ulti- M odal representation using attentive heterogeneous graph convolution. Our approach balances multi-modal representation fusion and graph relation extraction, enabling fine-grained interaction and generating discriminative features for the spammer classification task. We describe the methodology of dataset construction in detail and reveal the statistical properties of the collected dataset. SPAM-3 outperforms the former baseline models on both private and public benchmarks. Furthermore, we conduct comprehensive ablations and analyses to demonstrate our method. • Introducing a new challenge of spammer detection on short video applications. • Spammer detection data collecting and labeling. • Using heterogeneous graph to fuse multi-modal information and spam relation graph. • Outperforming former baseline models on the collected dataset. [ABSTRACT FROM AUTHOR]

[Display omitted] Boundary conditions are crucial for the construction of physical models of concrete-filled steel tube (CFST) arches, but the current model updating methods seldom consider boundary conditions and usually necessitate repeated modifications and recalculations of finite element models, which are not efficient enough. Moreover, the current physics-informed neural network (PINN) provides a new approach for solving physical equations through physical constraint merging into the neural network, but it is still limited by complex structures, multiple scales, and unknown physical model engineering problems. Therefore, this paper proposed a boundary recognition method for physically-enhanced machine learning. First, the basic variables of the physical equation and the variables to be optimized were taken as inputs to enhance the neural network's ability to predict the target response. Then, similar to the handling of physical loss constraints in the PINN, the physical loss between the physical model (FE) and the neural network predictions was used to update the parameters. Then, a physical feedback-enhanced neural network (PFENN) meta -model was established, and modified mutation particle swarm optimization (MMPSO) was adopted to optimize the identification of boundary parameters. The identification process involves validation via mutual verification using a finite element model, meta -model, and experimental model. The prediction accuracy is higher than that of classical Kriging, response surface (RS), support vector machine regression (SVR), and neural network (NN) meta -models; additionally, the proposed model has good optimization stability, and reliable optimization results, and its computational efficiency is increased by 4.57 times compared with that of the traditional non-metamodel method. Moreover, the proposed method enhances the accuracy and interpretability of surrogate models by enhancing physical parameters and reducing physical losses, which is suitable for boundary recognition and physical model construction of different structures. [ABSTRACT FROM AUTHOR]

In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE. PIE primarily addresses three issues: (i) To resolve the problem of existing learning-based methods often training a LLE model with strict pixel-correspondence image pairs, we eliminate the need for pixel-correspondence paired training data and instead train with unpaired images. (ii) To address the disregard for negative samples and the inadequacy of their generation in existing methods, we incorporate physics-inspired contrastive learning for LLE and design the Bag of Curves (BoC) method to generate more reasonable negative samples that closely adhere to the underlying physical imaging principle. (iii) To overcome the reliance on semantic ground truths in existing methods, we propose an unsupervised regional segmentation module, ensuring regional brightness consistency while eliminating the dependency on semantic ground truths. Overall, the proposed PIE can effectively learn from unpaired positive/negative samples and smoothly realize non-semantic regional enhancement, which is clearly different from existing LLE efforts. Besides the novel architecture of PIE, we explore the gain of PIE on downstream tasks such as semantic segmentation and face detection. Training on readily available open data and extensive experiments demonstrate that our method surpasses the state-of-the-art LLE models over six independent cross-scenes datasets. PIE runs fast with reasonable GFLOPs in test time, making it easy to use on mobile devices. Code available [ABSTRACT FROM AUTHOR]

[Display omitted] • A label-free PEC sensor based on soluble melem unit/g-C 3 N 4 materials was constructed. Development of label-free photoelectrochemical sensor to detection of aquatic Cu2+ is of great significance for disease prevention and environment preservation. Reported herein is the rational construction of a facile strategy, combined with a hydrothermal exfoliation and subsequent assembly process, which obtain the soluble melem unit/g-C 3 N 4 materials. This composite possesses porous structure and hydrophilic group (–OH), contributing to its great contact with aquatic solution and diffusion channel for target analytes. And abundant functional groups (-NH x) on the surface of soluble melem unit is capable of easily incorporating with aquatic Cu2+ to form the Cu-N bond, which was unveiled by experiential result of XPS spectra, providing tremendous potential for the application in Cu2+ detection. Consequently, a label-free photoelectrochemical sensor based on soluble melem unit/g-C 3 N 4 exhibited a significant signal change due to the formation of Cu2+-induced surface exciton trapping, thus achieve the aquatic Cu2+ monitoring with low cost and high sensitivity. [ABSTRACT FROM AUTHOR]

