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Toehold-mediated strand displacement (TMSD) provides a versatile toolbox for developing DNA digital computing systems. Although different logic circuits with diverse functions have achieved good performance in terms of complexity and scalability, most previous DNA logic circuits perform information processing only at the molecular level, and nonspecific signal leakages are often difficult to avoid. Here, we demonstrate the feasibility of constructing leakless digital computing systems in three-dimensionally ordered colloidal supercrystals. These systems possess a unique signal leakage resistance by integrating different TMSD-based logic gates with the catalytic assembly of DNA-functionalized gold colloids. A complete set of basic Boolean logic gates and different cascaded logic circuits is constructed on the basis of the catalytic assembly strategy enabled by a facilely designed catassembler, where the output signals are recognized by determining whether specific colloidal supercrystals are formed or not. In addition, a half adder is built through a combination of XOR and AND logic gates with two distinct crystal types as readouts. Finally, a leakless two-digit DNA keypad lock for information security protection is demonstrated. The combination of TMSD-based logic circuits with the universal nanoparticle catalytic assembly offers the possibility to develop highly complicated and leakage-free digital computing systems and promotes macroscopic colloidal superlattice materials with programmable logic functions.

Open-sided magnetic particle imaging (OS-MPI) has garnered significant interest due to its potential for interventional applications. However, the system matrix calibration in OS-MPI using sequential scans is a time-consuming task and susceptible to the low signal-to-noise ratio (SNR) resulting from the small calibration sample size. These challenges have hindered the practical implementation of system matrix-based reconstruction for sequentially scanned OS-MPI. To address these issues, we propose a novel calibration method, named sequen- tial scan-based single-dimension multi-voxel calibration (SS-SDMVC), to efficiently obtain a high-SNR system matrix. This method was implemented in a cylindrical field of view (FOV), where a bar calibration sample parallel to the field-free line (FFL) was shifted along a fixed radial direction. A standard image reconstruction process was also introduced to verify the feasibility of SS-SDMVC. Through simulations, we analyzed the effects of noise levels and scanner imperfections on the SS-SDMVC-based reconstruction and demonstrated its robustness. In experiments, we compared the imaging performance of SS-SDMVC and the sequential scan-based traditional cubic-FOV SMC. The results showed that SS-SDMVC reduced the number of measurements by a factor of 210.94 and achieved higher reconstruction quality. Therefore, SS-SDMVC is expected to improve the reconstruction quality of human- scale or high-gradient FFL MPI scanners.

Three-dimensional reconstruction from multiview images is considered as a longstanding problem in computer vision and graphics. In order to achieve high-fidelity geometry and appearance of 3-D scenes, this article proposes a novel geometric object learning method for multiview reconstruction with fuzzy set theory. We establish a new neural 3D reconstruction theoretical frame called neural fuzzy geometric representation (NeuFG), which is a special type of implicit representation of geometric scene that only takes value in [0, 1]. NeuFG is essentially a volume image, and thus can be visualized directly with the conventional volume rendering technique. Extensive experiments on two public datasets, i.e., DTU and BlendedMVS, show that our method has the ability of accurately reconstructing complex shapes with vivid geometric details, without the requirement of mask supervision. Both qualitative and quantitative comparisons demonstrate that the proposed method has superior performance over the state-of-the-art neural scene representation methods. The code will be released on GitHub soon.

Temperature rise determines the lifetime of the motor. Wuhan Institute of Marine Electric Propulsion has conceptualized and designed a 20 MW high-temperature superconducting motor (HTSM) with an oil-cooling air-core stator and a superconducting rotor. This paper describes the design and analysis process of the cooling system of this stator: Firstly, We simplified the full structure of the HTSM and established the model of the oil-cooling air-core stator for thermal analysis. Second, We analyzed and compared the cooling effect of different oil inlet forms under different thermal loads and coolant flow, and sequentially determined the optimized inlet form. The analysis results show that the average and maximum temperature of the oil and coil increase with a higher thermal load, or a smaller oil flow. Underrated operating conditions (240 kW@60 m3/h), the maximum temperature of the coil is 72 °C, and the maximum temperature of the cooling oil is 65 °C, meeting the temperature rise requirement. Finally, we present a structural solution for a compact oil-cooled air-core stator that can increase the torque density of the HTSM.

Device-to-device (D2D) communication enables direct data transmissions between devices, thereby filling in the blind zones of base stations (BSs) and alleviating their load pressures. In this work, we propose an adaptive single-hop and relay-selection-based double-hop D2D transmission scheme underlaying cellular networks with full-frequency reuse. When the communication distance between two D2D users is shorter than a threshold $d_{0}$, the single-hop D2D link can be established. However, when the distance between two D2D users is longer than $d_{0}$ but shorter than $2d_{0}$, and there exists at least one relay node in the overlapping coverage areas of both users, the double-hop D2D transmission is performed. Based on the stochastic geometry theory, we properly model the random distributions of D2D users, relay nodes, cellular users, and BSs, as well as the aggregate interference for each type of communication mode. We propose to divide the overlapping area of the coverage regions of two D2D users into small grids, and the relay selection priority is assigned to each grid according to the selection criterion. The griding of overlapping areas can greatly simplify the analysis of successful relaying by considering the possible locations of the selected relay. We also present two benchmark schemes, namely cellular only, cellular with D2D single-hop. Numerical results show that our proposed scheme can significantly improve the area throughput compared with the benchmark schemes.

