Knowledge domain and emerging trends in anxiety and depression after myocardial infarction research during 2002-2022: Bibliometric and visualized analysis.

Purpose: This study aimed to analyze developmental trends in anxiety and depression after myocardial infarction (ADMI) research in the past 20 years through bibliometrics analysis and predict future research directions. Methods: ADMI-related publications were retrieved from the Web of Science Core Collection. Bibliometric, VOSviewer, CiteSpace, and Bibliometrix software packages were used for bibliometric analysis and visualization. Results: Overall, 3220 ADMI-related publications were identified. The United States, China, and the Netherlands were the countries with the most publications. Carney RM, De Jonge P, and Blumenthal JA were the most influential researchers. In 2004, Van Melle JP, from the University of Groningen, published in Psychosomatic Medicine the most cited article. "Cardiac rehabilitation" was the primary focus area. "Cardiac rehabilitation," "management," "acute coronary syndrome," and "outcome" were the top four keywords in emerging research hotspots. Notably, the effect of traditional Chinese medicine on ADMI is an area of potential research value. Conclusion: Numerous studies have underscored the significance of cardiac rehabilitation. Present research focuses on managing anxiety and depression post-acute coronary syndrome and enhancing clinical outcomes through cardiac rehabilitation technology. Additionally, the therapeutic potential of traditional Chinese medicine for ADMI is expected to attract increased attention from researchers in the future.

Structural changes in corticospinal tract profiling via multishell diffusion models and their relation to overall survival in glioblastoma.

PURPOSE: Advanced MR fiber tracking imaging reflects fiber bundle invasion by glioblastoma, particularly of the corticospinal tract (CST), which is more susceptible as the largest downstream fiber tracts. We aimed to investigate whether CST features can predict the overall survival of glioblastoma. METHODS: In this prospective secondary analysis, 40 participants (mean age, 58 years; 16 male) pathologically diagnosed with glioblastoma were enrolled. Diffusion spectrum MRI was used for CST reconstruction. Fifty morphological and diffusion indicators (DTI, DKI, NODDI, MAP and Q-space) were used to characterize the CST. Optimal parameters capturing fiber bundle damage were obtained through various grouping methods. Eventually, the correlation with overall survival was determined by the hazard ratios (HRs) from various Cox proportional hazard model combinations. RESULTS: Only intracellular volume fraction (ICVF) and non-Gaussianity (NG) values on the affected tumor level were significant in all four groups or stratified comparisons (all P < .05). During the median follow-up 698 days, only the ICVF on the affected tumor level was independently associated with overall survival, even after adjusting for all classic prognostic factors (HR [95 % CI]: 0.611 [0.403, 0.927], P = .021). Moreover, stratification by the ICVF on the affected tumor level successfully predicted risk (P < .01) and improved the C-index of the multivariate model (from 0.695 to 0.736). CONCLUSIONS: This study demonstrates a relationship between NODDI-derived CST features, ICVF on the affected tumor level, and overall survival in glioblastoma. Independent of classical prognostic factors for glioblastoma, a lower ICVF on the affected tumor level might predict a lower overall survival.

Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models.

BACKGROUND: Diffusion-weighted imaging (DWI) has been developed to stage liver fibrosis. However, its diagnostic performance is inconsistent among studies. Therefore, it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort. AIM: To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances. METHODS: This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants. All participants underwent multi-b value DWI. The main DWI-derived parameters included Mono-apparent diffusion coefficient (ADC) from mono-exponential DWI, intravoxel incoherent motion model-derived true diffusion coefficient (IVIM-D), diffusion kurtosis imaging-derived apparent diffusivity (DKI-MD), stretched exponential model-derived distributed diffusion coefficient (SEM-DDC), fractional order calculus (FROC) model-derived diffusion coefficient (FROC-D) and FROC model-derived microstructural quantity (FROC-µ), and continuous-time random-walk (CTRW) model-derived anomalous diffusion coefficient (CTRW-D) and CTRW model-derived temporal diffusion heterogeneity index (CTRW-α). The correlations between DWI-derived parameters and fibrosis stages and the parameters' diagnostic efficacy in detecting significant fibrosis (SF) were assessed and compared. RESULTS: CTRW-D (r = -0.356), CTRW-α (r = -0.297), DKI-MD (r = -0.297), FROC-D (r = -0.350), FROC-µ (r = -0.321), IVIM-D (r = -0.251), Mono-ADC (r = -0.362), and SEM-DDC (r = -0.263) were significantly correlated with fibrosis stages. The areas under the ROC curves (AUCs) of the combined index of the six models for distinguishing SF (0.697-0.747) were higher than each of the parameters alone (0.524-0.719). The DWI models' ability to detect SF was similar. The combined index of CTRW model parameters had the highest AUC (0.747). CONCLUSION: The DWI models were similarly valuable in distinguishing SF in patients with liver disease. The combined index of CTRW parameters had the highest AUC.