The three-dimensional(3D) measurement technology based on structured light fringe projection has the advantages of high efficiency, simplicity and non-contact acquisition. However, its measurement quality is heavily dependent on the reflective properties of the target surface. For a highly reflective surface, overexposure may be caused in some areas of the image, bringing decoding errors and causing the degradation of accuracy or even measurement failure. In recent years, a lot of researches have been done on this problem and many methods have been proposed. However, a systematic survey of these methods is still lacking. In this work, a review of the structured light 3D measurement techniques for highly reflective surfaces is presented. Firstly, the challenges posed by surface reflectivity to structured light 3D measurement are analyzed. Secondly, recent methods, which are classified into two categories, are reviewed. Finally, the accuracy and efficiency of these methods are discussed, and the trends of future researches are summarized. [ABSTRACT FROM AUTHOR]

In this work, a donor‐acceptor (D‐A) type ligand is prepared to construct Cu(I) and Ag(I) complexes. These complexes possess similar molecular structures, distributions of molecular orbitals, and distributions of electrons and holes in excited states. However, they exhibit distinct photophysical properties, such as luminescent spectra and transient photo‐luminescent decay spectra. Through analysis of deep‐lying molecular orbitals in a tetrahedral field, it is determined that the variation in d‐p π conjugation between the metal cation's d‐orbitals and the π orbitals of the electron‐acceptor part of the D‐A ligand is the reason for the different photophysical properties. This research contributes to an in‐depth understanding of the role of metal ions in luminescent complexes, providing insights from the perspective of coordination chemistry. [ABSTRACT FROM AUTHOR]

Simple Summary: The genus Diploderma Hallowell, 1861 (Squamata, Agamidae), currently consists of 46 species, which are distributed across East Asia (inclusive of Japan) and the northern part of the Indochinese Peninsula, primarily inhabiting dry valley regions in Western China. This article introduces a novel species of Diploderma based on its unique morphological features and molecular evidence, discovered in the lower valley of the Dadu River in Sichuan Province, Western China. Phylogenetic analysis using ND2 data suggests that this new taxon is distinct from its congeners. Morphologically, the new species can be differentiated from other Diploderma species by an assemblage of 46 specific characteristics. Principal component analysis (PCA) further demonstrates that this new species is clearly distinguishable from its closest relative, D. splendidum. Given these multiple lines of evidence, we describe this species from the lower Dadu River valley as a newly identified species, D. daduense sp. nov. This discovery brings the total number of recognized species within the genus Diploderma to 47. This study describes a novel species of Diploderma (Squamata, Agamidae) from the lower valley of the Dadu River of the Sichuan Province of Western China based on its distinct morphological features and molecular evidence. D. daduense sp. nov. can be distinguished from its congeners by its tympanum concealed; head mainly green-yellow, supplemented by black; skin folds under the nuchal and dorsal crest obviously present in adult males only, its vertebral crest discontinuous between nuchal and dorsal sections with a distinct gap; transverse gular fold present but not obvious in some individuals; gular spot absent in both sexes; dorsolateral stripes green-yellow anteriorly, cyan in the center and blurry off-white posteriorly in adult males, the upper edge of dorsolateral stripes strongly jagged in adult males; no radial stripes around the eyes; inner-lip coloration smoky-white, and the coloration of the tongue and oral cavity as a light-flesh color in life; bright green-yellow transverse stripes on dorsal body in males; black patches are evenly distributed along the vertebral line between the dorsolateral stripes from the neck to the base of the tail in males; beech-brown or gray-brown line along the vertebral line with heart-shaped or diamond-shaped black patches on the dorsal body in females; and supratemporals fewer than four on at least one side. The phylogenetic tree based on mitochondrial ND2 sequences indicates that D. daduense sp. nov. forms an independent clade with strong support 1/100 in ML bootstrap/Bayesian posterior probability and is the sister group to D. splendidum. At the inter-species level, the p-distance is at least 6.95%, further confirming that an independent species had been identified. Our work raises the number of species within the genus Diploderma to 47. [ABSTRACT FROM AUTHOR]

To alleviate the selenium (Se) stress in fruit trees and improve its accumulation, the effects of exogenous indole-3-acetic acid (IAA) on the growth and Se accumulation of grapevine under Se stress were studied. The application of exogenous IAA increased the biomass of grapevine, and the concentration of exogenous IAA had a regression relationship with the biomass. The root and shoot biomass were the maximum at 60 mg L− 1 IAA, increasing by 15.61% and 23.95%, respectively, compared with the control. Exogenous IAA also increased the photosynthetic pigments and the activities of superoxide dismutase and peroxidase in grapevine. Moreover, exogenous IAA increased the contents of total Se, organic Se, and inorganic Se, and the concentration of exogenous IAA had a regression relationship with the total Se content. The highest contents of root total Se and shoot total Se were accumulated at 90 mg L− 1 IAA, increasing by 29.94% and 55.77% respectively,. In addition, the correlation and path analyses revealed that the carotenoid content and root total Se content were closely associated with the shoot total Se content. Therefore, the application of exogenous IAA can alleviate the stress of Se to grape and promote its uptake and the most effective amount for the uptake of Se is 90 mg L− 1 IAA. [ABSTRACT FROM AUTHOR]