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

Objective: Magnetic Particle Imaging (MPI) is a radiation-free tracer-based imaging technology that visualizes the spatial distribution of superparamagnetic iron oxide nanoparticles. Conventional spatial encoding methods in MPI rely on a gradient magnetic field with a constant gradient strength to generate a field-free point or line for spatial scanning. However, increasing the gradient strength can enhance theoretical spatial resolution but also lead to a decrease in the Signal-to-Noise Ratio (SNR) and sensitivity of the imaging system. This poses a technical challenge in balancing spatial resolution and sensitivity, necessitating intricate hardware design. Methods: To address this, we present a Space-Specific Mixing Excitation (SSME) technique for achieving high-SNR spatial encoding in MPI. By utilizing a dual-frequency excitation magnetic field with a non-homogeneous field strength, magnetic particles at each position generate unique intermodulation responses. By performing multi-channel acquisitions across the entire field of view, high SNR MPI signals can be acquired. When combined with reconstruction techniques based on system matrix, multi-dimensional SSME-MPI can be achieved. Results: The effectiveness of the proposed method was validated through phantom and in vivo imaging experiments. The results demonstrate significant improvements in sensitivity (3.6-fold improvement) and spatial resolution (better than 1 mm) without any hardware modifications. Conclusion: These findings demonstrate the capability of SSME to enhance both the spatial resolution and sensitivity of MPI. Significance: This method provides a solution to the ongoing challenge of balancing spatial resolution and sensitivity in MPI, potentially facilitating the implementation of MPI in a wider range of medical applications.

Multiscale features are of great importance in modern convolutional neural networks, showing consistent performance gains on numerous vision tasks. Therefore, many plug-and-play blocks are introduced to upgrade existing convolutional neural networks for stronger multiscale representation ability. However, the design of plug-and-play blocks is getting more and more complex, and these manually designed blocks are not optimal. In this work, we propose PP-NAS to develop plug-and-play blocks based on neural architecture search (NAS). Specifically, we design a new search space PPConv and develop a search algorithm consisting of one-level optimization, zero-one loss, and connection existence loss. PP-NAS minimizes the optimization gap between super-net and subarchitectures and can achieve good performance even without retraining. Extensive experiments on image classification, object detection, and semantic segmentation verify the superiority of PP-NAS over state-of-the-art CNNs (e.g., ResNet, ResNeXt, and Res2Net). Our code is available at https://github.com/ainieli/PP-NAS.

A core challenge in survival analysis is to model the distribution of time-to-event data, where the event of interest may be a death, failure, or occurrence of a specific event. Previous studies have showed that ranking and maximum likelihood estimation loss functions are widely-used learning approaches for survival analysis. However, ranking loss only focus on the ranking of survival time and does not consider potential effect of samples’ exact survival time values. Furthermore, the maximum likelihood estimation is unbounded and easily subject to outliers (e.g., censored data), which may cause poor performance of modeling. To handle the complexities of learning process and exploit valuable survival time values, we propose a time-adaptive coordinate loss function, TripleSurv, to achieve adaptive adjustments by introducing the differences in the survival time between sample pairs into the ranking, which can encourage the model to quantitatively rank relative risk of pairs, ultimately enhancing the accuracy of predictions. Most importantly, the TripleSurv is proficient in quantifying the relative risk between samples by ranking ordering of pairs, and consider the time interval as a trade-off to calibrate the robustness of model over sample distribution. Our TripleSurv is evaluated on three real-world survival datasets and a public synthetic dataset. The results show that our method outperforms the state-of-the-art methods and exhibits good model performance and robustness on modeling various sophisticated data distributions with different censor rates.

The extensive application of deep neural networks (DNN) in the field of welding penetration/keyhole status prediction, has led to a notable improvement in the accuracy of the predicted model in keyhole plasma arc welding (K-PAW), thereby ensuring a stable welding process and high-quality welding. However, numerous studies have only reported the prediction models based on one single dataset, and have demonstrated poor generalizability when evaluated on other datasets. This is due to the fact that traditional machine learning methods are based on the datasets assumption of independent identically distributed (I.I.D.), which do not involve a domain-invariant and feature-adapted scheme between multiple datasets. For different monitoring systems and welding scenarios, the distribution between the training data (source domain) and the test data (target domain) is different, which greatly affects the model prediction accuracy. Furthermore, numerous existing prediction modeling methods necessitate the use of extensive samples comprising weld pool images accompanied by the actual penetration/keyhole status labels. However, in practice, the labels are often only accessible in a laboratory setting, and difficult to obtain in the industrial production, making their acquisition in an industrial production environment challenging. This study has explored the improvement method of model generalization ability in the field of welding penetration/keyhole status prediction, while also investigating the modeling approach to weld pool image penetration/keyhole status in the absence of labels. In this article, three kinds of topside weld pool images (TWKS) datasets with visual discrepancies were established in different scenarios. Additionally, training strategies (ToS, PoS-ToT, ToST) based on traditional deep learning were proposed. To enhance model generalization and streamline the training process, a multi-scenario penetration/keyhole status prediction method was proposed by feature-based transfer learning and unsupervised domain adaptations (UDAs), designed as KS-DAN and KS-DSAN. The model performance results demonstrate that UDAs are viable and efficacious in the K-PAW penetration status prediction, and can establish the model of the topside weld pool image without genuine labels, thereby enabling unsupervised training and inference. Moreover, the results of model evaluation and visualization demonstrate that KS-DSAN outperforms KS-DAN.

Unmanned aerial vehicles rely heavily on global navigation satellite systems (GNSSs) for positioning and navigation when performing tasks. At the same time, low-cost GNSS jammers can pose a severe threat to the use of GNSSs. Interference affects the work of GNSS receivers and even causes GNSS positioning to fail, thereby increasing the possibility of drone mission failure. Aiming at the current common chirp interference, this article proposes an interference mitigation method based on the Zak transform to mitigate the impact of interference on GNSS receivers. The proposed method takes advantage of the sparsity of the interfering signal at the Doppler shift axis of the delay-Doppler domain to obtain an estimation of the interference signal, which is then subtracted from the received GNSS signal to remove the interference. Simulation results show that the proposed method is more effective in mitigating interference than other methods reported in the literature.

