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As the fleet of high-speed rail vehicles expands, ensuring train safety is of the utmost importance, emphasizing the critical need to enhance the precision of axel temperature warning systems. Yet, the limited availability of data on the unique features of high thermal axis temperature conditions in railway systems hinders the optimal performance of intelligent algorithms in alarm detection models. To address these challenges, this study introduces a novel dynamic principal component analysis preprocessing technique for tolerance temperature data to effectively manage missing data and outliers. Furthermore, a customized generative adversarial network is devised to generate distinct data related to high thermal axis temperature, focusing on optimizing the network's objective functions and distinctions to bolster the efficiency and diversity of the generated data. Finally, an integrated model with an optimized transformer module is established to accurately classify alarm levels, provide a comprehensive solution to pressing train safety issues, and, in a timely manner, notify drivers and maintenance departments (DEPOs) of high-temperature warnings. [ABSTRACT FROM AUTHOR]

Chronic low-grade inflammation (CLGI) is associated with obesity and is one of its pathogenetic mechanisms. Lipopolysaccharide (LPS), a component of Gram-negative bacterial cell walls, is the principal cause of CLGI. Studies have found that capsaicin significantly reduces the relative abundance of LPS-producing bacteria. In the present study, TRPV1-knockout (TRPV1−/−) C57BL/6J mice and the intestinal epithelial cell line Caco-2 (TRPV1−/−) were used as models to determine the effect of capsaicin on CLGI and elucidate the mechanism by which it mediates weight loss in vivo and in vitro. We found that the intragastric administration of capsaicin significantly blunted increases in body weight, food intake, blood lipid, and blood glucose in TRPV1−/− mice fed a high-fat diet, suggesting an anti-obesity effect of capsaicin. Capsaicin reduced LPS levels in the intestine by reducing the relative abundance of Proteobacteria such as Helicobacter, Desulfovibrio, and Sutterella. Toll-like receptor 4 (TLR4) levels decreased following decreases in LPS levels. Then, the local inflammation of the intestine was reduced by reducing the expression of tumor necrosis factor (TNF)-α and interleukin (IL)-6 mediated by TLR4. Attenuating local intestinal inflammation led to the increased expression of tight junction proteins zonula occludens 1 (ZO-1) and occludin and the restoration of the intestinal barrier function. Capsaicin increased the expression of ZO-1 and occludin at the transcriptional and translational levels, thereby increasing trans-endothelial electrical resistance and restoring intestinal barrier function. The restoration of intestinal barrier function decreases intestinal permeability, which reduces the concentration of LPS entering the circulation, and reduced endotoxemia leads to decreased serum concentrations of inflammatory cytokines such as TNF-α and IL-6, thereby attenuating CLGI. This study sheds light on the anti-obesity effect of capsaicin and its mechanism by reducing CLGI, increasing our understanding of the anti-obesity effects of capsaicin. It has been confirmed that capsaicin can stimulate the expression of intestinal transmembrane protein ZO-1 and cytoplasmic protein occludin, increase the trans-epithelial electrical resistance value, and repair intestinal barrier function. [ABSTRACT FROM AUTHOR]

Zeolite synthesis under acidic conditions has always presented a challenge. In this study, we successfully prepared series of ZSM‐5 zeolite nanosheets (Z‐5‐SCA‐X) over a broad pH range (4 to 13) without the need for additional supplements. This achievement was realized through aggregation crystallization of ZSM‐5 zeolite subcrystal (Z‐5‐SC) with highly short‐range ordering and ultrasmall size extracted from the synthetic system of ZSM‐5 zeolite. Furthermore, the crystallization behavior of Z‐5‐SC was investigated, revealing its non‐classical crystallization process under mildly alkaline and acidic conditions (pH<10), and the combination of classical and non‐classical processes under strongly alkaline conditions (pH≥10). What's particularly intriguing is that, the silanol nest content in the resultant Z‐5‐SCA‐X samples appears to be dependent on the pH values during the Z‐5‐SC crystallization process rather than its crystallinity. Finally, the results of the furfuryl alcohol etherification reaction demonstrate that reducing the concentration of silanol nests significantly enhances the catalytic performance of the Z‐5‐SCA‐X zeolite. The ability to synthesize zeolite in neutral and acidic environments without the additional mineralizing agents not only broadens the current view of traditional zeolite synthesis but also provides a new approach to control the silanol nest content of zeolite catalysts. [ABSTRACT FROM AUTHOR]

