High Ion-Conductive Hydrogel: Soft, Elastic, with Wide Humidity Tolerance and Long-Term Stability.

Ion-conductive hydrogels have received great attention due to their significant potential in flexible electronics. However, achieving hydrogels that simultaneously possess high ionic conductivity and stability under varying humidity conditions remains a challenge, limiting their practical applications. Herein, we propose a thermally controlled chemical cross-linking strategy to prepare an elastic and conductive hydrogel (ECH) of poly(vinyl alcohol) (PVA) with high content of H2SO4. The covalent cross-links formed effectively tackle the instability issue in high humidity of physically cross-linked PVA/H2SO4 hydrogels with high ionic conductivity, which were previously developed via the polymer-in-salt strategy. We systematically investigated the reaction conditions and clarified the methods to optimize the hydroxyl dehydration of PVA, resulting in excellent mechanical properties and ion conductivity simultaneously. The ECH demonstrates impressive ionic conductivity (up to 392 ± 49 mS cm-1) and elasticity (over 80% resilience upon stretching and compression after being equilibrated at various humidity levels for 24 days). Thanks to the excellent water retention of the high H2SO4 content, the ECH maintains an ionic conductivity exceeding 210 mS cm-1 for over 420 days at 50% relative humidity (RH) and retains over 100 mS cm-1 even after 3 days under extremely dry conditions (7% RH). These remarkable properties make the ECH an ideal candidate for applications requiring reliable ionic conductivity in diverse environmental conditions. Additionally, we demonstrated that the ECH can function as a stretchable Joule heater with high conformability for heating up objects with curved surfaces. The heating rate could reach a fast rate of ∼12 °C s-1 even when a human-safe alternating current voltage is below 36 V, attributed to the high ionic conductivity. We believe that the high performance and ease of fabrication make our hydrogels a promising candidate for use as electrolytes in flexible energy storage devices, electrolyte gates in electrochemical transistors, and artificial skin, which often face long-term stability challenges under varying humidity conditions.

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI.

Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivity maps (CSMs). k-space interpolation methods can effectively address this issue but conventional diffusion models are not readily applicable in k-space interpolation. To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method. Specifically, we utilize the iterative solver of the self-consistent term (i.e., k-space physical prior) in SPIRiT to formulate a novel stochastic differential equation (SDE) governing the diffusion process. Subsequently, k-space data can be interpolated by executing the diffusion process. This innovative approach highlights the optimization model's role in designing the SDE in diffusion models, enabling the diffusion process to align closely with the physics inherent in the optimization model-a concept referred to as model-driven diffusion. We evaluated the proposed SPIRiT-Diffusion method using a 3D joint intracranial and carotid vessel wall imaging dataset. The results convincingly demonstrate its superiority over image-domain reconstruction methods, achieving high reconstruction quality even at a substantial acceleration rate of 10. Our code are available at https://github.com/zhyjSIAT/SPIRiT-Diffusion.

[Spatial Heterogeneity and Key Influencing Factors of Soil Organic Carbon, Soil Total Nitrogen, and Carbon to Nitrogen Ratio in main plantations of Torreya grandis cv. Merrillii].

