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In order to explore the effects of postharvest exogenous melatonin (MT) treatment on the storage quality of spaghetti squash, squash was treated with 0.2 mmol/L MT solution to investigate the changes in fruit quality and antioxidant capacity during storage at (9±1) ℃ with relative humidity of 65%~70%. The results showed that MT treatment delayed the occurrence of decay of spaghetti squash by 40 days during storage compared with the control group. At 160 d of storage, the decay rate of MT-treated fruit was only 15.56%, which was significantly lower than that of the control fruit (34.07%, P

Abstract Electric dipoles and magnetic dipoles are the most fundamental particles in electromagnetic theory. Huygens and Janus sources, formed by the orthogonal combination of electric and magnetic dipoles, both show good directionality in the near field. Although the Huygens source has been widely used in antennas and metasurfaces, the applications of Janus source are heretofore limited. In this paper we report the physical construction of an active Janus source. Through full-wave simulations within the parallel plate waveguide (PPW) environment, we show that our source achieves the directional electromagnetic near-field and quasi-isotropic far-field requisite of the Janus source. Using this fact, we demonstrate that two active Janus and Huygens sources in close proximity (about 0.10 to 0.25 wavelengths) achieve a near 1000-fold reduced mutual coupling compared to electric dipole sources. Particularly, the simultaneous achievement of strong mutual coupling suppression and quasi-isotropic radiation make the Janus source an ideal candidate for consideration in future compact multi-input multi-output (MIMO) communication systems.

Simulated body fluid (SBF) is widely utilized in preclinical research for estimating the mineralization efficacy of biomaterials. Therefore, it is of great significance to construct an efficient and stable SBF mineralization system. The conventional SBF solutions cannot maintain a stable pH value and are prone to precipitate homogeneous calcium salts at the early stages of the biomimetic process because of the release of gaseous CO2. In this study, a simple but efficient five times SBF buffered by 5 % CO2 was developed and demonstrated to achieve excellent mineralized microstructure on a type of polymer-aligned nanofibrous scaffolds, which is strikingly similar to the natural human bone tissue. Scanning electron microscopy and energy-dispersive X-ray examinations indicated the growth of heterogeneous apatite with a high-calcium-to-phosphate ratio on the aligned nanofibers under 5 times SBF buffered by 5 % CO2. Moreover, X-ray diffraction spectroscopy and Fourier transform infrared analyses yielded peaks associated with carbonated hydroxyapatite with less prominent crystallization. In addition, the biomineralized aligned polycaprolactone nanofibers demonstrated excellent cell attachment, alignment, and proliferation characteristics in vitro. Overall, the results of this study showed that 5 × SBFs buffered by 5 % CO2 partial pressure are attractive alternatives for the efficient biomineralization of scaffolds in bone tissue engineering, and could be used as a model for the prediction of the bone-bonding bioactivity of biomaterials.

Abstract Diabetic cystopathy (DCP) is a prevalent etiology of bladder dysfunction in individuals with longstanding diabetes, frequently leading to bladder interstitial fibrosis. Research investigating the initial pathological alterations of DCP is notably scarce. To comprehend the development of fibrosis and find effective biomarkers for its diagnosis, we prepared streptozotocin-induced long-term diabetic SD rats exhibiting a type 1 diabetes phenotype and bladder fibrosis in histology detection. After observing myofibroblast differentiation from rats’ primary bladder fibroblasts with immunofluorescence, we isolated fibroblasts derived exosomes and performed exosomal miRNA sequencing. The co-differentially expressed miRNAs (DEMis) (miR-16-5p and let-7e-5p) were screened through a joint analysis of diabetic rats and long-term patients’ plasma data (GES97123) downloaded from the GEO database. Then two co-DEMis were validated by quantitative PCR on exosomes derived from diabetic rats’ plasma. Following with a series of analysis, including target mRNAs and transcription factors (TFs) prediction, hubgenes identification, protein–protein interaction (PPI) network construction and gene enrichment analysis, a miRNA-mediated genetic regulatory network consisting of two miRNAs, nine TFs, and thirty target mRNAs were identified in relation to fibrotic processes. Thus, circulating exosomal miR-16-5p and let-7e-5p are associated with bladder fibrosis of DCP, and the crucial genes in regulatory network might hold immense significance in studying the pathogenesis and molecular mechanisms of fibrosis, which deserves further exploration.