Magnetic particle imaging (MPI) uses nonlinear response signals to noninvasively detect magnetic nanoparticles in space, and its quantitative properties hold promise for future precise quantitative treatments. In reconstruction, the system matrix based method necessitates suitable regularization terms, such as Tikhonov or non-negative fused lasso (NFL) regularization, to stabilize the solution. While NFL regularization offers clearer edge information than Tikhonov regularization, it carries a biased estimate of the $\mathbf {l}_{\mathbf {{1}}}$ penalty, leading to an underestimation of the reconstructed concentration and adversely affecting the quantitative properties. In this paper, a new nonconvex regularization method including min-max concave (MC) and total variation (TV) regularization is proposed. This method utilized MC penalty to provide nearly unbiased sparse constraints and adds the TV penalty to provide a uniform intensity distribution of images. By combining the alternating direction multiplication method (ADMM) and the two-step parameter selection method, a more accurate quantitative MPI reconstruction was realized. The performance of the proposed method was verified on the simulation data, the Open-MPI dataset, and measured data from a homemade MPI scanner. The results indicate that the proposed method achieves better image quality while maintaining the quantitative properties, thus overcoming the drawback of intensity underestimation by the NFL method while providing edge information. In particular, for the measured data, the proposed method reduced the relative error in the intensity of the reconstruction results from 28% to 8%.

Magnetic Particle Imaging (MPI)-guided Magnetic Fluid Hyperthermia (MFH) has the potential for widespread utilization, as it allows for the prediction of magnetothermal dosage, real-time visualization of the thermal therapy process, and precise localization of the lesion area. However, the existing MPI-guided MFH (MPI-MFH) method is insensitive to concentration gradients of magnetic nanoparticles (MNPs) and is susceptible to causing damage to normal tissues with high MNP concentrations during MFH treatment, while inadequately heating tumor tissues with lower MNP concentrations. In this work, we established a relationship between MNP concentration and heating efficiency through simulations and phantom measurements, enabling the optimal selection of MFH parameters guided by MPI. Based on these findings, we developed a high-gradient field MPI-MFH method using a field-free point (FFP) approach to achieve precise local heating. Phantom experiments and in vivo glioma model experiments were conducted to validate this proposed method. The results demonstrated that the proposed method of MPI-MFH can improve the MNP concentration gradient sensitivity to $\pm$1 mg/ml, thereby enabling more effective lesion-site heating without damaging normal tissues. This method not only reduced glioma size effectively but also holds promise for application in various other types of cancers.

AbstractAims/BackgroundThe pathogenesis of irritable bowel syndrome encompasses various factors, including abnormal gastrointestinal motility, heightened visceral sensitivity, dysfunction in the brain-gut axis, psychological influences, and disturbances in the intestinal flora. These factors manifest primarily as persistent or intermittent abdominal pain, diarrhoea, alterations in bowel habits, or changes in stool characteristics. In our investigation, we delve into the repercussions of mechanical barrier damage and immune dysfunction on symptoms among patients with post-infectious irritable bowel syndrome.MethodsThis study recruited a total of 20 healthy controls and 49 patients diagnosed with irritable bowel syndrome. Among the irritable bowel syndrome patients, we categorised them into two groups based on the ROME IV diagnostic criteria: the post-infectious irritable bowel syndrome group (n=23) and the non-post-infectious irritable bowel syndrome group (n=26). To compare clinical features, we utilised the Gastrointestinal Symptom Rating Scale, Self-Rating Depression Scale, and Self-Rating Anxiety Scale. Furthermore, we employed various techniques including haematoxylin and eosin (HE) staining, electron microscopy, Enzyme-linked Immunosorbent Assay, and flow cytometry to assess changes in immune cells, immune factors, inflammatory biomarkers, and intestinal barrier function.ResultsUnder haematoxylin and eosin staining, post-infectious irritable bowel syndrome patients demonstrated increased neutrophils and plasma cells compared to the control group. Additionally, electron microscopy revealed ultrastructural changes such as the widening of the epithelial cell gap in the intestinal mucosa among post-infectious irritable bowel syndrome patients. Comparatively, the Gastrointestinal Symptom Rating Scale, Self-Rating Anxiety Scale, and Self-Rating Depression Scale scores were significantly elevated in the post-infectious irritable bowel syndrome group in contrast to both the control group and the non- post-infectious irritable bowel syndrome group (p< 0.05). Moreover, post-infectious irritable bowel syndrome patients exhibited a notably higher neutrophil-to-lymphocyte ratio compared to the control group (p< 0.05). Furthermore, the levels of interleukin-17 (IL-17) were elevated in post-infectious irritable bowel syndrome patients compared to the control group (p< 0.05). Additionally, the post-infectious irritable bowel syndrome group displayed a higher percentage of T helper 17 (Th17) cells compared to both the control and non-post-infectious irritable bowel syndrome groups (p< 0.05).ConclusionAcute gastrointestinal infection can disrupt the balance of intestinal flora, leading to dysbiosis. This dysbiosis can trigger the release of pro-inflammatory factors, including interleukin-17, which contributes to the impairment of the intestinal mucosal barrier. Consequently, this sets the stage for the development of long-lasting, mild chronic intestinal inflammation, ultimately culminating in the onset of post-infectious irritable bowel syndrome. Furthermore, within the framework of the gut-brain axis interaction, anxiety and depression may exacerbate intestinal inflammation in post-infectious irritable bowel syndrome patients. This interaction can perpetuate and prolong clinical symptoms in individuals with post-infectious irritable bowel syndrome, further complicating the management of the condition.

