An in vitro vesicle formation assay reveals cargo clients and factors that mediate vesicular trafficking

The fidelity of protein transport in the secretory pathway relies on the accurate sorting of proteins to their correct destinations. To deepen our understanding of the underlying molecular mechanisms, it is important to develop a robust approach to systematically reveal cargo proteins that depend on specific sorting machinery to be enriched into transport vesicles. Here, we used an in vitro assay that reconstitutes packaging of human cargo proteins into vesicles to quantify cargo capture. Quantitative mass spectrometry (MS) analyses of the isolated vesicles revealed cytosolic proteins that are associated with vesicle membranes in a GTP-dependent manner. We found that two of them, FAM84B (also known as LRAT domain containing 2 or LRATD2) and PRRC1, contain proline-rich domains and regulate anterograde trafficking. Further analyses revealed that PRRC1 is recruited to endoplasmic reticulum (ER) exit sites, interacts with the inner COPII coat, and its absence increases membrane association of COPII. In addition, we uncovered cargo proteins that depend on GTP hydrolysis to be captured into vesicles. Comparing control cells with cells depleted of the cargo receptors, SURF4 or ERGIC53, we revealed specific clients of each of these two export adaptors. Our results indicate that the vesicle formation assay in combination with quantitative MS analysis is a robust and powerful tool to uncover novel factors that mediate vesicular trafficking and to uncover cargo clients of specific cellular factors.</p

Optimization Model and Method of Variable Speed Limit for Urban Expressway

The urban expressway network is the main part of the urban traffic network carrying most of the city’s traffic pressure for its continuity and rapidity, but the control method of the traffic flow was too simple to other control methods in application in addition to the ramp control and the fixed speed control. In this paper, the theory of variable speed limit (VSL) was used to develop an optimal control model based on the improved traffic flow simulation model according to the characteristics of urban expressway traffic flow. The objective of the proposed model is to minimize the delay and maximize the traffic flow. It can adjust the traffic flow on the network in space time so that the whole network is in a state of equilibrium which not only is conducive to the control of the local traffic congestion and avoids the spread of congestion but also improves the traffic safety. The SPSA-based solution algorithm was proposed by taking into account the needs of real-time online applications. It can not only ensure the accuracy of the solution but also meet the requirements of the simulation time. The simulation results show that the variable speed limit can be optimized in moderate demand, and the proposed model and algorithm are effective and feasible in this paper. The conclusions are useful to help the traffic management department to formulate reasonable traffic control strategies

Planar quadrature RF transceiver design using common-mode differential-mode (CMDM) transmission line method for 7T MR imaging.

The use of quadrature RF magnetic fields has been demonstrated to be an efficient method to reduce transmit power and to increase the signal-to-noise (SNR) in magnetic resonance (MR) imaging. The goal of this project was to develop a new method using the common-mode and differential-mode (CMDM) technique for compact, planar, distributed-element quadrature transmit/receive resonators for MR signal excitation and detection and to investigate its performance for MR imaging, particularly, at ultrahigh magnetic fields. A prototype resonator based on CMDM method implemented by using microstrip transmission line was designed and fabricated for 7T imaging. Both the common mode (CM) and the differential mode (DM) of the resonator were tuned and matched at 298MHz independently. Numerical electromagnetic simulation was performed to verify the orthogonal B1 field direction of the two modes of the CMDM resonator. Both workbench tests and MR imaging experiments were carried out to evaluate the performance. The intrinsic decoupling between the two modes of the CMDM resonator was demonstrated by the bench test, showing a better than -36 dB transmission coefficient between the two modes at resonance frequency. The MR images acquired by using each mode and the images combined in quadrature showed that the CM and DM of the proposed resonator provided similar B1 coverage and achieved SNR improvement in the entire region of interest. The simulation and experimental results demonstrate that the proposed CMDM method with distributed-element transmission line technique is a feasible and efficient technique for planar quadrature RF coil design at ultrahigh fields, providing intrinsic decoupling between two quadrature channels and high frequency capability. Due to its simple and compact geometry and easy implementation of decoupling methods, the CMDM quadrature resonator can possibly be a good candidate for design blocks in multichannel RF coil arrays

