An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction.

Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods.Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM.Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts.Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.

Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework.

Magnetic particle imaging (MPI) is an emerging medical imaging technique that has high sensitivity, contrast, and excellent depth penetration. In MPI, x-space is a reconstruction method that transforms the measured voltages into particle concentrations. The reconstructed native image can be modeled as a convolution of the magnetic particle concentration with a point-spread function (PSF). The PSF is one of the important parameters in deconvolution. However, accurately measuring or modeling the PSF in the hardware used for deconvolution is challenging due to the various environment and magnetic particle relaxation. The inaccurate PSF estimation may lead to the loss of the content structure of the MPI image, especially in low gradient fields. In this study, we developed a Dual Adversarial Network (DAN) with patch-wise contrastive constraint to deblur the MPI image. This method can overcome the limitations of unpaired data in data acquisition scenarios and remove the blur around the boundary more effectively than the common deconvolution method. We evaluated the performance of the proposed DAN model on simulated and real data. Experimental results confirmed that our model performs favorably against the deconvolution method that is mainly used for deblurring the MPI image and other GAN-based deep learning models.

Comparative microbiome analysis reveals the variation in microbial communities between 'Kyoho' grape and its bud mutant variety.

Microbes are an important part of the vineyard ecosystem, which significantly influence the quality of grapes. Previously, we identified a bud mutant variety (named 'Fengzao') from 'Kyoho' grapes. The variation of microbial communities in grape and its bud mutant variety has not been studied yet. So, in this study, with the samples of both 'Fengzao' and 'Kyoho', we conducted high-throughput microbiome sequencing and investigated their microbial communities in different tissues. Obvious differences were observed in the microbial communities between 'Fengzao' and 'Kyoho'. The fruit and the stem are the tissues with relatively higher abundance of microbes, while the leaves contained less microbes. The fruit and the stem of 'Kyoho' and the stem of 'Fengzao' had relatively higher species diversity based on the alpha diversity analysis. Proteobacteria, Enterobacteriaceae and Rhodobacteraceae had significantly high abundance in 'Fengzao'. Firmicutes and Pseudomonas were highly abundant in the stems of 'Kyoho', and family of Spirochaetaceae, Anaplasmataceae, Chlorobiaceae, and Sphingomonadaceae, and genera of Spirochaeta, Sphingomonas, Chlorobaculum and Wolbachia were abundant in the fruits of 'Kyoho'. These identified microbes are main components of the microbial communities, and could be important regulators of grapevine growth and development. This study revealed the differences in the microbial compositions between 'Kyoho' and its bud mutant, and these identified microbes will be significant resources for the future researches on the quality regulation and disease control of grapevines.

Enhancing Freshwater Production via Customizable and Highly Efficient Solar-Driven Seawater Desalination.

Solar-powered water generation is an appealing strategy for cost-effective and energy-sustainable seawater purification/desalination, where rational material selection and device design is crucial. Nevertheless, prevailing carbon-based photothermal materials in such systems still suffer from mediocre steam-to-water efficiency, failing to satisfy an adequate freshwater supply. Herein, we demonstrate a biomimetic corrugated evaporator (CE) affording carbon nanotube (CNT) encapsulated Fe nanocluster-decoration in the pursuit of high-efficiency seawater purification. The thus-customized CE demonstrates a maximum evaporation rate of 4.2 kg m-2 h-1 with a refraction angle of 60° and a water-lifting height of 5.5 cm, outperforming most state-of-the-art carbon-based counterparts. By employing a tailored architectural design and optimized condensing volume, the steam-to-water efficiency increases from 65.8 to 88.2% as the volume enlarges from 0.8 to 5.3 L, further harvesting a peak value of 91% under negative pressure. Light intensity simulation and experimental mechanistic investigation disclose the dual property-performance relationships between evaporator microstructure and evaporation rate, as well as between condensing device volume and steam-to-water efficiency. The universality of the theoretical guidance of this work will offer insight into the development of solar-driven evaporator construction toward simultaneous seawater desalination and clean water generation.