In industrial applications, there is a lack of methods applicable to large-scale, rapid and non-destructive detection of voids and cracks inside carbon bricks. This paper used the ultrasonic testing (UT) A-scan method to investigate the influence of quantity (void fraction:1.4%-4.6%; number of artificial cracks:1–7) and location (five layers) of voids and cracks inside the resin on ultrasonic waves. Both fast Fourier transform (FFT) and synchrosqueezed wavelet transform (SWT) are employed to analyse the amplitude, main frequency and normalised peak time of ultrasonic waves. The results indicate that increased void fraction and number of cracks lead to greater amplitude attenuation, promoting the transition from the fundamental wave to harmonics. Void and crack location symmetrically affect ultrasound amplitude, main frequency, and normalised peak time around the sample centre. Meanwhile, fitting equations are established for the main frequency of the fundamental wave (f1) in relation to defect quantity. A high-precision quantitative defects detection standard is also developed based on normalised peak time. The validation based on carbon bricks indicates that the derived empirical relations between these parameters and defects can lay the foundation for large-scale and rapid non-destructive detection in carbon bricks. [ABSTRACT FROM AUTHOR]

Pyrolysis behaviors of polypropylene (PP), low-density polyethylene (LDPE), and their mixtures are investigated by Thermal Gravimetric Analysis (TGA) under varying heating rates to research the co-pyrolysis kinetic and synergistic effect. Model-free methods (Flynn-Wall-Ozawa method, Kissinger-Akahira-Sunose method, and Starink method) and model-fitting techniques (Malek method, Coats-Redfern method) were utilized to determine key kinetic parameters. The average activation energy are 224.69–225.19 kJ/mol for PP, 186–188.5 kJ/mol for LDPE, 195.64–197.48 kJ/mol for PP/LDPE composites. Model-fitting analysis was employed to elucidate the thermal pyrolysis mechanisms. For PP and LDPE, the A3/2 mechanism was found to be the governing pathway, whereas the PP/LDPE blends exhibited the A1 mechanism. Subsequently, compensation effect equations were derived to elucidate the synergistic interactions between PP and LDPE during co-pyrolysis process. The co-pyrolysis of PP and LDPE mixtures exhibit a lower pyrolysis temperature compared to the individual materials. Furthermore, the activation energy and pre-exponential factor results indicate a promotion effect in the early stage and an inhibiting effect in the later stage of the pyrolysis process. [ABSTRACT FROM AUTHOR]

To improve the efficiency of multi-coil data compression and recover the compressed image reversibly, increasing the possibility of applying the proposed method to medical scenarios. A deep learning algorithm is employed for MR coil compression in the presented work. The approach introduces a variable augmentation network for invertible coil compression (VAN-ICC). This network utilizes the inherent reversibility of normalizing flow-based models. The aim is to enhance the readability of the sentence and clearly convey the key components of the algorithm. By applying the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains the invertible network by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC carries much higher compression effects. The proposed method trains the invertible network by finding an invertible and bijective function, which improves the defects of traditional coil compression method by utilizing inherent reversibility of normalizing flow-based models. In addition, the application of variable augmentation technology ensures the implementation of reversible networks. In short, VAN-ICC offered a competitive advantage over other traditional coil compression algorithms. [ABSTRACT FROM AUTHOR]

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation. This study, utilizing deep learning to model semantic saturation, shows its tie to repetitive visual stimuli processing by the primary visual cortex, suggesting a bottom-up neurocognitive process. [ABSTRACT FROM AUTHOR]

This study was designed to investigate the state quo of the appropriateness of alerts overrides of the medication-related clinical decision support system (MRCDSS) in China. The medication-related alerts in one hospital from Jan 2022 to Dec 2022 were acquired and sampled. Rates of alert overrides, appropriateness of alert generation and physicians' responses were observed. Total 14,612 medicationrelated alerts (≤ level 3) were recorded, of those, 12,659 (86.6%) alerts were overridden. The top 3 alert types were: drug and diagnosis contraindications (23.8%), drug and test value contraindications (23.3%), and compatibility issues (17.7%). Of all sampled 1,501 alerts, 80.2% of them were appropriately overridden by the physicians. The appropriate rate of alert generation was 57.9% and the inappropriate rate was 42.1%. The inappropriate rate of physicians' responses was 17.8%, and 2.0% physicians' responses were undetermined. A few medications accounted for over 10% of overrides, 88.3% of "overridden reasons" inputted by the physicians were meaningless characters or values, indicating an obvious "alert fatigue" in these physicians. Our results indicated that the overridden rate of MRCDSS in China was still high, and appropriateness of generation of alert was quite low. These data indicated that the MRCDSS currently using in China still needs constantly optimization and timely maintenance. Proper sensitivity to reduce triggering of useless alerts and generation of alert fatigue might play a vital role. We believed that these findings are helpful for better understanding the state quo of MRCDSS in China and providing useful insights for future developing and improving MRCDSS. [ABSTRACT FROM AUTHOR]

