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Dynamic Tensor Inversion (DTI) is an emerging issue in recent research, prevalent in artificial intelligence development frameworks such as TensorFlow and PyTorch. Traditional numerical methods suffer significant lagging error when addressing this issue. To address this, Zeroing-type Neural Dynamics (ZND) and Gradient-type Neural Dynamics (GND) are employed to tackle the DTI. However, these two methods exhibit inherent limitations in the resolution process, i.e. high computational complexity and low solution accuracy, respectively. Motivated by this technology gap, this paper proposes an Adaptive Coefficient Gradient Neural Dynamics (ACGND) for dynamically solving the DTI with an efficient and precise manner. Through a series of simulation experiments and validations in engineering applications, the ACGND demonstrates advantages in resolving DTI. The ACGND enhances computational efficiency by circumventing matrix inversion, thereby reducing computational complexity. Moreover, its incorporation of adaptive coefficients and activation functions enables real-time adjustments of the computational solution, facilitating rapid convergence to theoretical solutions and adaptation to non-statinary scenarios. Code is available at https://github.com/Maia2333/ACGND-Code-Implementation. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved significant success in the super-resolution of remote sensing images. However, existing deep learning models still suffer from the issue of blurry pseudo-artifacts when restoring high-frequency details and textures. In this paper, a novel dual-path feature reuse multi-scale network (DFMNet) is proposed to more effectively utilize multi-scale features in remote sensing images, enhancing the detailed information in the restored images. Specifically, the designed dual-path feature reuse module adopts a symmetrical dual-path structure, with each path composed of convolutional layers of different sizes. This module enables deep feature reuse and multi-scale aggregation, improving the network’s ability to handle and restore high-frequency details in the images. Furthermore, a cross-attention module is introduced to facilitate deep interactive fusion of multi-scale image features produced by the encoder output. Comparative experiments conducted on challenging UCMerced and AID remote sensing datasets demonstrate that the proposed DFMNet achieves superior performance in both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

A lot of current work based on convolutional neural networks (CNNs) has fetched good visual results on AWGN (additive white Gaussian noise) removal. However, ordinary neural networks are unable to recover detailed information for complex tasks, and the application of a single Gaussian denoising model is greatly limited. To improve the practicality of the denoising algorithm, we trained a DCNN (deep convolutional neural network) to perform multiple denoising tasks, including Gaussian denoising and blind Gaussian denoising. The proposed CNN denoising model with a residual structure and apply feature attention to exploit channel dependency. The network structure mainly consists of sparse block (SB), feature fusion block (FFB), feature compression block (FCB), information interaction block (IIB) and reconstruction block (RB). The SB with sparse mechanism obtains global and local features by alternating between dilated convolution and common convolution. The FFB collects and fuses global and local features to provide additional information for the latter network. The FCB refines the extracted information and compresses the network. The IIB is used for feature integration and dimensionality reduction. Finally, the RB is used to reconstruct the denoised image. A channel attention mechanism is added to the network, and a trade-off is made between the denoising effect and the complexity of the network. A large number of experiments are conducted on five datasets, and the results showed that the proposed method achieves highly competitive performance in both objective evaluation indicators and subjective visual effects. [ABSTRACT FROM AUTHOR]

Perovskite-type lead-free piezoelectric ceramics allow access to illustrious piezoelectric coefficients (d33) through intricate composition design and experimental modulation. Developing a swift and accurate technology for identifying (K, Na)NbO3 (KNN)-based ceramic compositions with high d33 in exceedingly large "compositional" space will establish an innovative research paradigm surpassing the traditional empirical trial-and-error method. Herein, we demonstrate an interpretable machine learning (ML) framework for quick evaluation of KNN-based ceramics with high d33 based on data from published literature. Specifically, a thorough feature construction was carried out from the global and local dimensions to establish tree regression models with d33 as the target property. Subsequently, the feature-property mapping rules of KNN-based piezoelectric ceramics are further optimized through feature screening. To intuitively understand the correlation mechanisms between ML regression targets and features, the sure independence screening and sparsifying operator (SISSO) method was employed to extract the essential descriptors to explain d33. A straightforward descriptor, e (NM B − MV B) ⋅ ST / (I D A) 2 , consisting of only four easily accessible parameters, can accelerate the evaluation of a series of novel KNN-based ceramics with high d33 while exhibiting strong theoretical interpretability. This work not only provides a tool for the rapid discovery of high piezoelectric performance in KNN-based ceramics but also offers a data-driven route for the design of property descriptors in perovskites. [ABSTRACT FROM AUTHOR]

Vanadium redox flow batteries are recognized as well-developed flow batteries. The flow rate and current density of the electrolyte are important control mechanisms in the operation of this type of battery, which affect its energy power. The thermal behavior and performance of this battery during charging and discharging modes are also important. As a consequence, the aim of this investigation is to deeply study the impact of different working parameters on the temperature distribution and state of charge of these batteries. To achieve these goals, a single battery thermal model is established. The effects of various operating parameters, including working temperature, molar concentration, flow rate, and current density of the electrolyte, on the thermal behavior, state of charge, and performance of this type of battery are investigated. It is observed that the temperature distribution of high flow rate (90 mL min−1) is more uniform than that of other flow rates (30 and 60 mL min−1). In the end of the discharging mode, the battery voltage performance increases with the increase in the electrolyte flow rate. The temperature distribution of high current density (80 mA cm−2) is relatively uneven, and the local heating is produced at the battery outlet. The end time in the charging and discharging modes for the case of the high current density (80 mA cm−2) is faster than other current densities (20 and 40 mA cm−2). [ABSTRACT FROM AUTHOR]

