On-Demand Transport Bubbles Adhering to Noncontiguous Patterned Superhydrophobic Surfaces Using a Superhydrophobic Tweezer.

Bubble transportation and related flotation are ubiquitous phenomena in nature and industry. Various surfaces with distinct morphologies and specific wettability properties have been engineered by organisms in nature and by humans to facilitate the targeted movement of bubbles. However, existing methods predominantly rely on continuous surfaces, limiting the ability of bubbles to deviate from their path before reaching their intended destination. Therefore, directional transportation of bubbles using noncontiguous surfaces still remains a significant challenge. Inspired by water spiders' ability to capture bubbles underwater using their hydrophobic surface for survival, we propose a novel transport strategy that utilizes patterned superhydrophobic surfaces (PSHSs) and a superhydrophobic tweezer. This strategy is implemented by switching between the hood mode and puncture mode of the moving three-phase contact lines to load and unload the bubble. To quantitatively evaluate the loss ratio of the bubble during transportation, a simple and exquisite bubble-weighing apparatus is devised. Our findings indicate that circular PSHSs demonstrate superior bubble adhesion and achieve the highest bubble transport ratio of 95.1%. In order to validate the promising application of this novel method, we employ the computer numerical control (CNC) technology to facilitate the autonomous loading and precise transportation of underwater bubbles, as well as the blending and ionization of combustible gas bubbles with air bubbles at different volume ratios.

The histone-like nucleoid-structuring protein encoded by the plasmid pMBL6842 regulates both plasmid stability and host physiology of Pseudoalteromonas rubra SCSIO 6842.

Plasmids orchestrate bacterial adaptation across diverse environments and facilitate lateral gene transfer within bacterial communities. Their presence can perturb host metabolism, creating a competitive advantage for plasmid-free cells. Plasmid stability hinges on efficient replication and partition mechanisms. While plasmids commonly encode histone-like nucleoid-structuring (H-NS) family proteins, the precise influence of plasmid-encoded H-NS proteins on stability remains elusive. In this study, we examined the conjugative plasmid pMBL6842, harboring the hns gene, and observed its positive regulation of parAB transcription, critical for plasmid segregation. Deletion of hns led to rapid plasmid loss, which was remedied by hns complementation. Further investigations unveiled adverse effects of hns overexpression on the bacterial host. Transcriptome analysis revealed hns's role in regulating numerous bacterial genes, impacting both host growth and swimming motility in the presence of the hns gene. Therefore, our study unveils the multifaceted roles of H-NS in both plasmid stability and host physiology, underscoring its biological significance and paving the way for future inquiries into the involvement of H-NS in horizontal gene transfer events.

CircZNF609 inhibited bladder cancer immunotherapy sensitivity via enhancing fatty acid uptake through IGF2BP2/CD36 pathway.

Circular RNAs (circRNAs) are gaining attention for their involvement in immune escape and immunotherapy sensitivity regulation. CircZNF609 is a well-known oncogene in various solid tumours. Our previous research revealed its role in reducing the chemosensitivity of bladder cancer (BCa) to cisplatin. However, the underlying role of circZNF609 in BCa immune escape and immunotherapy sensitivity remains unknown. We conducted BCa cells-CD8 + T cells co-culture assays, cell line-derived xenograft and patient-derived xenograft mouse models with human immune reconstitution to further confirm the role of circZNF609 in BCa immune escape and immunotherapy sensitivity. Overexpression of circZNF609 promoted BCa immune escape in vitro and in vivo. Mechanistically, circZNF609 was bound to IGF2BP2, enhancing its interaction with the 3'-untranslated region of CD36. This increased the stability of the CD36 mRNA, leading to enhanced fatty acid uptake by BCa cells and fatty acid depletion within the tumour microenvironment. Additionally, the nuclear export of circZNF609 was regulated by DDX39B. CircZNF609 promoted immune escape and suppressed BCa immunotherapy sensitivity by regulating the newly identified circZNF609/IGF2BP2/CD36 cascade. Therefore, circZNF609 holds potential as both a biomarker and therapeutic target in BCa immunotherapy.

Analysis of the number of topological defects in active nematic fluids under applied shear flow.

