An ATP13A1-assisted topogenesis pathway for folding multi-spanning membrane proteins.

Many multi-spanning membrane proteins contain poorly hydrophobic transmembrane domains (pTMDs) protected from phospholipid in mature structure. Nascent pTMDs are difficult for translocon to recognize and insert. How pTMDs are discerned and packed into mature, muti-spanning configuration remains unclear. Here, we report that pTMD elicits a post-translational topogenesis pathway for its recognition and integration. Using six-spanning protein adenosine triphosphate-binding cassette transporter G2 (ABCG2) and cultured human cells as models, we show that ABCG2's pTMD2 can pass through translocon into the endoplasmic reticulum (ER) lumen, yielding an intermediate with inserted yet mis-oriented downstream TMDs. After translation, the intermediate recruits P5A-ATPase ATP13A1, which facilitates TMD re-orientation, allowing further folding and the integration of the remaining lumen-exposed pTMD2. Depleting ATP13A1 or disrupting pTMD-characteristic residues arrests intermediates with mis-oriented and exposed TMDs. Our results explain how a "difficult" pTMD is co-translationally skipped for insertion and post-translationally buried into the final correct structure at the late folding stage to avoid excessive lipid exposure.

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization.

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966-2020).

Background: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH). Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots. Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected. Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.

A Multi-Layer Intrusion Detection System for SOME/IP-Based In-Vehicle Network.

The automotive Ethernet is gradually replacing the traditional controller area network (CAN) as the backbone network of the vehicle. As an essential protocol to solve service-based communication, Scalable service-Oriented MiddlewarE over IP (SOME/IP) is expected to be applied to an in-vehicle network (IVN). The increasing number of external attack interfaces and the protocol's vulnerability makes SOME/IP in-vehicle networks vulnerable to intrusion. This paper proposes a multi-layer intrusion detection system (IDS) architecture, including rule-based and artificial intelligence (AI)-based modules. The rule-based module is used to detect the SOME/IP header, SOME/IP-SD message, message interval, and communication process. The AI-based module acts on the payload. We propose a SOME/IP dataset establishment method to evaluate the performance of the proposed multi-layer IDS. Experiments are carried out on a Jetson Xavier NX, showing that the accuracy of AI-based detection reached 99.7761% and that of rule-based detection was 100%. The average detection time per packet is 0.3958 ms with graphics processing unit (GPU) acceleration and 0.6669 ms with only a central processing unit (CPU). After vehicle-level real-time analyses, the proposed IDS can be deployed for distributed or select critical advanced driving assistance system (ADAS) traffic for detection in a centralized layout.

UBE4A catalyzes NRF1 ubiquitination and facilitates DDI2-mediated NRF1 cleavage.

The transcription factor nuclear factor erythroid 2 like 1 (NFE2L1 or NRF1) regulates constitutive and inducible expression of proteasome subunits and assembly chaperones. The precursor of NRF1 is integrated into the endoplasmic reticulum (ER) and can be retrotranslocated from the ER to the cytosol where it is processed by ubiquitin-directed endoprotease DDI2. DDI2 cleaves and activates NRF1 only when NRF1 is highly polyubiquitinated. It remains unclear how retrotranslocated NRF1 is primed with large amount of ubiquitin and/or very long polyubiquitin chain for subsequent processing. Here, we report that E3 ligase UBE4A catalyzes ubiquitination of retrotranslocated NRF1 and promotes its cleavage. Depletion of UBE4A reduces the amount of ubiquitin modified on NRF1, shortens the average length of polyubiquitin chain, decreases NRF1 cleavage efficiency and causes accumulation of non-cleaved, inactivated NRF1. Expression of a UBE4A mutant lacking ligase activity impairs the cleavage, likely due to a dominant negative effect. UBE4A interacts with NRF1 and the recombinant UBE4A can promote ubiquitination of retrotranslocated NRF1 in vitro. In addition, knocking out UBE4A reduces transcription of proteasomal subunits in cells. Our results indicate that UBE4A primes NRF1 for DDI2-mediated activation to facilitate expression of proteasomal genes.

p53 promotes peroxisomal fatty acid ß-oxidation to repress purine biosynthesis and mediate tumor suppression.

The metabolic pathways through which p53 functions as a potent tumor suppressor are incompletely understood. Here we report that, by associating with the Vitamin D receptor (VDR), p53 induces numerous genes encoding enzymes for peroxisomal fatty acid ß-oxidation (FAO). This leads to increased cytosolic acetyl-CoA levels and acetylation of the enzyme 5-Aminoimidazole-4-Carboxamide Ribonucleotide Formyltransferase/IMP Cyclohydrolase (ATIC), which catalyzes the last two steps in the purine biosynthetic pathway. This acetylation step, mediated by lysine acetyltransferase 2B (KAT2B), occurs at ATIC Lys 266, dramatically inhibits ATIC activity, and inversely correlates with colorectal cancer (CRC) tumor growth in vitro and in vivo, and acetylation of ATIC is downregulated in human CRC samples. p53-deficient CRCs with high levels of ATIC is more susceptible to ATIC inhibition. Collectively, these findings link p53 to peroxisomal FAO, purine biosynthesis, and CRC pathogenesis in a manner that is regulated by the levels of ATIC acetylation.