The simultaneous removal of NOx and dioxins has been considered an economical and effective technology of controlling multipollutant flue gas in the context of “carbon peaking and carbon neutrality”. However, this technology has not yet been implemented in practical situations, because the interactive relationship between the selective catalytic reduction (SCR) reaction and dioxin catalytic oxidation lacks a deep understanding, especially on a carbon-based catalyst. In this research, the influence of NO and NH3on the oxidation characteristics and byproducts distribution of dibenzofuran (DBF) was studied on V2O5/AC catalyst. Results indicated that NH3has a stronger inhibition effect for DBF catalytic oxidation than NO due to obvious competitive adsorption between NH3and DBF on the V2O5/AC catalyst. In addition, although both NO and NH3inhibit the complete degradation of DBF, their effects on the byproduct distribution are not consistent. NO primarily affects the level of oxygen-containing byproducts, while NH3primarily affects the level of alkane byproducts. Furthermore, the SCR reaction activity demonstrated a reduction when DBF was present. The occupation of V2O5sites by DBF and its oxidizing intermediates has hindered the production of monodentate nitrate and the reactivity of bridged nitrate, resulting in a decrease in SCR activity via the L-H mechanism. This work aims to provide theoretical guidance for simultaneous removal of NOx and dioxins in industrial fumes.

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Magnetic particle imaging (MPI) is an innovative medical imaging modality with tremendous potential for various clinical applications. The handheld MPI device, due to its high biosafety and portability, holds promise for use in intraoperative diagnosis and other clinical settings. The handheld MPI system has all its hardware components located on one side of the imaged object, enabling convenient mobile detection. However, this single-sided construction leads to a reduced magnetic field gradient of the selection field, resulting in a compromised imaging field of view (FOV) and resolution. To address these challenges, this study proposes a handheld MPI system that utilizes excitation field intensity encoding. By gradually increasing the calibrated excitation field, spatial encoding is achieved throughout the FOV. To improve the imaging resolution of the system, three different excitation waveforms are applied to the excitation coil to expand the available harmonic signals. In addition, a bidirectional adjustable external compensation strategy is introduced to achieve more precise direct feedthrough suppression, restoring the baseband signal. Experimental results demonstrate that the proposed handheld MPI achieves a spatial resolution of 1 mm within a depth range of 30 mm while maintaining imaging FOV, resolution, and detection sensitivity of 50-ng Fe, laying a foundation for future clinical applications.

Combining magnetic particle spectroscopy (MPS) with functionalized magnetic nanoparticles (MNPs) has the potential to provide a rapid, sensitive, and user-friendly point-of-care (PoC) testing platform. Conventional MPS techniques typically apply harmonic ratios (such as, the third to fifth harmonic ratio) as the sensing metric to eliminate interference caused by MNP concentration. However, the quantification accuracy of this approach largely depends on the consistency of the signal-to-noise ratio (SNR) between the selected harmonics, imposing stringent requirements on hardware design and the nonlinear magnetic behavior of the MNPs. In this study, we introduce “peak shift” to field-swept MPS (FS-MPS) as a novel metric for MNP characterization and biosensing. An integrated FS-MPS device is used to capture the second harmonic spectrum, which records the second harmonic amplitudes under different perpendicular static magnetic fields. Importantly, the peak position of the spectrum remains constant under different MNP concentrations and shifts upon changes in MNP hydrodynamic size. In this way, the peak shift sensitively detects MNP binding to the targeted analytes, enabling robust biosensing based solely on the second harmonics. The feasibility of the proposed method was demonstrated using streptavidin-grafted MNPs and biotinylated polystyrene beads. Diagnostic performance was assessed using ACE2 functionalized MNPs and SARS-CoV-2 S1 protein and SARS-CoV-2 pseudovirus assays. The proposed strategy can detect virus concentrations as low as 350 TCID50/mL. Additionally, a good linear correlation exists between the peak shift and virus titer in the 350–7000 TCID50/mL range, demonstrating the potential of our method to quantify virus content.

Background: The role of intraoperative near-infrared fluorescence angiography with indocyanine green in reducing anastomotic leakage (AL) has been demonstrated in colorectal surgery, however, its perfusion assessment mode, and efficacy in reducing anastomotic leakage after laparoscopic intersphincteric resection (LsISR) need to be further elucidated. Aim: Aim was to study near-infrared fluorescent angiography to help identify bowel ischemia to reduce AL after LsISR. Material and methods: A retrospective case-matched study was conducted in one referral center. A total of 556 consecutive patients with ultra-low rectal cancer including 140 patients with fluorescence angiography of epiploic appendages (FAEA)were enrolled. Perfusion assessment by FAEA in the monochrome fluorescence mode. Patients were divided into two groups based on perfusion assessment by FAEA. The primary endpoint was the AL rate within 6 months, and the secondary endpoint was the structural sequelae of anastomotic leakage (SSAL). Results: After matching, the study group (n= 109) and control group (n= 190) were well-balanced. The AL rate in the FAEA group was lower before (3.6% vs. 10.1%, P= 0.026) and after matching (3.7% vs. 10.5%, P= 0.036). Propensity scores matching analysis (OR 0.275, 95% CI 0.035–0.937, P 0.039), inverse probability of treatment weighting (OR 0.814, 95% CI 0.765–0.921, P 0.002), and regression analysis (OR 0.298, 95% CI 0.112–0.790, P= 0.015), showed that FAEA was an independent protector factor for AL. This technique can significantly shorten postoperative hospital stay [9 (6–13) vs. 10 (8–13), P= 0.024] and reduce the risk of SSAL (1.4% vs. 6.0%, P= 0.029). Conclusions: Perfusion assessment by FAEA can achieve better visualization in LsISR and reduce the incidence of AL, subsequently avoiding SSAL after LsISR.