Cross-domain knowledge transfer based parallel-cascaded multi-scale attention network for limited view reconstruction in projection magnetic particle imaging.

Projection magnetic particle imaging (MPI) can significantly improve the temporal resolution of three-dimensional (3D) imaging compared to that using traditional point by point scanning. However, the dense view of projections required for tomographic reconstruction limits the scope of temporal resolution optimization. The solution to this problem in computed tomography (CT) is using limited view projections (sparse view or limited angle) for reconstruction, which can be divided into: completing the limited view sinogram and image post-processing for streaking artifacts caused by insufficient projections. Benefiting from large-scale CT datasets, both categories of deep learning-based methods have achieved tremendous progress; yet, there is a data scarcity limitation in MPI. We propose a cross-domain knowledge transfer learning strategy that can transfer the prior knowledge of the limited view learned by the model in CT to MPI, which can help reduce the network requirements for real MPI data. In addition, the size of the imaging target affects the scale of the streaking artifacts caused by insufficient projections. Therefore, we propose a parallel-cascaded multi-scale attention module that allows the network to adaptively identify streaking artifacts at different scales. The proposed method was evaluated on real phantom and in vivo mouse data, and it significantly outperformed several advanced limited view methods. The streaking artifacts caused by an insufficient number of projections can be overcome using the proposed method.

Sensitive magnetic particle imaging of haemoglobin degradation for the detection and monitoring of intraplaque haemorrhage in atherosclerosis.

BACKGROUND: Intraplaque haemorrhage (IPH) drives atherosclerosis progression and is a key imaging biomarker of unstable plaques. Non-invasive and sensitive monitoring of IPH is challenging due to the compositional complexity and dynamic nature of atherosclerotic plaques. Magnetic particle imaging (MPI) is a highly sensitive, radiation-free, and no-tissue-background tomographic technique that detects superparamagnetic nanoparticles. Thus, we aimed to investigate whether MPI can in vivo detect and monitor IPH. METHODS: Thirty human carotid endarterectomy samples were collected and scanned with MPI. The tandem stenosis (TS) model was employed to establish unstable plaques with IPH in ApoE-/- mice. MPI and 7 T T1-weighted magnetic resonance imaging (MRI) were performed on TS ApoE-/- mice. Plaque specimens were analyzed histologically. FINDINGS: Human carotid endarterectomy samples exhibited endogenous MPI signals, which histologically colocalized with IPH. In vitro experiments identified haemosiderin, a haemoglobin degradation product, as a potential source of MPI signals. Longitudinal MPI of TS ApoE-/- mice detected IPH at unstable plaques, of which MPI signal-to-noise ratio values increased from 6.43 ± 1.74 (four weeks) to 10.55 ± 2.30 (seven weeks) and reduced to 7.23 ± 1.44 (eleven weeks). In contrast, 7 T T1-weighted MRI did not detect the small-size IPH (329.91 ± 226.82 µm2) at four weeks post-TS. The time-course changes in IPH were shown to correlate with neovessel permeability providing a possible mechanism for signal changes over time. INTERPRETATION: MPI is a highly sensitive imaging technology that allows the identification of atherosclerotic plaques with IPH and may help detect and monitor unstable plaques in patients. FUNDING: This work was supported in part by the Beijing Natural Science Foundation under Grant JQ22023; the National Key Research and Development Program of China under Grant 2017YFA0700401; the National Natural Science Foundation of China under Grant 62027901, 81827808, 81730050, 81870178, 81800221, 81527805, and 81671851; the CAS Youth Innovation Promotion Association under Grant Y2022055 and CAS Key Technology Talent Program; and the Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai HLHPTP201703).

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging.

Objective. Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI.Methods. AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data.Results. The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods.Significance. The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.

PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging.