The number of topological defects in the shear flow of active nematic fluids is numerically investigated in this study. The evolution of the flow state of extensile active nematic fluids is explored by increasing the activity of active nematic fluids. Evidently, medium-activity active nematic fluids exhibit a highly ordered vortex lattice fluid state. However, high-activity active nematic fluids exhibit a meso-scale turbulent flow accompanied by topological defects. The number of topological defects (Ndef) increases with increasing shear Reynolds number (Res). Fluid viscosity strongly influences Ndef, while the influence of fluid density is relatively weak. Ndef decreases with increasing activity length scale (lζ) value. A small Res value strongly influences Ndef, whereas a large lζ value only weakly influences Ndef. As the activity increases, Ndef in contractile active nematic fluids becomes larger than that of extensile active nematic fluids.

Spiral time-of-flight magnetic resonance angiography for intracranial vascular imaging: performance compared to conventional Cartesian angiogram.

Background: Computed tomography angiography (CTA) and digital subtraction angiography (DSA) usually raise the risk of potential malignancies with cumulative radiation doses. Current time-of-flight magnetic resonance angiography (TOF-MRA) (dubbed as cTOF), which is based on Cartesian sampling mode, may show limited diagnostic conspicuity at sinuous or branching regions. It is also prone to relatively high false positive diagnoses and undesirable display of distal intracranial vessels. This study aimed to use spiral TOF-MRA (sTOF) as a noninvasive alternative to explore possible improvement, such that the application of magnetic resonance angiography (MRA) can be extended to facilitate clinical examination or cerebrovascular disease diagnosis and follow-up studies. Methods: Initially, 37 patients with symptoms of dizziness or transient ischemic attack were consecutively recruited for suspected intracranial vascular disease examination from Zhongshan Hospital of Xiamen University between July 2020 and April 2021 in this cross-sectional prospective study. After excluding 1 patient with severe scanning artifacts, 1 patient whose scanning scope did not meet the requirement, and 1 patient with confounding tumor lesions, a total of 34 participants were included according to the inclusion and exclusion criteria. Each participant underwent intracranial vascular imaging with both sTOF and cTOF sequences on a 3.0 T MR scanner with a conventional head-neck coil of 16 channels. Contrast CTA or DSA was also performed for 15 patients showing pathology. Qualitative comparisons in terms of image quality and diagnostic efficacy ratings, quantitative comparisons in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel length, and sharpness were evaluated. Pair-wise Wilcoxon test was performed to evaluate the imaging quality derived from cTOF and sTOF acquisitions and weighted Cohen's Kappa was conducted to assess the rating consistency between different physicians. Results: Compared to cTOF, sTOF showed better performance with fewer artifacts. It can effectively alleviate false positives of normal vessels being misdiagnosed as aneurysm or stenosis. Improved conspicuity was observed in cerebral distal regions with more clearly identifiable vasculature at finer scales. Quantitative comparisons in selected regions revealed significant improvement of sTOF in SNR (P<0.01 or P<0.001), CNR (P<0.001), vessel length (P<0.001), and sharpness (P<0.001) as compared to cTOF. Besides, sTOF can depict details of M1 and M2 segments of middle cerebral artery (MCA) at metallic implant region, showing its resistance to magnetic susceptibility. Conclusions: The sTOF shows higher imaging quality and lesion detectability with reduced artifacts and false positives, representing a potentially feasible surrogate in intracranial vascular imaging for future clinic routines.

A self-cleaning micro-fluidic chip biospired by the filtering system of manta rays.

Size-based particle filtration has become indispensable in numerous biomedical and environmental applications. In this study, bioinspired by the filter-feeding mechanism (lobe filtration) of manta rays, we designed a U-shaped biomimetic gill rake filter that combined lobe filtration and Dean flow to filter monodisperse suspensions, bi-disperse suspensions and yeast cells. Compared with other equipment using the inertial focusing technology, our equipment can perform high-throughput (up to 8 mL min-1) and high-efficiency filtration of particles (maximum filtration efficiencies of 96.08% and 97.14% for 10 and 15 µm monodisperse suspensions at the optimum flow rate of 6 mL min-1). The complex velocity field of the micro-fluidic flow within the filter is numerically simulated, and in combination with experiments, a threshold for the flow rate is identified. When the inlet flow rate exceeds the threshold value, the efficiency of particle filtration is increased rapidly. Afterwards, by analysing the filtration mechanism, we develop three novel filtration processes. The equilibrium positions of the particles and yeast cells in the main channel are close to the outer wall at high flow rate, which diminishes the likelihood of particles and yeast cells entering the side channel. This configuration establishes a self-cleaning mechanism, ensuring prolonged and efficient operation of the filter with high-throughput processing. Furthermore, the influence of the filter lobe angle and channel width on the filtration efficiency and outlet flow rate ratio are explored, and an optimisation plan is prepared.