Due to the complex environment in the field, the number of nodes and the energy consumption of nodes should be considered in the deployment of aquaculture water quality monitoring system. Therefore, according to the actual network framework of aquaculture water quality monitoring system, based on the energy balance mechanism of clustering routing protocol, clustering mode and path energy consumption model, a new node layout and energy consumption optimization strategy is proposed in this paper, by improving artificial bee colony algorithm and genetic algorithm, the number of relay nodes and energy consumption of network are reduced. Through simulation and comparison, it is verified that the network coverage can be increased by 36.92% when the proposed optimization strategy and PSO perform the node placement task in the same scenario. The improved artificial bee colony algorithm has a significant improvement in the network coverage of the monitored area with the same number of nodes. On the basis of this, the final node layout scheme obtained by GA extends the life cycle of the network to a certain extent, and proves the guidance and application value of the strategy in the process of system building. [ABSTRACT FROM AUTHOR]

Structural superlubricity, a state of nearly zero friction and no wear between two contact surfaces under relative sliding, holds immense potential for research and application prospects in micro-electro-mechanical systems devices, mechanical engineering, and energy resources. A critical step towards the practical application of structural superlubricity is the mass transfer and high throughput performance evaluation. Limited by the yield rate of material preparation, existing automated systems, such as roll printing or massive stamping, are inadequate for this task. In this paper, a machine learning-assisted system is proposed to realize fully automated selective transfer and tribological performance measurement for structural superlubricity materials. Specifically, the system has a judgment accuracy of over 98% for the selection of micro-scale graphite flakes with structural superlubricity properties and complete the 100 graphite flakes assembly array to form various pre-designed patterns within 100 mins, which is 15 times faster than manual operation. Besides, the system is capable of automatically measuring the tribological performance of over 100 selected flakes on Si3N4, delivering statistical results for new interface which is beyond the reach of traditional methods. With its high accuracy, efficiency, and robustness, this machine learning-assisted system promotes the fundamental research and practical application of structural superlubricity. A critical step to enable practical structural superlubricity (SSL) applications is to enable high throughput to both fabrication and performance evaluation. Here, the authors demonstrate an automated system for efficient and multiple SSL materials transfer and tribological measurement. [ABSTRACT FROM AUTHOR]

As one of the most promising alternatives to graphite negative electrodes, silicon oxide (SiOx) has been hindered by its fast capacity fading. Solid electrolyte interphase (SEI) aging on silicon SiOx has been recognized as the most critical yet least understood facet. Herein, leveraging 3D focused ion beam-scanning electron microscopy (FIB-SEM) tomographic imaging, we reveal an exceptionally characteristic SEI microstructure with an incompact inner region and a dense outer region, which overturns the prevailing belief that SEIs are homogeneous structure and reveals the SEI evolution process. Through combining nanoprobe and electron energy loss spectroscopy (EELS), it is also discovered that the electronic conductivity of thick SEI relies on the percolation network within composed of conductive agents (e.g., carbon black particles), which are embedded into the SEI upon its growth. Therefore, the free growth of SEI will gradually attenuate this electron percolation network, thereby causing capacity decay of SiOx. Based on these findings, a proof-of-concept strategy is adopted to mechanically restrict the SEI growth via applying a confining layer on top of the electrode. Through shedding light on the fundamental understanding of SEI aging for SiOx anodes, this work could potentially inspire viable improving strategies in the future. Observing the evolution of the solid electrolyte interphase on SiOx-based electrodes in Li-ion batteries is challenging. Here, authors use three-dimensional tomography to visualize the growth of the interphase on single SiOx particles and propose a mechanical confinement strategy to prevent aging. [ABSTRACT FROM AUTHOR]

Zeroing neural networks (ZNN) have shown their state-of-the-art performance on dynamic problems. However, ZNNs are vulnerable to perturbations, which causes reliability concerns in these models owing to the potentially severe consequences. Although it has been reported that some models possess enhanced robustness but cost worse convergence speed. In order to address these problems, a robust neural dynamic with an adaptive coefficient (RNDAC) model is proposed, aided by the novel adaptive activation function and robust evolution formula to boost convergence speed and preserve robustness accuracy. In order to validate and analyze the performance of the RNDAC model, it is applied to solve the dynamic matrix square root (DMSR) problem. Related experiment results show that the RNDAC model reliably solves the DMSR question perturbed by various noises. Using the RNDAC model, we are able to reduce the residual error from 10 1 to 10 - 4 with noise perturbed and reached a satisfying and competitive convergence speed, which converges within 3 s. [ABSTRACT FROM AUTHOR]

A physical cross-linking method was used to construct an injectable hydrogel composite comprised of bioactive glass (BG) particles and polyvinyl alcohol (PVA). BG/PVA hydrogel morphology, mechanical properties, and viscoelasticity were explored. The hydrogel composite had a homogeneous and uniform distribution of BG particles, as evidenced by scanning electronic microscopy. The elastic modulus and static compressive strength were elevated by 152% and 345%, respectively, by adding 2.5 wt% BG particles to the hydrogel composite constructions. A self-standing elastic hydrogel composite with a smooth injectability was found to have a storage mould (G) more remarkable than a loss module (G″) and over the entire frequency range examined. The cell viability and live/dead assay investigation suggested the suitability of the BG/PVA hydrogel composite compared to the control PVA hydrogel groups. Adult male rats were implanted subcutaneously with the BG/PVA hydrogel composite. No inflammatory cell was found inside or over the implant after 4 weeks of implantation, indicating that the hydrogel composite was highly biocompatible. The newly constructed injectable hydrogel BG/PVA nanocomposite properties establish its ability to repair the shoulder joint head for hemiarthroplasty. [ABSTRACT FROM AUTHOR]