Soil organic carbon （SOC） and soil total nitrogen （STN） serve as important indicators of the elemental balance within forest ecosystems reflecting soil fertility and quality. Accurate knowledge regarding the spatial variability of regional SOC, STN, and C∶N ratio and their influencing factors is of great significance for precise fertilization and soil health. In this study, a total of 117 topsoil samples （0-20 cm in depth） based on a 1 km×1 km grid were collected in the Torreya grandis cv. Merrillii plantation in Zhejiang Province. A combination of multi-dimensional statistical approaches （random forest model, structural equation model, redundancy analysis, and variation partitioning analysis） and diverse spatial analytical techniques （geostatistics, Moran's I index, etc.） were applied to reveal the spatial distributions and influencing factors of SOC, STN, and C∶N ratio in the Torreya. grandis cv. Merrillii region. The results showed that the average ω（SOC）, ω（STN）, and C∶N ratio were 17.63 g·kg-1, 1.48 g·kg-1, and 12.65, respectively, and their coefficients of variation were 68.08%, 67.41%, and 46.03%, respectively, indicating a moderate degree of variability. In general, the SOC, STN, and C∶N ratio of the Torreya grandis cv. Merrillii plantations were at an intermediate level in the national plantation. The semi-variance results showed that the nugget/sill values of SOC, STN, and C∶N ratio were 49.98%, 45.88%, and 49.93%, respectively, demonstrating a moderate level of spatial autocorrelation. The spatial distribution results showed that SOC, STN, and C∶N ratio decreased from northeast to southwest, with the majority of the region exhibiting above-medium fertility levels of SOC. The results of correlation analysis and redundancy analysis indicated that AN, AP, and AK were significantly correlated with both SOC, STN, and C∶N ratio （P<0.05）. The results of random forest, structural equation model, and variation partitioning analysis evidenced that the main influencing factors of SOC and STN were soil-available nutrients （AN, AP, and AK）. Therefore, our results could provide important insights for enhancing soil carbon and nitrogen pools in special plantations in Zhejiang Province, enhancing the capacity of plantations to adapt to regional climate change through ecological measures such as appropriate fertilization practices and strategic understory vegetation cultivation.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data. Broadly, this insight drives a shift in the generation paradigm from random noise to a more deterministic approach grounded in the existing LF k-space data. Building on this, we first establish a relationship between the interpolation of HF k-space data from LF k-space data and the reverse heat diffusion process, providing a fundamental framework for designing diffusion models that generate missing HF data. To further improve reconstruction accuracy, we integrate a traditional physics-informed k-space interpolation model into our diffusion framework as a data fidelity term. Experimental validation using publicly available datasets demonstrates that our approach significantly surpasses traditional k-space interpolation methods, deep learning-based k-space interpolation techniques, and conventional diffusion models, particularly in HF regions. Finally, we assess the generalization performance of our model across various out-of-distribution datasets. Our code are available at https://github.com/ZhuoxuCui/Heat-Diffusion.

Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Background: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study. Methods: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001). Conclusion: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

Novel method for identifying the heaviest QED atom.

QED atoms are composed of unstructured and point-like lepton pairs bound together by the electromagnetic force. The smallest and heaviest QED atom is formed by a τ+τ- pair. Currently, the only known atoms of this type are the e+e- and µ+e- atoms, which were discovered 64 years ago and remain the sole examples found thus far. We demonstrate that the Jτ (τ+τ- atom with JPC=1--) atom signal can be observed with a significance larger than 5σ including both statistical and systematic uncertainties, via. the process e+e-→X+Y-Ɇ (X,Y=e,µ,π,K, or ρ, and Ɇ is the missing energy due to unobserved neutrinos) with 1.5ab-1 data taken around the τ pair production threshold. The τ lepton mass can be measured with a precision of 1 keV with the same data sample. This is within one year's running time of the proposed super tau-charm facility in China or super charm-tau factory in Russia.

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

DPP: deep phase prior for parallel imaging with wave encoding.

Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.

A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma.

Background: The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis. Methods: In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method. Results: Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates. Conclusions: These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.

DNase I targeted degradation of neutrophil extracellular traps to reduce the damage on IgAV rat.