Phosphomevalonate Kinase Controls ß-Catenin Signaling via the Metabolite 5-Diphosphomevalonate.

ß-catenin signaling is abnormally activated in cancer. Here, this work screens the mevalonate metabolic pathway enzyme PMVK to stabilize ß-catenin signaling using a human genome-wide library. On the one hand, PMVK-produced MVA-5PP competitively binds to CKIα to prevent ß-catenin Ser45 phosphorylation and degradation. On the other hand, PMVK functions as a protein kinase to directly phosphorylate ß-catenin Ser184 to increase its protein nuclear localization. This synergistic effect of PMVK and MVA-5PP together promotes ß-catenin signaling. In addition, PMVK deletion impairs mouse embryonic development and causes embryonic lethal. PMVK deficiency in liver tissue alleviates DEN/CCl4 -induced hepatocarcinogenesis. Finally, the small molecule inhibitor of PMVK, PMVKi5, is developed and PMVKi5 inhibits carcinogenesis of liver and colorectal tissues. These findings reveal a non-canonical function of a key metabolic enzyme PMVK and a novel link between the mevalonate pathway and ß-catenin signaling in carcinogenesis providing a new target for clinical cancer therapy.

Cybersecurity Testing for Automotive Domain: A Survey.

Modern vehicles are more complex and interconnected than ever before, which also means that attack surfaces for vehicles have increased significantly. Malicious cyberattacks will not only exploit personal privacy and property, but also affect the functional safety of electrical/electronic (E/E) safety-critical systems by controlling the driving functionality, which is life-threatening. Therefore, it is necessary to conduct cybersecurity testing on vehicles to reveal and address relevant security threats and vulnerabilities. Cybersecurity standards and regulations issued in recent years, such as ISO/SAE 21434 and UNECE WP.29 regulations (R155 and R156), also emphasize the indispensability of cybersecurity verification and validation in the development lifecycle but lack specific technical details. Thus, this paper conducts a systematic and comprehensive review of the research and practice in the field of automotive cybersecurity testing, which can provide reference and advice for automotive security researchers and testers. We classify and discuss the security testing methods and testbeds in automotive engineering. Furthermore, we identify gaps and limitations in existing research and point out future challenges.

Design of IoT Gateway for Crop Growth Environmental Monitoring Based on Edge-Computing Technology.

With the extensive use of the Internet of Things (IoT) in agriculture, the number of terminals are also grow rapidly. This will increase the network traffic and computing pressure of the centralized server. The centralized data processing mode used in traditional agriculture cannot meet the needs of the Internet of everything era. This paper designs a gateway based on edge-computing technology for monitoring crop growth environment. It uses virtualized container technology to package long-range wide-area network (LoRaWAN) server, pest identification, and environmental information data fusion functions into images. It forms integrated operation mode of multiple function in agriculture. The gateway applies message-oriented middleware to standardize and customize the data transmission among functional modules, clouds, and edges. Through simulation and field test, the designed gateway can achieve the functions of each module at the same time, the resource utilization, and the transmission quality are stable. The edge-computing gateway has the advantages of low cost, low latency, and low power consumption which has practical significance.

Design Methodology of Automotive Time-Sensitive Network System Based on OMNeT++ Simulation System.

Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network.

Multi-Modal Vehicle Trajectory Prediction by Collaborative Learning of Lane Orientation, Vehicle Interaction, and Intention.

Accurate trajectory prediction is an essential task in automated driving, which is achieved by sensing and analyzing the behavior of surrounding vehicles. Although plenty of research works have been invested in this field, it is still a challenging subject due to the environment's complexity and the driving intention uncertainty. In this paper, we propose a joint learning architecture to incorporate the lane orientation, vehicle interaction, and driving intention in vehicle trajectory forecasting. This work employs a coordinate transform to encode the vehicle trajectory with lane orientation information, which is further incorporated into various interaction models to explore the mutual trajectory relations. Extracted features are applied in a dual-level stochastic choice learning to distinguish the trajectory modality at both the intention and motion levels. By collaborative learning of lane orientation, interaction, and intention, our approach can be applied to both highway and urban scenes. Experiments on the NGSIM, HighD, and Argoverse datasets demonstrate that the proposed method achieves a significant improvement in prediction accuracy compared with the baseline.

Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera.