Multi-channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction.

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Crown-ether threaded covalent organic polyrotaxane framework (COPF) towards synergistic crown/Zn2+/photothermal/photodynamic antibacterial and infected wound healing therapy.

Controllable preparation of materials with new structure has always been the top priority of polymer materials science research. Here, the supramolecular binding strategy is adopted to develop covalent organic frameworks (COFs) with novel structures and functions. Based on this, a two-dimensional crown-ether ring threaded covalent organic framework (COF), denoted as Crown-COPF with intrinsic photothermal (PTT) and photodynamic (PDT) therapeutic capacity, was facilely developed using crown-ether threaded rotaxane and porphyrin as building blocks. Crown-COPF with discrete mechanically interlocked blocks in the open pore could be used as a molecular machine, in which crown-ether served as the wheel sliding along the axle under the laser stimulation. As a result, Crown-COPF combining with the bactericidal power of crown ether displayed a significant photothermal and photodynamic antibacterial activity towards both the Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus), far exceeding the traditional Crown-free COF. Noteworthily, the bactericidal performance could be further enhanced via impregnation of Zn2+ ions (Crown-COPF-Zn) flexible coordinated with the multiple coordination sites (crown-ether, bipyridine, and porphyrin), which not only endow the positive charge with the skeleton, enhancing its ability to bind to the bacterial membrane, but also introduce the bactericidal ability of zinc ions. Notably, in vivo experiments on mice with back infections indicates Crown-COPF-Zn with self-adaptive multinuclear zinc center, could effectively promote the repairing of wounds. This study paves a new avenue for the effectively preparation of porous polymers with brand new structure, which provides opportunities for COF and mechanically interlocked polymers (MIPs) research and applications.

Spectrum learning for super-resolution tomographic reconstruction.

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information.Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network's ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality.Significance. The proposed SPEAR network's ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

Coalescence-Induced Jumping of Nanodroplets in a Perpendicular Electric Field: A Molecular Dynamics Study.

Coalescence-induced jumping has promised a substantial reduction in the droplet detachment size and consequently shows great potential for heat-transfer enhancement in dropwise condensation. In this work, using molecular dynamics simulations, the evolution dynamics of the liquid bridge and the jumping velocity during coalescence-induced nanodroplet jumping under a perpendicular electric field are studied for the first time to further promote jumping. It is found that using a constant electric field, the jumping performance at the small intensity is weakened owing to the continuously decreased interfacial tension. There is a critical intensity above which the electric field can considerably enhance the stretching effect with a stronger liquid-bridge impact and, hence, improve the jumping performance. For canceling the inhibition effect of the interfacial tension under the condition of the weak electric field, a square-pulsed electric field with a paused electrical effect at the expansion stage of the liquid bridge is proposed and presents an efficient nanodroplet jumping even using the weak electric field.

Genome-wide analysis of the switchgrass YABBY family and functional characterization of PvYABBY14 in response to ABA and GA stress in Arabidopsis.