Abstract Aiming at the problem that it is difficult to determine the reasonable working resistance of support in fully-mechanized top coal caving face with large mining height, this paper discusses it from the perspective of preventing rib spalling of coal wall, and holds that the support resistance of working face should not only maintain the stability of roof structure of working face, but also prevent rib spalling of coal wall. Taking Caojiatan 122,107 working face as the engineering background. Using theoretical analysis, numerical simulation, orthogonal test, and similar simulation, it is concluded that reducing the end distance b, reducing the distance l between the action point of the support force and the coal wall, increasing the vertical force Pv and the horizontal force P h of the support are beneficial to the integrity and stability of the coal wall. The proportion index η of the plastic zone of coal wall is established, and the fitting relationship between η and support parameters is: $$\eta { = } - 0.21{\text{g}}P_{v} + 0.04{\text{g}}P_{h} + 0.551{\text{g}}b - 0.039{\text{g}}l + 0.665$$ η = - 0.21 g P v + 0.04 g P h + 0.551 g b - 0.039 g l + 0.665 . Taking η as the index, the calculation formula of the lower limit value of support strength is constructed, and the lower limit value of support strength required to prevent coal wall spalling in 122,107 working face is 12,369 kN. On-site monitoring and adjustment have achieved good results.

A new two-dimensional (2D) non-MXene transition metal carbide, Mo3C2, was found using the USPEX code. Comprehensive first-principles calculations show that the Mo3C2 monolayer exhibits thermal, dynamic, and mechanical stability, which can ensure excellent durability in practical applications. The optimized structures of Lix@(3×3)-Mo3C2 (x = 1–36) and Nax@(3×3)-Mo3C2 (x = 1–32) were identified as prospective anode materials. The metallic Mo3C2 sheet exhibits low diffusion barriers of 0.190 eV for Li and 0.118 eV for Na and low average open circuit voltages of 0.31–0.55 V for Li and 0.18–0.48 V for Na. When adsorbing two layers of adatoms, the theoretical energy capacities are 344 and 306 mA h g−1 for Li and Na, respectively, which are comparable to that of commercial graphite. Moreover, the Mo3C2 substrate can maintain structural integrity during the lithiation or sodiation process at high temperature. Considering these features, our proposed Mo3C2 slab is a potential candidate as an anode material for future Li- and Na-ion batteries.

Starting from a $ 3\times3 $ matrix spectral problem and the characteristic polynomial of the Lax matrix, we propose a trigonal curve, the associated meromorphic functions and three kinds of Abelian differentials. By discussing the asymptotic properties for the Baker-Akhiezer functions and their Riemann theta function expressions, we get quasi-periodic solutions of the three-component Burgers hierarchy. Finally, we straighten out the three-component Burgers flows.

Abstract The prediction of production capacity in tight gas wells is greatly influenced by the characteristics of gas–water two-phase flow and the fracture network permeability parameters. However, traditional analytical models simplify the nonlinear problems of two-phase flow equations to a large extent, resulting in significant errors in dynamic analysis results. To address this issue, this study considers the characteristics of gas–water two-phase flow in the reservoir and fracture network, utilizes a trilinear flow model to characterize the effects of hydraulic fracturing, and takes into account the stress sensitivity of the reservoir and fractures. A predictive model for gas–water two-phase production in tight fractured horizontal wells is established. By combining the mass balance equation with the Newton–Raphson iteration method, the nonlinear parameters of the flow model are updated step by step using the average reservoir pressure. The accuracy of the model is validated through comparisons with results from commercial numerical simulation software and field case applications. The research results demonstrate that the established semi-analytical solution method efficiently handles the nonlinear two-phase flow problems, allowing for the rapid and accurate prediction of production capacity in tight gas wells. Water production significantly affects gas well productivity, and appropriate fracture network parameters are crucial for improving gas well productivity. The findings of this work could provide more clear understanding of the gas production performance from the fractured tight-gas horizontal well.