BACKGROUND: In the past two years, studies have found a significant increase in neutrophil extracellular traps (NETs) in patients with IgA vasculitis (IgAV), which is correlated with the severity of the disease. NETs have been reported as an intervention target in inflammatory and autoimmune diseases. This study aimed to investigate the effect of targeted degradation of NETs using DNase I in IgAV rat model. METHODS: Twenty-four Sprague-Dawley rats were randomly divided into three groups: the IgAV model group, the DNase I intervention group and the normal control group, with an average of 8 rats in each group. The model group was established by using Indian ink, ovalbumin, and Freund's complete adjuvant. In the intervention group, DNase I was injected through tail vein 3 days before the end of established model. The circulating cell free-DNA (cf-DNA) and myeloperoxidase-DNA (MPO-DNA) were analyzed. The presence of NETs in the kidney, gastric antrum and descending duodenum were detected using multiple fluorescences immunohistochemistry and Western blots. Morphological changes of the tissues were observed. RESULTS: After the intervention of DNase I, there was a significant reduction in cf-DNA and MPO-DNA levels in the intervention group compared to the IgAV model group (all P<0.001). The presence of NETs in renal, gastric, and duodenal tissues of the intervention group exhibited a significant decrease compared to the IgAV model group (P < 0.01). Moreover, the intervention group demonstrated significantly lower levels of renal MPO and citrullinated histone H3 (citH3) protein expression when compared to the IgAV model group (all P < 0.05). The HE staining results of intervention group demonstrated a significant reduction in congestion within glomerular and interstitial capillaries. Moreover, there was a notable improvement in gastric and intestinal mucosa necrosis, congestion and bleeding. Additionally, there was a substantial decrease in inflammatory cells infiltration. CONCLUSION: The degradation of NETs can be targeted by DNase I to mitigate tissue damage in IgAV rat models. Targeted regulation of NETs holds potential as a therapeutic approach for IgAV.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

A meta-analysis of the association between mindfulness and motivation.

Introduction: Mindfulness reflects attention to the present moment in a non-judgmental way and has been linked to individual autonomy and motivation, but conclusions are inconsistent. The purpose of this review was to summarize previous studies to explore the relationship between mindfulness and motivation and its intervention effects. Methods: Literature searches were conducted in five electronic databases. Both correlational studies assessing the association between motivation and mindfulness and experimental studies to verify the effect of intervention were included. Results: Six papers with seven intervention studies and twenty-three papers with twenty-seven correlational studies met the inclusion criteria. Meta-analysis showed that mindfulness was positively correlated with intrinsic motivation (r = 0.28, p < 0.0001) and total motivation (r = 0.37, p < 0.0001) but had no significant correlation with extrinsic motivation (r = 0.01, p = 0.93) or amotivation (r = -0.17, p = 0.14). Effect-size estimates suggested that mindfulness intervention was beneficial to motivation promotion, but the effect was at a low level (g = 0.12). Conclusion: We found consistent support for mindfulness practice relating to motivation promotion, especially on intrinsic motivation development. However, there was still a portion of heterogeneity that could not be explained and needed to be identified in future studies.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging.

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module. This implies that the unrolled network output may not align with the regularization models. Furthermore, there are few established theories that guarantee global convergence and robustness (regularity) of unrolled networks under practical assumptions. To address this gap, we propose a safeguarded methodology for network unrolling. Specifically, for parallel MR imaging, we unroll a zeroth-order algorithm, where the network module serves as a regularizer itself, allowing the network output to be covered by a regularization model. Additionally, inspired by deep equilibrium models, we conduct the unrolled network before backpropagation to converge to a fixed point and then demonstrate that it can tightly approximate the actual MR image. We also prove that the proposed network is robust against noisy interferences if the measurement data contain noise. Finally, numerical experiments indicate that the proposed network consistently outperforms state-of-the-art MRI reconstruction methods, including traditional regularization and unrolled deep learning techniques.

K-UNN: k-space interpolation with untrained neural network.

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier (PF), regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including transform sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can characterize the physical priors of MR images well. Additionally, experiments show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and is even competitive against supervised-trained deep learning methods in PF and regular undersampling reconstruction.

Neutrophil extracellular traps involved in the pathogenesis of IgA vasculitis: Confirmed in two IgAV rat models.

