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Ionizing radiation in space, radiation devices or nuclear disasters are major threats to human health and public security. Expanding countermeasures for dealing with accidental or occupational radiation exposure is crucial for the protection of radiation injuries. Circulating microRNAs (miRNAs) have emerged as promising radiation biomarkers in recent years. However, the origin, distribution and functions of radiosensitive circulating miRNAs remain unclear, which obstructs their clinical applications in the future. In this study, we found that mmu-miR-342-3p (miR-342) in mouse serum presents a stable and significant decrease after X-ray total-body irradiation (TBI). Focusing on this miRNA, we investigated the influences of circulating miR-342 on the radiation-induced injury. Through tail vein injection of Cy5-labeled synthetic miR-342, we found the exogenous miR-342-Cy5 was mainly enriched in metabolic and immune organs. Besides, the bioinformatic analysis predicted that miR-342 might involve in immune-related processes or pathways. Further, mice were tail vein injected with synthetic miR-342 mimetics (Ago-miR-342) after irradiation to upregulate the level of miR-342 in circulating blood. The results showed that the upregulation of circulating miR-342 alleviated the radiation-induced depletion of CD3+CD4+ T lymphocytes and influenced the levels of IL-2 and IL-6 in irradiated mice. Moreover, the injection of Ago-miR-342 improved the survival rates of mice with acute radiation injury. Our findings demonstrate that upregulation of circulating miR-342 alleviates the radiation-induced immune system injury, which provides us new insights into the functions of circulating miRNAs and the prospect as the targets for mitigation of radiation injuries. [ABSTRACT FROM AUTHOR]

Inhibition of thioredoxin reductase (TrxR) is a crucial strategy for the discovery of antineoplastic drugs and radiosensitizers. As an anticancer candidate derived from Michelia , micheliolide (MCL) is converted readily from parthenolide (PTL), and has better stability and solubility than PTL. However, the anticancer mechanism of MCL has not been fully dissected. We present here for the first time that MCL-targeted inhibition of TrxR not only promotes oxidative stress-mediated HeLa cell apoptosis but also sensitizes ionizing radiation (IR) treatment. Further mechanistic studies demonstrate that MCL covalently binds to Sec at position 498 of TrxR to restrain the biological function of TrxR. It exhibits the inhibition of TrxR activity, enhancement of oxidized Trx, and sensitization of IR in the cellular environment, accompanied by the accumulation of reactive oxygen species (ROS) and the collapse of the intracellular redox balance. In addition, HeLa- shTrxR1 cells with knockdown of TrxR were more sensitive than the HeLa- shNT cells to either MCL-treated or IR-induced cytotoxicity, ROS, and apoptosis, suggesting that inhibition of TrxR by MCL is likely responsible for increased cytotoxicity and enhanced radiation response. These findings further establish the mechanistic understanding and preclinical data to support the further investigation of MCL's potential as a prospective radiosensitizer and cancer chemotherapeutic agent. [Display omitted] • Revealing thioredoxin reductase (TrxR) as a novel anticancer target of micheliolide (MCL). • Identifying MCL as a novel radiosensitizer and establishing the critical role of TrxR in MCL-mediated radiosensitization. • Sensitizing cancer cells by MCL to X-ray via ROS-mediated signaling. • Inhibiting TrxR by MCL to induce oxidative stress-mediated apoptosis in cancer cells. • Targeting TrxR by MCL to expand its anticancer mechanism and unleash its therapeutic and sensitizing potential. [ABSTRACT FROM AUTHOR]

Global, coarse mesh and local, refined mesh modeling strategy is a common solution approach for domains spanning many orders in length scale. In this article, a nonintrusive computational strategy for iterative solution to the nonlinear behavior in the subdomains is proposed. To estimate the residual force on the interface connecting the subdomains with nonmatching discretizations, variational principles with an intermediate framework are proposed to assure satisfaction of virtual work principle in the subdomains and to assure displacement/traction compatibility at the interface. To map nodal force from one subdomain to the other, both global Lagrange multiplier (GLM) and local Lagrange multiplier (LLM) methods are discussed. Quasi-Newton iterative updates are used to correct displacements on the interface connecting the different subdomains until equilibrium is reached. A symmetric rank 1 update as well as the Broyden–Fletcher–Goldfarb–Shanno (BFGS) update are considered for accelerating the solution convergence. The developed method is suitable for the modular construction of submodels with nonmatching discretizations and even allows coupling between subdomains analyzed using different (commercial or custom) codes. The iterative global–local coupling strategy is validated on several examples, including an L-shaped domain with local nonlinearity and a rectangular plate with a propagating crack. Another example illustrating the coupling of domains independently analyzed using commercial finite element codes ANSYS and ABAQUS is next demonstrated. Finally, the method is demonstrated to analyze semiconductor chip assemblies where plastic ratcheting of the interconnect line causes passivation coating fracture. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet, image information is growing. It requires a lot of image storage and transmission. In order to reduce the storage and get better image quality, image compression algorithm is studied. The paper proposes a new image compression algorithm that combines principal component analysis (PCA) and Contourlet Transform (CT). Because PCA has good image quality, but the compression ratio is low, and CT compression algorithm has high compression ratio and good PNSR value. The image is decomposed by PCA. The image data is divided into blocks, and each block is used as a sample vector, then select covariance matrix of k larger eigenvalues corresponding eigenvector to realize image compression. Then the image is compressed again using CT compression algorithm. Compared with the results of JEPG2000 and CT compression algorithm, the results show that the proposed algorithm has better performance than JEPG2000 and CT compression algorithm. In the same compression ratio, PNSR value of proposed algorithm is about 3dB higher than that of JEPG2000, and 2dB higher than that of CT compression algorithm.