This paper presents a monolithically integrated aptasensor composed of a piezoresistive microcantilever array and an on-chip signal processing circuit. Twelve microcantilevers, each of them embedded with a piezoresistor, form three sensors in a Wheatstone bridge configuration. The on-chip signal processing circuit consists of a multiplexer, a chopper instrumentation amplifier, a low-pass filter, a sigma-delta analog-to-digital converter, and a serial peripheral interface. Both the microcantilever array and the on-chip signal processing circuit were fabricated on the single-crystalline silicon device layer of a silicon-on-insulator (SOI) wafer with partially depleted (PD) CMOS technology followed by three micromachining processes. The integrated microcantilever sensor makes full use of the high gauge factor of single-crystalline silicon to achieve low parasitic, latch-up, and leakage current in the PD-SOI CMOS. A measured deflection sensitivity of 0.98 × 10−6 nm−1 and an output voltage fluctuation of less than 1 μV were obtained for the integrated microcantilever. A maximum gain of 134.97 and an input offset current of only 0.623 nA were acquired for the on-chip signal processing circuit. By functionalizing the measurement microcantilevers with a biotin-avidin system method, human IgG, abrin, and staphylococcus enterotoxin B (SEB) were detected at a limit of detection (LOD) of 48 pg/mL. Moreover, multichannel detection of the three integrated microcantilever aptasensors was also verified by detecting SEB. All these experimental results indicate that the design and process of monolithically integrated microcantilevers can meet the requirements of high-sensitivity detection of biomolecules. [ABSTRACT FROM AUTHOR]

Toll-like receptors (TLRs) are the important participants of the innate immune response. Their spatial organization is well studied for the ligand-binding domains, while a lot of questions remain unanswered for the membrane and cytoplasmic regions of the proteins. Here we use solution NMR spectroscopy and computer simulations to investigate the spatial structures of transmembrane and cytoplasmic juxtamembrane regions of TLR2, TLR3, TLR5, and TLR9. According to our data, all the proteins reveal the presence of a previously unreported structural element, the cytoplasmic hydrophobic juxtamembrane α-helix. As indicated by the functional tests in living cells and bioinformatic analysis, this helix is important for receptor activation and plays a role, more complicated than a linker, connecting the transmembrane and cytoplasmic parts of the proteins. Toll-like receptors (TLRs) play a key role in the innate immune system. Here, Kornilov et al. resolve the 3D structures of the membrane-associated parts of four TLRs to reveal properties of the juxta-membrane domain. [ABSTRACT FROM AUTHOR]

The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can improve detection efficiency and provide recommendations to inexperienced doctors. Current methods and instruments either fail to automate the identification process fully or have low performance and need suitable leukocyte data sets for further study. To improve the current status, we need to develop more intelligent strategies. This paper investigates fulfilling high-performance automatic detection for leukocytes using a deep learning-based method. We established a new dataset more suitable for leukocyte detection, containing 6273 images (8595 leukocytes) and considering nine common clinical interference factors. Based on the dataset, the performance evaluation of six mainstream detection models is carried out, and a more robust ensemble model is proposed. The mean of average precision (mAP) @IoU = 0.50:0.95 and mean of average recall (mAR)@IoU = 0.50:0.95 of the ensemble model on the test set are 0.853 and 0.922, respectively. The detection performance of poor-quality images is robust. For the first time, it is found that the ensemble model yields an accuracy of 98.84% for detecting incomplete leukocytes. In addition, we also compared the test results of different models and found multiple identical false detections of the models, then provided correct suggestions for the clinic. [ABSTRACT FROM AUTHOR]

The noise and redundant information are the main reasons for the performance bottleneck of medical image segmentation algorithms based on the deep learning. To this end, we propose a deep network embedded with rough fuzzy discretization (RFDDN) for OCT fundus image segmentation. Firstly, we establish the information decision table of OCT fundus image segmentation, and regard each category of segmentation region as a fuzzy set. Then, we use the fuzzy c-means clustering to get the membership degrees of pixels to each segmentation region. According to membership functions and the equivalence relation generated by the brightness attribute, we design the individual fitness function based on the rough fuzzy set, and use a genetic algorithm to search for the best breakpoints to discretize the features of OCT fundus images. Finally, we take the feature discretization based on the rough fuzzy set as the pre-module of the deep neural network, and introduce the deep supervised attention mechanism to obtain the important multi-scale information. We compare RFDDN with U-Net, ReLayNet, CE-Net, MultiResUNet, and ISCLNet on the two groups of 3D retinal OCT data. RFDDN is superior to the other five methods on all evaluation indicators. The results obtained by ISCLNet are the second only inferior to those obtained by RFDDN. DSC, sensitivity, and specificity of RFDDN are evenly 3.3%, 2.6%, and 7.1% higher than those of ISCLNet, respectively. HD95 and ASD of RFDDN are evenly 6.6% and 19.7% lower than those of ISCLNet, respectively. The experimental results show that our method can effectively eliminate the noise and redundant information in Oct fundus images, and greatly improve the accuracy of OCT fundus image segmentation while taking into account the interpretability and computational efficiency. [ABSTRACT FROM AUTHOR]

In this work, we introduce our project on Dunhuang cultural heritage protection using artificial intelligence. The Dunhuang Mogao Grottoes in China, also known as the Grottoes of the Thousand Buddhas, is a religious and cultural heritage located on the Silk Road. The grottoes were built from the 4th century to the 14th century. After thousands of years, the in grottoes decaying is serious. In addition, numerous historical records were destroyed throughout the years, making it difficult for archaeologists to reconstruct history. We aim to use modern computer vision and machine learning technologies to solve such challenges. First, we propose to use deep networks to automatically perform the restoration. Through out experiments, we find the automated restoration can provide comparable quality as those manually restored from an archaeologist. This can significantly speed up the restoration given the enormous size of the historical paintings. Second, we propose to use detection and retrieval for further analyzing the tremendously large amount of objects because it is unreasonable to manually label and analyze them. Several state-of-the-art methods are rigorously tested and quantitatively compared in different criteria and categorically. In this work, we created a new dataset, namely, AI for Dunhuang, to facilitate the research. Version v1.0 of the dataset comprises of data and label for the restoration, style transfer, detection, and retrieval. Specifically, the dataset has 10,000 images for restoration, 3455 for style transfer, and 6147 for property retrieval. Lastly, we propose to use style transfer to link and analyze the styles over time, given that the grottoes were build over 1000 years by numerous artists. This enables the possibly to analyze and study the art styles over 1000 years and further enable future researches on cross-era style analysis. We benchmark representative methods and conduct a comparative study on the results for our solution. The dataset will be publicly available along with this paper. [ABSTRACT FROM AUTHOR]