Zeolite synthesis under acidic conditions has always presented a challenge. In this study, we successfully prepared series of ZSM‐5 zeolite nanosheets (Z‐5‐SCA‐X) over a broad pH range (4 to 13) without the need for additional supplements. This achievement was realized through aggregation crystallization of ZSM‐5 zeolite subcrystal (Z‐5‐SC) with highly short‐range ordering and ultrasmall size extracted from the synthetic system of ZSM‐5 zeolite. Furthermore, the crystallization behavior of Z‐5‐SC was investigated, revealing its non‐classical crystallization process under mildly alkaline and acidic conditions (pH<10), and the combination of classical and non‐classical processes under strongly alkaline conditions (pH≥10). What's particularly intriguing is that, the silanol nest content in the resultant Z‐5‐SCA‐X samples appears to be dependent on the pH values during the Z‐5‐SC crystallization process rather than its crystallinity. Finally, the results of the furfuryl alcohol etherification reaction demonstrate that reducing the concentration of silanol nests significantly enhances the catalytic performance of the Z‐5‐SCA‐X zeolite. The ability to synthesize zeolite in neutral and acidic environments without the additional mineralizing agents not only broadens the current view of traditional zeolite synthesis but also provides a new approach to control the silanol nest content of zeolite catalysts. [ABSTRACT FROM AUTHOR]

To investigate the impact of multisensory fusion training (MSFT) combined with 7, 8-dihydroxyflavone (DHF) on the behavioral characteristics, protein expression, microbiome, and serum metabolome using the AD model in mice induced with amyloid-β (Aβ). Methods: We assessed cognitive ability, anxiety-like and depression-like behaviors in Aβ mice using behavioral measures. Western blotting was employed to detect the expression of relevant proteins. The 16S rRNA gene sequencing and metabolomics were used to analyze changes in the intestinal microbial composition and serum metabolic profile, respectively, of Aβ mice. Results: The behavioral outcomes indicated that a 4-week intervention combining DHF and MSFT yielded remarkable improvements in cognitive function and reduced anxiety and depression-like behaviors in Aβ mice. In the hippocampus of Aβ mice, the combined intervention increased the levels of BDNF, VGF, PSD-95, Nrf2, p-GSK3β and p-CREB proteins. Analyses of sequence and metabolomic data revealed that Bacteroides and Ruminococcaceae were remarkably more abundant following the combined intervention, influencing the expression of specific metabolites directly linked to the maintenance of neuronal and neurobehavioral functions. These metabolites play a crucial role in vital processes, such as amino acid metabolism, lipid metabolism, and neurotransmitter metabolism in mice. Conclusion: Our study highlighted that MSFT combined with DHF improves cognitive impairment, anxiety, and depression-like behavior in Aβ mice through multiple mechanisms, and further validated the correlation between the gut microbiome and serum metabolome. These findings open up a promising avenue for future investigations into potential treatment strategies for AD. [ABSTRACT FROM AUTHOR]

Stock price trend prediction is the focus of academics and economists. Selecting appropriate forecasting techniques can help investors to avoid potential financial risks in some extent, and help to determine the future volatility of assets. However, financial time series signals are characterized by non-stationary, long memory and low signal-to-noise ratio, which make it difficult to be forecasted. In order to address the issue, a frequency-domain enhanced bi-directional recurrent quantum network (FDBRQ) is proposed in this paper, which takes into account the influence of external factors and is able to achieve multistep prediction. In the model, a frequency-domain embedding module (FDEM) is used to extract the frequency-domain features of the price series and convert the input stock price time series data into embedding vectors. And we introduce the external feature attention enhancement (EFAE), which utilizes external factor data to enhance the representation of the embedding vectors. To enhance the interpretation of stock price time series and improve the prediction accuracy, a quantum neural network with bi-directional recurrent structure is designed to capture the long-term dependence of the financial time series. The network is built by quantum circuit units (QCUs), and each QCU processes the embedding vector at each time step. Finally, the model outputs the predicted value for multiple future time steps through the fully connected layer, aiming to provide investors with a useful basis for decisions. Experimental results show that our proposed method outperforms other models on the four stock datasets from Chinese stock market. [ABSTRACT FROM AUTHOR]

In order to break the limitation that the gap is fixed once it was synthesized for traditional flexible surface-enhanced Raman scattering substrate, in this work, a laser-induced reduction method was used to fabricate ordered silver nanodot arrays on flexible substrates. By using rhodamine 6G as a probe molecule, Raman characterization is carried out on the hydrogel film/silver substrate at expanded and shrunken states. The substrate shows great reproducibility, and the average relative standard deviation of the probe is 8.7%. In addition, when the hydrogel film was shrunk by 75%, the intensity at 1650 cm−1 will be enhanced by about 36 times, and the detection concentration of the R6G molecule can reach 10−7 mol/l. [ABSTRACT FROM AUTHOR]