Simple Summary: Ketosis (KET), a metabolic disorder frequently observed in dairy cows, is characterized by increased levels of ketone bodies. The "gold standard" for diagnosing ketosis is the concentration of blood β-hydroxybutyrate (BHB). The increasing number of studies focusing on BHB highlights the increasing significance of metabolic disorders in both the dairy industry and the scientific community. Additionally, the surge in research on the genetic and economic facets of the KET is likely correlated with the recent accessibility of extensive datasets containing blood BHB concentrations as routinely recorded traits in certain production systems. Such data are essential to accurately estimating the genetic parameters associated with these traits. Consequently, the objective of this study was to estimate genetic parameters and identify candidate genes related to blood BHB through genome-wide association analysis to provide research directions for dairy ketosis. Ketosis is a common metabolic disorder in the early lactation of dairy cows. It is typically diagnosed by measuring the concentration of β-hydroxybutyrate (BHB) in the blood. This study aimed to estimate the genetic parameters of blood BHB and conducted a genome-wide association study (GWAS) based on the estimated breeding value. Phenotypic data were collected from December 2019 to August 2023, comprising blood BHB concentrations in 45,617 Holstein cows during the three weeks post-calving across seven dairy farms. Genotypic data were obtained using the Neogen Geneseek Genomic Profiler (GGP) Bovine 100 K SNP Chip and GGP Bovine SNP50 v3 (Illumina Inc., San Diego, CA, USA) for genotyping. The estimated heritability and repeatability values for blood BHB levels were 0.167 and 0.175, respectively. The GWAS result detected a total of ten genome-wide significant associations with blood BHB. Significant SNPs were distributed in Bos taurus autosomes (BTA) 2, 6, 9, 11, 13, and 23, with 48 annotated candidate genes. These potential genes included those associated with insulin regulation, such as INSIG2, and those linked to fatty acid metabolism, such as HADHB, HADHA, and PANK2. Enrichment analysis of the candidate genes for blood BHB revealed the molecular functions and biological processes involved in fatty acid and lipid metabolism in dairy cattle. The identification of novel genomic regions in this study contributes to the characterization of key genes and pathways that elucidate susceptibility to ketosis in dairy cattle. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge. Purpose: Existing deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings. Methods: In this paper, we propose a few‐shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high‐order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours. Results: We compared our proposed method with state‐of‐the‐art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators. Conclusion: Our approach utilizes the output of few‐shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples. [ABSTRACT FROM AUTHOR]

Recent studies have emphasized the importance of dynamic activity in the development of myelopathy. However, current knowledge of how degenerative factors affect the spinal cord during motion is still limited. This study aimed to investigate the effect of various types of preexisting herniated cervical disc and the ligamentum flavum ossification on the spinal cord during cervical flexion and extension. A detailed dynamic fluid-structure interaction finite element model of the cervical spine with the spinal cord was developed and validated. The changes of von Mises stress and maximum principal strain within the spinal cord in the period of normal, hyperflexion, and hyperextension were investigated, considering various types and grades of disc herniation and ossification of the ligamentum flavum. The flexion and extension of the cervical spine with spinal canal encroachment induced high stress and strain inside the spinal cord, and this effect was also amplified by increased canal encroachments and cervical hypermobility. The spinal cord might evade lateral encroachment, leading to a reduction in the maximum stress and principal strain within the spinal cord in local-type herniation. Although the impact was limited in the case of diffuse type, the maximum stress tended to appear in the white matter near the encroachment site while compression from both ventral and dorsal was essential to make maximum stress appear in the grey matter. The existence of canal encroachment can reduce the safe range for spinal cord activities, and hypermobility activities may induce spinal cord injury. Besides, the ligamentum flavum plays an important role in the development of central canal syndrome. Significance. This model will enable researchers to have a better understanding of the influence of cervical degenerative diseases on the spinal cord during extension and flexion. [ABSTRACT FROM AUTHOR]

The predefined-time cooperative output regulation of second-order nonlinear multiagent systems with an unknown exosystem is investigated in this paper. Existing similar work is almost limited to exactly known exosystems, and the upper bound of convergence time for closed-loop systems is difficult to adjust and predict, which may not meet practical needs. In this paper, we first propose a new dynamic compensator called predefined-time distributed observer, which guarantees that each agent estimates the state and parameters about the linear exosystem with unknown system matrix through the general directed communication network within the time set by an explicit parameter. Subsequently, combining the improved dynamic gain control method and the proposed predefined-time distributed observer, a novel distributed dynamic predefined-time control scheme is proposed, which solves the problem of this paper by simplifying the tedious arithmetic process caused by the traditional backstepping method. The final simulation experiments verify the effectiveness of the proposed control scheme. • We extended similar articles to more general leader and controlled objects. • The settling time of our controller can be set directly and structure is simple. • We present a new distributed observer for unknown leader and general digraph. • The designed observer can work for an arbitrarily set period of time. • We transform an unknown leader into a known one in a predefined time. [ABSTRACT FROM AUTHOR]