Swine manure treatment plants are important reservoirs of plasmid-harboring antibiotic resistance genes (ARGs) and physicochemical contaminants, but the changes in the abundances of plasmids and ARGs, and their interactions with the physicochemical properties of manure, are still unclear. Thus, in the present study, plasmidome and metagenome analyses were conducted for samples collected at different stages in the swine manure treatment process. The results indicated that anaerobic digestion and aerobic digestion were the most efficient stages for reducing the abundances of ARGs in swine manure. However, the plasmids associated with ARGs were not effectively removed in these stages. Through the whole treatment process, the IncL/M, IncQ1, IncHI2A, IncA/C, and IncN plasmid groups had strong correlations (r > 0.8, P < 0.01) with most ARG types, thereby indicating that these plasmids play important roles in the persistence of ARGs in this environment. Furthermore, the pH, total nitrogen, total phosphorus, and four heavy metals (Cu, Zn, As, and Fe) significantly affected the abundances of seven ARG subtypes (tetB(P), ant(6)-Ia, tet44, aph(3′′)-Ib, mefB, tet(L), and tet(39)). In particular, florfenicol had the most positive correlations with ARGs. Our results indicated that nutrients, heavy metals, and antibiotics all contributed to the presence and persistence of plasmid-harboring ARGs. This study provides insights into the fate of plasmids and ARGs, and related factors during the swine manure treatment process, thereby facilitating the development of a new treatment technique for removing ARGs and reducing the public health risk associated with livestock production. [ABSTRACT FROM AUTHOR]

The construction of a three-dimensional (3D) conductive network is a potent approach to achieving high electrical and thermal conductivity of silicone rubber at low filler content. In this work, silicone rubber composites with high performance were prepared by constructing a 2D-3D collaborative network structure in a matrix. The results indicate that there is a strong interfacial interaction between flake graphites (FGs), conductive sponges (CSs), and silicone rubber. With the addition of FG and CS, the thermal and electrical conductivities of silicone rubber composites increase of 0.51 W/m·K and 52 S/m, which were 320% and 13 orders of magnitude higher than that of pure silicone rubber, respectively. The silicone rubber composites also display good thermal stability and mechanical properties. Hence, this research provides a new paradigm for achieving high-performance silicone rubber. [ABSTRACT FROM AUTHOR]

The pursuit of atomic precision structure of porous covalent organic frameworks (COFs) is the key to understanding the relationship between structures and properties, and further developing new materials with superior performance. Yet, a challenge of how to determine their atomic structures has always existed since the first COFs reported seventeen years ago. Here, we present a universal method for ab initio structure determination of polycrystalline three-dimensional (3D) COFs at atomic level using enhanced cryo-continuous rotation electron diffraction (cryo-cRED), which combines hierarchical cluster analysis with cryo-EM technique. The high-quality datasets possess not only up to 0.79-angstrom resolution but more than 90% completeness, leading to unambiguous solution and precise refinement with anisotropic temperature factors. With such a powerful method, the dynamic structures with flexible linkers, degree of interpenetration, position of functional groups, and arrangement of ordered guest molecules are successfully revealed with atomic precision in five 3D COFs, which are almost impossible to be obtained without atomic resolution structure solution. This study demonstrates a practicable strategy for determining the structures of polycrystalline COFs and other beam-sensitive materials and to help in the future discovery of novel materials on the other. Structure determination of covalent organic frameworks (COFs) is the key to pushing the development of COF-based materials further but precise determination of the structure of COFs is challenging. Here, the authors develop a universal ab initio structure determination method for polycrystalline 3D COFs using cryo-cRED by combining hierarchical cluster analysis with cryo-EM technique and demonstrate COF structures with atomic preciscion and up to 0.79-angstrom resolution. [ABSTRACT FROM AUTHOR]

High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem. For the unmet need to choose the suitable doublet detection method, an ensemble machine learning algorithm called Chord was developed, which integrates multiple methods and achieves higher accuracy and stability on different scRNA-seq datasets. [ABSTRACT FROM AUTHOR]

The detection and removal methods of impulse noise often need to estimate the noise level of the damaged image in advance to obtain a better detection rate. An effective method of random-value impulse noise level estimation based on local similarity is proposed in this paper. Firstly, quantify the similarity between the center pixel x and any pixel y in the neighborhood of x based on their Euclidean distance and gray difference, then the similarity of pixel x and each pixel in its neighborhood is accumulated and summed to obtain the local similarity (LS) of pixel x. The value of LS represents the local consistency of a given pixel with respect to its neighboring pixels in which can also determine if a pixel is an impulse noise or a clean pixel. Hence, the LS value of a pixel could be regarded as an effective index to measure whether it is a clean pixel. Then the noise pixels in multiple flat regions of the noise image are detected to obtain the noise level of each region, and the noise level of these flat regions are processed with average operation to estimate the impulse noise level of the entire image finally. Extensive experiments were conducted to verify the effectiveness of the method and the experimental results show that the method is effective in scenarios with various noise levels, and the estimation error of the noise level of most images is within 1%. By comparing the RMSE and Std of different noise level estimation algorithms, it can be found that the algorithm proposed in this paper has higher robustness and accuracy, which can be well applied to practical applications with impulse noise level as the key parameter. [ABSTRACT FROM AUTHOR]

