Human DDX17 Unwinds Rift Valley Fever Virus Non-Coding RNAs

Rift Valley fever virus (RVFV) is a mosquito-transmitted virus from the Bunyaviridae family that causes high rates of mortality and morbidity in humans and ruminant animals. Previous studies indicated that DEAD-box helicase 17 (DDX17) restricts RVFV replication by recognizing two primary non-coding RNAs in the S-segment of the genome: the intergenic region (IGR) and 5′ non-coding region (NCR). However, we lack molecular insights into the direct binding of DDX17 with RVFV non-coding RNAs and information on the unwinding of both non-coding RNAs by DDX17. Therefore, we performed an extensive biophysical analysis of the DDX17 helicase domain (DDX17_135–555) and RVFV non-coding RNAs, IGR and 5’ NCR. The homogeneity studies using analytical ultracentrifugation indicated that DDX17_135–555, IGR, and 5’ NCR are pure. Next, we performed small-angle X-ray scattering (SAXS) experiments, which suggested that DDX17 and both RNAs are homogenous as well. SAXS analysis also demonstrated that DDX17 is globular to an extent, whereas the RNAs adopt an extended conformation in solution. Subsequently, microscale thermophoresis (MST) experiments were performed to investigate the direct binding of DDX17 to the non-coding RNAs. The MST experiments demonstrated that DDX17 binds with the IGR and 5’ NCR with a dissociation constant of 5.77 ± 0.15 µM and 9.85 ± 0.11 µM, respectively. As DDX17_135–555 is an RNA helicase, we next determined if it could unwind IGR and NCR. We developed a helicase assay using MST and fluorescently-labeled oligos, which suggested DDX17_135–555 can unwind both RNAs. Overall, our study provides direct evidence of DDX17_135–555 interacting with and unwinding RVFV non-coding regions.     

Identification of the Primary Factors Determining the Specificity of Human VKORC1 Recognition by Thioredoxin-Fold Proteins

Redox (reduction–oxidation) reactions control many important biological processes in all organisms, both prokaryotes and eukaryotes. This reaction is usually accomplished by canonical disulphide-based pathways involving a donor enzyme that reduces the oxidised cysteine residues of a target protein, resulting in the cleavage of its disulphide bonds. Focusing on human vitamin K epoxide reductase (hVKORC1) as a target and on four redoxins (protein disulphide isomerase (PDI), endoplasmic reticulum oxidoreductase (ERp18), thioredoxin-related transmembrane protein 1 (Tmx1) and thioredoxin-related transmembrane protein 4 (Tmx4)) as the most probable reducers of VKORC1, a comparative in-silico analysis that concentrates on the similarity and divergence of redoxins in their sequence, secondary and tertiary structure, dynamics, intraprotein interactions and composition of the surface exposed to the target is provided. Similarly, hVKORC1 is analysed in its native state, where two pairs of cysteine residues are covalently linked, forming two disulphide bridges, as a target for Trx-fold proteins. Such analysis is used to derive the putative recognition/binding sites on each isolated protein, and PDI is suggested as the most probable hVKORC1 partner. By probing the alternative orientation of PDI with respect to hVKORC1, the functionally related noncovalent complex formed by hVKORC1 and PDI was found, which is proposed to be a first precursor to probe thiol–disulphide exchange reactions between PDI and hVKORC1.     

Fluorescence Correlation Spectroscopy Analysis of Effect of Molecular Crowding on Self-Assembly of β-Annulus Peptide into Artificial Viral Capsid

Recent progress in the de novo design of self-assembling peptides has enabled the construction of peptide-based viral capsids. Previously, we demonstrated that 24-mer β-annulus peptides from tomato bushy stunt virus spontaneously self-assemble into an artificial viral capsid. Here we propose to use the artificial viral capsid through the self-assembly of β-annulus peptide as a simple model to analyze the effect of molecular crowding environment on the formation process of viral capsid. Artificial viral capsids formed by co-assembly of fluorescent-labelled and unmodified β-annulus peptides in dilute aqueous solutions and under molecular crowding conditions were analyzed using fluorescence correlation spectroscopy (FCS). The apparent particle size and the dissociation constant (K_d) of the assemblies decreased with increasing concentration of the molecular crowding agent, i.e., polyethylene glycol (PEG). This is the first successful in situ analysis of self-assembling process of artificial viral capsid under molecular crowding conditions.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