Abstract Objectives Rupture of intracranial aneurysm is very dangerous, often leading to death and disability. In this study, deep learning and radiomics techniques were used to automatically detect and differentiate ruptured and unruptured intracranial aneurysms. Materials and methods 363 ruptured aneurysms and 535 unruptured aneurysms from Hospital 1 were included in the training set. 63 ruptured aneurysms and 190 unruptured aneurysms from Hospital 2 were used for independent external testing. Aneurysm detection, segmentation and morphological features extraction were automatically performed with a 3-dimensional convolutional neural network (CNN). Radiomic features were additionally computed via pyradiomics package. After dimensionality reduction, three classification models including support vector machines (SVM), random forests (RF), and multi-layer perceptron (MLP) were established and evaluated via area under the curve (AUC) of receiver operating characteristics. Delong tests were used for the comparison of different models. Results The 3-dimensional CNN automatically detected, segmented aneurysms and calculated 21 morphological features for each aneurysm. The pyradiomics provided 14 radiomics features. After dimensionality reduction, 13 features were found associated with aneurysm rupture. The AUCs of SVM, RF and MLP on the training dataset and external testing dataset were 0.86, 0.85, 0.90 and 0.85, 0.88, 0.86, respectively, for the discrimination of ruptured and unruptured intracranial aneurysms. Delong tests showed that there was no significant difference among the three models. Conclusions In this study, three classification models were established to distinguish ruptured and unruptured aneurysms accurately. The aneurysms segmentation and morphological measurements were performed automatically, which greatly improved the clinical efficiency. Clinical relevance statement Our fully automatic models could rapidly process the CTA data and evaluate the status of aneurysms in one minute. Graphical Abstract

BackgroundThe incidence of prostate cancer (PCa) has continued to increase since the US Preventive Services Task Force (USPSTF) recommendations against prostate-specific antigen (PSA)-based screening for all men in 2012, approximately half of additional diagnosed cases are advanced-stage, including regional PCa and metastatic PCa (mPCa). It is very important to investigate the shift in mPCa incidence and mPCa-related mortality risk, as the survival of mPCa remains poor.ObjectiveTo investigate the incidence temporal trend of mPCa stratified by metastatic site, including bone and visceral metastatic involvement, and potential survival improvements.MaterialsBased on the recently released Surveillance, Epidemiology, and End Results (SEER) data (2010-2019), the age-adjusted incidence rates of mPCa with bone and visceral involvement with annual percentage changes (APCs) were assessed by a joinpoint regression model in men aged 45 years and older by age and race groups, and potential recent improvements in overall survival (OS) and cancer-specific survival (CSS) were estimated by the Kaplan−Meier method and Cox regression model.ResultsFrom 2010 to 2019, a total of 19081 (84.8%) and 3413 (15.2%) mPCa patients with bone and visceral involvement, respectively, were recorded in the SEER database. Considering all races and age groups, the incidence rate of mPCa with bone metastasis remained stable during 2017-2019 (APC, 0.9%; p=0.421) after increasing during 2010-2017 (APC, 5.8%; p

A new type of graphene electric heating solid wood composite floor and its heat transfer model were designed to enable users to have a higher-quality and safe living experience. A heat transfer mathematical model was developed. The structural entity of the composite graphene heating floor was drawn using Solidworks software. The floor structure was abstracted as a two-dimensional model using MATLAB software to obtain the temperature rise curves and corresponding time of each group. Then, six groups of the best data were selected from the experimental data to simulate the heat storage capacity of graphene floors. The optimal group of the model was verified via experiments. According to the simulation, the comprehensive performance was optimal when the overall thickness of the floor was 18 mm, the thickness of the floor surface was 4 mm, and the thickness of the heat-accumulating layer was 2 mm. The experimental results showed a maximum difference between the measured and calculated data of only 3.2%, which shows the scientific validity, accuracy, and advancement of the model. The composite graphene electric heating energy storage floor designed in this study can be regarded as safe, reliable, environmentally friendly, and healthy.

To achieve optimal system performance, it’s recommended to operate an LLC resonant converter at its resonant frequency. However, in mass production, the resonant parameters of different products can deviate significantly. This means that we need to actively track the resonant frequency during practical usage. To address this issue, this paper presents a novel automatic control scheme for LLC converter that tracks the resonant frequency. The proposed control scheme is based on an accurate small-signal model, and an extended state observer which provides small-signal characteristics at the operating frequency. The controller checks the voltage gain of the converter and combines large-signal with small-signal models to control the LLC converter effectively. Compared to conventional automatic resonant frequency tracking (ARFT) control schemes, the proposed control scheme is easier to implement and requires fewer sensors. Experimental results demonstrate its effectiveness in tracking the resonant frequency. In summary, this manuscript proposes an effective ARFT control scheme for LLC converter that can track the resonant frequency with reduced sensors and is easier to implement.