Cell embedment into a solid support matrix is considered essential for the culture of intestinal epithelial organoids and tumoroids, but this technique presents challenges that impede scalable culture expansion, experimental manipulation, high-throughput screening and diagnostic applications. We have developed a low-viscosity matrix (LVM) suspension culture method that enables efficient establishment and propagation of organoids and tumoroids from the human large intestine. Organoids and tumoroids cultured in LVM suspension recapitulate the morphological development observed in solid matrices, with tumoroids reflecting the histological features and genetic heterogeneity of primary colorectal cancers. We demonstrate the utility of LVM suspension culture for organoid and tumoroid bioreactor applications and biobanking, as well as tumoroid high-throughput drug sensitivity testing. These methods provide opportunities for the study and use of patient-derived organoids and tumoroids from the large intestine. Given the practical limitations of solid matrix-based protocols in organoid culture, Yumiko Hirokawa et al. assess the ability of low-concentration Matrigel conditions to promote intestinal organoid growth. Their results suggest that a low-viscosity culture system can improve live cell yield compared to the existing dome method, while maintaining similar morphology, and represents a useful approach for high-throughput applications of organoids. [ABSTRACT FROM AUTHOR]

Toll-like receptors (TLRs) play an important role in the innate immune response. While a lot is known about the structures of their extracellular parts, many questions are still left unanswered, when the structural basis of TLR activation is analyzed for the TLR intracellular domains. Here we report the structure and dynamics of TLR1 toll-interleukin like (TIR) cytoplasmic domain in crystal and in solution. We found that the TLR1-TIR domain is capable of specific binding of Zn with nanomolar affinity. Interactions with Zn are mediated by cysteine residues 667 and 686 and C667 is essential for the Zn binding. Potential structures of the TLR1-TIR/Zn complex were predicted in silico. Using the functional assays for the heterodimeric TLR1/2 receptor, we found that both Zn addition and Zn depletion affect the activity of TLR1, and C667A mutation disrupts the receptor activity. Analysis of C667 position in the TLR1 structure and possible effects of C667A mutation, suggests that zinc-binding ability of TLR1-TIR domain is critical for the receptor activation. Lushpa et al report the structure and dynamics of the TLR1 toll-interleukin like (TIR) cytoplasmic domain in both crystal and solution. They demonstrate that the TLR1 TIR domain is capable of specific binding of Zn with nanomolar affinity, which appears to be critical for receptor activation, and provide potential structures TLR1-TIR/Zn complex based on in silico data. [ABSTRACT FROM AUTHOR]

Stochastic excitations inevitably pose a significant threat to the safety and reliability of structures, making it imperative to develop stochastic vibration control techniques. This paper utilizes the mass amplification property of inerter to construct inertial nonlinear energy sink (INES) to suppress the random response of a spring-mass-damper structure under random excitation. Specifically, the governing equations of a main oscillator coupled with INES forced by a stochastic excitation are derived. After that, the equivalent form of system equations is treated by the stochastic averaging technique together with the Jacobi transformation to obtain the steady-state probability density function (PDF), leading to determination of stochastic response as well as parameters optimization. The effects of parameters including inertance ratio and intensity of the external excitation on the random response of main structure are examined. This paper illustrates that INES exhibits both superior random vibration control capability and lightweight design characteristic. Besides, excellent consistency is found between the results of the present approach and the available simulation data. Note that this work presented herein will promote the design and application of INES. [ABSTRACT FROM AUTHOR]

Copy move has become a simple and effective operation for image forgeries due to the advancement of image editing software, which is still challenging to be detected. In this paper, a novel method is proposed for copy move forgery detection based on Keypoint and Patch Match. Local Intensity Order Pattern (LIOP), a robust keypoint descriptor, is combined with SIFT to obtain reliable keypoints. After using g2NN to match the extracted keypoints, a new matched keypoint pair description model and a density grid-based filtering strategy are applied to removing the redundancy matched keypoint pairs. Finally an enhanced patch match approach is utilized to examine the matched keypoint pairs to accurately determine the existence of forgery. Compared with the state-of-the-art methods, the proposed method can detect copy move region more precisely according to the experimental result, even when detected objects are distorted by some processing such as rotation, scaling, JPEG compression and additional noise. [ABSTRACT FROM AUTHOR]

Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks. [ABSTRACT FROM AUTHOR]

In order to facilitate cooperation to solve problems within a nation-state, a new approach which conceptualizes citizenship in terms of shared fate has been promoted to potentially ameliorate the tensions identified between civic liberty and solidarity. Proponents of an emphasis on shared fate frame it not in terms of a particular shared national identity, but in terms of participation in the shared project(s) of the nation-state. The approach of singular shared fate rightly emphasizes the urgency of finding a common ground for people to cultivate obligations to others and achieve sincere cooperation in a society. Unfortunately, in some cases it leaves room for some people to undermine the common ground and its good intention, however, as the promotion of a view of singular shared fate risks producing a hegemonic singular nation building project and predesigning an agreement before a truly inclusive and just dialogue among relevant stakeholders proceeds. To make the good intentions of the notion of shared fate realizable, a modification is explored, in recasting the concept of singular "shared fate" to plural "shared fates". Given the situation that people in societies have the plural shared fates de facto, the view of plural shared fates recognizes that people will reject any singular substantial nation building project that has a predesigned direction. In current, divided societies, acknowledging multiple fates at the beginning, rather than predesigning a singular fate, can better provide a platform for all stakeholders (including citizens, would-be citizens, newcomers, and immigrants) to discuss their obligations to others toward sincere cooperation through equal co-construction of what is shared and what is not shared. To enhance civic education in a multicultural society, we suggest that complementing the concept of singular "shared fate" with a recognition of the value of plural "shared fates" can provide a context for all people to work together towards a more inclusive and just future. [ABSTRACT FROM AUTHOR]