Metal line ratcheting and passivation cracking in back-end-of-line (BEOL) structures are significant reliability concerns for molded packages that are in widespread use at the present time. When metal lines plastically deform due to ratcheting, the passivation overcoat accumulates stress at the corner upon temperature cycling and is eventually susceptible to fracture. Since packaging materials’ interaction with the die is the cause of the failure, the problem is inherently multiscale in nature requiring the analysis model to span from package dimension to BEOL length scale. In this article, the mechanistic cause for stress accumulation is elucidated. Furthermore, a global–local modeling strategy is applied to model the passivation crack initiation and growth. A global model with coarse mesh was built of the package. The local region around the interconnect metal line in the die was modeled using boundary conditions extracted from the global model. A novel load decomposition technique is developed to identify the loading mode that best correlates with the experimentally observed fracture. It is shown that shear is the dominant loading mode inducing the observed die cracks. Furthermore, it is demonstrated that the thermal expansion mismatch between the mold compound and the lead frame with the silicon die induces the shear load on the BEOL structure. Due to the fact that mold compound is applied at a temperature that is higher than that seen during thermal cycling, the direction of the induced shear load is constant regardless of whether the package is heated or cooled. As the metal line plastically yields during every temperature cycle, the plastic deformation ratchets or accumulates in the same direction over the course of the thermal cycling test. The yielding of metal line results in stiffness reduction, leading to steady accumulation of stress in the passivation corner, causing it to fracture eventually. [ABSTRACT FROM AUTHOR]

The hashing technique has been extensively used in large-scale image retrieval applications due to its low storage and fast computing speed. Most existing deep hashing approaches cannot fully consider the global semantic similarity and category-level semantic information, which result in the insufficient utilization of the global semantic similarity for hash codes learning and the semantic information loss of hash codes. To tackle these issues, we propose a novel deep hashing approach with triplet labels, namely, deep category-level and regularized hashing (DCRH), to leverage the global semantic similarity of deep feature and category-level semantic information to enhance the semantic similarity of hash codes. There are four contributions in this article. First, we design a novel global semantic similarity constraint about the deep feature to make the anchor deep feature more similar to the positive deep feature than to the negative deep feature. Second, we leverage label information to enhance category-level semantics of hash codes for hash codes learning. Third, we develop a new triplet construction module to select good image triplets for effective hash functions learning. Finally, we propose a new triplet regularized loss (Reg-L) term, which can force binary-like codes to approximate binary codes and eventually minimize the information loss between binary-like codes and binary codes. Extensive experimental results in three image retrieval benchmark datasets show that the proposed DCRH approach achieves superior performance over other state-of-the-art hashing approaches. [ABSTRACT FROM AUTHOR]

Mitochondrial antiviral signaling (MAVS) protein, located in the mitochondrial out-membrane, is necessary for IFN-beta induction and IFN-stimulated gene expression in response to external stress such as viral invasion and ionizing radiation (IR). Although the involvement of radiation induced bystander effect (RIBE) has been investigated for decades for secondary cancer risk related to radiotherapy, the underlying regulatory mechanisms remain largely unclear, especially the roles played by the immune factors such as MAVS. MAVS gene knockout cells using CRISPR/Cas9 technology were used as donor cells or recipient cells to assess the role of MAVS in RIBE by means of co-cultured system. The micronucleus and γH2AX foci in the recipient cells were counted to demonstrate the degree of RIBE. The reactive oxygen species (ROS) level in the recipient was measured using the fluorescent dye 2′7′-dichlorofluorescein. Firstly, we found that MAVS expression level was different in A549, BEAS-2B, U937 and HepG2 cells. Cell co-culture experiments showed that MAVS participate in RIBE. Interestingly, the RIBE response was more significant in recipient cells with higher level of MAVS (i.e. A549) than that in recipient cells showing lower level of MAVS (i.e. BEAS-2B). Further, the bystander response was dramatically suppressed in MAVS-silenced A549 and BEAS-2B recipient cells. MAVS-silenced recipient cells exhibited lower level of ROS induced by IR. Our results indicated that the innate immune signaling molecule MAVS in recipient cells participate in RIBE. ROS is an important factor in RIBE via MAVS pathway and MAVS may be a potential target for the precise radiotherapy and radioprotection. [ABSTRACT FROM AUTHOR]