The increasing intensity and frequency of extreme climate events have made improving the adaptability to extreme climate events a strategic imperative for manufacturing companies. This paper investigates whether manufacturing enterprises increase green technology innovation affected by different extreme climate events. Based on panel data of Chinese listed manufacturing enterprises, we show that extreme precipitation events can positively promote green technology innovation, yet extreme temperature events do not. Heterogeneity analyses suggest that the effect of extreme precipitation events on green technology innovation is more significant for non-state-owned enterprises, poor performance enterprises, and high R&D intensity enterprises than other enterprises. Furthermore, the facilitating effect of extreme precipitation events on green technology innovation is merely temporary. [ABSTRACT FROM AUTHOR]

Many countries have adopted payment for ecosystem services (PES) programs to reduce deforestation. Empirical evaluations find such programs, which pay forest owners to conserve forest, can lead to anywhere from no impact to a 50% reduction in deforestation level. To better understand the potential effectiveness of PES contracts, we use a principal–agent model, in which the agent has an observable amount of initial forest land and a privately known baseline conservation level. Commonly used conditional contracts perform well when the environmental value of forest is sufficiently high or sufficiently low, but can do arbitrarily poorly compared with the optimal contract for intermediate values. We identify a linear contract with a distribution-free per-unit price that guarantees at least half of the optimal contract payoff. A numerical study using U.S. land use data supports our findings and illustrates when linear or conditional contracts are likely to be more effective. This paper was This paper was accepted by Beril Toktay, Special Section of Management Science on Business and Climate Change. Funding: W. Dai Li was supported by a Stanford Interdisciplinary Graduate Fellowship. I. Lo was supported by an Environmental Venture Project Grant from the Stanford Woods Institute for the Environment. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2021.4273. [ABSTRACT FROM AUTHOR]

Background: Computational thinking (CT) has become a crucial skill for individuals in the 21st century, and while more educators are starting to recognize the importance of CT education, there is still a lack of research on how to teach young children CT, particularly outside of traditional school settings. Objectives: To fill the gap in knowledge, we aimed to investigate the effectiveness of a web‐based parent education program on improving children's CT skills. Additionally, we sought to determine if children's age, gender and family socioeconomic status had any impact on the development of CT skills. Methods: We selected 86 adult–child pairs in the K3 age group to participate in a 4‐week intervention program using a quasi‐experimental approach. Results: After 4 weeks, children in the intervention group had improved their CT skills more than their peers in the control group. This shows that the intervention was successful in enhancing children's CT skills. Age had a moderating effect on CT enhancement, with older children showing a more significant improvement than younger children. However, children's gender and family socioeconomic status did not have any moderating effects. Conclusions: These results demonstrate that CT education can be effectively implemented in the home setting through web‐mediated parent education. Encouraging the use of unplugged CT activities at home can aid children in acquiring CT skills. Lay Description: What is already known about this topic: Computational thinking (CT) education can be started at the early childhood stage.Both plugged‐in and unplugged activities can promote children's CT.Unplugged activities have many advantages and are suitable for young children. What this paper adds: Web‐mediated parent education is useful for equipping parents with the knowledge and skills to conduct CT activities at home.CT education has a positive effect on children in the home setting.Parents play an important leading role in children's CT education.Parent–child unplugged activities are beneficial to children's CT enhancement. Implications for practice and/or policy: A web‐mediated approach is an appropriate way to encourage parent participation in CT training.CT‐related parent education should be scaled up so that parents can play an active educational role at home.Unplugged CT activities should be designed more for the home settings in addition to school settings. [ABSTRACT FROM AUTHOR]

The field of computer vision has been applied in many topics and scenes, especially in the shipping business which occupies a large position in the world trade. With the development of ship intellectualization, the task of detection, tracking, segmentation and classification of interested targets become more and more important. Publicly available dataset is the foundation to promote research in shipping. Based on this intention, we systematically present a review of maritime datasets on maritime perception. In this paper, comparison is made in terms of data type, environment, ground authenticity, and applicable research directions. The aim of writing this paper is to help researchers quickly identify the most suitable dataset for their work. [ABSTRACT FROM AUTHOR]

Nitrification in the rhizosphere is a crucial process in controlling nitrogen‐use efficiency (NUE) in flooded paddy soils. To understand the relationship between the nitrification ability of the rhizosphere and NUE, pot experiments using 15N tracer technique were conducted to investigate the impacts of the rhizosphere soil net nitrification rate on NUE and denitrification losses at different rice growth stages in two paddy soils, which were sampled from Jurong (JR) and Yancheng (YC) in Jiangsu Province in China. The results showed that the nitrification rate in JR rhizosphere soil was lower than in YC rhizosphere soil at all rice growth stages. The abundance of ammonia‐oxidizing bacteria (AOB), ammonia‐oxidizing archaea (AOA), and pH in YC rhizosphere soils were always higher than in JR rhizosphere soils. Rice yield, biomass, NUE, leaf glutamine synthetase (GS) activity, and nitrate reductase (NR) activity were higher in JR soils with low nitrification rates than in YC soils with high nitrification rates (p < 0.05). In contrast, denitrification loss from JR soil (12.69%–23.41%) was lower than that from YC soil (26.83%–40.98%; p < 0.05) for each rice growth stage. The biomass and NUE decreased significantly as the net nitrification rate, the abundance of AOA and AOB of both the JR and YC rhizosphere soils increased (p < 0.05), and the denitrification loss was enhanced as the rhizosphere nitrification rate increased in the JR and YC soils (p < 0.05) during the rice growth period. The rhizosphere‐dominant AOB community Nitrosospira is the key factor affecting the nitrification rate and then decreasing rice NUE. In general, the rhizosphere nitrification rate in paddy soils is a primary factor controlling the rice NUE and denitrification loss. [ABSTRACT FROM AUTHOR]