Digital image are easy to be tampered by the photo editing software. Therefore, digital image forensics which aims at validating the authenticity of the digital image are received wide public concern. Region duplication is a commonly used operation in digital image forgeries. The main aims of the region duplication are to overemphasize or conceal some contents by duplicating some regions on the image. Most of the region duplication methods can be categorized into two main classes:block-based and keypoint-based methods. In this paper, a novel region duplication detection scheme is proposed based on hybrid feature and evaluative clustering. The proposed scheme is divided into two stages: the rough matching and the exact matching. In the rough matching, first, hybrid keypoints are extracted from the input image, and those keypoints are described by the unified descriptors. Second, those keypoints are matched by the g2NN strategy. Third, those matched keypoints are grouped by the proposed clustering based on evaluation. Fourth, affine transformations are estimated between these groups, and Bag of Word is used to filter inaccuracy affine transformations to improve the results of pixel level. When no affine transformation is obtained, in the exact matching, each suspicious region is handled separately. Experimental results indicate that the proposed scheme outperforms the state-of-the-art methods under various conditions. [ABSTRACT FROM AUTHOR]

Purpose: There is a large inter-individual variation in the efficacy of valproic acid (VPA) against epilepsy. The genetic polymorphism influence of sodium channels on VPA response remains a matter of debate. The aim of the study was to explore the effect of SCN1A and SCN2A gene polymorphisms on VPA response in the treatment of epilepsy among Chinese patients. Methods: A total of 354 epileptic patients with VPA treatment were genotyped for five single nucleotide polymorphisms (SNP), including SCN1A rs10188577 T>C, rs2298771 T>C, rs3812718 G>A, and SCN2A rs2304016 A>G, rs17183814 G>A. A binary logistic regression analysis was performed to evaluate the association of genotype with VPA antiepileptic effects, adjusting the influence of confounding factors. Results: Genotype distributions of all selected SNPs were consistent with the Hardy–Weinberg equilibrium in epilepsy patients. SCN1A rs3812718 and SCN2A rs2304016 were found to be significantly associated with VPA response, both in monotherapy and in VPA-based polytherapy. Patients with the rs3812718 A allele were more frequently seen in the VPA-responsive group (P < 0.05), and the rs2304016 G allele was related to an increased risk of resistance to VPA therapy (P < 0.05). Conclusions: Our study revealed that SCN1A rs3812718 and SCN2A rs2304016 polymorphisms might be markers of VPA response in Chinese epilepsy patients. Trial registration: ChiCTR-1800016477 [ABSTRACT FROM AUTHOR]

Key message: Coexpression network revealing genes with Co-variation Expression pattern (CE) and those with Top rank of Expression fold change (TE) played different roles in responding to new environment of Miscanthus lutarioriparius.Abstract: Variation in gene expression level, the product of genetic and/or environmental perturbation, determines the robustness-to-plasticity spectrum of a phenotype in plants. Understanding how expression variation of plant population response to a new field is crucial to domesticate energy crops. Weighted Gene Coexpression Network Analysis (WGCNA) was used to explore the patterns of expression variation based on 72 Miscanthus lutarioriparius transcriptomes from two contrasting environments, one near the native habitat and the other in one harsh domesticating region. The 932 genes with Co-variation Expression pattern (CE) and other 932 genes with Top rank of Expression fold change (TE) were identified and the former were strongly associated with the water use efficiency (r ≥ 0.55, P ≤ 10−7). Functional enrichment of CE genes were related to three organelles, which well matched the annotation of twelve motifs identified from their conserved noncoding sequence; while TE genes were mostly related to biotic and/or abiotic stress. The expression robustness of CE genes with high genetic diversity kept relatively stable between environments while the harsh environment reduced the expression robustness of TE genes with low genetic diversity. The expression plasticity of CE genes was increased less than that of TE genes. These results suggested that expression variation of CE genes and TE genes could account for the robustness and plasticity of acclimation ability of Miscanthus, respectively. The patterns of expression variation revealed by transcriptomic network would shed new light on breeding and domestication of energy crops. [ABSTRACT FROM AUTHOR]

In this paper, a novel region duplication detection method is proposed based on image segmentation and keypoint contexts. The proposed method includes the primary region duplication detection based on keypoints and the supplementary region duplication detection based on blocks. In the primary region duplication detection, an image is divided into non-overlapped patches by using SLIC. Furthermore, the keypoints are matched and clustered within the same patch as patch feature. Then the patches are matched and an affine transformation is tried to be estimated from a pair of patches. When the estimation fails, in the supplementary region duplication detection, a transformation matrix is tried to be estimated from a pair of keypoints by the proposed Keypoint Contexts (KC) approach. The experimental results indicate that the proposed method can achieve much better comprehensive performances than the state-of-the-art methods on the public databases, even under various challenging conditions. [ABSTRACT FROM AUTHOR]

A wireless sensor network is applied for detecting information, by nodes, then generates and transfers the packets to the clustering head for further transmission. In practice, due to the influence of environmental factors, traffic loads of nodes and barrier, a node may fail to detect the information which occurs within its sensing area. Thus, the redundant deployment is often conducted. There are some frequent questions need to be considered when deploying the WSN: (1) how many nodes are required for obtaining a confident coverage of the area; (2) how well does the WSN perform as time goes by, because it is an energy consumption technique; (3) when is the best opportunity to reallocate the nodes. To answer the above questions, we conduct this research. We use $$k$$ -out-of- $$n$$ model to calculate the $$k$$ -coverage probability, which determines the minimal number of nodes that are needed to be deployed in the monitored area. We formulate the node availability and WSN availability with the assumption that the information arrival is a Poisson process. By applying the age-based replacement model, we obtain the optimal reallocation interval with lowest long run expected cost rate. [ABSTRACT FROM AUTHOR]