The process of sustainable urban development is accompanied by frequent and complex land cover changes, and thus, clarify accurate information on land cover changes can provide scientific data for urban management. To characterize urban development at an accurate spatiotemporal scale, a change detection model is not only required to provide accurate location (Where) and time (When) of the changes, but also semantic information on the change types (What). Accordingly, this study proposed a deep learning method for temporal semantic segmentation change detection (TSSCD) that obtains information on the where, when, and what of changes simultaneously. TSSCD model bridges the semantic gap between remote sensing time series abrupt changes and land cover changes by learning the month-to-month mapping from spectral information to land cover types. We implemented a temporal semantic segmentation model based on the most classic fully convolutional network, where all two-dimensional convolutions and pooling operations were replaced with one-dimensional. We conducted tests on the TSSCD in several urban study areas, and it consistently exhibited good accuracy. In most cases, it outperformed the BFAST and CCDC algorithms, except when only a single spectral band was used. Simultaneously, we analyzed the minimum data requirements for training a TSSCD. The TSSCD currently faces challenges in achieving strong generalization beyond the training data distribution. Additionally, we observed that change detection for specific land cover types can be achieved through the flexible configuration of TSSCD. Finally, we explored a method for constructing datasets using existing products to minimize data annotation efforts, yielding promising results. However, there is still some gap compared to complete manual annotation. Overall, the TSSCD model provided a novel solution to accurately characterize sustainable urban development at the spatiotemporal scale. • A deep learning-based time series change detection model. • Monthly synthetic Sentinel-2 at 10 m and 20 m resolutions were used. • The first application of semantic segmentation to time series change detection. • Simultaneous gathering of three change details: where, when, and what. • It can provide land cover maps for any given time. [ABSTRACT FROM AUTHOR]

[Display omitted] • Enhancing PE hydrocracking via pore-mouth catalysis and Ru structural regulation. • Unveiling the influence of Ru electronic effects on olefin intermediate desorption. • Achieving efficient production of iso -alkanes using commercially available catalysts. The subject of extensive research, the low-temperature hydrocracking of polyethylene reveals the one-step synthesis of iso -alkanes via metal–acid bifunctional catalysis as an emerging strategy. Zeolites, with their superior compatibility with existing petrochemical infrastructure, serve as the acidic supports of choice. In this research, Ru nanoparticles were loaded onto ZSM-22, a zeolite known for its effective pore-mouth catalysis. Mechanical ball milling enhanced the pore-mouth area and the proximity of metal–acid sites, significantly boosting the hydrocracking reaction. The electronic properties of Ru were modified by selecting various Ru precursors and adjusting loading amounts. In-situ infrared studies showed the detachment of olefin intermediates from Ru sites as a critical factor in controlling hydrocracking and the terminal hydrogenolysis reactions. By combining catalyst characterization with reaction barrier calculations from density functional theory (DFT), it was found that oxidized Ru species aid in the release of olefin intermediates, facilitating Brønsted acid-driven β-scission and isomerization reactions. The study achieved a liquid-phase yield exceeding 82 wt% and an iso/n ratio surpassing 60% under low-energy consumption conditions using commercially available catalysts. [ABSTRACT FROM AUTHOR]

Nitrogen-doped molybdenum trioxide (MoO3/NC) has drawbacks such as volume expansion during long-term charging and discharging cycles, which severely limit its further application. This work proposes the addition of titanium dioxide nanoparticles (TiO2 NPs) for performance improvement of MoO3/NC. TiO2 NPs embedded on the surface of a MoO3/NC nanosheet can alleviate its volume expansion and the accumulation of lithiated products and improve the conductivity of the electrode material. The results show that the MoO3/NC nanosheet decorated with TiO2 NPs (TiO2@MoO3/NC), when used as an electrode material, exhibited a discharge specific capacity of 419 mA h g−1 at a current density of 0.05 A g−1 and retained a discharge specific capacity of 517 mA h g−1 after 600 cycles at a current density of 1 A g−1. [ABSTRACT FROM AUTHOR]