BACKGROUND: Neutrophil extracellular traps (NETs) have been found to play a role in the development of autoimmune diseases. In the past two years, studies have demonstrated a significantly increase of NETs in skin tissues during the early stages of IgAV, indicating their involvement in disease activity among children with IgAV. However, the presence of NETs in IgAV animal models has not yet been reported. The objective of this study is to investigate whether NETs are involved in the pathogenesis of IgA vasculitis (IgAV) rats. METHODS: Twenty-four SD rats were randomly divided into three groups: the ovalbumin group, the gliadin group, and the control group. The IgAV rat models were established administering Indian ink with ovalbumin (ovalbumin group) or gliadin (gliadin group) with Freund's complete adjuvant. The cell-free DNA (cf-DNA) was quantified by using dsDNA quantification kit, while the levels of Immunoglobulins, complement C3 and myeloperoxidase-DNA (MPO-DNA) in serum were tested using enzyme linked immunosorbent assay (ELISA). The IgA, complement C3 and NETs in tissues were detected through multiple immunofluorescences. RESULTS: Both the ovalbumin group and gliadin group showed IgA and C3 deposition in various tissues, including the glomerular mesangial region, skin, and digestive tract, while the control group showed no such deposition. The levels of circulatory cf-DNA and MPO-DNA, which are components of NETs, were significantly elevated in both ovalbumin and gliadin groups compared with the control group. Furthermore, the presence of NETs were found in gastrointestinal and renal tissues of the ovalbumin and gliadin groups, but not in the control group. CONCLUSIONS: IgAV model rat can be established through the combination of ovalbumin and gliadin with Indian ink and Freund's complete adjuvant. This study provides the first confirmation that NETs are involved in the pathogenesis of IgAV rat.

Hypobaric Hypoxia Aggravates Renal Injury by Inducing the Formation of Neutrophil Extracellular Traps through the NF-κB Signaling Pathway.

OBJECTIVE: The hypersensitivity of the kidney makes it susceptible to hypoxia injury. The involvement of neutrophil extracellular traps (NETs) in renal injury resulting from hypobaric hypoxia (HH) has not been reported. In this study, we aimed to investigate the expression of NETs in renal injury induced by HH and the possible underlying mechanism. METHODS: A total of 24 SD male rats were divided into three groups (n=8 each): normal control group, hypoxia group and hypoxia+pyrrolidine dithiocarbamate (PDTC) group. Rats in hypoxia group and hypoxia+PDTC group were placed in animal chambers with HH which was caused by simulating the altitude at 7000 meters (oxygen partial pressure about 6.9 kPa) for 7 days. PDTC was administered at a dose of 100 mg/kg intraperitoneally once daily for 7 days. Pathological changes of the rat renal tissues were observed under a light microscope; the levels of serum creatinine (SCr), blood urea nitrogen (BUN), cell-free DNA (cf-DNA) and reactive oxygen species (ROS) were measured; the expression levels of myeloperoxidase (MPO), citrullinated histone H3 (cit-H3), B-cell lymphoma 2 (Bcl-2), Bax, nuclear factor kappa B (NF-κB) p65 and phospho-NF-κB p65 (p-NF-κB p65) in rat renal tissues were detected by qRT-qPCR and Western blotting; the localization of NF-κB p65 expression in rat renal tissues was observed by immunofluorescence staining and the expression changes of NETs in rat renal tissues were detected by multiplex fluorescence immunohistochemical staining. RESULTS: After hypoxia, the expression of NF-κB protein in renal tissues was significantly increased, the levels of SCr, BUN, cf-DNA and ROS in serum were significantly increased, the formation of NETs in renal tissues was significantly increased, and a large number of tubular dilatation and lymphocyte infiltration were observed in renal tissues. When PDTC was used to inhibit NF-κB activation, NETs formation in renal tissue was significantly decreased, the expression level of Bcl-2 in renal tissues was significantly increased, the expression level of Bax was significantly decreased, and renal injury was significantly alleviated. CONCLUSION: HH induces the formation of NETs through the NF-κB signaling pathway, and it promotes apoptosis and aggravates renal injury by decreasing Bcl-2 and increasing Bax expression.