Multi-Target tracking is a central aspect of modeling the environment of autonomous vehicles. A mono camera is a necessary component in the autonomous driving system. One of the biggest advantages of the mono camera is it can give out the type of vehicle and cameras are the only sensors able to interpret 2D information such as road signs or lane markings. Besides this, it has the advantage of estimating the lateral velocity of the moving object. The mono camera is now being used by companies all over the world to build autonomous vehicles. In the expressway scenario, the forward-looking camera can generate a raw picture to extract information from and finally achieve tracking multiple vehicles at the same time. A multi-object tracking system, which is composed of a convolution neural network module, depth estimation module, kinematic state estimation module, data association module, and track management module, is needed. This paper applies the YOLO detection algorithm combined with the depth estimation algorithm, Extend Kalman Filter, and Nearest Neighbor algorithm with a gating trick to build the tracking system. Finally, the tracking system is tested on the vehicle equipped with a forward mono camera, and the results show that the lateral and longitudinal position and velocity can satisfy the need for Adaptive Cruise Control (ACC), Navigation On Pilot (NOP), Auto Emergency Braking (AEB), and other applications.

The MAP3K13-TRIM25-FBXW7α axis affects c-Myc protein stability and tumor development.

c-Myc (Myc) is a master transcription factor that is often deregulated and highly expressed by at least 50% of cancers. In many cases, Myc protein levels correlate with resistance to therapy and poor prognosis. However, effective direct inhibition of Myc by pharmacologic approaches has remained unachievable. Here, we identify MAP3K13 as a positive regulator of Myc to promote tumor development. Our findings show that MAP3K13 upregulation is predictive of poor outcomes in patients with hepatocellular carcinoma (HCC). Mechanistically, MAP3K13 phosphorylates the E3 ubiquitin ligase TRIM25 at Ser12 to decrease its polyubiquitination and proteasomal degradation. This newly stabilized TRIM25 then directly ubiquitinates Lys412 of FBXW7α, a core subunit of the SKP1-Cullin-F-box (SCF) ubiquitin ligase complex involved in Myc ubiquitination, thereby stabilizing Myc. Together, these results reveal a novel regulatory pathway that supervises Myc protein stability via the MAP3K13-TRIM25-FBXW7α signaling axis. In addition, they provide a potential therapeutic target in Myc over-expressing human cancers.

High resolution profiling of human exon methylation by liquid hybridization capture-based bisulfite sequencing.

BACKGROUND: DNA methylation plays important roles in gene regulation during both normal developmental and disease states. In the past decade, a number of methods have been developed and applied to characterize the genome-wide distribution of DNA methylation. Most of these methods endeavored to screen whole genome and turned to be enormously costly and time consuming for studies of the complex mammalian genome. Thus, they are not practical for researchers to study multiple clinical samples in biomarker research. RESULTS: Here, we display a novel strategy that relies on the selective capture of target regions by liquid hybridization followed by bisulfite conversion and deep sequencing, which is referred to as liquid hybridization capture-based bisulfite sequencing (LHC-BS). To estimate this method, we utilized about 2 µg of native genomic DNA from YanHuang (YH) whole blood samples and a mature dendritic cell (mDC) line, respectively, to evaluate their methylation statuses of target regions of exome. The results indicated that the LHC-BS system was able to cover more than 97% of the exome regions and detect their methylation statuses with acceptable allele dropouts. Most of the regions that couldn't provide accurate methylation information were distributed in chromosomes 6 and Y because of multiple mapping to those regions. The accuracy of this strategy was evaluated by pair-wise comparisons using the results from whole genome bisulfite sequencing and validated by bisulfite specific PCR sequencing. CONCLUSIONS: In the present study, we employed a liquid hybridisation capture system to enrich for exon regions and then combined with bisulfite sequencing to examine the methylation statuses for the first time. This technique is highly sensitive and flexible and can be applied to identify differentially methylated regions (DMRs) at specific genomic locations of interest, such as regulatory elements or promoters.

Comprehensive comparison of three commercial human whole-exome capture platforms.

BACKGROUND: Exome sequencing, which allows the global analysis of protein coding sequences in the human genome, has become an effective and affordable approach to detecting causative genetic mutations in diseases. Currently, there are several commercial human exome capture platforms; however, the relative performances of these have not been characterized sufficiently to know which is best for a particular study. RESULTS: We comprehensively compared three platforms: NimbleGen's Sequence Capture Array and SeqCap EZ, and Agilent's SureSelect. We assessed their performance in a variety of ways, including number of genes covered and capture efficacy. Differences that may impact on the choice of platform were that Agilent SureSelect covered approximately 1,100 more genes, while NimbleGen provided better flanking sequence capture. Although all three platforms achieved similar capture specificity of targeted regions, the NimbleGen platforms showed better uniformity of coverage and greater genotype sensitivity at 30- to 100-fold sequencing depth. All three platforms showed similar power in exome SNP calling, including medically relevant SNPs. Compared with genotyping and whole-genome sequencing data, the three platforms achieved a similar accuracy of genotype assignment and SNP detection. Importantly, all three platforms showed similar levels of reproducibility, GC bias and reference allele bias. CONCLUSIONS: We demonstrate key differences between the three platforms, particularly advantages of solutions over array capture and the importance of a large gene target set.