Meet the authors: The Ji lab.

We talk to the Ji lab about their paper, "RNA Pol II preferentially regulates ribosomal protein expression by trapping disassociated subunits" (in this issue), lessons from their scientific journey so far, and what inspires them along their scientific paths.

RNA Pol II preferentially regulates ribosomal protein expression by trapping disassociated subunits.

RNA polymerase II (RNA Pol II) has been recognized as a passively regulated multi-subunit holoenzyme. However, the extent to which RNA Pol II subunits might be important beyond the RNA Pol II complex remains unclear. Here, fractions containing disassociated RPB3 (dRPB3) were identified by size exclusion chromatography in various cells. Through a unique strategy, i.e., "specific degradation of disassociated subunits (SDDS)," we demonstrated that dRPB3 functions as a regulatory component of RNA Pol II to enable the preferential control of 3' end processing of ribosomal protein genes directly through its N-terminal domain. Machine learning analysis of large-scale genomic features revealed that the little elongation complex (LEC) helps to specialize the functions of dRPB3. Mechanistically, dRPB3 facilitates CBC-PCF11 axis activity to increase the efficiency of 3' end processing. Furthermore, RPB3 is dynamically regulated during development and diseases. These findings suggest that RNA Pol II gains specific regulatory functions by trapping disassociated subunits in mammalian cells.

Targeted protein degradation reveals RNA Pol II heterogeneity and functional diversity.

RNA polymerase II (RNA Pol II) subunits are thought to be involved in various transcription-associated processes, but it is unclear whether they play different regulatory roles in modulating gene expression. Here, we performed nascent and mature transcript sequencing after the acute degradation of 12 mammalian RNA Pol II subunits and profiled their genomic binding sites and protein interactomes to dissect their molecular functions. We found that RNA Pol II subunits contribute differently to RNA Pol II cellular localization and transcription processes and preferentially regulate RNA processing (such as RNA splicing and 3' end maturation). Genes sensitive to the depletion of different RNA Pol II subunits tend to be involved in diverse biological functions and show different RNA half-lives. Sequences, associated protein factors, and RNA structures are correlated with RNA Pol II subunit-mediated differential gene expression. These findings collectively suggest that the heterogeneity of RNA Pol II and different genes appear to depend on some of the subunits.

Increased transcriptional elongation and RNA stability of GPCR ligand binding genes unveiled via RNA polymerase II degradation.

RNA polymerase II drives mRNA gene expression, yet our understanding of Pol II degradation is limited. Using auxin-inducible degron, we degraded Pol II's RPB1 subunit, resulting in global repression. Surprisingly, certain genes exhibited increased RNA levels post-degradation. These genes are associated with GPCR ligand binding and are characterized by being less paused and comprising polycomb-bound short genes. RPB1 degradation globally increased KDM6B binding, which was insufficient to explain specific gene activation. In contrast, RPB2 degradation repressed nearly all genes, accompanied by decreased H3K9me3 and SUV39H1 occupancy. We observed a specific increase in serine 2 phosphorylated Pol II and RNA stability for RPB1 degradation-upregulated genes. Additionally, α-amanitin or UV treatment resulted in RPB1 degradation and global gene repression, unveiling subsets of upregulated genes. Our findings highlight the activated transcription elongation and increased RNA stability of signaling genes as potential mechanisms for mammalian cells to counter RPB1 degradation during stress.

SIPAS: A comprehensive susceptibility imaging process and analysis studio.

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.

Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure.

The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging.

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.

Anti-Inflammatory Activity of Mulberry Leaf Flavonoids In Vitro and In Vivo.

Mulberry (Morus alba L.) is a flowering tree traditionally used in Chinese herbal medicine. Mulberry leaf flavonoids (MLFs) have been reported to exert important anti-inflammatory and antioxidant properties. The purpose of this study was to select the MLF with the best anti-inflammatory and antioxidative activities from MLFs eluted by different ethanol concentrations (30%, 50%, and 75%) and explore its pharmacological properties. Three types of MLFs inhibited the production of nitric oxide (NO), prostaglandin E2 (PGE2), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and inflammatory cytokines in lipopolysaccharide (LPS)-induced RAW 264.7 cells. All MLFs boosted the antioxidative capacity by decreasing the reactive oxygen species (ROS) production and the scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals and improving the metal ion chelating activity and reducing power. The results revealed that the MLFs eluted by 30% ethanol exhibited the best anti-inflammatory and antioxidative activities. A nontargeted metabolomic analysis was used to analyze 24 types of differential flavonoids between the MLFs. Quercetin, kaempferol, and their derivatives in 30%MLF were more abundant than the other two MLFs. Furthermore, we evaluated the pharmacological activities of 30%MLF in dextran sodium sulfate (DSS)-induced ulcerative colitis (UC) mice. The 30%MLF could alleviate the clinical symptoms, reduce the secretion of inflammatory cytokines, and inhibit the activation of the inflammatory pathway in DSS-induced colitis mice. This study will provide valuable information for the development of MLFs eluted by 30% ethanol as a functional food.