Fiber optic sensors are often used as acoustic sensors to detect sound waves because of their apparent advantages, such as anti-electromagnetic interference and strong adaptation to the environment. The transverse vibration mode of the fiber caused by the acoustic wave can be obtained, and the principle of the optical fiber sensor to detect the acoustic wave signal was explored by using a simple model. It is found that the acoustic wave can effectively cause the change in birefringence of the fiber only when the number of azimuthal modes is 2, and the acoustic wave was detected by using a fiber sensor. It is found, by analyzing the detection mechanism, that the spectral width is proportional to the acoustic impedance of the surrounding medium, and the acoustic interaction between the TR22 mode and the surrounding medium is much weaker than that of the TR21 mode. This provides a theoretical basis for the detection of acoustic signals by fiber optic sensors. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL. [ABSTRACT FROM AUTHOR]

It is a challenge work to estimate the 3D human motion from image sequence. There are some problems, such as unsatisfactory estimation error, ambiguous matching and transient occlusion. Although the prior information of learning large‐scale samples exists, these problems are still difficult to be solved. How to extract the feature of the high‐dimensional (HD) sample of 3D human motion and find the desired one will become the key to solve these problems above. Some dimension reduction methods can extract the sample features and build the low‐dimensional (LD) space to view their LD features, but how to search the relevant valid and desired LD samples remains the bottleneck problem, which can be used to reconstruct the 3D human motions denoted by the corresponding high‐dimensional samples. Thus, a new method called dynamic manifold Boltzmann optimization (DMBO) is proposed to estimate the 3D human motion from multi‐view images. DMBO can find the best matching 3D human motion model by the help of the self‐supervised learning from Gaussian incremental dimension reduction model (GIDRM). DMBO can avoid the local optimum during searching and solve the problems above, so that the generation of the accurate 3D human motion corresponding to multi‐view images can be achieved. [ABSTRACT FROM AUTHOR]

Fine‐grained visual categorization (FGVC) is a challenging task, facing the issues such as inter‐class similarities, large intra‐class variances, scale variation, and angle variation. To address these issues, the authors propose a novel multiscale attention dynamic aware network (MADA‐Net). The core of network consists of three parallel sub‐networks, which learn features from different scales. Each sub‐network is composed of three serial sub‐modules: (1) A self‐attention module (SAM) locates objects according to relative importance scattered throughout feature map. (2) A multiscale feature extractor (MFE) learns the non‐linear features of objects. (3) A dynamic aware module (DAM) enhances the learning capability of spatial deformation of the network to generate high‐quality feature map. In addition, the authors propose a multiscale adjusted loss (MA‐Loss) to improve the performance of network. Experiments on three prevailing benchmark datasets demonstrate that our method can achieve state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR]

The heterogeneous Cu-based solid catalysts have attracted enormous attention of researchers in different potential applications. In this work, a graphitic carbon nitride/copper-doped carbon dots (g-C3N4/Cu-CDs) nanocomposite with both intrinsic peroxidase- and oxidase-like (POD- and OXD-like) activities was successfully prepared. Due to the synergistic catalytic enhancement and electron transmission provided by g-C3N4, both POD- and OXD-like activities of g-C3N4/Cu-CDs were significantly improved compared with those of g-C3N4 and Cu-CDs. Moreover, upon the addition of H2O2, g-C3N4/Cu-CDs could catalyze the oxidation of colorless o-phenylenediamine (OPD) to form a yellow fluorescent product 2,3-diaminophenazine (DAP) with yellow fluorescence. Interestingly, the OPD + H2O2 + g-C3N4/Cu-CDs system could be inhibited by phenolic compounds, which could efficiently decrease the DAP fluorescence. Based on this, a method for the quantitative detection of total phenolic substances was established. Meanwhile, the use of OXD-like activity of nanocomposites was extended for the degradation of phenols (e.g., 2-CP), which showed a good degradation efficiency. Based on the result that the conversion of Cu+/Cu2+/Cu0 plays pivotal roles in promoting the generation of radicals (i.e., •OH and •O2–), a possible catalytic mechanism of g-C3N4/Cu-CDs was deduced. These findings showed that the proposed g-C3N4/Cu-CDs exhibit great potential to become a green catalyst for the degradation of phenolic pollutants in the environment. [ABSTRACT FROM AUTHOR]

Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance, and night vision. Deep learning-based detectors have achieved major progress, which usually need large amount of labelled training data. However, labelled data for object detection in thermal images is scarce and expensive to collect. How to take advantage of the large number labelled visible images and adapt them into thermal image domain is expected to solve. This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images and preserves the annotation information of the visible source domain. The image generation includes a CycleGAN-based image-to-image translation and an intensity inversion transformation. Generated fake thermal images are used as renewed source domain, and then the off-the-shelf domain adaptive faster RCNN is utilized to reduce the gap between the generated intermediate domain and the thermal target domain. Experiments demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants. [ABSTRACT FROM AUTHOR]

Multiple ship tracking (MST) is an important task in marine surveillance and ship situational awareness systems. Considerable work has been conducted on multiple object tracking in recent years, but it has focused primarily on pedestrians and automobiles, leaving a gap in studies on MST due to the particularities of complex marine scenes, such as ship scale variations, the long-tailed distribution of ships, and long-term occlusions caused by ship movements. In this article, we present a robust deep affinity network (RoDAN) for MST. To overcome the above difficulties in MST, we start with the basic deep affinity network (DAN) and improve it in three aspects: scale, region, and motion. For the scale dimension, we integrate an atrous spatial pyramid pooling (ASPP) module to improve the modeling ability for multiscale ships. For the region dimension, we propose the joint global region modeling (JGRM) module, which further strengthens the modeling ability of DAN and exploit it to overcome the long-tailed distribution property of ships. For the motion dimension, we propose the motion-matching optimization (MMO) module to fine-tune the tracking results and make our tracker more robust, less reliant on the front-end detector, and ameliorate long-term occlusions. The experimental results demonstrate that our MST method outperforms the state-of-the-art methods. In particular, it reduces the number of ID switches (IDSs) and trajectory fragmentations (FMs), achieving holistically preferable performance. Meanwhile, our method achieves a comparable speed. [ABSTRACT FROM AUTHOR]

Abnormal expression of Ten eleven translocation‐2 (Tet2) contributes to the pathogenesis of Alzheimer's disease (AD). However, to date, the role of Tet2 in modulating neuronal morphology upon amyloid‐β (Aβ)‐induced neurotoxicity has not been shown in a mouse model of AD. Here, we have developed a model of injured mouse hippocampal neurons induced by Aβ42 oligomers in vitro. We also investigated the role of Tet2 in injured neurons using recombinant plasmids‐induced Tet2 inhibition or over‐expression. We found that the reduced expression of Tet2 exacerbated neuronal damage, whereas the increased expression of Tet2 was sufficient to protect neurons against Aβ42 toxicity. Our results indicate that the brains of aged APPswe/PSEN1 double‐transgenic (2 × Tg‐AD) mice exhibit an increase in Aβ plaque accumulation and a decrease in Tet2 expression. As a result, we have also explored the underlying mechanisms of Tet2 in cognition and amyloid load in 2 × Tg‐AD mice via adeno‐associated virus‐mediated Tet2 knockdown or over‐expression. Recombinant adeno‐associated virus was microinjected into bilateral dentate gyrus regions of the hippocampus of the mice. Knocking down Tet2 in young 2 × Tg‐AD mice resulted in the same extent of cognitive dysfunction as aged 2 × Tg‐AD mice. Importantly, in middle‐aged 2 × Tg‐AD mice, knocking down Tet2 accelerated the accumulation of Aβ plaques, whereas over‐expressing Tet2 alleviated amyloid burden and memory loss. Furthermore, our hippocampal RNA‐seq data, from young 2 × Tg‐AD mice, were enriched with aberrantly expressed lncRNAs and miRNAs that are modulated by Tet2. Tet2‐modulated lncRNAs (Malat1, Meg3, Sox2ot, Gm15477, Snhg1) and miRNAs (miR‐764, miR‐211, and miR‐34a) may play a role in neuron formation. Overall, these results indicate that Tet2 may be a potential therapeutic target for repairing neuronal damage and cognitive impairment in AD. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) human motion capture is a hot researching topic at present. The network becomes advanced nowadays, the appearance of 3D human motion is indispensable in the multimedia works, such as image, video, and game. 3D human motion plays an important role in the publication and expression of all kinds of medium. How to capture the 3D human motion is the key technology of multimedia product. Therefore, a new algorithm called incremental dimension reduction and projection position optimization (IDRPPO) is proposed in this paper. This algorithm can help to learn sparse 3D human motion samples and generate the new ones. Thus, it can provide the technique for making 3D character animation. By taking advantage of the Gaussian incremental dimension reduction model (GIDRM) and projection position optimization, the proposed algorithm can learn the existing samples and establish the relevant mapping between the low dimensional (LD) data and the high dimensional (HD) data. Finally, the missing frames of input 3D human motion and the other type of 3D human motion can be generated by the IDRPPO. [ABSTRACT FROM AUTHOR]