With a rich breeding history, Nanyang cattle (NY cattle) have undergone extensive natural and artificial selection, resulting in distinctive traits such as high fertility, excellent meat quality, and disease resistance. This makes them an ideal model for studying the mechanisms of environmental adaptability. To assess the population structure and genetic diversity of NY cattle, we performed whole-genome resequencing on 30 individuals. These data were then compared with published whole-genome resequencing data from 432 cattle globally. The results indicate that the genetic structure of NY cattle is significantly different from European commercial breeds and is more similar to North–Central Chinese breeds. Furthermore, among all breeds, NY cattle exhibit the highest genetic diversity and the lowest population inbreeding levels. A genome-wide selection signal analysis of NY cattle and European commercial breeds using Fst, θπ-ratio, and θπ methods revealed significant selection signals in genes associated with reproductive performance and immunity. Our functional annotation analysis suggests that these genes may be responsible for reproduction (MAP2K2, PGR, and GSE1), immune response (NCOA2, HSF1, and PAX5), and olfaction (TAS1R3). We provide a comprehensive overview of sequence variations in the NY cattle genome, revealing insights into the population structure and genetic diversity of NY cattle. Additionally, we identify candidate genes associated with important economic traits, offering valuable references for future conservation and breeding efforts of NY cattle. [ABSTRACT FROM AUTHOR]

Background: Deep learning methods driven by the low‐rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability. Purpose: This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation‐Net and use it for accelerating dynamic MRI. Methods: Based on the equivalence between Hankel matrix product and convolution, we utilize convolutional networks to learn the null space transform for characterizing low‐rankness. We employ low‐rankness to represent interframe correlations in dynamic MR imaging, while combining with sparse constraints in the compressed sensing framework. The corresponding optimization problem is solved in an iterative form with the semi‐quadratic splitting method (HQS). The iterative steps are unrolled into a network, dubbed Annihilation‐Net. All the regularization parameters and null space transforms are set as learnable in the Annihilation‐Net. Results: Experiments on the cardiac cine dataset show that the proposed model outperforms other competing methods both quantitatively and qualitatively. The training set and test set have 800 and 118 images, respectively. Conclusions: The proposed Annihilation‐Net improves the reconstruction quality of accelerated dynamic MRI with better interpretability. [ABSTRACT FROM AUTHOR]

To identify suitable potassium fertilizers for grape (Vitis vinifera L.) production and study their mechanism of action, the effects of four potassium-containing fertilizers (complex fertilizer, potassium nitrate, potassium sulfate, and potassium dihydrogen phosphate) on sugar and organic acid metabolism in grape fruits were investigated. Potassium-containing fertilizers increased the activity of sugar and organic acid metabolism-related enzymes at all stages of grape fruit development. During the later stages of fruit development, potassium-containing fertilizers increased the total soluble solid content and the sugar content of the different sugar fractions and decreased the titratable acid content and organic acid content of the different organic acid fractions. At the ripening stage of grape fruit, compared with the control, complex fertilizer, potassium nitrate, potassium sulfate, and potassium dihydrogen phosphate increased the total soluble solid content by 1.5, 1.2, 3.5, and 3.4 percentage points, decreased the titratable acid content by 0.09, 0.06, 0.18, and 0.17 percentage points, respectively, and also increased the total potassium content in grape fruits to a certain degree. Transcriptome analysis of the differentially expressed genes (DEGs) in the berries showed that applying potassium-containing fertilizers enriched the genes in pathways involved in fruit quality, namely, carbon metabolism, carbon fixation in photosynthetic organisms, glycolysis and gluconeogenesis, and fructose and mannose metabolism. Potassium-containing fertilizers affected the expression levels of genes regulating sugar metabolism and potassium ion uptake and transport. Overall, potassium-containing fertilizers can promote sugar accumulation and reduce acid accumulation in grape fruits, and potassium sulfate and potassium dihydrogen phosphate had the best effects among the fertilizers tested. [ABSTRACT FROM AUTHOR]

With the change of people's dietary structures, obesity, diabetes, and other related chronic metabolic diseases characterized by energy metabolism disorders have become a public health problem worldwide. Stable isotope-resolved metabolomics (SIRM) as a novel technology to explore energy metabolism will certainly contribute to the development of functional foods to improve energy metabolism disorders. The purpose of this review is to elaborate on the technology processes of SIRM in the research of functional foods with improved energy metabolism disorders, and to promote the applications of this technology in functional food. The research strategies for functional foods for improving energy metabolism disorders were summarized. The progress of the application of SIRM on functional foods was elaborated and policy recommendations were also provided. In conclusion, the precise property of SIRM in revealing metabolic pathways will inevitably facilitate the development of functional foods in improving energy metabolism disorders. [ABSTRACT FROM AUTHOR]

Five new sesquiterpenoids, including a campherenane-type (1), a bergamotane-type (2), a drimane-type (3), and two bisabolane-type (5-6) sesquiterpenoids have been isolated from Biscogniauxia sp. 71-10-1-1. Their structures were determined by spectroscopic analyses, quantum chemical ECD calculations,13C chemical shifts calculations, and X-ray crystallography. This is the first report of campherenane-type and drimane-type sesquiterpenoids from Biscogniauxia. Furthermore, the anti-inflammatory assays of all compounds are evaluated, and the results showed that compounds 3 and 7 exhibited the effects against the production of the pro-inflammatory cytokine TNF-α. [ABSTRACT FROM AUTHOR]

