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Several decades after its discovery, the hepatitis B virus (HBV) still displays one of the most successful pathogens in human populations worldwide. The identification and characterization of interactions between cellular and pathogenic components are essential for the development of antiviral treatments. Due to its small-sized genome, HBV highly depends on cellular functions to produce and export progeny particles. Deploying biochemical-silencing methods and molecular interaction studies in HBV-expressing liver cells, we herein identified the cellular ERGIC-53, a high-mannose-specific lectin, and distinct components of the endoplasmic reticulum (ER) export machinery COPII as crucial factors of viral trafficking and egress. Whereas the COPII subunits Sec24A, Sec23B and Sar1 are needed for both viral and subviral HBV particle exit, ERGIC-53 appears as an exclusive element of viral particle propagation, therefore interacting with the N146-glycan of the HBV envelope in a productive manner. Cell-imaging studies pointed to ER-derived, subcellular compartments where HBV assembly initiates. N-butyl-N-(4-hydroxybutyl) nitrosamine Moreover, our findings provide evidence that HBV exploits the functions of ERGIC-53 and Sec24A after the envelopment of nucleocapsids at these compartments in conjunction with endosomal sorting complexes required for transport (ESCRT) components. These data reveal novel insights into HBV assembly and trafficking, illustrating therapeutic prospects for intervening with the viral life cycle.Biocompatible amphiphilic statistical copolymers P(MEA/MPC m ) composed of 2-methoxyethyl acrylate (MEA) and 2-methacryloyloxyethyl phosphorylcholine (MPC) were prepared with three different mol% of the hydrophilic unit MPC (m = 6, 12 and 46 mol%). The monomer reactivity ratios of MEA (rMEA) and MPC (rMPC) were 0.53 and 2.21, respectively. The rMEA × rMPC value of 1.17 demonstrated that statistical copolymerization was successful. P(MEA/MPC12) and P(MEA/MPC46) copolymers did not undergo aggregation in water, whereas the P(MEA/MPC6) copolymer formed micelles in water with a hydrodynamic radius (Rh) of 96.9 nm and a critical aggregation concentration, which was determined using pyrene fluorescence, at 0.0082 g/L. The restricted motion of the protons in the hydrophobic MEA units in the micelles' cores provided additional evidence of self-association in P(MEA/MPC6).While ionizing radiation (IR) is a powerful tool in medical diagnostics, nuclear medicine, and radiology, it also is a serious threat to the integrity of genetic material. Mutagenic effects of IR to the human genome have long been the subject of research, yet still comparatively little is known about the genome-wide effects of IR exposure on the DNA-sequence level. In this study, we employed high throughput sequencing technologies to investigate IR-induced DNA alterations in human gingiva fibroblasts (HGF) that were acutely exposed to 0.5, 2, and 10 Gy of 240 kV X-radiation followed by repair times of 16 h or 7 days before whole-genome sequencing (WGS). Our analysis of the obtained WGS datasets revealed patterns of IR-induced variant (SNV and InDel) accumulation across the genome, within chromosomes as well as around the borders of topologically associating domains (TADs). Chromosome 19 consistently accumulated the highest SNVs and InDels events. Translocations showed variable patterns but with recurrent chromosomes of origin (e.g., Chr7 and Chr16). IR-induced InDels showed a relative increase in number relative to SNVs and a characteristic signature with respect to the frequency of triplet deletions in areas without repetitive or microhomology features. Overall experimental conditions and datasets the majority of SNVs per genome had no or little predicted functional impact with a maximum of 62, showing damaging potential. A dose-dependent effect of IR was surprisingly not apparent. We also observed a significant reduction in transition/transversion (Ti/Tv) ratios for IR-dependent SNVs, which could point to a contribution of the mismatch repair (MMR) system that strongly favors the repair of transitions over transversions, to the IR-induced DNA-damage response in human cells. Taken together, our results show the presence of distinguishable characteristic patterns of IR-induced DNA-alterations on a genome-wide level and implicate DNA-repair mechanisms in the formation of these signatures.Peroxisomes are eukaryotic organelles that are essential for growth and development. They are highly metabolically active and house many biochemical reactions, including lipid metabolism and synthesis of signaling molecules. Most of these metabolic pathways are shared with other compartments, such as Endoplasmic reticulum (ER), mitochondria, and plastids. Peroxisomes, in common with all other cellular organelles are dependent on a wide range of cofactors, such as adenosine 5'-triphosphate (ATP), Coenzyme A (CoA), and nicotinamide adenine dinucleotide (NAD). The availability of the peroxisomal cofactor pool controls peroxisome function. The levels of these cofactors available for peroxisomal metabolism is determined by the balance between synthesis, import, export, binding, and degradation. Since the final steps of cofactor synthesis are thought to be located in the cytosol, cofactors must be imported into peroxisomes. This review gives an overview about our current knowledge of the permeability of the peroxisomal membrane with the focus on ATP, CoA, and NAD. Several members of the mitochondrial carrier family are located in peroxisomes, catalyzing the transfer of these organic cofactors across the peroxisomal membrane. Most of the functions of these peroxisomal cofactor transporters are known from studies in yeast, humans, and plants. Parallels and differences between the transporters in the different organisms are discussed here.Epigenetics, CpG methylation of CpG islands (CGI) and gene bodies (GBs), plays an important role in gene regulation and cancer biology, the former established as a transcription regulator. Genome wide CpG methylation, summarized over GBs and CGIs, was analyzed for impact on overall survival (OS) in cancer. The averaged GB and CGI methylation status of each gene was categorized into methylated and unmethylated (defined) or undefined. Differentially methylated GBs and genes associated with their GB methylation status were compared to the corresponding CGI methylation states and biologically annotated. No relevant correlations of GB and CGI methylation or GB methylation and gene expression were observed. Summarized GB methylation showed impact on OS in ovarian, breast, colorectal, and pancreatic cancer, and glioblastoma, but not in lung cancer. In ovarian, breast, and colorectal cancer more defined GBs correlated with unfavorable OS, in pancreatic cancer with favorable OS and in glioblastoma more methylated GBs correlated with unfavorable OS. The GB methylation of genes were similar over different samples and even over cancer types; nevertheless, the clustering of different cancers was possible. Gene expression differences associated with summarized GB methylation were cancer specific. A genome-wide dysregulation of gene-body methylation showed impact on the outcome in different cancers.One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.Autonomous vehicles are gaining popularity throughout the world among researchers and consumers. However, their popularity has not yet reached the level where it is widely accepted as a fully developed technology as a large portion of the consumer base feels skeptical about it. Proving the correctness of this technology will help in establishing faith in it. That is easier said than done because of the fact that the formal verification techniques has not attained the level of development and application that it is ought to. In this work, we present Statistical Model Checking (SMC) as a possible solution for verifying the safety of autonomous systems and algorithms. We apply it on Heuristic Autonomous Intersection Management (HAIM) algorithm. The presented verification routine can be adopted for other conflict point based autonomous intersection management algorithms as well. Along with verifying the HAIM, we also demonstrate the modeling and verification applied at each stage of development to verify the inherent behavior of the algorithm. The HAIM scheme is formally modeled using a variant of the language of Timed Automata. The model consists of automata that encode the behavior of vehicles, intersection manager (IM) and collision checkers. To verify the complete nature of the heuristic and ensure correct modeling of the system, we model it in layers and verify each layer separately for their expected behavior. Along with that, we perform implementation verification and error injection testing to ensure faithful modeling of the system. Results show with high confidence the freedom from collisions of the intersection controlled by the HAIM algorithm.In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.