In this work, based on a pH-responsive hydrogel, we report on a reversible data encryption–decryption technique in which the pH channel is employed for data manipulation. Upon alkali or acid stimulation, the hydrogel exhibits a network expansion or shrinkage in response to the pH variations. In particular, pre-doping the hydrogel with silver ions, we demonstrate that data input can be encoded in the hydrogel platform through the direct writing and patterning of silver nanodots. By this means, the scattering signals from the patterned nanodot pixels are converted to the binarized data. Meanwhile, we show the threshold behaviour of the hydrogel system in which dynamic switching of the encoded plasmonic pattern to the optically resolvable/irresolvable state is viable only at finite pH values. By delicately matching pixel spacings of the encoded pattern and the diffraction limit of the deciphering microscopic system, reversible sub-diffraction limit data encryption is achieved by selectively imposing acid and alkali stimulations. The suggested strategy offers a potential solution for optical storage, multiplexed data manipulation, and optical data security. [ABSTRACT FROM AUTHOR]

Eggs are one of the most common foods in the world, and their safety is a major concern for the public. We aimed to develop a novel clean-up approach named Bond Elut Enhanced Matrix Removal-Lipid (EMR-Lipid) for the simultaneous determination of 325 pesticides in the chicken egg by UHPLC–MS/MS and GC–MS/MS. The initial step of extraction was adopted by acetonitrile with 5% formic acid and subsequent adoption of EMR-Lipid d-SPE for further clean-up. Compared with the traditional C18 procedure, EMR-Lipid clean-up achieved a superior degreasing effect. The recoveries of at least 85% of pesticides were in the range of 60–120% at three fortified levels and the relative standard deviation of nearly 96% of analytes were within the limits of criteria. Improved linearity was evaluated using a matrix-matched calibration at eight concentrations in the range 0.1–40 µg/kg; the correlation coefficients of 309 analytes were exceeding 0.99. The new method offered quick, reliable and consistent detection and quantification of 325 pesticide residues, thereby demonstrating promising future for sample testing. [ABSTRACT FROM AUTHOR]

An uncomplicated, valid, and convenient way for detection of lycopene and β-carotene in fruit juice samples using acid–base-induced deep eutectic solvent (DES) liquid-liquid microextraction (DES-ABLLME) prior to HPLC was developed. The ternary fatty acid DESs were prepared by C9:C10:C11 (2:1:1), which serve as hydrogen bond donors and hydrogen bond acceptors synchronously. In extraction procedure, NH3·H2O was used as the emulsifier and reacted with DESs with a milk solution, which can significantly increase the extraction efficiency. HCl was used as the de-emulsifier, which could break the state of emulsification. At optimum conditions, the calibration graphs were linear in the concentration range from 0.1 to 100 μg mL−1 for lycopene and from 0.025 to 5.00 μg mL−1 for β-carotene. The limits of detection (LODs) were 0.05 and 0.002 μg mL−1 for lycopene and β-carotene, respectively. The RSD values were varied from 1.3 to 4.1%. Finally, the proposed method was applied to analyze β-carotene and lycopene in fruit juices. [ABSTRACT FROM AUTHOR]

Surface flaw inspection is of great importance for quality control in the field of manufacture. In this paper, a novel surface flaw inspection algorithm is proposed based on adaptive multiscale image collection (AMIC) using convolutional neural networks. First, the inspection networks are pretrained with ImageNet data set. Second, the AMIC is established, which consists of adaptive multiscale image extraction and with-contour local extraction from training images. Through the AMIC, the training data set is greatly augmented, and labels of images can be accomplished automatically without artificial consumption. Then, transfer learning is performed with the AMIC established from training data set. Finally, an automatic surface flaw inspection instrument for large-volume metal components embedded with the proposed inspection algorithm is designed. Experiments with small metal components are performed to analyze the influence of parameters, and comparative experiments are carried out. The inspecting precisions for indentation, scratch, and pitted surface of the proposed method are 97.3%, 99.5%, and 100%, respectively. The experimental results demonstrate the effectiveness of the proposed method in the detection of various surface flaws. [ABSTRACT FROM AUTHOR]