Melatonin inhibiting the survival of human gastric cancer cells under ER stress involving autophagy and Ras-Raf-MAPK signalling.

Melatonin exhibits antitumour activities in the treatment of many human cancers. In the present study, we aimed to improve the therapeutic potential of melatonin in gastric cancer. Our results confirmed that melatonin dose-dependently suppressed the proliferation and necrosis, and increased G0/G1 phase arrest, apoptosis, autophagy and endoplasmic reticulum (ER) stress. The Ras-Raf-MAPK signalling pathway was activated in cells after melatonin treatment. RNA-seq was performed and GSEA analysis further confirmed that many down-regulated genes in melatonin-treated cells were associated with proliferation. However, GSEA analysis also indicated that many pathways related to metastasis were increased after melatonin treatment. Subsequently, combinatorial treatment was conducted to further investigate the therapeutic outcomes of melatonin. A combination of melatonin and thapsigargin increased the apoptotic rate and G0/G1 cell cycle arrest when compared to treatment with melatonin alone. Melatonin in combination with thapsigargin triggered the increased expression of Bip, LC3-II, phospho-Erk1/2 and phospho-p38 MAPK. In addition, STF-083010, an IRE1a inhibitor, further exacerbated the decrease in survival rate induced by combinatorial treatment with melatonin and thapsigargin. Collectively, melatonin was effective in gastric cancer treatment by modifying ER stress.

Comparative Proteomic Analysis of Tolerant and Sensitive Varieties Reveals That Phenylpropanoid Biosynthesis Contributes to Salt Tolerance in Mulberry.

Mulberry, an important woody tree, has strong tolerance to environmental stresses, including salinity, drought, and heavy metal stress. However, the current research on mulberry resistance focuses mainly on the selection of resistant resources and the determination of physiological indicators. In order to clarify the molecular mechanism of salt tolerance in mulberry, the physiological changes and proteomic profiles were comprehensively analyzed in salt-tolerant (Jisang3) and salt-sensitive (Guisangyou12) mulberry varieties. After salt treatment, the malondialdehyde (MDA) content and proline content were significantly increased compared to control, and the MDA and proline content in G12 was significantly lower than in Jisang3 under salt stress. The calcium content was significantly reduced in the salt-sensitive mulberry varieties Guisangyou12 (G12), while sodium content was significantly increased in both mulberry varieties. Although the Jisang3 is salt-tolerant, salt stress caused more reductions of photosynthetic rate in Jisang3 than Guisangyou12. Using tandem mass tags (TMT)-based proteomics, the changes of mulberry proteome levels were analyzed in salt-tolerant and salt-sensitive mulberry varieties under salt stress. Combined with GO and KEGG databases, the differentially expressed proteins were significantly enriched in the GO terms of amino acid transport and metabolism and posttranslational modification, protein turnover up-classified in Guisangyou12 while down-classified in Jisang3. Through the comparison of proteomic level, we identified the phenylpropanoid biosynthesis may play an important role in salt tolerance of mulberry. We clarified the molecular mechanism of mulberry salt tolerance, which is of great significance for the selection of excellent candidate genes for saline-alkali soil management and mulberry stress resistance genetic engineering.

Integrated Transcriptomic and Un-Targeted Metabolomics Analysis Reveals Mulberry Fruit (Morus atropurpurea) in Response to Sclerotiniose Pathogen Ciboria shiraiana Infection.