BACKGROUND: The small YABBY plant-specific transcription factor has a prominent role in regulating plant growth progress and responding to abiotic stress. RESULTS: Here, a total of 16 PvYABBYs from switchgrass (Panicum virgatum L.) were identified and classified into four distinct subgroups. Proteins within the same subgroup exhibited similar conserved motifs and gene structures. Synteny analyses indicated that segmental duplication contributed to the expansion of the YABBY gene family in switchgrass and that complex duplication events occurred in rice, maize, soybean, and sorghum. Promoter regions of PvYABBY genes contained numerous cis-elements related to stress responsiveness and plant hormones. Expression profile analysis indicated higher expression levels of many PvYABBY genes during inflorescence development and seed maturation, with lower expression levels during root growth. Real-time quantitative PCR analysis demonstrated the sensitivity of multiple YABBY genes to PEG, NaCl, ABA, and GA treatments. The overexpression of PvYABBY14 in Arabidopsis resulted in increased root length after treatment with GA and ABA compared to wild-type plants. CONCLUSIONS: Taken together, our study provides the first genome-wide overview of the YABBY transcription factor family, laying the groundwork for understanding the molecular basis and regulatory mechanisms of PvYABBY14 in response to ABA and GA responses in switchgrass.

Iterative Residual Optimization Network for Limited-Angle Tomographic Reconstruction.

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems, leading to edge divergence with degraded image quality. Recently, deep learning has been introduced into image reconstruction and achieved great results. However, existing deep reconstruction methods have not fully explored data consistency, resulting in poor performance. In addition, deep reconstruction methods are still mathematically inexplicable and unstable. In this work, we propose an iterative residual optimization network (IRON) for limited-angle tomographic reconstruction. First, a new optimization objective function is established to overcome false negative and positive artifacts induced by limited-angle measurements. We integrate neural network priors as a regularizer to explore deep features within residual data. Furthermore, the block-coordinate descent is employed to achieve a novel iterative framework. Second, a convolution assisted transformer is carefully elaborated to capture both local and long-range pixel interactions simultaneously. Regarding the visual transformer, the multi-head attention is further redesigned to reduce computational costs and protect reconstructed image features. Third, based on the relative error convergence property of the convolution assisted transformer, a mathematical convergence analysis is also provided for our IRON. Both numerically simulated and clinically collected real cardiac datasets are employed to validate the effectiveness and advantages of the proposed IRON. The results show that IRON outperforms other state-of-the-art methods.

Intramuscular therapeutic doses of enrofloxacin affect microbial community structure but not the relative abundance of fluoroquinolones resistance genes in swine manure.

Livestock manure is a major source of veterinary antibiotics and antibiotic resistance genes (ARGs). Elucidation of the residual characteristics of ARGs in livestock manure following the administration of veterinary antibiotics is critical to assess their ecotoxicological effects and environmental contamination risks. Here, we investigated the effects of enrofloxacin (ENR), a fluoroquinolone antibiotic commonly used as a therapeutic drug in animal husbandry, on the characteristics of ARGs, mobile genetic elements, and microbial community structure in swine manure following its intramuscular administration for 3 days and a withdrawal period of 10 days. The results revealed the highest concentrations of ENR and ciprofloxacin (CIP) in swine manure at the end of the administration period, ENR concentrations in swine manure in groups L and H were 88.67 ± 45.46 and 219.75 ± 88.05 mg/kg DM, respectively. Approximately 15 fluoroquinolone resistance genes (FRGs) and 48 fluoroquinolone-related multidrug resistance genes (F-MRGs) were detected in swine manure; the relative abundance of the F-MRGs was considerably higher than that of the FRGs. On day 3, the relative abundance of qacA was significantly higher in group H than in group CK, and no significant differences in the relative abundance of other FRGs, F-MRGs, or MGEs were observed between the three groups on day 3 and day 13. The microbial community structure in swine manure was significantly altered on day 3, and the altered community structure was restored on day 13. The FRGs and F-MRGs with the highest relative abundance were qacA and adeF, respectively, and Clostridium and Lactobacillus were the dominant bacterial genera carrying these genes in swine manure. In summary, a single treatment of intramuscular ENR transiently increased antibiotic concentrations and altered the microbial community structure in swine manure; however, this treatment did not significantly affect the abundance of FRGs and F-MRGs.

Visual Exploratory Analysis for Designing Large-Scale Network-on-Chip Architectures: A Domain Expert-Led Design Study.