Aerial reconfigurable intelligent surface (ARIS) system refers to a novel transmission scheme, in which an RIS is mounted on aerial platforms, such as unmanned aerial vehicles (UAVs), to achieve intelligent signal reflection over the air. Compared to terrestrial RIS, ARIS extends the service area and enables panoramic signal reflections. Benefiting from its high altitude, the UAV is more likely to establish line-of-sight (LoS) connections with ground nodes. However, implementing ARIS systems is challenging. Owing to atmospheric turbulence or propeller rotations, the UAV can experience random fluctuations, which lead to misalignment of reflected signal phases. In this letter, a three-dimensional (3D) ARIS-assisted channel model is proposed, in which UAV fluctuations are described by random processes. Statistics including temporal correlation function (TCF), Doppler power spectral density (PSD), and ergodic achievable rate are obtained. Results show that even for a minor vibration, the UAV wobbling can introduce extra Doppler frequency components, and significantly reduce the temporal correlation and achievable rate of the ARIS-assisted channel when systems operating at high frequency bands.

Magnetic particle imaging (MPI) is a burgeoning imaging modality that facilitates the reconstruction of position and concentration of superparamagnetic iron oxide nanoparticles. Its potential in clinical applications is substantial, leading to an increasing demand for larger bore sizes in MPI systems. However, the challenge lies in maintaining high resolution, sensitivity, and real-time imaging advantages in larger systems. In this article, we present the design of a 3-D MPI system tailored for nonhuman primates (NHP-MPI) with a bore size of 190 mm. The application of a 3-D digital-scanned focus field is applied to obtain highly sensitive imaging, and the system operates cooling-free under low gradient conditions. In imaging experiments involving a macaque’s brain, our system demonstrated good quantitative performance. The 3-D MPI image yielded satisfactory results for fusion with other structural imaging modalities.

Objective: Open-sided field-free line magnetic particle imaging (OS FFL MPI) is a novel medical imaging system configuration that has received significant attention in recent years. However, the measurement-based system matrix (SM) image reconstruction for OS FFL MPI typically requires multiple angle calibration (MAC), which is time-consuming in practice. Methods: To address this issue, we propose a fast 2D SM generation method that requires only a single angle calibration (SAC). The SAC method exploits the rotational invariance of the system function. Based on the measured single angle system function, the system function is rotated to generate system functions at other angles, and then the SM for image reconstruction is constructed. Then, we conducted various simulation experiments and built an OS FFL MPI scanner to evaluate the proposed SAC method. Results: The experiments demonstrating the effectiveness of SAC in reducing calibration workload, requiring fewer scanning numbers while maintaining a similar image reconstruction quality compared to MAC method. Furthermore, the SM generated by SAC produces consistent imaging results with the SM generated by MAC, regardless of the interpolation algorithms, the number of rotation angles, or the signal-to-noise ratios employed in phantom imaging experiments. Conclusion: SAC has been experimentally verified to reduce acquisition time while maintaining accurate and robust reconstruction performance. Significance: The significance of SAC lies in its contribution to improving calibration efficiency in OS FFL MPI, potentially facilitating the implementation of MPI in a wider range of applications.

Magnetic particle imaging (MPI) is a tracer-based imaging modality known for its high sensitivity and temporal resolution. It makes the quantitative visualization of the concentration and spatial location of magnetic nanoparticles (MNPs) possible. The $x$ -space method is widely used in MPI for high-speed image reconstruction. In this method, the received signal is mapped directly onto the corresponding spatial position. However, the anisotropic 2-D point spread function (PSF) in the $x$ -space method introduces artifacts and blur in the reconstructed images. In this study, an isotropic MPI method, transverse MNP signal-based isotropic imaging (TSI), was developed. It makes isotropic imaging possible in 2-D unidirectional Cartesian MPI. Unlike conventional methods that rely on MNP signals registered from the same direction as the excitation, TSI only utilizes MNP signals received perpendicular to the excitation direction. In TSI, the velocity of the time domain signal is first normalized to obtain a “raw-image.” The raw-image is then integrated, differentiated, and added to generate an isotropic MPI image. Because all the operations in TSI are analytical, it preserves the real-time imaging characteristics of the $x$ -space while isotropic imaging is performed. The PSF of TSI was calculated through analytical derivation and the resolution isotropy was validated through simulation and imaging experiments on an in-house-built MPI system. The simulation and experimental results demonstrate that, by improving the isotropy, TSI achieves an approximately 2.5- $\times $ increase in resolution in the nonexcitation direction compared with the conventional $x$ -space method. The proposed TSI algorithm does not increase hardware complexity and requires only half the scanning time of the previously proposed multichannel method. In addition, the transverse MNP signal used in TSI is geometrically decoupled from the excitation magnetic field, eliminating the need for a special compensation design for the receiving coils. This shows promise for future MPI designs with flexible surface coils.