Three new phenylpropanoid glycosides, piperpubelide (1), 1-propionyl-3-hydroxy-phenyl-4-O-β-D-glucopyranoside (2), and 1-propionyl-4-hydroxy-phenyl-3-O-β-D-glucopyranoside (3), a new tyramine-type alkamide, puberulumine L (4), together with thirteen known compounds (5−17) were isolated from Piper puberulum (Benth.) Maxim. Their structures were elucidated by analysis of spectroscopic data involving NMR, IR, UV, and HRESIMS data. Calculated and experimental ECD was used to confirm the configuration of compound 1. Compounds 14, 16, and 17 exhibited relatively positive DPPH radical scavenging activities, with corresponding EC50 of 10.23, 24.12, and 21.83 μM, respectively. In addition, compound 5 inhibited LPS-induced NO production in BV-2 microglia with an IC50 value of 18.05 μM. [ABSTRACT FROM AUTHOR]

High-density zwitterionic groups functionalized graphene oxides (PSBMA@GO) were incorporated into sulfonated poly(ether ether ketone) (SPEEK) membranes to create continuous channels for efficient proton/vanadium ion selective conduction. The incorporation of zwitterionic polymers, specifically poly(sulfobetaine methacrylate) (PSBMA) combined with graphene oxide (GO) sheets, enhanced the compatibility of additives with the SPEEK matrix. Furthermore, the impact of PSBMA@GO content on the properties of S/PSBMA@GO hybrid membranes for vanadium redox flow battery (VRFB) application was investigated. S/PSBMA@GO-1 hybrid membrane exhibits ultrahigh ion selectivity 55.3×103 S min cm-3, and the cell with this membrane shows excellent coulombic efficiencies (98.2-99.1%), energy efficiencies (83.6-74.5%) at 100-180 mA cm-2, long cycle life and high capacity retention (35.8% after 100 cycles at 100 mA cm-2). This study demonstrates that PSBMA@GO have great application potential to solve the trade-off effect between proton conductivity and vanadium ion permeability of PEM. [Display omitted] • The PSBMA@GO with high-density zwitterionic groups is prepared. • Efficient ion selective conduction channels are formed in S/PSBMA@GO membrane. • The S/PSBMA@GO membrane shows ultrahigh ion selectivity. • The S/PSBMA@GO membrane exhibits high cell efficiency and good stability. [ABSTRACT FROM AUTHOR]

Objective. This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD). Approach. With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method. Main Results. The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance. In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. Methods: A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Results: Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Conclusion: Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts. [ABSTRACT FROM AUTHOR]

BACKGROUND: Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet. OBJECTIVE: This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, and thus helps determining the optimal system settings. METHODS: To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance. RESULTS: Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging. CONCLUSIONS: Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings. [ABSTRACT FROM AUTHOR]

Precise image segmentation of adhesive cells is a challenge during the cell detection process for biomedical image analyses. In this article, a new segmentation and extraction method for adhesive cells based on concave points detection and matching is proposed. First, image preprocessing is used to reduce interference information, which mainly includes sequential gray scale transformation, binarization, hole filling, adhesive cells recognition, and median filtering process. Second, an improved concave detection algorithm is presented which can reduce the interfering feature points and detect the concave points on the adhesive cells accurately. Third, precise extraction of adhesive cell contour is realized by concave matching based on ellipse fitting. Experiments of blood cell sample detection show that the average accuracy is 97.18% with a costing time of 3.36 s. The proposed method can effectively reduce the interference of multiple concave points and extract the cell contour from adhesive regions with higher accuracy and less time, which shows great application potential in the field of biological image detection and analyses. [ABSTRACT FROM AUTHOR]

Objectives: To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. Materials and methods: From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. Results: The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970). Conclusion: In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. Clinical relevance statement: Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. Key Points: • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions. [ABSTRACT FROM AUTHOR]

The newly released 'Snow White' (SW), a white-fleshed loquat (Eriobotrya japonica Lindl.) cultivar, holds promise for commercial production. However, the specifics of the phenolic composition in white-fleshed loquats, along with the antioxidant substances and their regulatory mechanisms, are not yet fully understood. In this study, we examined the dynamic changes in the phenolic compounds, enzyme activities, antioxidant capacity, and gene expression patterns of SW during the key stages of fruit development and ripening. A total of 18 phenolic compounds were identified in SW, with chlorogenic acid, neochlorogenic acid, and coniferyl alcohol being the most predominant. SW demonstrated a stronger antioxidant capacity in the early stages of development, largely due to total phenolics and flavonoids. Neochlorogenic acid may be the most significant antioxidant contributor in loquat. A decline in enzyme activities corresponded with fruit softening. Different genes within a multigene family played distinct roles in the synthesis of phenolics. C4H1, 4CL2, 4CL9, HCT, CCoAOMT5, F5H, COMT1, CAD6, and POD42 were implicated in the regulation of neochlorogenic acid synthesis and accumulation. Consequently, these findings enhance our understanding of phenolic metabolism and offer fresh perspectives on the development of germplasm resources for white-fleshed loquats. [ABSTRACT FROM AUTHOR]