2.5 mol% Ni-modified BiNaTiO (BNT-Ni-0.025) ceramic has been fabricated based on the sol-gel method. The BNT-Ni-0.025 ceramic possesses a rhombohedral perovskite structure with denser microstructure than that of pure BNT ceramic. The introduction of certain amount of Ni (e.g. 2.5 mol%) in BNT can reduce the sintering temperature (50 °C lower), effectively suppress the grain growth, greatly decrease the dielectric loss and then produce multifunctionality. The BNT-Ni-0.025 ceramic also exhibits good ferroelectric properties with a large remanent polarization of ~26 μC/cm. The observed weak ferromagnetism of the ceramic can be ascribed to the exchange interaction between free carriers (oxygen vacancy induced delocalized electrons) and the localized Ni ions. Under the excitation of 370 nm, the BNT-Ni-0.025 ceramic shows broadband NIR luminescence centered at ~1480 nm from Ni with long emission lifetime (28 µs), which can cover the optical communication window and be applied in broadband optical amplifiers. The multifunctional BNT-Ni-0.025 ceramic shows potentials in the application of optical-electrical-magnetic areas. [ABSTRACT FROM AUTHOR]

Nowadays, fully unsupervised video object segmentation is still a challenge in computer vision. Furthermore, it is more difficult to segment the object from a set of clips. In this paper, we propose an unsupervised and on-line method that efficiently segments common objects from a set of video clips. Our approach is based on the hypothesis, that common or similar objects in multiple video clips are salient, and they share similar features. At first, we try to find out the regions in every clip which are salient and share similar features by proposing a new co-saliency scheme based on superpixels. Then, the most salient superpixels are chosen as the initial object marker superpixels. Starting from these superpixels, we merge neighboring and similar regions, and segment out the final object parts. The experimental results demonstrate that the proposed method can efficiently segment the common objects from a group of video clips with generally lower error rate than some state-of-the-art video co-segmentation methods. [ABSTRACT FROM AUTHOR]

A novel scheme for non-rigid video object tracking using segment-based object candidates is proposed in this paper. Rather than using a conventional bounding box, the tracker is based on segments and considers the target object to be a combination of segments, where the hierarchical hue-saturation-value histogram is extracted as a feature. The objectness method is employed and integrated into the tracker to generate candidates for a similarity measure. Moreover, segment-based motion weights are introduced to give higher weights to candidates with motion consistency. A confidence-collecting scheme is proposed for similar candidates. To validate our method, experiments were conducted using several image sequences with different non-rigid challenges. The experimental results show that the proposed scheme can achieve better performance than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A novel scheme for efficient shape classification using region descriptors and extreme learning machine with kernels is proposed. The skeleton and boundary of the input shape image are first extracted. Then the boundary is simplified to remove noise and minor variations. Finally, region descriptors for the local skeleton, and the simplified shape signature are constructed to form a hybrid feature vector. Training and classification are then performed using kernel extreme learning machine (k-ELM) for efficient shape classification. Experimental results show that the proposed scheme is very fast and can archive high classification accuracy of 91.43 % on the challenging MPEG-7 dataset, outperforming existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

ITH-type germanosilicate, Cu- and Co-containing ITH-zeotype materials, were synthesized by the hydrothermal method in the presence of an organic structure-directing agent, N, N, N′, N′-tetramethyl-1,6-hexanediamine. The effects of reaction compositions, including the molar ratios of SiO/GeO, SiO/HO, and MeO/SiO (Me = Cu and Co transition atoms) on the synthesis of the pure product were investigated. The synthesized samples were characterized by powder and single-crystal X-ray diffractions, scanning electron microscopy, inductively coupled plasma (ICP) analysis, energy dispersive spectrometry, thermogravimetric analysis, UV-Vis diffuse reflectance spectrum (DRS) measurements, X-ray photoelectron spectroscopy, and N adsorption and desorption analysis. The results reveal that the concentration of HO in the reaction mixture has an important impact on the synthesis of pure ITH-type zeolite. The framework's Si/Ge ratio is slightly changed from 3.1 to 3.9 when the initial SiO/GeO molar ratio is increased from 0.8 to 10. Structure refinement results show that the Ge atoms preferably occupy sites in the double-four ring units. Cu and Co atoms have been introduced for the first time into ITH-type materials. The trace amounts of amorphous Cu- and Co-compounds in the resulting materials can be readily detected by backscattered electron imaging, so that pure samples can be selected for ICP and UV-Vis DRS measurements. [ABSTRACT FROM AUTHOR]

A novel non-rigid object tracking based on interactive user-define marker and superpixel Gaussian kernel is proposed in this paper. In the initialization stage, instead of using the traditional bounding box to locate the targeted object, we have employed an interactive segmentation with user-defined marker to segment the object accurately in the first frame of the input video to avoid the background influence in the traditional bounding box. During the tracking stage, by using a Gaussian kernel as movement constraint, each superpixel is tracked independently to locate the object in the next frame. Experimental results show that the proposed method compared to state of the art methods can achieve better robustness and accuracy for various challenging video clips. [ABSTRACT FROM AUTHOR]

Our objective is to explore the tumor-specific mutated genes by transcriptome sequencing of patients with acute myeloblastic leukemia. 96 patients with subtype M2 acute myeloid leukemia (AML), admitted during January 2007 to January 2012, were selected. Bone marrow and peripheral blood samples from the patients after the first visit and the patients who were improved or alleviated, were subjected to high-throughput sequencing to compare the gene expression. The single nucleotide mutation related to subtype M2 AML was detected. Meanwhile, real-time fluorescent quantitation RT-PCR was used to detect the AML1/ETO fusion gene and its correlation with prognosis after treatment. Among 96 patients, AML1- ETO fusion gene was positive in 52 cases, the positive rate was 54.17 %. The complete relief (CR) rate of AML1- ETO fusion gene positive patients was 84.62 %, and the CR rate of AML1/ETO fusion gene negative patients was 77.27 %; the CR rate of AML1- ETO positive patients was higher than that of patients without the fusion gene, however there was no statistical difference. In the analysis of recurrent gene mutation in AML-M2 patients, IDH2, ASXL1, TET2, JAK1 and JAK2 gene expressions were not significantly different before treatment and after CR, however, IDHI, JAK3, ABL1 and BCR gene expressions were significantly different. In the study of transcriptome in AML-M2 patients, high-throughput sequencing could effectively detect the difference of the gene expression before treatment and after CR. Furthermore, positive expression of AML1- ETO fusion gene had effect on the prognosis of patients. [ABSTRACT FROM AUTHOR]