Neuroplasticity of visual brain network induced by hypoxia.

The effects of hypoxia on brain function remain largely unknown. This study aimed to clarify this issue by visual-stimulated functional magnetic resonance imaging design. Twenty-three college students with a 30-d high-altitude exposure were tested before, 1 week and 3 months after returning to sea level. Brain functional magnetic resonance imaging and retinal electroretinogram were acquired. One week after returning to sea level, decreased blood oxygenation level dependent in the right lingual gyrus accompanied with increased blood oxygenation level dependent in the frontal cortex and insular cortex, and decreased amplitude of electroretinogram a-wave in right eye; moreover, the bilateral lingual gyri showed increased functional connectivity within the dorsal visual stream pathway, and the blood oxygenation level dependent signals in the right lingual gyrus showed positive correlation with right retinal electroretinogram a-wave. Three months after returning to sea level, the blood oxygenation level dependent signals recovered to normal level, while intensively increased blood oxygenation level dependent signals in a broad of brain regions and decreased retinal electroretinogram were also existed. In conclusion, hypoxic exposure has long-term effects on visual cortex, and the impaired retinal electroretinogram may contribute to it. The increased functional connectivity of dorsal stream may compensate for the decreased function of retinal photoreceptor cells to maintain normal visual function.

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning.

Objective. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach. FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results. Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error by about 50% on fractional anisotropy and 15% on mean diffusivity, compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance. FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.

The motion of micro-swimmers over a cavity in a micro-channel.

This article combines the lattice Boltzmann method (LBM) with the squirmer model to investigate the motion of micro-swimmers in a channel-cavity system. The study analyses various influential factors, including the value of the squirmer-type factor (ß), the swimming Reynolds number (Rep), the size of the cavity, initial position and particle size on the movement of micro-swimmers within the channel-cavity system. We simultaneously studied three types of squirmer models, Puller (ß > 0), Pusher (ß < 0), and Neutral (ß = 0) swimmers. The findings reveal that the motion of micro-swimmers is determined by the value of ß and Rep, which can be classified into six distinct motion modes. For Puller and Pusher, when the ß value is constant, an increase in Rep will lead to transition in the motion mode. Moreover, the appropriate depth of cavity within the channel-cavity system plays a crucial role in capturing and separating Neutral swimmers. This study, for the first time, explores the effect of complex channel-cavity systems on the behaviour of micro-swimmers and highlights their separation and capture ability. These findings offer novel insights for the design and enhancement of micro-channel structures in achieving efficient separation and capture of micro-swimmers.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.

On particle motion in a confined square domain filled with active fluids.

The motion of passive particles in a confined square domain filled with active fluids has been numerically simulated using a direct-fictitious domain method. The ratio of particle diameter to the side length of the square domain (dp/L) is adopted to classify the degree of confinement (i.e., strong or weak confinement). The translational mean-squared displacement (MSDT) of weakly-confined particles scales well with the reported theoretical and experimental results in a short time and eventually reaches a plateau because of the confined environment. Additionally, the radial probability densities of the particle positions gradually increase with increasing distance from the center of the square domain at relatively high activity levels, displaying an apparent rise near the boundary and maximize near the corner. Conversely, the strongly confined particles migrate toward the center of the square domain or approach the corner with continuous rotation. In addition, the localized minima of the angular velocity of the particles show a periodic behavior, with the vortices periodically becoming more organized. Moreover, with increasing activity, two distinct linearly correlated regimes emerge in the relationship between the particle's rotational velocity and the activity. A comprehensive analysis of the collective dynamics reveals that the cutoff length is Rc ≈ 0.19(2.375dp), pointing to the distance at which the velocities of two particles are uncorrelated. Moreover, the spatial correlation function (Ip) shows a small peak at Rr ≈ 0.12(1.5dp), suggesting a relatively strong correlation between a given particle and another particle located at a distance Rr from it. Interestingly, both Rc and Rr are smaller than those observed in an unbounded flow, which indicates that boundary confinement significantly influences the ability of the particles to form coherent structures.