Purpose: Positron emission tomography/magnetic resonance imaging (PET/MRI) is a powerful tool for brain imaging, but the spatial resolution of the PET scanners currently used for brain imaging can be further improved to enhance the quantitative accuracy of brain PET imaging. The purpose of this study is to develop an MR-compatible brain PET scanner that can simultaneously achieve a uniform high spatial resolution and high sensitivity by using dual-ended readout depth encoding detectors. Methods: The MR-compatible brain PET scanner, named SIAT bPET, consists of 224 dual-ended readout detectors. Each detector contains a 26 × 26 lutetium yttrium oxyorthosilicate (LYSO) crystal array of 1.4 × 1.4 × 20 mm3 crystal size read out by two 10 × 10 silicon photomultiplier (SiPM) arrays from both ends. The scanner has a detector ring diameter of 376.8 mm and an axial field of view (FOV) of 329 mm. The performance of the scanner including spatial resolution, sensitivity, count rate, scatter fraction, and image quality was measured. Imaging studies of phantoms and the brain of a volunteer were performed. The mutual interferences of the PET insert and the uMR790 3 T MRI scanner were measured, and simultaneous PET/MRI imaging of the brain of a volunteer was performed. Results: A spatial resolution of better than 1.5 mm with an average of 1.2 mm within the whole FOV was obtained. A sensitivity of 11.0% was achieved at the center FOV for an energy window of 350–750 keV. Except for the dedicated RF coil, which caused a ~ 30% reduction of the sensitivity of the PET scanner, the MRI sequences running had a negligible effect on the performance of the PET scanner. The reduction of the SNR and homogeneity of the MRI images was less than 2% as the PET scanner was inserted to the MRI scanner and powered-on. High quality PET and MRI images of a human brain were obtained from simultaneous PET/MRI scans. Conclusion: The SIAT bPET scanner achieved a spatial resolution and sensitivity better than all MR-compatible brain PET scanners developed up to date. It can be used either as a standalone brain PET scanner or a PET insert placed inside a commercial whole-body MRI scanner to perform simultaneous PET/MRI imaging. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) has emerged as a powerful technique to image dilute labile protons. However, its measurement depends on the RF saturation duration (T sat) and relaxation delay (T rec). Although the recently developed quasi-steady-state (QUASS) solution can reconstruct equilibrium CEST effects under continuous-wave RF saturation, it does not apply to pulsed-CEST MRI on clinical scanners with restricted hardware or specific absorption rate limits. This study proposed a QUASS algorithm for pulsed-CEST MRI and evaluated its performance in muscle CEST measurement. An approximated expression of a steady-state pulsed-CEST signal was incorporated in the off-resonance spin-lock model, from which the QUASS pulsed-CEST effect was derived. Numerical simulation, creatine phantom, and healthy volunteer scans were conducted at 3 T. The CEST effect was quantified with asymmetry analysis in the simulation and phantom experiments. CEST effects of creatine, amide proton transfer, phosphocreatine, and combined magnetization transfer and nuclear Overhauser effects were isolated from a multi-pool Lorentzian model in muscles. Apparent and QUASS CEST measurements were compared under different T sat /T rec and duty cycles. Paired Student's t -test was employed with P < 0.05 as statistically significant. The simulation, phantom, and human studies showed the strong impact of T sat /T rec on apparent CEST measurements, which were significantly smaller than the corresponding QUASS CEST measures, especially under short T sat /T rec times. In comparison, the QUASS algorithm mitigates such impact and enables accurate CEST measurements under short T sat /T rec times. In conclusion, the QUASS algorithm can accelerate robust pulsed-CEST MRI, promising the efficient detection and evaluation of muscle diseases in clinical settings. [ABSTRACT FROM AUTHOR]

We propose a dual-branch Siamese network for visual object tracking. Our network architecture comprises two distinct branches: a shallow network branch and a deep network branch. The shallow network branch focuses on precise object localization and improving resistance to interference from similar objects. Meanwhile, the deep network branch emphasizes capturing abstract semantic features of the object. To enhance localization accuracy, we integrate a multi-scale KFFM into the shallow network. In addition, we leverage the attention mechanism to further enhance the model's robustness. Through extensive experiments on three publicly available datasets, we demonstrate that our method surpasses state-of-the-art tracking algorithms in terms of performance and accuracy. The source code of this work is available online at https://github.com/mbgzwn/SiamDUL.git. [ABSTRACT FROM AUTHOR]