Visualization design studies bring together visualization researchers and domain experts to address yet unsolved data analysis challenges stemming from the needs of the domain experts. Typically, the visualization researchers lead the design study process and implementation of any visualization solutions. This setup leverages the visualization researchers' knowledge of methodology, design, and programming, but the availability to synchronize with the domain experts can hamper the design process. We consider an alternative setup where the domain experts take the lead in the design study, supported by the visualization experts. In this study, the domain experts are computer architecture experts who simulate and analyze novel computer chip designs. These chips rely on a Network-on-Chip (NOC) to connect components. The experts want to understand how the chip designs perform and what in the design led to their performance. To aid this analysis, we develop Vis4Mesh, a visualization system that provides spatial, temporal, and architectural context to simulated NOC behavior. Integration with an existing computer architecture visualization tool enables architects to perform deep-dives into specific architecture component behavior. We validate Vis4Mesh through a case study and a user study with computer architecture researchers. We reflect on our design and process, discussing advantages, disadvantages, and guidance for engaging in a domain expert-led design studies.

Staging liver fibrosis by a continuous-time random-walk diffusion model.

PURPOSE: Noninvasive assessment of liver fibrosis holds significant clinical importance. We aimed to evaluate the clinical potential of using a continuous-time random-walk diffusion model (CTRW) for staging liver fibrosis. METHODS: This prospective study included 52 patients suspected of liver disease and scheduled for liver biopsy. All patients underwent multi-b value diffusion-weighted imaging (DWI) using a 1.5 T MR scanner to derive the anomalous diffusion coefficient (D) and temporal (α) and spatial (ß) diffusion heterogeneity indexes sourced from the CTRW. The mono-exponential DWI-derived apparent diffusion coefficient (ADC), transient elastography-derived liver stiffness measurement (LSM), aspartate aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 (FIB-4) index were calculated. We assessed and compared the correlations of these parameters with fibrosis stages and their efficacy in staging liver fibrosis. RESULTS: Significant correlations with fibrosis stages were found for APRI (r = 0.336), FIB-4 (r = 0.351), LSM (r = 0.523), D (r = -0.458), and ADC (r = -0.473). Significant differences were observed between APRI, LSM, D, and ADC of different fibrosis stages. The diagnostic performance of an index that combined D, α, ß, ADC, and LSM was superior to that of ADC or LSM alone for fibrosis stage F ≥ 2 and better than the index that combined D, α, ß for fibrosis stage F ≥ 4. CONCLUSIONS: Accurate liver fibrosis staging was achieved with a model that combined CTRW-derived parameters (D, α, and ß), ADC, and LSM. The model could serve as a reliable tool for noninvasive fibrosis evaluation.

Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis.

Objective: To explain adult-type diffuse gliomas heterogeneity through diffusion kurtosis imaging-based habitat characteristics and develop and validate a comprehensive model for predicting isocitrate dehydrogenase (IDH) status. Materials and methods: In this prospective secondary analysis, 103 participants (mean age, 52 years; range, 21-77; 54 [52%] male) pathologically diagnosed with adult-type diffuse gliomas were enrolled between June 2018 and February 2022. The Otsu method was used to generate habitat maps with mean diffusivity (MD) and mean kurtosis (MK) for a total of 4 subhabitats containing 16 habitat features. Habitat heatmaps were created based on the Pearson correlation coefficient. The Habitat imAging aNd clinicraD INtegrated prEdiction SyStem (HANDINESS) was created by combining clinical features, conventional MRI morphological features, and habitat image features. ROC, calibration curve, and decision curve analyses were used to select the optimal model after 32 pipelines for model training and validation. Results: In the restricted diffusion and high-density subhabitat, MK was highly correlated with MD (R2 = 0.999), volume (0.608) and percentage of volume (0.663), and this region had the highest MK value (P<.001). The unrestricted diffusion and low-density subhabitat had the highest MD value (P<.001). When MK was less than the Otsu threshold, there was still a difference between restricted diffusion and low-density and unrestricted diffusion and low-density subhabitats (P<.01). The HANDINESS enabled more accurate prediction of the IDH status in the training (AUC=0.951 [0.902-0.987]) and internal validation cohorts (0.938 [0.881-0.949]). AUC values for single-modality models and independent factors ranged from 0.593 to 0.916. Calibration and decision curve analyses showed that the HANDINESS demonstrated a high level of clinical applicability and predictive consistency. Conclusion: Diffusion kurtosis imaging-based habitat analysis provides additional important information on microscopic tumor spatial heterogeneity. The HANDINESS has higher diagnostic performance and robustness than single-modality models.