The zinc finger protein DHHC09 S-acylates the kinase STRK1 to regulate H2O2 homeostasis and promote salt tolerance in rice.

Soil salinity results in oxidative stress and heavy losses to crop production. The S-acylated protein SALT TOLERANCE RECEPTOR-LIKE CYTOPLASMIC KINASE 1 (STRK1) phosphorylates and activates CATALASE C (CatC) to improve rice (Oryza sativa L.) salt tolerance, but the molecular mechanism underlying its S-acylation involved in salt signal transduction awaits elucidation. Here, we show that the DHHC-type zinc finger protein DHHC09 S-acylates STRK1 at Cys5, Cys10, and Cys14 and promotes salt and oxidative stress tolerance by enhancing rice H2O2-scavenging capacity. This modification determines STRK1 targeting to the plasma membrane or lipid nanodomains and is required for its function. DHHC09 promotes salt signaling from STRK1 to CatC via transphosphorylation, and its deficiency impairs salt signal transduction. Our findings demonstrate that DHHC09 S-acylates and anchors STRK1 to the plasma membrane to promote salt signaling from STRK1 to CatC, thereby regulating H2O2 homeostasis and improving salt stress tolerance in rice. Moreover, overexpression of DHHC09 in rice mitigates grain yield loss under salt stress. Together, these results shed light on the mechanism underlying the role of S-acylation in RLK/RLCK-mediated salt signal transduction and provide a strategy for breeding highly salt-tolerant rice.

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. METHODS: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen in training compared to the end-to-end deep learning method. RESULTS: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. CONCLUSION: This study provides an intelligent, reliable and robust MRS quantification. SIGNIFICANCE: QNet is the first LLS quantification aided by deep learning.

Radial magnetic resonance image reconstruction with a deep unrolled projected fast iterative soft-thresholding network.

Radially sampling of magnetic resonance imaging (MRI) is an effective way to accelerate the imaging. How to preserve the image details in reconstruction is always challenging. In this work, a deep unrolled neural network is designed to emulate the iterative sparse image reconstruction process of a projected fast soft-threshold algorithm (pFISTA). The proposed method, an unrolled pFISTA network for Deep Radial MRI (pFISTA-DR), include the preprocessing module to refine coil sensitivity maps and initial reconstructed image, the learnable convolution filters to extract image feature maps, and adaptive threshold to robustly remove image artifacts. Experimental results show that, among the compared methods, pFISTA-DR provides the best reconstruction and achieved the highest PSNR, the highest SSIM and the lowest reconstruction errors.

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis.

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Origin tracing and adulteration identification of bird's nest by high- and low-field NMR combined with pattern recognition.

Edible bird's nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.

Decrease in Cerebral Blood Flow after Reoxygenation Is Associated with Neurological Syndrome Sequelae and Blood Pressure.

Changes in cerebral blood flow (CBF) and regulation of cerebral circulation occur at high altitude (HA). However, the changes in CBF and their associations with neurological syndrome sequelae and blood pressure after subjects return to the lowlands remain unclear. In this study, the subjects were 23 college students who were teaching at an altitude of 4300 m for 30 days. These subjects were studied before reaching the HA (Test 1), one week after returning to the lowlands (Test 2), and three months after returning to the lowlands (Test 3). Symptom scores for de-acclimatization syndrome were evaluated. Changes in CBF were measured using the magnetic resonance imaging arterial spin labeling (ASL) technique. Additionally, the velocity of CBF in the cerebral arteries was measured using a transcranial doppler (TCD). In Test 2 vs. Test 1, the peak systolic velocity and mean velocity in the basilar artery were significantly decreased. CBF exhibited significant decreases in the left putamen/cerebellum crus1/vermis and right thalamus/inferior temporal gyrus, while significant increases were observed in the left postcentral gyrus/precuneus and right middle cingulate gyrus/superior frontal gyrus. In Test 3 vs. Test 1, the basilar artery velocity returned to the baseline level, while CBF continued to decrease. The mean global CBF showed a decreasing trend from Test 1 to Test 3. Furthermore, the mean global CBF had a negative correlation with the systolic pressure, pulse pressure, and mean arterial pressure. The decrease in CBF after reoxygenation may underlie the neurological symptoms in subjects returning to the lowlands. Increased blood pressure could serve as a predictor of a decrease in CBF.