The system matrix (SM) is an important component of magnetic particle imaging (MPI) because it describes the mapping between the particle concentration and voltage signal. The acquisition of SM predominantly relies on measurement-based calibration. However, the measurement procedure is time-consuming and susceptible to noise interference, leading to a decrease in reconstruction quality. The need for measuring the SM is because existing MPI forward physical process models are not sufficiently accurate for model-based SM generation. To address this problem, we propose a model-based SM generation method based on the modified Jiles–Atherton (MJA) for accurate and fast modeling of the MPI forward physical process (MJA-SMGM). In the proposed method, the MJA accurately describes the dynamic magnetization responses of superparamagnetic iron-oxide (SPIO) nanoparticles. The MJA model considers the relaxation effect to improve accuracy and reduce complexity to accelerate solving. Additionally, time-harmonic electromagnetic field analysis for inhomogeneity and the transfer function of the receive signal chain are included for SM generation. The proposed MJA-SMGM method can achieve fast and accurate SM generation, thereby improving the quality of reconstructed images. Compared to the complex Fokker–Planck method, MJA-SMGM is approximately 70 times faster and reduces error by 30.2% according to the results of homemade MPI scanner experiments. Additionally, the proposed method improves image quality by 2.2 times in terms of the signal-to-noise ratio. These results indicate that MJA-SMGM has the potential to improve the applicability of SM methods in various MPI scanners.

The pathophysiology of autism spectrum disorders (ASDs) is causally linked to postsynaptic scaffolding proteins, as evidenced by numerous large-scale genomic studies [1, 2] and in vitro and in vivo neurobiological studies of mutations in animal models [3, 4]. However, due to the distinct phenotypic and genetic heterogeneity observed in ASD patients, individual mutation genes account for only a small proportion (<2%) of cases [1, 5]. Recently, a human genetic study revealed a correlation between de novo variants in FERM domain-containing-5 (FRMD5) and neurodevelopmental abnormalities [6]. In this study, we demonstrate that deficiency of the scaffolding protein FRMD5 leads to neurodevelopmental dysfunction and ASD-like behavior in mice. FRMD5 deficiency results in morphological abnormalities in neurons and synaptic dysfunction in mice. Frmd5-deficient mice display learning and memory dysfunction, impaired social function, and increased repetitive stereotyped behavior. Mechanistically, tandem mass tag (TMT)-labeled quantitative proteomics revealed that FRMD5 deletion affects the distribution of synaptic proteins involved in the pathological process of ASD. Collectively, our findings delineate the critical role of FRMD5 in neurodevelopment and ASD pathophysiology, suggesting potential therapeutic implications for the treatment of ASD.

Novel strategies utilizing light in the second near-infrared region (NIR-II; 900–1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis. Furthermore, the use of NIR-II light promises to improve cancer phototherapy by enabling the selective delivery of increased therapeutic energy to tissues at greater depths. Initial clinical studies of NIR-II-based imaging and phototherapy have indicated impressive potential to decrease cancer recurrence, reduce complications and prolong survival. Despite the encouraging results achieved, clinical translation of innovative NIR-II techniques remains challenging and inefficient; multidisciplinary cooperation is necessary to bridge the gap between preclinical research and clinical practice, and thus accelerate the translation of technical advances into clinical benefits. In this Review, we summarize the available clinical data on NIR-II-based imaging and phototherapy, demonstrating the feasibility and utility of integrating these technologies into the treatment of cancer. We also introduce emerging NIR-II-based approaches with substantial potential to further enhance patient outcomes, while also highlighting the challenges associated with imminent clinical studies of these modalities.

Magnetic particle imaging (MPI) is an emerging molecular imaging technique that relies on the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles. Open-sided MPI (OS-MPI) has attracted considerable interest for its potential to enhance interventional procedures. However, improving the spatial resolution of OS-MPI poses a significant challenge, mainly due to the inefficiencies of selection coils in generating, rotating, and shifting a high-gradient field-free line (FFL). To address this issue, we propose a novel approach called the permanent magnet-based sequential scan to develop a high-resolution 3-D OS-MPI system with ultralow power consumption. By utilizing permanent magnets, we can create high-gradient selection fields without any power consumption. The sequential scan technique involves rotating the FFL angle by angle and shifting the FFL layer by layer. To maintain thermal stability during sequential scans, we used drive coils wound with hollow copper wires and implemented forced oil cooling. Additionally, measures such as copper shielding, feed-through compensation, and filtering were employed to minimize interference from the permanent magnets, drive coils, and motor drivers. Experimental results demonstrated that the developed system successfully achieved a spatial resolution of 1 mm while operating with an overall low power consumption of 282.16 W.

Sjögren's syndrome is a systemic autoimmune disease characterized by dysfunction of the affected exocrine glands. Lymphocytic infiltration within the inflamed glands and aberrant B-cell hyperactivation are the two salient pathologic features in Sjögren's syndrome. Increasing evidence indicates that salivary gland epithelial cells act as a key regulator in the pathogenesis of Sjögren's syndrome, as revealed by the dysregulated innate immune signaling pathways in salivary gland epithelium and increased expression of various proinflammatory molecules as well as their interaction with immune cells. In addition, salivary gland epithelial cells can regulate adaptive immune responses as nonprofessional antigen-presenting cells and promote the activation and differentiation of infiltrated immune cells. Moreover, the local inflammatory milieu can modulate the survival of salivary gland epithelial cells, leading to enhanced apoptosis and pyroptosis with the release of intracellular autoantigens, which further contributes to SG autoimmune inflammation and tissue destruction in Sjögren's syndrome. Herein, we reviewed recent advances in elucidating the role of salivary gland epithelial cells in the pathogenesis of Sjögren's syndrome, which may provide rationales for potential therapeutic targeting of salivary gland epithelial cells to alleviate salivary gland dysfunction alongside treatments with immunosuppressive reagents in Sjögren's syndrome.Graphical abstract