A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition

In this paper, a new and robust audio watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD) is proposed. Specifically, the watermark data is efficiently inserted in the coefficients of the LWT low frequency subband taking advantage of both SVD and quantization index modulation (QIM). The use of QIM renders our scheme blind in nature. Furthermore, the synchronization code technique is also integrated into our hybrid LWT–SVD audio watermarking method. Experimental and analysis results demonstrate that the proposed LWT–SVD method is not only robust against both general signal processing attacks and desynchronization attacks but also achieve a very good tradeoff between robustness, imperceptibility and payload. Comparisons with the typical and related audio watermarking algorithms also show that our proposed method outperforms most of the selected algorithms

Image homogenization using pre-emphasis method for high field MRI

Radiofrequency (RF) field (B 1) inhomogeneity due to shortened wavelength at high field is a major cause of magnetic resonance imaging (MRI) nonuniformity in high dielectric biological samples (e.g., human body). In this work, we propose a method to improve the B 1 and MRI homogeneity by using pre-emphasized non-uniform B 1 distribution. The intrinsic B 1 distribution that could be generated by a RF volume coil, specifically a microstrip transmission line (MTL) coil used in this work, was pre-emphasized in the sample's periphery region of interest to compensate for the central brightness induced by high frequency interference effect due to shortened wave length. This pre-emphasized non-uniform B 1 can be realized by varying the parameters of microstrip elements, such as the substrate thickness of MTL volume coil. Both numerical simulation and phantom MR imaging studies were carried out to investigate the feasibility and merit of the proposed method in achieving homogeneous MR images. The simulation results demonstrate that by using a pre-emphasized B 1 distribution generated by the MTL volume coil, relatively uniform B 1 distribution and homogeneous MR image (98% homogeneity) within the spherical phantom (15 cm diameter) were achieved with 4.5 mm thickness. The B 1 and MRI intensity distributions of a 16-element MTL volume coil with fixed substrate thickness and five varied saline loads were modeled and experimentally tested. Similar results from both simulation and experiments were obtained, suggesting substantial improvements of B 1 and MRI homogeneities within the phantom containing 125 mM saline. The overall results demonstrate an efficient B 1 shimming approach for improving high field MRI

PCMT1 is a potential target related to tumor progression and immune infiltration in liver cancer

Abstract Background Liver cancer is a prevalent and deadly form of cancer with high incidence and mortality rates. The PCMT1 protein has been linked to cell anti-apoptosis and tumor metastasis, but its significance in liver hepatocellular carcinoma (LIHC) remains largely unexplored. Methods We conducted a pan-cancer analysis to examine the expression differences of PCMT1. Kaplan–Meier curves were employed to assess the prognostic impact of PCMT1 on LIHC patients, and we investigated the association between PCMT1 and clinical features, which we validated using a GEO therapeutic dataset. Gene enrichment analysis helped identify signaling pathways associated with PCMT1 expression. Moreover, we evaluated the relationship between PCMT1 and immune cell infiltration, as well as the differences in gene mutations between high-expression and low-expression groups. In vitro and in vivo experiments were performed to assess the effect of PCMT1 on tumor cell lines and mouse tumor models, and potential pathways were explored through gene sequencing. Result PCMT1 is highly expressed in most tumors and exhibits a significant association with prognosis in LIHC patients. Pathway enrichment analysis revealed that PCMT1 is involved in cell cycle regulation, immunity, and other processes. Further immune analysis demonstrated that high expression of PCMT1 could reduce tumor-killing immune cell infiltration. In vitro experiments indicated that PCMT1 knockdown could inhibit cancer cell proliferation and migration while promoting apoptosis. In vivo experiments showed that PCMT1 knockdown significantly reduced tumor growth rate, enhanced CD8+T cell infiltration, and increased caspase-3 expression in the tumor area. Gene sequencing suggested that PCMT1 may function through the PI3K–AKT pathway. Conclusion Our findings suggest that PCMT1 acts as a promoter of liver cancer progression and may serve as a novel prognostic indicator and therapeutic target for patients with LIHC

Deep Learning in Medical Ultrasound Analysis: A Review

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed. Keywords: Deep learning, Medical ultrasound analysis, Classification, Segmentation, Detectio