Intravoxel incoherent motion magnetic resonance imaging reconstruction from highly under-sampled diffusion-weighted PROPELLER acquisition data via physics-informed residual feedback unrolled network.

Objective. The acquisition of diffusion-weighted images for intravoxel incoherent motion (IVIM) imaging is time consuming. This work aims to accelerate the scan through a highly under-sampling diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) scheme and to develop a reconstruction method for accurate IVIM parameter mapping from the under-sampled data.Approach.The proposed under-sampling DW-TSE-PROPELLER scheme for IVIM imaging is that a few blades perb-value are acquired and rotated along theb-value dimension to cover high-frequency information. A physics-informed residual feedback unrolled network (PIRFU-Net) is proposed to directly estimate distortion-free and artifact-free IVIM parametric maps (i.e., the perfusion-free diffusion coefficientDand the perfusion fractionf) from highly under-sampled DW-TSE-PROPELLER data. PIRFU-Net used an unrolled convolution network to explore data redundancy in the k-q space to remove under-sampling artifacts. An empirical IVIM physical constraint was incorporated into the network to ensure that the signal evolution curves along theb-value follow a bi-exponential decay. The residual between the realistic and estimated measurements was fed into the network to refine the parametric maps. Meanwhile, the use of synthetic training data eliminated the need for genuine DW-TSE-PROPELLER data.Main results.The experimental results show that the DW-TSE-PROPELLER acquisition was six times faster than full k-space coverage PROPELLER acquisition and within a clinically acceptable time. Compared with the state-of-the-art methods, the distortion-freeDandfmaps estimated by PIRFU-Net were more accurate and had better-preserved tissue boundaries on a simulated human brain and realistic phantom/rat brain/human brain data.Significance.Our proposed method greatly accelerates IVIM imaging. It is capable of directly and simultaneously reconstructing distortion-free, artifact-free, and accurateDandfmaps from six-fold under-sampled DW-TSE-PROPELLER data.

A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging.

OBJECTIVE: Multi-shot interleaved echo planer imaging (Ms-iEPI) can obtain diffusion-weighted images (DWI) with high spatial resolution and low distortion, but suffers from ghost artifacts introduced by phase variations between shots. In this work, we aim at solving the ms-iEPI DWI reconstructions under inter-shot motions and ultra-high b-values. METHODS: An iteratively joint estimation model with paired phase and magnitude priors is proposed to regularize the reconstruction (PAIR). The former prior is low-rankness in the k-space domain. The latter explores similar edges among multi-b-value and multi-direction DWI with weighted total variation in the image domain. The weighted total variation transfers edge information from the high SNR images (b-value = 0) to DWI reconstructions, achieving simultaneously noise suppression and image edges preservation. RESULTS: Results on simulated and in vivo data show that PAIR can remove inter-shot motion artifacts very well (8 shots) and suppress the noise under the ultra-high b-value (4000 s/mm2) significantly. CONCLUSION: The joint estimation model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions and a low signal-to-noise ratio. SIGNIFICANCE: PAIR has potential in advanced clinical DWI applications and microstructure research.

Untargeted Metabolomics Analysis Revealed the Difference of Component and Geographical Indication Markers of Panax notoginseng in Different Production Areas.

Panax notoginseng (P. notoginseng) has excellent medicinal and food dual-use characteristics. However, P. notoginseng with a unique origin label has become the target of fraud because of people confusing or hiding its origin. In this study, an untargeted nuclear magnetic resonance (NMR)-based metabolomics approach was used to discriminate the geographical origins of P. notoginseng from four major producing areas in China. Fifty-two components, including various saccharides, amino acids, saponins, organic acids, and alcohols, were identified and quantified through the NMR spectrum, and the area-specific geographical identification components were further screened. P. notoginseng from Yunnan had strong hypoglycemic and cardiovascular protective effects due to its high acetic acid, dopamine, and serine content, while P. notoginseng from Sichuan was more beneficial for diseases of the nervous system because of its high content of fumarate. P. notoginseng from Guizhou and Tibet had high contents of malic acid, notoginsenoside R1, and amino acids. Our results can help to distinguish the geographical origin of P. notoginseng and are readily available for nutritional recommendations in human consumption.