The valve-side single-phase-to-ground (SPG) fault is one of the key challenges for the MMC-based bipolar HVdc system. In this article, a grounded thyristor loop (GTL)-based hybrid MMC (HMMC) is proposed to protect the bipolar HVdc system under valve-side SPG faults. In the GTL-HMMC, the thyristor branches are proposed to connect the different arm inductors and the ground. The operation for the GTL-HMMC in case of valve-side SPG faults is also proposed, where the GTL-HMMC can handle the faults with the advantages of short-fault current interruption time, low capacitor overvoltage, low arm overcurrent, low semiconductor cost, and low power loss. Electromagnetic transient (EMT) simulation studies using Power system computer aided design / electromagnetic transients including DC (PSCAD/EMTDC) tool and experimental studies with a laboratory scaled hardware prototype are conducted to confirm the effectiveness of the proposed technique.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

The interaction of selenium (Se) and melatonin in plants is unclear. The aim of this study was to investigate the effects of Se root application on melatonin synthesis in sweet cherry. Cultivar ‘Russia No.8’ was treated with Se6+(sodium selenate) or Se2−(selenomethionine). Parameters of plant antioxidant levels, growth parameters, and the synthesis enzyme activities and precursor contents of melatonin were measured. Results showed that the plant benefited more from Se6+than Se2−and were not exposed to Se stress by four low doses of Se6+(0, 0.2, 0.4, and 0.6 mg kg−1) treatments for 40 days. The trends of tryptophan hydroxylase (TPH), tryptamine-5-hydroxylas, and arylalkylamine N-acetyltransferase activities were similar, while tryptophan decarboxylase (TDC), serotonin-N-acetyltransferase (SNAT), caffeic acid-O-methyltransferase, and N-acetyl-5-hydroxytryptamine-O-methyltransferase (ASMT) activities were different in leaves and roots following different Se6+treatments. Tryptophan and 5-hydorxy tryptophan contents were not influenced by the Se6+dose. Tryptamine was not detected in leaves or roots, but the serotonin content was increased by Se6+treatment. The leaf content of N-acetyl-5-tryptophan was not influenced, but its synthesis was significantly induced in roots as the Se dose increased. Melatonin synthesis in leaves and roots was increased only following treatment with 96 mg Se6+. The leaf Se and melatonin content showed no significant linear relationship in the ‘Russia No.8’ cultivar. Treatment with Se2−increased the enzyme levels of TDC, TPH, SNAT, and ASMT more significantly than Se6+. Overall, lower doses of Se6+did not significantly induce melatonin synthesis, but the relationship between melatonin and Se2−merits further attention.

Epigenetic regulations play an important role in disease development. In this study, we have investigated epigenetic regulations in restrictive cardiomyopathy mice with cTnI 193His mutation. Our results demonstrated that phosphodiesterase (PDEs) 4d was down-regulated in the heart of these mice. Further studies showed that the epigenetic modifications were associated with enhanced acetylation of histone 3 lysine 4 and lysines 9, whereas tri-methylation of histone 3 lysine 4, were decreased in histones near the PDE4d gene promoter regions. The binding levels of histone transmethylase SMYD1 and histone deacetylase HDAC1 were increased in the gene promoter regions in cTnI193His transgenic hearts. Using immune-fluorescent labeling we found an evidence of cTnI existence in the nucleus of cardiomyocytes and Western blotting further confirmed that both wild type and mutated cTnI could be detected in the cell nucleus of the hearts. Furthermore, an interaction between cTnI and SMYD1, or cTnI and HDAC1 was observed. Overexpression of the mutated cTnI in cultured cardiomyocytes reduced the expression of PDE4d. Our data suggest that the decrease of PDE4d expression in RCM mice caused by cTnI mutations may be related to epigenetic regulation, i.e., histone acetylation and methylation, and cTnI might be involved in this procedure via an interaction with HDAC1 and SMAD1 in the hearts.

Oxidative amination for the installation of nitrogen functional molecules from nitrogen nucleophiles has always been a very challenging topic in organic synthesis. Here we report a novel conversion of different aldehydes with secondary amines for the synthesis of diversified α-amino ketones. This method can be achieved through oxidative rearrangement of an in situ-generated enamine intermediate promoted by commercially available sodium percarbonate. Furthermore, this one-pot process is also suitable for the functional modification of complex molecules.

With the development of computation-intensive applications, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) provides task offloading service for the users with or without terrestrial infrastructure support. Meanwhile, the next generation of UAVs communication systems are expected to be user-centric. Therefore, more attention should be paid to users’ satisfaction with the offered service. In this paper, we study the service satisfaction-oriented task offloading and UAV scheduling problem for UAV-enabled MEC networks, where the task priorities are considered based on the delay requirements of users’ tasks and the remaining energy status of users. Specifically, we firstly divide the users into different groups via the K-means-based grouping algorithm. Then, we develop a novel user satisfaction model by jointly considering the task processing delay and energy saving, based on which a total user satisfaction maximization problem is formulated to jointly optimize the task offloading decisions and UAV scheduling strategy. To solve the formulated problem, we decompose it into two sub-problems, i.e., the UAV scheduling sub-problem and the task offloading sub-problem. To solve the first sub-problem, we develop a genetic algorithm (GA)-based UAV scheduling algorithm through dealing with multiple balanced assignment problems. To address the second sub-problem, a GA-based task offloading algorithm is developed. Then, we propose a joint task offloading and UAV scheduling optimization algorithm to solve the original optimization problem. Finally, simulation results demonstrate that the proposed optimization algorithm not only converges fast, but also improves the total users’ satisfaction greatly. The total users’ satisfaction improved by the proposed scheme is up to 16.9% in the case of 170 users.

Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. TMB-related features were detected using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and the correlation between features and TMB was evaluated in an external validation set (237 patients). TMB-related features were used for predicting overall survival (OS) of patients to investigate whether these features have potential for prognostic prediction. Moreover, biological pathways underlying the prognostic value of the features were further explored. Histopathologic features derived from whole-slide images were significantly associated with patient TMB (P = 0.007 in the external validation set). TMB-related features showed excellent performance for OS prediction, and patients with lower-grade gliomas could be further stratified into different risk groups according to the features (P = 0.00013; hazard ratio, 4.004). Pathways involved in the cell cycle and execution of immune response were enriched in patients with higher OS risk. The TMB-related features could be used to estimate TMB and aid in prognostic risk stratification of patients with glioma with dysregulated biological pathways.

Power loss management is one of the most important challenges for improving the reliability of modular multilevel converters (MMCs). This article proposes a current integral comparison (CIC)-based power loss balancing control (PLBC) for full-bridge submodule (FBSM)-based MMCs, where two types of bypassed modes of the FBSM are selected by comparing the current difference integral values of the semiconductors in the FBSM. The proposed control can not only improve the power loss distribution in each FBSM, but also reduce the temperature fluctuation of semiconductors, and accordingly, the proposed control significantly improves the lifetime of MMCs. Simulation studies with professional tool PSCAD/EMTDC and experimental studies with a downscale MMC prototype are conducted, and their results confirm the effectiveness of the proposed CIC-PLBC.

This article proposes a fast frequency domain dielectric spectroscopy test scheme for diagnosing oil–paper insulation. The high-voltage output section introduces dynamic power supply tracking (DPST) and cascading techniques to generate high-voltage excitation signals. The DPST can increase the amplifier's voltage slew rate (SR) from 13 V per microsecond to 36.3 V per microsecond without crossover distortion. In the microcurrent measurement section, the error active suppression structure of bias current and bias current under time-varying temperature conditions is proposed to improve the temperature stability of the measurement circuit. The fast testing technology shortens the test period by 65% in the low-frequency band by using the method of multifrequency signal synthesis and partial cycle waveform fitting. The experimental results show that the total harmonic distortion and noise (THD+N) of the output voltage of the instrument are less than or equal to 0.078%. The voltage range is 780 Vpp. The standard deviation of current is less than 1.5%, and the standard deviation of tan δ is less than 1.7%, which verifies the stability of the designed instrument.

Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer’s characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.

ABSTRACT Confined stabilized soil (CSS) was the composite system that used polyvinyl chloride pipe to confine stabilized soil with the binder consisting of expansive and cementing components, aimed at treating [...]

A method is proposed to fuse the velocity data of the global navigation satellite system (GNSS) and leveling height via combined adjustment with constraints. First, stable GNSS–leveling points are uniformly selected, and the constraints of the geodetic height change velocity and normal height change velocity are given. Then, the GNSS vertical velocities and leveling height difference are used as observations of combined adjustment, and robust least-squares estimation are used to estimate the velocities of the unknown points. Finally, a vertical movement model is established with the GNSS vertical velocities and leveling vertical velocities obtained via combined adjustment. Data from the second-order leveling network and GNSS control points in Shandong Province are taken as test data, and eight calculation schemes are used for discussion. One of the schemes, the bifactor robust combined adjustment method based on variance component estimation with two kinds of vertical velocity constraints achieves the optimal results. The method applied in the scheme can be recommended for data fusion of GNSS and leveling, further improving the reliability of vertical crustal movement in Shandong Province.

C27H39N3O2Sn, monoclinic, P21/c(no. 14), a= 10.3317(6) Å, b= 15.8067(9) Å, c= 16.5497(9) Å, β= 90.5380(10)°, V= 2702.6(3) Å3, Z= 4, Rgt(F) = 0.0370, wRref(F2) = 0.0954, T = 296(2) K.

Rechargeable lithium metal batteries (LMBs) offer excellent opportunities for applications requiring high-energy-density battery systems. So far, it has received a lot of interest in pairing higher-energy-density high-voltage nickel-rich cathodes. Here, fluorinated solvents were used instead of the usual carbonate solvents to prepare gel polymer electrolytes (FGPE) by in situ polymerization of polymers introducing the fluorine-containing groups. Theoretically and experimentally, FGPE has proven to be ultra-compatible with the lithium metal anode and LiNi0.8Co0.1Mn0.1O2cathode. A stable plating/stripping process of over 2000 h can be achieved for symmetrical lithium cells using FGPE. The Li||FGPE||NCM811 cell has a longer cycle life at a high voltage (4.5 V). In addition, the zero self-extinguishing time indicates that the FGPE has sufficient safety. In summary, the design of this electrolyte provides ideas to improve the safety and energy density of LMBs.

Many industrial applications require time-consuming and resource-intensive evaluations of suitable solutions within very limited time frames. Therefore, many surrogate-assisted evaluation algorithms (SAEAs) have been widely used to optimize expensive problems. However, due to the curse of dimensionality and its implications, scaling SAEAs to high-dimensional expensive problems is still challenging. This paper proposes a variable surrogate model-based particle swarm optimization (called VSMPSO) to meet this challenge and extends it to solve 200-dimensional problems. Specifically, a single surrogate model constructed by simple random sampling is taken to explore different promising areas in different iterations. Moreover, a variable model management strategy is used to better utilize the current global model and accelerate the convergence rate of the optimizer. In addition, the strategy can be applied to any SAEA irrespective of the surrogate model used. To control the trade-off between optimization results and optimization time consumption of SAEAs, we consider fitness value and running time as a bi-objective problem. Applying the proposed approach to a benchmark test suite of dimensions ranging from 30 to 200 and comparisons with four state-of-the-art algorithms show that the proposed VSMPSO achieves high-quality solutions and computational efficiency for high-dimensional problems.

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I–III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities—clinical, genomic, and histopathological—to improve the predictive accuracy for localised renal cell carcinoma recurrence.