BACKGROUND: Magnetic particle imaging (MPI) is a novel tomographic imaging modality that scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time-consuming to scan multiview two-dimensional (2D) projections for three-dimensional (3D) reconstruction in projection MPI, such as computed tomography (CT). An intuitive idea is to use the sparse-view projections for reconstruction to improve the temporal resolution. Tremendous progress has been made toward addressing the sparse-view problem in CT, because of the availability of large data sets. For the novel tomography of MPI, to the best of our knowledge, studies on the sparse-view problem have not yet been reported. PURPOSE: The acquisition of multiview projections for 3D MPI imaging is time-consuming. Our goal is to only acquire sparse-view projections for reconstruction to improve the 3D imaging temporal resolution of projection MPI. METHODS: We propose to address the sparse-view problem in projection MPI by generating novel projections. The data set we constructed consists of three parts: simulation data set (including 3000 3D data), four phantoms data, and an in vivo mouse data. The simulation data set is used to train and validate the network, and the phantoms and in vivo mouse data are used to test the network. When the number of novel generated projections meets the requirements of filtered back projection, the streaking artifacts will be absent from MPI tomographic imaging. Specifically, we propose a projection generative network (PGNet), that combines an attention mechanism, adversarial training strategy, and a fusion loss function and can generate novel projections based on sparse-view real projections. To the best of our knowledge, we are the first to propose a deep learning method to attempt to overcome the sparse-view problem in projection MPI. RESULTS: We compare our method with several sparse-view methods on phantoms and in vivo mouse data and validate the advantages and effectiveness of our proposed PGNet. Our proposed PGNet enables the 3D imaging temporal resolution of projection MPI to be improved by 6.6 times, while significantly suppressing the streaking artifacts. CONCLUSION: We proposed a deep learning method operated in projection domain to address the sparse-view reconstruction of MPI, and the data scarcity problem in projection MPI reconstruction is alleviated by constructing a sparse-dense simulated projection data set. By our proposed method, the number of acquisitions of real projections can be reduced. The advantage of our method is that it prevents the generation of streaking artifacts at the source. Our proposed sparse-view reconstruction method has great potential for application to time-sensitive in vivo 3D MPI imaging.

Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging.

BACKGROUND: Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology.

Ehm2 transcript variant 1 inhibits breast cancer progression and increases E-cadherin stability.

Ehm2/1, an Ehm2 transcript variant, regulates the cytoskeleton by binding to plasma membrane proteins. However, the role of Ehm2/1 in breast cancer development remains poorly understood. This study shows that, the expression of Ehm2/1 was decreased in breast cancer and that patients with low Ehm2/1 expression had a significantly poorer prognosis than those with high expression of Ehm2/1. Overexpression of Ehm2/1 in MCF-7 breast cancer cells inhibited cell migration and invasion. Ehm2/1 markedly increased the stability and half-life of E-cadherin. Moreover, Ehm2/1 was collocated with E-cadherin in the plasma membrane of MCF-7 cells. Furthermore, downregulation of Ehm2/1 promoted ubiquitination of E-cadherin, whereas overexpression of Ehm2/1 inhibited ubiquitination of E-cadherin. These results suggest that Ehm2/1 could suppress the migration and invasion of breast cancer cells by increasing E-cadherin stability.

Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study.

COVID-19 has been spreading continuously since its outbreak, and the detection of its manifestations in the lung via chest computed tomography (CT) imaging is essential to investigate the diagnosis and prognosis of COVID-19 as an indispensable step. Automatic and accurate segmentation of infected lesions is highly required for fast and accurate diagnosis and further assessment of COVID-19 pneumonia. However, the two-dimensional methods generally neglect the intraslice context, while the three-dimensional methods usually have high GPU memory consumption and calculation cost. To address these limitations, we propose a two-stage hybrid UNet to automatically segment infected regions, which is evaluated on the multicenter data obtained from seven hospitals. Moreover, we train a 3D-ResNet for COVID-19 pneumonia screening. In segmentation tasks, the Dice coefficient reaches 97.23% for lung segmentation and 84.58% for lesion segmentation. In classification tasks, our model can identify COVID-19 pneumonia with an area under the receiver-operating characteristic curve value of 0.92, an accuracy of 92.44%, a sensitivity of 93.94%, and a specificity of 92.45%. In comparison with other state-of-the-art methods, the proposed approach could be implemented as an efficient assisting tool for radiologists in COVID-19 diagnosis from CT images.