Mulberry sclerotiniose caused by Ciboria shiraiana is a devastating disease of mulberry (Morus alba L.) fruit in Northwest China. At present, no disease-resistant varieties are used in production, as the molecular mechanisms of this disease are not well understood. In this study, to explore new prevention methods and provide direction for molecular breeding, transcriptomic sequencing and un-targeted metabolomics were performed on healthy (CK), early-stage diseased (HB1), and middle-stage diseased (HB2) mulberry fruits. Functional annotation, gene ontology, a Kyoto encyclopedia of genes and genomes (KEGG) analysis, and a Mapman analysis of the differentially expressed genes revealed differential regulation of genes related to plant hormone signal transduction, transcription factors, and phenylpropanoid biosynthesis. A correspondence between the transcript pattern and metabolite profile was observed in the phenylpropanoid biosynthesis pathway. It should be noted that the log2 ratio of eugenol (isoeugenol) in HB1 and HB2 are 85 times and 23 times higher than CK, respectively. Our study shows that phenylpropanoid biosynthesis may play an essential role in response to sclerotiniose pathogen infection and eugenol(isoeugenol) enrichment in mulberry fruit, which may provide a novel method for mulberry sclerotiniose control.

Background field removal for susceptibility mapping of human brain with large susceptibility variations.

PURPOSE: In quantitative susceptibility mapping (QSM) of human brain, the background field induced by air-tissue interface varies significantly with respect to the rotation angle between the head and the static field, which may result in substantial error in the estimated magnetic susceptibility values. The goal of this study was to develop a strategy to better remove such orientation dependent background field. METHODS: An improved background field removal method is proposed based on the sophisticated harmonic artifact reduction for phase data using a region adaptive kernel (R-SHARP), named iRSHARP. It uses a spatially weighted spherical Gaussian kernel exploiting the amplitude, gradient, and wrap count of the phase map. The method was validated using both numerical simulations and in vivo human brain data at multiple head orientations. Performance was compared with the variable kernel (V-SHARP) and R-SHARP methods. RESULTS: The proposed iRSHARP method showed improved background removal over R-SHARP while cutting the computational time in half. As compared to V-SHARP and R-SHARP, the iRSHARP generated local field and susceptibility maps showed fewer artifacts in regions of large susceptibility variations, and for the in vivo human brain, the susceptibilities of the deep gray matter nuclei were consistent with the in vivo gold-standard "Calculation of Susceptibility through Multiple Orientation Sampling" (COSMOS) values. CONCLUSION: iRSHARP can remove the orientation dependent background field effectively. Using iRSHARP, the paranasal sinus regions can be preserved in the brain mask and the brain integrity was conserved, which may facilitate further data analysis and clinical application.

Fast quantitative susceptibility reconstruction via total field inversion with improved weighted L0 norm approximation.

Quantitative susceptibility mapping (QSM) is a meaningful MRI technique owing to its unique relation to actual physical tissue magnetic properties. The reconstruction of QSM is usually decomposed into three sub-problems, which are solved independently. However, this decomposition does not conform to the causes of the problems, and may cause discontinuity of parameters and error accumulation. In this paper, a fast reconstruction method named fast TFI based on total field inversion was proposed. It can accelerate the total field inversion by using a specially selected preconditioner and advanced solution of the weighted L0 regularization. Due to the employment of an effective model, the proposed method can efficiently reconstruct the QSM of brains with lesions, where other methods may encounter problems. Experimental results from simulation and in vivo data verified that the new method has better reconstruction accuracy, faster convergence ability and excellent robustness, which may promote clinical application of QSM.

A comparative study of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters for the characterization of common solid hepatic tumors.

BACKGROUND: The performance of diffusion-weighted imaging parameters for characterizing hepatic tumors is controversial. PURPOSE: To compare the performances of apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM)-derived parameters, including the pure diffusion coefficient (D), perfusion coefficient (D*), and perfusion fraction (f), in the characterization of common solid hepatic tumors. MATERIAL AND METHODS: Twelve healthy volunteers and 43 patients underwent free-breath diffusion-weighted magnetic resonance imaging (DW-MRI) of the liver using eight b values (10-800 s/mm(2)). Twelve regions of interest (ROIs) of normal liver tissue in healthy volunteers and 49 hepatic lesions (23 hepatocellular carcinomas [HCCs], 16 hemangiomas, and 10 metastases) were measured. Conventional ADC(0,500) and ADCtotal obtained by the mono-exponential model, as well as D, D*, and f were calculated. Student t-tests and receiver operating characteristic (ROC) analysis were also performed. RESULTS: ADC(0,500), ADCtotal, and D were significantly lower in the malignant group ([1.48 ± 0.35] × 10(-3) mm(2)/s; [1.35 ± 0.30] × 10(-3) mm(2)/s; [1.18 ± 0.33] × 10(-3) mm(2)/s) compared to the hemangioma group ([2.74 ± 1.03] × 10(-3) mm(2)/s; [2.61 ± 0.81] × 10(-3) mm(2)/s; [1.97 ± 0.79] × 10(-3) mm(2)/s]. D* did not differ among multiple comparisons. For the area under the ROC curve (AUC-ROC), the maximum value was attained with ADCtotal (0.983) and was closely followed by ADC(0,500) (0.967), with lower values obtained for D (0.837), f (0.649), and D* (0.599). Statistically significant differences were found between the AUC-ROC of both ADCs (ADCtotal and ADC(0,500)) and D. There was no statistically significant difference between the AUC-ROC of ADCtotal and ADC(0,500). CONCLUSION: ADCs showed superior diagnostic performance compared to IVIM-derived parameters in detecting differences between the malignant group and hemangioma group.