Working Regime Criteria for Microscale Electrohydrodynamic Conduction Pumps.

We investigated the microscale electrohydrodynamic (EHD) conduction pumps in a wide range of working regimes, from the saturation regime to the ohmic regime. We showed that the existing macro- and microscale theoretical models could not accurately predict the electric force of microscale EHD conduction pumps, especially for the cases of a strong diffusion effect. We clarified that the failure is caused by a rough estimate of the heterocharge layer thickness. We revised the expression of heterocharge layer thickness by considering the diffusion effect and developed a new theoretical model for the microscale EHD conduction pumps based on the revised expression of heterocharge layer thickness. The results showed that our model can accurately predict the dimensionless electric force of the microscale EHD conduction pumps even for the cases of a strong diffusion effect. Furthermore, we developed a working regime map of microscale EHD conduction pumps and found that the microscale EHD conduction pumps more easily fall into the saturation regime compared with the macroscale EHD conduction pumps due to the enhanced diffusion effect; in other words, the microscale EHD conduction pumps have a wider saturation regime. We showed that the conduction number C0 could not distinguish the working regime of the microscale EHD conduction pumps because it does not take the diffusion effect into account. By employing the revised expression of heterocharge layer thickness, we proposed a new dimensionless number, C0D to distinguish the working regimes of microscale EHD conduction pumps.

Vacuum ultraviolet irradiation for reduction of the toxicity of wastewater towards mammalian cells: Removal mechanism, changes in organic compounds, and toxicity alternatives.

Vacuum ultraviolet (VUV, 185 + 254 nm) irradiation performs well for oxidation of model pollutants. However, oxidation of pollutants does not necessarily lead to a reduction in toxicity. Currently, a comprehensive understanding of the effect of VUV irradiation on the toxicity of real wastewater is still lacking. In this study, the influence of VUV irradiation on the toxicity of secondary effluents to Chinese hamster ovary (CHO) cells was investigated. The induction units of endogenous reactive oxygen species (ROS) and 8-hydroxyguanosine (8-OHdG) in cells continuously decreased with prolonged irradiation time. After 36 min of irradiation, the cytotoxicity and the genotoxicity of the secondary effluents were reduced by 57%-63% and 56%-61%, respectively. The UV (254 nm), •OH, and other substances generated during the VUV irradiation directly drive toxicity changes of wastewater. The contribution of •OH generated during VUV irradiation to the reductions in cytotoxicity and genotoxicity of the secondary effluents reached 72%-78% and 77%-84%, respectively. Hydroxyl radicals generated during VUV irradiation played an important role in the detoxification. The relative signal intensity of dissolved organic carbon (DOC) > 500 Da was partially removed, whereas that of DOC < 500 Da was small changed. Since the content of DOC > 500 Da in the samples was much lower than that of DOC < 500 Da, the removal of total DOC was only 15.8%-20.0% after 36 min of irradiation. The UV254 values and the fluorescence intensity values for different molecular weights (MWs) were all reduced effectively by VUV irradiation. Electron-rich organic compounds of all MWs were all sensitive to VUV irradiation. There were mono-linear relationships between changes in chemical indexes and changes in cytotoxicity or genotoxicity. The total fluorescence intensity (Ex: 220-420 nm, Em: 280-560 nm) was identified as the best indicator of the reduction in toxicity.

Effect of Mowing on Wheat Growth at Seeding Stage.