The aim of the current study was to prepare and identify mouse anti-human rotavirus (RV) VP7 monoclonal antibodies and explore their protective effects on RV infection. The mouse anti-human RV VP7 monoclonal antibody was produced using the ascites method and identified via western blot analysis. In vitro neutralization of mouse anti-human RV VP7 monoclonal antibodies was detected by performing an MTT assay. The TCID50 value was calculated to obtain antibody neutralization titers. A mouse RV infection model was generated to assess the protective effect of the mouse anti-human RV VP7 monoclonal antibody in experimental animals. Monoclonal antibodies were successfully prepared and their purity reached ≥90%. Western blotting demonstrated that monoclonal antibodies specifically bound to the purified Wa RV strain, with a specific reaction band at ~40 kDa. Monoclonal antibody in vitro neutralization results demonstrated that cell survival rate in the virus + monoclonal antibody group was higher than that in virus + maintenance fluid group (P<0.05). Monoclonal antibody neutralization titer detection revealed that the cytopathic effect did not extend beyond 4 days. In addition, the calculated monoclonal antibody neutralization titer was 1:446. The results revealed that the positive rate of colloidal gold RV in the 100 µl monoclonal antibody group was significantly lower than that in the control group (P<0.05). Furthermore, the protection rate of the 100 µl monoclonal antibody group was 71.4%, whereas the 50 µl monoclonal antibody group was 42.9% and the ribavirin group was 57.1%. In conclusion, the results of the current study demonstrated that mouse anti-human RV VP7 monoclonal antibodies can be successfully prepared using ascites method. These antibodies also effectively neutralize the cytotoxic effects of the human RV Wa strain in vitro and mouse anti-human RV VP7 monoclonal antibodies also exhibited a good protective role in mice. Furthermore, greater protective effects were observed at a higher dose and the protective effects of these high dose treatments were superior to that of ribavirin. [ABSTRACT FROM AUTHOR]

It is a difficult task to estimate the human transition motion without the specialized software. The 3-dimensional (3D) human motion animation is widely used in video game, movie, and so on. When making the animation, human transition motion is necessary. If there is a method that can generate the transition motion, the making time will cost less and the working efficiency will be improved. Thus a new method called latent space optimization based on projection analysis (LSOPA) is proposed to estimate the human transition motion. LSOPA is carried out under the assistance of Gaussian process dynamical models (GPDM); it builds the object function to optimize the data in the low dimensional (LD) space, and the optimized data in LD space will be obtained to generate the human transition motion. The LSOPA can make the GPDM learn the high dimensional (HD) data to estimate the needed transition motion. The excellent performance of LSOPA will be tested by the experiments. [ABSTRACT FROM AUTHOR]

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum $b$ -matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of $O(M+N)$ is also proposed, where $M$ is the number of users and $N$ is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs. [ABSTRACT FROM AUTHOR]

Background: Seasonal patterns of influenza A subtypes and B lineages in tropical/subtropical regions across age have remained to be explored. The impact of the 2009 H1N1 pandemic on seasonal influenza activity have not been well understood.Methods: Based on a national sentinel hospital-based influenza surveillance system, the epidemiology of influenza virus during 2006/07-2015/16 was characterized in the subtropical city, Chengdu. Chengdu is one of the most populous cities in southwestern China, where the first reported case of A/H1N1pdm09 in mainland China was identified. Wavelet analysis was applied to identify the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria, and Yamagata across age, respectively. The persistence and age distribution patterns were described during the pre-pandemic (2006/07-2008/09), pandemic (2009/10), and post-pandemic (2010/11-2015/16) seasons.Results: A total of 10,981 respiratory specimens were collected, of which 2516 influenza cases were identified. Periodicity transition from semi-annual cycles to an annual cycle was observed for composite influenza virus as well as A/H3N2 along in Chengdu since the 2009 H1N1 pandemic. Semi-annual cycles of composite influenza virus and A/H3N2 along were observed again during 2014/15-2015/16, coinciding with the emergence and predominance of A/H3N2 significant antigenic drift groups. However, A/H1N1pdm09, Victoria, and Yamagata generally demonstrated an annual winter-spring peak in non-pandemic seasons. Along with periodicity transitions, age groups with higher positive rates shifted from school-aged children and adults to adults and the elderly for A/H1N1pdm09 during 2009/10-2010/11 and for A/H3N2 during 2014/15-2015/16.Conclusions: Differences in periodicity and age distribution by subtype/lineage and by season highlight the importance of increasing year-round influenza surveillance and developing subtype/lineage- and age-specific prevention and control measures. Changes of periodicity and age shifts should be considered in public health response to influenza pandemics and epidemics. In addition, it is suggested to use quadrivalent influenza vaccines to provide protection against both influenza B lineages. [ABSTRACT FROM AUTHOR]