Deep learning for improving the spatial resolution of magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast.Approach.Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings.Main results.We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two.Significance.This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

Highly efficient Cd(â ¡) removal using 3D N-doped carbon derived from MOFs: Performance and mechanisms.

Cadmium (Cd) removal is imperative to ensure the safety of aquatic-ecosystem, yet its effective removal technology has remained elusive by far. To address this concern, three-dimensional N-doped carbon (NC) polyhedrons affording ample porosity is fabricated based upon the thermal carbonization and KOH activation of zeolitic imidazolate framework-8 (ZIF-8) precursor. Thus-derived activated NC (a-NC) adsorbent not only overcomes the inherent instability of ZIF-8 but also harvests a maximum Cd(Ⅱ) adsorption capacity of 370.2 mg g-1, which evidently surpasses those of bare NC counterpart as well as previously reported adsorbents. Impressively, a-NC achieves ca. 100% removal of aqueous Cd(Ⅱ) in a broad working pH range (5-9), and particularly attains stable performances (81-92%) in various realistic water. Theoretical calculations in combination with experimental characterizations further offer mechanistic insight into the enhanced removal exerted by a-NC. Notably, owing to the increased specific surface area (3041 vs. 389 m2 g-1) and enhanced sp2 carbon content (91.7 vs. 68.8%) of a-NC as compared to NC, advanced Cd(Ⅱ) adsorption via a-NC can be exhibited. Our designed a-NC material harnessing favorable recycling capability would be in particular attractive in the realm of practical Cd(Ⅱ) remediation.

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis.

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

Insights into aggregation and transport of graphene oxide in aqueous and saturated porous media: Complex effects of cations with different molecular weight fractionated natural organic matter.

The stability of nanomaterials in aquatic environment is a critical factor that governs their fate and ecotoxicity. Meanwhile, the interaction between nanomaterials and ubiquitous natural organic matter (NOM) is a vital process that influences the transport and biological effects of nanomaterials in the environment. However, impacts of NOM on the aggregation and transport of two-dimensional nanomaterials, especially for the increasingly used graphene oxide (GO), are not well understood. Particularly, there is lack of exploration on potential impacts of the heterogeneous properties of NOM on GO behaviour, especially that induced by the wide molecular weight (MW) span of NOM. In this study, effects of several kinds of well-characterized MW fractionated Suwannee River NOM (Mf-SRNOMs) on the aggregation and transport of GO in aqueous media and saturated porous media were investigated. Our results suggest that the stability and migration capacity of GO under most investigated electrolyte conditions are promoted by all Mf-SRNOMs, and efficiencies of different Mf-SRNOMs are generally positively correlated with their MW. Primarily, mechanisms including MW-dependent steric hindrance and sorption of Mf-SRNOMs onto GO are critical in stabilizing GO, and thus facilitating its transport. However, the stronger sorption of higher Mf-SRNOMs onto the GO basal plane through π-π interaction further facilitated the cation bridging between both ends of Mf-SRNOM and GO, and resulted in heteroaggregation of NOM-GO. Moreover, the weight analysis indicated that despite the fact that high Mf-SRNOMs only occupied a small percentage of pristine-SRNOM, they showed a stronger contribution towards pristine-SRNOM's capacity in stabilizing GO, when compared with that of lower MW counterpart. These findings pointed out that complex effects of the heterogeneities of NOM and cations should be highly relevant when the aggregation and transport behaviour of two-dimensional nanomaterials is investigated, and NOM fractions that are highly aromatic and of a higher MW should receive greater attention.