Winter wheat is used as forage at the tillering stage in many countries; however, the regrowth pattern of wheat after mowing remains unclear. In this study, the growth patterns of wheat were revealed through cytological and physiological assessments as well as transcriptome sequencing. The results of agronomic traits and paraffin sections showed that the shoot growth rate increased, but root growth was inhibited after mowing. The submicroscopic structure revealed a decrease in heterochromatin in the tillering node cell and a change in mitochondrial shape in the tillering node and secondary root. Analysis of the transcriptome showed the number of differentially expressed genes (DEGs) involved in biological processes, cellular components, and molecular functions; 2492 upregulated DEGs and 1534 downregulated DEGs were identified. The results of the experimental study showed that mowing induced expression of DEGs in the phenylpropanoid biosynthesis pathway and increased the activity of PAL and 4CL. The upregulated DEGs in the starch and sucrose metabolism pathways and related enzyme activity alterations indicated that the sugar degradation rate increased. The DEGs in the nitrogen metabolism pathway biosynthesis of the amino acids, phenylpropanoid biosynthesis metabolism, and in the TCA pathway also changed after mowing. Hormone content and related gene expression was also altered in the tillering and secondary roots after mowing. When jasmonic acid and ethylene were used to treat the wheat after mowing, the regeneration rate increased, whereas abscisic acid inhibited regrowth. This study revealed the wheat growth patterns after mowing, which could lead to a better understanding of the development of dual-purpose wheat.

Antifouling Properties of Amine-Oxide-Containing Zwitterionic Polymers.

Biofouling due to nonspecific proteins or cells on the material surfaces is a major challenge in a range of applications such as biosensors, medical devices, and implants. Even though poly(ethylene glycol) (PEG) has become the most widely used stealth material in medical and pharmaceutical products, the number of reported cases of PEG-triggered rare allergic responses continues to increase in the past decades. Herein, a new type of antifouling material poly(amine oxide) (PAO) has been evaluated as an alternative to overcome nonspecific foulant adsorption and impart comparable biocompatibility. Alkyl-substituted PAO containing diethyl, dibutyl, and dihexyl substituents are prepared, and their solution properties are studied. Photoreactive copolymers containing benzophenone as the photo-cross-linker are prepared by reversible addition-fragmentation chain-transfer polymerization and fully characterized by gel permeation chromatography and dynamic light scattering. Then, these water-soluble polymers are anchored onto a silicon wafer with the aid of UV irradiation. By evaluating the fouling resistance properties of these modified surfaces against various types of foulants, protein adsorption and bacterial attachment assays show that the cross-linked PAO-modified surface can efficiently inhibit biofouling. Furthermore, human blood cell adhesion experiments demonstrate that our PAO polymer could be used as a novel surface modifier for biomedical devices.

Application of multi-echo Dixon and MRS in quantifying hepatic fat content and staging liver fibrosis.

This study associated the liver proton density fat fraction (PDFF), measured by multi-echo Dixon (ME-Dixon) and breath-hold single-voxel high-speed T2-corrected multi-echo 1H magnetic resonance spectroscopy (HISTO) at 1.5 T, with serum biomarkers and liver fibrosis stages. This prospective study enrolled 75 patients suspected of liver fibrosis and scheduled for liver biopsy and 23 healthy participants with normal liver function. The participant underwent ME-Dixon and HISTO scanning. The agreement of PDFF measured by ME-Dixon (PDFF-D) and HISTO (PDFF-H) were compared. Correlations between PDFF and serum fat biomarkers (total cholesterol, triglyceride, and high- and low-density lipoproteins) and the liver fibrosis stages were assessed. PDFF were compared among the liver fibrosis stages (F0-F4) based on clinical liver biopsies. The Bland-Altman plot showed agreement between PDFF-D and PDFF-H(LoA, - 4.44 to 6.75), which have high consistency (ICC 0.752, P < 0.001). The correlations with the blood serum markers were mild to moderate (PDFF-H: r = 0.261-0.410, P < 0.01; PDFF-D: r = 0.265-0.367, P < 0.01). PDFF-D, PDFF-H, and steatosis were distributed similarly among the liver fibrosis stages. PDFF-H showed a slight negative correlation with the liver fibrosis stages (r = - 0.220, P = 0.04). Both ME-Dixon and HISTO sequences measured liver fat content noninvasively. Liver fat content was not directly associated with liver fibrosis stages.