Due to physiological and anatomical variations across users, myoelectric interfaces trained by multiple users cannot be adapted to the unique hand movement patterns of the new user. Most current work requires the new user to provide one or more trials per gesture (dozens to hundreds of samples), applying domain adaptation methods to calibrate the model and achieve promising movement recognition performance. However, the user burden associated with time-consuming electromyography signal acquisition and annotation is a key factor hindering the practical application of myoelectric control. As shown in this work, once the number of calibration samples is reduced, the performance of previous cross-user myoelectric interfaces will degrade due to the lack of enough statistics to characterize the distributions. In this paper, a few-shot supervised domain adaptation (FSSDA) framework is proposed to address this issue. It aligns the distributions of different domains by calculating the distribution distances of point-wise surrogates. Specifically, we introduce a positive-negative pair distance loss to find a shared embedding subspace where each scarce sample from the new user will be closer to the positive samples and away from the negative samples of multiple users. Thus, FSSDA allows every target domain sample to be paired with all source domain samples and optimizes the feature distance between each target domain sample and the source domain samples within the same batch, instead of direct estimation of the data distribution of the target domain. The proposed method is validated on two high-density EMG datasets, which achieves the averaged recognition accuracies of 97.59% and 82.78% with only 5 samples per gesture. In addition, FSSDA is also effective even when only one sample per gesture is provided. The experimental results show that FSSDA greatly reduces the user burden and further facilitates the development of myoelectric pattern recognition techniques.

Abstract Background The brain vascular basement membrane (brain-VBM) is an important component of the brain extracellular matrix, and the three-dimensional structure of the cerebrovascular network nested with many cell-adhesive proteins may provide guidance for brain tissue regeneration. However, the potential of ability of brain-VBM to promote neural tissue regeneration has not been examined due to the technical difficulty of isolating intact brain-VBM. Methods The present study developed a simple, effective method to isolate structurally and compositionally intact brain-VBM. Structural and component properties of the brain-VBM were characterized to confirm the technique. Seed cells were cocultured with brain-VBM in vitro to analyze biocompatibility and neurite extension. An experimental rat model of focal traumatic brain injury (TBI) induced by controlled cortical impact were conducted to further test the tissue regeneration ability of brain-VBM. Results Brain-VBM isolated using genipin showed significantly improved mechanical properties, was easy to handle, supported high cell viability, exhibited strong cell adhesive properties, and promoted neurite extension and outgrowth. Further testing of the isolated brain-VBM transplanted at lesion sites in an experimental rat model of focal TBI demonstrated considerable promise for reconstructing a complete blood vessel network that filled in the lesion cavity and promoting repopulation of neural progenitor cells and neurons. Conclusion The technique allows isolation of intact brain-VBM as a 3D microvascular scaffold to support brain tissue regeneration following TBI and shows considerable promise for the production of naturally-derived biomaterials for neural tissue engineering. Graphical Abstract

Abstract In order to promote the development of environmental protection, and the usage rate of green energy utilization, a progressive, innovative laser process method employing helium assisted is proposed, which optimizes the joint cutting process under the same energy consumption. This method provides a new idea for the wood process industry. The uniqueness of this paper establishes a mathematical model to address the diffusion of helium injection and the heat transfer of the laser beam on the processed surface. From the results, it can be exhibited that the oxygen concentration reduces when the helium is injected on the processed surface. The helium could destroy the combustion-supporting conditions and decrease the combustion zone of the processed joint cutting. Thus, the carbonized area of the processed surface is reduced, which could effectively enhance the processing quality of joint cutting. Notably, the helium with injection speed forms a sweeping effect on the processed surface, which could remove parts of the carbonized particles and residues on the processed surface, as well as improve the processing quality. Comparing the traditional laser process and helium-assisted laser process, the gas-assisted laser process owns higher process quality than that of traditional laser processing and cutting. In detail, it features the advantages of smaller joint cutting width, lower surface roughness and smoother surface. Eventually, a mathematical model based on the response surface method with the evaluation criteria of the kerf width, kerf depth, and surface roughness is established to analyze the interaction of laser power, cutting speed and inert gas pressure on the response factors. Comparing the error between the predicted and experimental measurement value, and the optimized process parameters could be acquired. In this paper, the helium-assisted laser process method proposed is meaningful and encouraging, which not only obtains better processing quality, but also provides a guide for developing green industry.