The aim of this work was to present the common anaerobic digestion technologies in a typical farm-scale biogas plant in China. The comprehensive benefits of most biogas plants in China have not been fully assessed in past decades due to the limited information of the anaerobic digestion processes in biogas plants. This paper analyzed four key aspects (i.e., operational performance, nonrenewable energy (NE) savings, CO emission reduction (CER) and economic benefits (EBs)) of a typical farm-scale biogas plant, where beef cattle manure was used as feedstock. Owing to the monitoring system, stable operation was achieved with a hydraulic retention time of 18-22 days and a production of 876,000 m of biogas and 37,960 t of digestate fertilizer annually. This could substantially substitute for the nonrenewable energy and chemical fertilizer. The total amount of NE savings and CER derived from biogas and digestate fertilizer was 2.10×10 MJ (equivalent to 749.7 tce) and 9.71×10 kg, respectively. The EBs of the biogas plant was 6.84×10 CNY·yr with an outputs-to-inputs ratio of 2.37. As a result, the monitoring system was proved to contribute significantly to the sound management and quantitative assessment of the biogas plant. Biogas plants could produce biogas which could be used to substitute fossil fuels and reduce the emissions of greenhouse gases, and digestate fertilizer is also an important bio-product. [ABSTRACT FROM AUTHOR]

A constant ratio of ferritin heavy chain homolog (HCH) and light chain homolog (LCH) subunits seems to be required to compose the ferritin heteropolymer protein in insects. However, the mechanism by which insect LCH genes regulate protein levels remains unclear. We report that alternative promoters and alternative splicing contribute to maintaining a constant ratio of the two subunits, BdFer1HCH and BdFer2LCH (ferritin 1 HCH and ferritin 2 LCH), in Bactrocera dorsalis, a notorious quarantine pest. The genes BdFer1HCH and BdFer2LCH were identified with a series of potential transcription factor binding sites and were shown to be clustered within the genome in a "head to head" fashion. Thus, we unearthed a potential post-transcriptional mechanism to regulate the levels of LCH subunits, and confirmed that the expressions of BdFer1HCH and BdFer2LCH were induced by 20-hydroecdysone, iron overload, and immune challenge [ABSTRACT FROM AUTHOR]

Background: The inhibitor of growth (ING) family is involved in multiple cellular functions, but the role of ING2 in gastric cancer progression is unclear. Aim: To investigate the effects of ING2 gene knockdown on chemosensitivity to 5-fluorouracil (5-FU) in human gastric cancer cells and its possible mechanisms. Methods: Short hairpin RNA (shRNA) targeting ING2 (shING2) was transfected into MGC-803 cells using Lipofectamine 2000, and stable transfection cell lines were established using G418. Cell viability, cell cycle distribution, cell apoptosis, and invasive ability were measured to determine the influence of ING2 knockdown on cell biologic characteristics. Messenger RNA (mRNA) and protein levels of ING2, cyclin D1, NF-kappaB/p65, and several matrix metalloproteinases (MMPs) were determined by use of real-time polymerase chain reaction (PCR) or Western blotting, respectively. Results: Our results showed that ING2 knockdown induced cell apoptosis and inhibited cell viability significantly ( P < 0.05). Additionally, ING2 knockdown induced a specific G0/G1 arrest. Furthermore, the suppression of ING2 could enhance the chemosensitivity of gastric cancer cells to 5-FU significantly. Moreover, knockdown of ING2 expression significantly reduced cellular metastatic ability and expression of MMPs in MGC-803 cells. The expression of cyclin D1 and NF-kappaB/p65 was also markedly inhibited in MGC-803/shING2 cells compared with control cells. Conclusions: ING2 not only plays an essential role in the growth and invasion of MGC-803 cells but also represents a potential approach to chemosensitization therapy in human gastric cancer. [ABSTRACT FROM AUTHOR]

This paper presents a two-dimensional CFD study of the falling film evaporation of horizontal tubes with different shapes applied in the seawater desalination. The flow and heat transfer characteristics of the falling water film on one circular tube and two non-circular shaped tubes, a drop-shaped tube and an oval-shaped tube, are analyzed, respectively. The Volume Of Fluid (VOF) method is employed to investigate the influence of the mass flow rate and the feeder height on the distribution of the film thickness and the heat transfer performance. The numerical results show that the minimum value of the film thickness appears approximately at the angular positions of 125°, 160° and 170° for the smooth circular, oval- and drop-shaped tubes, respectively. The film thickness grows with the increase of the mass flow rate and the decrease of the feeder height, while the variation pattern varies for different tubes. Moreover, compared with the circular tube, the drop- and oval-shaped tubes have a lower dimensionless temperature and a thinner thermal boundary layer, which means a better heat transfer performance. Finally, the numerical results correlate well with the experimental and predicted data in literature [ABSTRACT FROM AUTHOR]

We present a novel and convenient method for producing selective stylized simplification of images. The user uses a brush to interactively mark certain areas of the input image which are to be left unaltered. Boundaries of these areas are then automatically optimized to underlying object boundaries in the image. Our method then performs stylized simplification of the unmarked areas, while preserving the marked areas. The method ensures a smooth transition between stylized and unaltered regions to leave a mixed reality image which combines the real and the abstract. Stylized simplification is performed using nonlinear diffusion, which can generate sophisticated results. We modify the classic model of nonlinear diffusion to incorporate bilateral filtering; we apply diffusion speed control of each pixel based on the user's input. The level of simplification can be controlled intuitively based on the diffusion time; another parameter controls the abstraction style, giving a simple and intuitive user interface. Our contributions include a simple-to-use method to produce a novel NPR style and a modified nonlinear diffusion model suited to this selective stylized simplification task. Experimental results show that the final mixed reality results are harmonious. [ABSTRACT FROM AUTHOR]