We give instructions for the construction and operation of a simple apparatus for performing optically detected magnetic resonance measurements on diamond samples containing high concentrations of nitrogen-vacancy (NV) centers. Each NV center has a spin degree of freedom that can be manipulated and monitored by a combination of visible and microwave radiation. We observe Zeeman shifts in the presence of small external magnetic fields and describe a simple method to optically measure magnetic field strengths with a spatial resolution of several microns. The activities described are suitable for use in an advanced undergraduate lab course, powerfully connecting core quantum concepts to cutting edge applications. An even simpler setup, appropriate for use in more introductory settings, is also presented. [ABSTRACT FROM AUTHOR]

Objective. Systemic lupus erythematosus (SLE) is an autoimmune disease identified by a plethora of production of autoantibodies. Autoreactive T cells may play an important role in the process. Attenuated T cell vaccination (TCV) has proven to benefit some autoimmune diseases by deleting or suppressing pathogenic T cells. However, clinical evidence for TCV in SLE is still limited. Therefore, this self-controlled study concentrates on the clinical effects of TCV on SLE patients. Methods. 16 patients were enrolled in the study; they accepted TCV regularly. SLEDAI, clinical symptoms, blood parameters including complements 3 and 4 levels, ANA, and anti-ds-DNA antibodies were tested. In addition, the side effects and drug usage were observed during the patients' treatment and follow-up. Results. Remissions in clinical symptoms such as facial rash, vasculitis, and proteinuria were noted in most patients. There are also evident reductions in SLEDAI, anti-ds-DNA antibodies, and GC dose and increases in C3 and C4 levels, with no pathogenic side effects during treatment and follow-up. Conclusions. T cell vaccination is helpful in alleviating and regulating systemic lupus erythematosus manifestation. [ABSTRACT FROM AUTHOR]

Background: The complex pathogenesis of Helicobacter pylori (H. pylori) and the features of the host influence the diverse clinical outcomes. A mass of studies about virulence genes have accelerated the exploration of pathogenesis of H. pylori infection. Induced by contact with epithelium gene A (iceA) is one of the biggest concerned virulence genes. In this study, we explored the relationship between iceA and the magnitude of the risk for clinical outcomes and the prevalence of iceA-positive H. pylori in People's Republic of China and other countries.Methods: We searched the electronic databases of PubMed, Embase, CNKI, VIP, and Wanfang by literature search strategy. The studies conforming to the inclusion criteria were assessed. With these data, we systematically analyzed the relationship between the iceA gene of H. pylori and clinical outcomes.Results: Nineteen articles with 22 studies, a total of 2,657 cases, were involved in the study. The iceA1 gene was significantly associated with peptic ulcer disease (odds ratio =1.28, 95% confidence interval =1.03-1.60; P=0.03), especially in People's Republic of China (odds ratio =1.40, 95% confidence interval =1.07-1.83; P=0.01). Moreover, the prevalence of iceA1 was significantly higher than iceA2 in People's Republic of China (P<0.0001). The prevalence of both iceA1 and iceA2 was significantly different (P<0.0001) in People's Republic of China and in other countries.Conclusion: The system analysis showed that infection with the iceA1-positive H. pylori significantly increased the overall risk for peptic ulcer disease, especially in People's Republic of China. The iceA2 gene status and clinical outcome of H. pylori infection have no significant correlation. H. pylori iceA1 genotype is the major epidemic strain in People's Republic of China. [ABSTRACT FROM AUTHOR]

Gentisides are a class of chemical compounds which is considered as potential therapeutic substance for treatment of neurodegenerative disorders. The heat reflux extraction conditions were optimized for seven kinds of gentisides from the root and rhizome of Gentiana rigescens Franch. ex Hemsl. by employing response surface method. Based on univariate test, a Box-Behnken design (BBD) was applied to the survey of relationships between response value (gentisides yield) and independent variables which were chosen from various extraction processes, including extraction temperature, extraction time, and solvent-material ratio. The optimized conditions for this extraction are as follows: extraction time of 3.40 h, extraction temperature of 74.33°C, and ratio of solvent to raw material of 10.21 : 1 mL/g. Verification assay revealed that the predicted value (99.24%) of extraction parameters from this model was mainly conformed to the experimentally observed values (98.61±0.61). [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm called low dimensional space incremental learning (LDSIL) to estimate the human motion in 3D from the silhouettes of human motion multiview images. The proposed algorithm takes the advantage of stochastic extremum memory adaptive searching (SEMAS) and incremental probabilistic dimension reduction model (IPDRM) to collect new high dimensional data samples. The high dimensional data samples can be selected to update the mapping from low dimensional space to high dimensional space, so that incremental learning can be achieved to estimate human motion from small amount of samples. Compared with three traditional algorithms, the proposed algorithm can make human motion estimation achieve a good performance in disambiguating silhouettes, overcoming the transient occlusion, and reducing estimation error. [ABSTRACT FROM AUTHOR]