Next-generation viral nanoparticles for targeted delivery of therapeutics: Fundamentals, methods, biomedical applications, and challenges.

INTRODUCTION: Viral nanoparticles (VNPs) are virus-based nanocarriers that have been studied extensively and intensively for biomedical applications. However, their clinical translation is relatively low compared to the predominating lipid-based nanoparticles. Therefore, this article describes the fundamentals, challenges, and solutions of the VNP-based platform, which will leverage the development of next-generation VNPs. AREAS COVERED: Different types of VNPs and their biomedical applications are reviewed comprehensively. Strategies and approaches for cargo loading and targeted delivery of VNPs are examined thoroughly. The latest developments in controlled release of cargoes from VNPs and their mechanisms are highlighted too. The challenges faced by VNPs in biomedical applications are identified, and solutions are provided to overcome them. EXPERT OPINION: In the development of next-generation VNPs for gene therapy, bioimaging and therapeutic deliveries, focus must be given to reduce their immunogenicity, and increase their stability in the circulatory system. Modular virus-like particles (VLPs) which are produced separately from their cargoes or ligands before all the components are coupled can speed up clinical trials and commercialization. In addition, removal of contaminants from VNPs, cargo delivery across the blood brain barrier (BBB), and targeting of VNPs to organelles intracellularly are challenges that will preoccupy researchers in this decade.

[Effects of Long-term Sod Cultivation on Chinese Hickory Plantation Soil Fungal Community and Enzyme Activities].

A long-term field experiment was conducted at a Chinese hickory (Carya cathayensis) plantation from 2011 to 2021, with the purpose of researching the effects of long-term sod cultivation on hickory plantation soil fungal communities and enzyme activities and providing experience for ecological management in other plantations. Sod cultivation included oilseed rape (Brassica chinensis, BR), Chinese milk vetch (Astragalus sinicus, AS), and oilseed rape+Chinese milk vetch (BA), and clear tillage (CT) served as a contrast. The soil fertility, fungal community composition and diversity, and soil enzyme activities were determined. The results showed that:① long-term sod cultivation significantly increased soil nutrient contents and availability, and pH increased variably from different sod cultivation treatments (P<0.05). ②The soil fungal community composition was changed by long-term sod cultivation. The relative abundance of Ascomycota, which utilized the readily decomposed organic matter, was increased, whereas the relative abundance of Basidiomycota, which degraded stubborn organic matter, decreased. Long-term sod cultivation shifted the soil dominant genera, as BR and BA increased the relative abundance of somemycorrhizal fungi that could form mutually beneficial structures with dominant plant genera after sod cultivation,whereas AS increased the relative abundance of saprophytic fungi that could decompose the remains of dead plants and animals. The soil fertility factors including pH, available nitrogen, microbial biomass nitrogen, and water-soluble organic carbon were revealed to have a significant influence on the soil fungal composition (P<0.05). ③ Moreover, long-term sod cultivation stimulated the activities of soil enzymes involved in the carbon and nitrogen cycle. Apart from BA, sod cultivation treatments decreased the activities of alkaline phosphatase, which was involved in the soil P turnover. The correlation analysis demonstrated that the correlations between activities of enzymes decomposing carbon and nitrogen and soil fertility were significant (P<0.05 or P<0.01). The activities of phosphatase were positively correlated with soil microbial biomass carbon and nitrogen. Long-term sod cultivation could improve soil nutrient content and availability, optimized soil fungal community structure, and promoted soil nutrient turnover enzyme activities.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network.

BACKGROUND: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data. PURPOSE: To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free. METHODS: Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively. RESULTS: In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice. CONCLUSIONS: The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.