Thioredoxin reductase (TrxR) enzymes are critical in regulating redox homeostasis in cells. We report the first two-photon fluorescent probe of mammalian TrxR (TP-TRFS). TP-TRFS retains high specificity in recognizing TrxR. More importantly, the two-photon absorbing character of TP-TRFS enables it to be used in vivo. With the aid of TP-TRFS, a remarkable decline of the TrxR function was observed in the brain of a mouse model of stroke for the first time, providing a mechanistic link of TrxR dysfunction with stroke. [ABSTRACT FROM AUTHOR]

With the rapid progress of satellite and aircraft technologies, cross-modal remote sensing image–voice retrieval has been studied in geography recently. However, there still exist some bottlenecks: how to consider the characteristics of remote sensing data adequately and how to reduce the memory and improve the retrieval efficiency in large-scale remote sensing data. In this article, we propose a novel deep cross-modal remote sensing image–voice retrieval approach, namely, deep image–voice retrieval (DIVR), to capture more information of remote sensing data to generate hash codes with low memory and fast retrieval properties. Especially, the DIVR approach proposes inception dilated convolution module to capture multiscale contextual information of remote sensing images and voices. Moreover, in order to enhance cross-modal similarity, the deep features’ similarity term is designed to make paired similar deep features as close as possible and paired dissimilar deep features as mutually far as possible. In addition, the quantization error term is designed to drive hash-like codes to approximate hash codes, which can effectively reduce the quantization error for hash codes’ learning. Extensive experimental results on three remote sensing image–voice data sets show that the proposed DIVR approach can outperform other cross-modal retrieval approaches. [ABSTRACT FROM AUTHOR]

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods. [ABSTRACT FROM PUBLISHER]

The effective and minimally invasive radiation biomarkers are valuable for exposure scenarios in nuclear accidents or space missions. Recent studies have opened the new sight of circulating small non-coding RNA (sncRNA) as radiation biomarkers. The tRNA-derived small RNA (tsRNA) is a new class of sncRNA. It is more abundant than other kinds of sncRNAs in extracellular vesicles or blood, presenting great potential as promising biomarkers. However, the circulating tsRNAs in response to ionizing radiation have not been reported. In this research, Kunming mice were total-body exposed to 0.05–2 Gy of carbon ions, protons, or X-rays, and the RNA sequencing was performed to profile the expression of sncRNAs in serum. After conditional screening and validation, we firstly identified 5 tsRNAs including 4 tRNA-related fragments (tRFs) and 1 tRNA half (tiRNA) which showed a significant level decrease after exposure to three kinds of radiations. Moreover, the radiation responses of these 5 serum tsRNAs were reproduced in other mouse strains, and the sequences of them could be detected in serum of humans. Furthermore, we developed multi-factor models based on tsRNA biomarkers to indicate the degree of radiation exposure with high sensitivity and specificity. These findings suggest that the circulating tsRNAs can serve as new minimally invasive biomarkers and can make a triage or dose assessment from blood sample collection within 4 h in exposure scenarios. [ABSTRACT FROM AUTHOR]

With the rapid progress of remote sensing (RS) observation technologies, cross-modal RS image-sound retrieval has attracted some attention in recent years. However, these methods perform cross-modal image-sound retrieval by leveraging high-dimensional real-valued features, which can require more storage than low-dimensional binary features (i.e., hash codes). Moreover, these methods cannot directly encode relative semantic similarity relationships. To tackle these issues, we propose a new, deep, cross-modal RS image-sound hashing approach, called deep triplet-based hashing (DTBH), to integrate hash code learning and relative semantic similarity relationship learning into an end-to-end network. Specially, the proposed DTBH method designs a triplet selection strategy to select effective triplets. Moreover, in order to encode relative semantic similarity relationships, we propose the objective function, which makes sure that that the anchor images are more similar to the positive sounds than the negative sounds. In addition, a triplet regularized loss term leverages approximate l1-norm of hash-like codes and hash codes and can effectively reduce the information loss between hash-like codes and hash codes. Extensive experimental results showed that the DTBH method could achieve a superior performance to other state-of-the-art cross-modal image-sound retrieval methods. For a sound query RS image task, the proposed approach achieved a mean average precision (mAP) of up to 60.13% on the UCM dataset, 87.49% on the Sydney dataset, and 22.72% on the RSICD dataset. For RS image query sound task, the proposed approach achieved a mAP of 64.27% on the UCM dataset, 92.45% on the Sydney dataset, and 23.46% on the RSICD dataset. Future work will focus on how to consider the balance property of hash codes to improve image-sound retrieval performance. [ABSTRACT FROM AUTHOR]