Role of the HSP70 Co-Chaperone SIL1 in Health and Disease

Cell surface and secreted proteins provide essential functions for multicellular life. They enter the endoplasmic reticulum (ER) lumen co-translationally, where they mature and fold into their complex three-dimensional structures. The ER is populated with a host of molecular chaperones, associated co-factors, and enzymes that assist and stabilize folded states. Together, they ensure that nascent proteins mature properly or, if this process fails, target them for degradation. BiP, the ER HSP70 chaperone, interacts with unfolded client proteins in a nucleotide-dependent manner, which is tightly regulated by eight DnaJ-type proteins and two nucleotide exchange factors (NEFs), SIL1 and GRP170. Loss of SIL1′s function is the leading cause of Marinesco-Sjögren syndrome (MSS), an autosomal recessive, multisystem disorder. The development of animal models has provided insights into SIL1′s functions and MSS-associated pathologies. This review provides an in-depth update on the current understanding of the molecular mechanisms underlying SIL1′s NEF activity and its role in maintaining ER homeostasis and normal physiology. A precise understanding of the underlying molecular mechanisms associated with the loss of SIL1 may allow for the development of new pharmacological approaches to treat MSS.     

An Injectable Hydrogel for Simultaneous Photothermal Therapy and Photodynamic Therapy with Ultrahigh Efficiency Based on Carbon Dots and Modified Cellulose Nanocrystals

The convenience of injectable hydrogels that can provide high loading of diverse phototherapy agents and further long-time retention at the tumor site has attracted tremendous interest in simultaneous photothermal and photodynamic cancer therapies. However, to incorporate the phototherapy agents into hydrogels, complex modifications are generally unavoidable. Moreover, these phototherapy agents usually suffer from low efficiency and work at different irradiation wavelengths outside the near infrared windows. Hence, a method for the fabrication of an injectable hydrogel for simultaneous photothermal therapy and photodynamic therapy, through the Schiff-base reaction between amido modified carbon dots (NCDs) and aldehyde modified cellulose nanocrystals is proposed. The NCDs act as both phototherapy agents and crosslinkers to form hydrogels. Significantly, the NCDs demonstrate an extremely high photothermal conversion efficiency of 77.6% which is among the highest levels for photothermal agents and a high singlet quantum yield of 0.37 under a single 660 nm light-emitting diode irradiation. The hydrogels are examined through in vitro and in vivo animal experiments which show nontoxic and effectively tumor inhibition. Thus, the strategy of direct reaction of phototherapy agents and the matrix not only provides new strategies for injectable hydrogel fabrication but paves a new road for advanced tumor treatment.     

基于电磁超材料的微波单向传输圆波导设计

微波隔离器是微波系统中不可或缺的器件,常见的隔离器都采用了铁氧体旋磁材料配合吸收负载实现电磁波的单向传输。这种器件虽然可以使电磁波单向传输,但是并不能改善能量的浪费问题,还增加了系统的复杂度。针对上述问题,本文基于电磁超材料设计了一种微波单向传输的圆波导,使用波导内壁涂覆折射率逐渐变化的材料来影响电磁波的传输特性,从而实现电磁波单向传输。本文给出了微波单向传输的电磁计算模型和超材料结构及属性,并通过简化这种超材料使其易于实现;最后通过电磁仿真分析了这种材料的电磁特性并给出了这种材料的实现方法。     

认知双向中继网络在κ-μ衰落信道下的中断性能分析

针对认知双向中继网络在进行数据传输时面临的复杂无线信道场景问题,采用广义κ-μ分布构建认知双向中继网络中的视距和非视距无线传输信道,推导次网络在κ-μ衰落信道下的统一中断概率,并分析次网络在多种单一和混合衰落信道情况下的中断性能。仿真结果表明,无线信道的衰落程度对次网络的中断概率影响显著,依据衰落信道类型合理设置网络参数将有助于提升次网络中断性能。     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.