Quantitative susceptibility mapping based basal ganglia segmentation via AGSeg: leveraging active gradient guiding mechanism in deep learning.

Background: With better visual contrast and the ability for magnetic susceptibility quantification analysis, quantitative susceptibility mapping (QSM) has emerged as an important magnetic resonance imaging (MRI) method for basal ganglia studies. Precise segmentation of basal ganglia is a prerequisite for quantification analysis of tissue magnetic susceptibility, which is crucial for subsequent disease diagnosis and surgical planning. The conventional method of localizing and segmenting basal ganglia heavily relies on layer-by-layer manual annotation by experts, resulting in a tedious amount of workload. Although several morphology registration and deep learning based methods have been developed to automate segmentation, the voxels around the nuclei boundary remain a challenge to distinguish due to insufficient tissue contrast. This paper proposes AGSeg, an active gradient guidance-based susceptibility and magnitude information complete (MIC) network for real-time and accurate basal ganglia segmentation. Methods: Various datasets, including clinical scans and data from healthy volunteers, were collected across multiple centers with different magnetic field strengths (3T/5T/7T), with a total of 210 three-dimensional (3D) susceptibility measurements. Manual segmentations following fixed rules for anatomical borders annotated by experts were used as ground truth labels. The proposed network took QSM maps and Magnitude images as two individual inputs, of which the features are selectively enhanced in the proposed magnitude information complete (MIC) module. AGSeg utilized a dual-branch architecture, with Seg-branch aiming to generate a proper segmentation map and Grad-branch to reconstruct the gradient map of regions of interest (ROIs). With the support of the newly designed active gradient module (AGM) and gradient guiding module (GGM), the Grad-branch provided attention guidance for the Seg-branch, facilitating it to focus on the boundary of target nuclei. Results: Ablation studies were conducted to assess the functionality of the proposed modules. Significant performance decrement was observed after ablating relative modules. AGSeg was evaluated against several existing methods on both healthy and clinical data, achieving an average Dice similarity coefficient (DSC) =0.874 and average 95% Hausdorff distance (HD95) =2.009. Comparison experiments indicated that our model had superior performance on basal ganglia segmentation and better generalization ability over existing methods. The AGSeg outperformed all implemented comparison deep learning algorithms with average DSC enhancement ranging from 0.036 to 0.074. Conclusions: The current work integrates a deep learning-based method into automated basal ganglia segmentation. The high processing speed and segmentation robustness of AGSeg contribute to the feasibility of future surgery planning and intraoperative navigation. Experiments show that leveraging active gradient guidance mechanisms and magnitude information completion can facilitate the segmentation process. Moreover, this approach also offers a portable solution for other multi-modality medical image segmentation tasks.

A spatially adaptive regularization based three-dimensional reconstruction network for quantitative susceptibility mapping.

Objective.Quantitative susceptibility mapping (QSM) is a new imaging technique for non-invasive characterization of the composition and microstructure ofin vivotissues, and it can be reconstructed from local field measurements by solving an ill-posed inverse problem. Even for deep learning networks, it is not an easy task to establish an accurate quantitative mapping between two physical quantities of different units, i.e. field shift in Hz and susceptibility value in ppm for QSM.Approach. In this paper, we propose a spatially adaptive regularization based three-dimensional reconstruction network SAQSM. A spatially adaptive module is specially designed and a set of them at different resolutions are inserted into the network decoder, playing a role of cross-modality based regularization constraint. Therefore, the exact information of both field and magnitude data is exploited to adjust the scale and shift of feature maps, and thus any information loss or deviation occurred in previous layers could be effectively corrected. The network encoding has a dynamic perceptual initialization, which enables the network to overcome receptive field intervals and also strengthens its ability to detect features of various sizes.Main results. Experimental results on the brain data of healthy volunteers, clinical hemorrhage and simulated phantom with calcification demonstrate that SAQSM can achieve more accurate reconstruction with less susceptibility artifacts, while perform well on the stability and generalization even for severe lesion areas.Significance. This proposed framework may provide a valuable paradigm to quantitative mapping or multimodal reconstruction.

Disulfide bonds as a molecular switch of enzyme-activatable anticancer drug precise release for fluorescence imaging and enhancing tumor therapy.

Enzyme-activatable drug delivery systems have been developed for cancer diagnosis and therapy. However, targeted intracellular drug delivery is a challenge for precisely tumor imaging and therapy due to the increased stability of copolymer nanoparticles (NPs) is accompanied by a notable decrease in enzyme degradation. Herein, disulfide bond was designed as an enzyme-activatable molecular switch of SS-P(G2)2/DOX NPs. The copolymer NPs consists of polyvinylpyrrolidone (PVP) with disulfide bonds in the center and enzyme-degradable peptide dendrites (Phe-Lys) to form dendritic-linear-dendritic triblock copolymers (TBCs). The amphiphilic TBCs could be split into two identical amphiphilic diblock copolymers (DBCs) by glutathione (GSH) in cancer cells specifically while maintaining the same hydrophilic-lipophilic equilibrium. This structural transformation significantly reduced the stability of copolymer NPs and enhanced sensitivity of DOX release by cathepsin B-activated. Subsequently, the released DOX acted as an indicator of fluorescence imaging and chemotherapy drug for cancer cells. The polymeric NPs achieved excellent drug-loaded stability and prolonged blood circulation in vivo, and realized fluorescence imaging and specific cancer cell killing capabilities by responding to the overexpression of GSH and cathepsin B in tumor cells. Furthermore, the copolymer NPs demonstrated excellent blood compatibility and biosafety. Therefore, a novel strategy based on one tumor marker acting as the switch for another tumor microenvironment responsive drug delivery system could be designed for tumor intracellular imaging and chemotherapy.

Metabolomic and transcriptomic analysis of flavonoids biosynthesis mechanisms in mulberry fruit (Hongguo 2) under exogenous hormone treatments.

The mulberry fruit is prized for its superior nutrition value and abundant color due to its high flavone content. To enhance comprehension of flavone biogenesis induced by external hormones, we sprayed exogenous ethylene (ETH), indoleacetic acid (IAA) and spermine (SPM) on mulberry fruit (Hongguo 2) during its color-changed period. The levels of anthocyanin, titratable acid, soluble sugar and endogenous hormones were determined after hormone treatment, integrated transcriptome and metabolome analysis were performed for mechanism exploration. Our results indicated that exogenous ETH, SPM, and IAA play important roles in mulberry ripening, including acid reduction, sugar increase and flavonoid synthesis.

Mulberry leaves supplementation alters lipid metabolism and promotes fatty acid ß oxidation in growing mutton sheep.

Mulberry leaves (MLs) are an unconventional feed with fiber and various active ingredients, and are acknowledged as likely to regulate lipid metabolism, while the molecular mechanism remains undefined. Therefore, our objective was to define the role of MLs on the overall lipid metabolism. We conducted a feeding experiment of three groups on growing mutton sheep fed with dried mulberry leaves (DMLs), with fermented mulberry leaves (FMLs), or without MLs (as control). Analyses of transcriptome and widely target lipids demonstrated the addition of MLs triggered big perturbations in genes and metabolites related to glycerolipid, phospholipid, ether lipid, and sphingolipid metabolism. Additionally, the variations of the above lipids in the treatment of MLs possibly facilitate immunity enhancement of growing mutton sheep via the activation of complement and coagulation cascades. Furthermore, treatments with MLs could expedite proceedings of lipid degradation and fatty acid ß oxidation in mitochondria, thereby to achieve the effect of lipid reduction. Besides, added DMLs also fuel fatty acid ß-oxidation in peroxisomes and own much stronger lipolysis than added FMLs, possibly attributed to high fiber content in DMLs. These findings establish the novel lipid-lowering role and immune protection of MLs, which lays the foundation for the medicinal application of MLs.

Mulberry leaves (MLs) are rich in a wide variety of active ingredients and are also a kind of traditional Chinese medicine with the same origin as medicine and food. Previous studies have found that MLs may regulate lipid metabolism. But the exact mechanism remains unclear. Our study reveals that ML supplement not only alters lipid metabolism including glycerol phospholipid, ether lipid as well as sphingolipid metabolism, which may help to improve immunity but also promote fatty acid degradation as well as ß oxidation to achieve the effect of fat reduction.

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.