Abstract Since the entry into genome‐enabled biology several decades ago, much progress has been made in determining, describing, and disseminating the functions of genes and their products. Yet, this information is still difficult to access for many scientists and for most genomes. To provide easy access and a graphical summary of the status of genome function annotation for model organisms and bioenergy and food crop species, we created a web application (https://genomeannotation.rheelab.org) to visualize, search, and download genome annotation data for 28 species. The summary graphics and data tables will be updated semi‐annually, and snapshots will be archived to provide a historical record of the progress of genome function annotation efforts. Clear and simple visualization of up‐to‐date genome function annotation status, including the extent of what is unknown, will help address the grand challenge of elucidating the functions of all genes in organisms.

Brain-computer interface (BCI) based on Steady-State Visual Evoked Potentials (SSVEP) provides an effective method for human-computer communication. In practical application scenarios, SSVEP-BCI systems are easily interfered by physiological noises such as electromyography (EMG) and electrooculography (EOG). The performance of traditional SSVEP recognition methods will degrade in such a noisy environment, which limits their real-world applications. To alleviate the interference of noise, existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological noises. In this study, we utilize adversarial training (AT) and neural networks (NNs) to construct a robust recognition method for SSVEP contaminated by physiological noise. During model training, we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome them. In this way, we strengthen the robustness of the model to potential noises, such as physiological noises. In this study, we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and DeepConvNet. The experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and EOG. We also verified that introducing AT can slightly improve the performance of models under a cross-subject scenario. Our method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.

TP53 is the most frequently mutated gene in muscle invasive bladder cancer (MIBC) and there are two gene signatures regarding TP53 developed for MIBC prognosis. However, they are limited to immune genes only and unable to be used individually across platforms due to their quantitative manners. We used 827 gene expression profiles from seven MIBC cohorts with varied platforms to build a pairwise TP53-derived transcriptome signature, 13 gene pairs (13-GPs). Since the 13-GPs model is a single sample prognostic predictor, it can be applied individually in practice and is applicable to any gene-expression platforms without specific normalization requirements. Survival difference between high-risk and low-risk patients stratified by the 13-GPs test was statistically significant (HR range: 2.26–2.76, all P

Abstract An overreliance on commercial, kit-based RNA extraction in the molecular diagnoses of infectious disease presents a challenge in the event of supply chain disruptions and can potentially hinder testing capacity in times of need. In this study, we adapted a well-established, robust TRIzol-based RNA extraction protocol into a high-throughput format through miniaturization and automation. The workflow was validated by RT-qPCR assay for SARS-CoV-2 detection to illustrate its scalability without interference to downstream diagnostic sensitivity and accuracy. This semi-automated, kit-free approach offers a versatile alternative to prevailing integrated solid-phase RNA extraction proprietary systems, with the added advantage of improved cost-effectiveness for high volume acquisition of quality RNA whether for use in clinical diagnoses or for diverse molecular applications.

Abstract There exist the problems of the low utilization rate of DC voltage, power imbalance and current backflow in the modulation process of traditional unsymmetrical cascaded units. In view of these, a pulse width modulation strategy with an improved trapezoidal wave as the modulation wave and its corresponding power balance method are proposed. First, through double Fourier analysis of the output line voltage, an appropriate sine coefficient δ is obtained. Second, the waveform of the modulation wave is determined according to δ. Third, through the simple logic operation of the initial driving signals of the switch transistors, the required switch driving signals are obtained, and the power balance of each unit with the same voltage level is realized. In order to verify the effectiveness of the strategy and the method, a simulation and experiment platform are built, and a large number of simulations and experiments are carried out. The results show that compared with the traditional sinusoidal modulation method, the utilization rate of DC voltage obtained by the method can be improved by 22.28% and compared with the trapezoidal wave modulation method, the waveform distortion coefficient of the output voltage can be significantly reduced, and the utilization rate of DC voltage can be further improved. Meanwhile, the power balance can be achieved within one voltage cycle and the phenomenon of current backflow is eliminated.

The traffic scene understanding is the core technology in Intelligent Transportation Systems (ITS) and Advanced Driver Assistance System (ADAS), and it is becoming increasingly important for smart or autonomous vehicles. The recent methods for traffic scene understanding, such as Traffic Sign Recognition (TSR), Pedestrian Detection, and Vehicle Detection, have three major shortcomings. First, most models are customized for recognizing a specific category of traffic target instead of general traffic targets. Second, as for these recognition modules, the task of traffic scene understanding is to recognize objects rather than make driving suggestions or strategies. Third, numerous independent recognition modules disadvantage to fusing multi-modal information to make a comprehensive decision for driving operation in accordance with complicated traffic scenes. In this paper, we first introduce the image captioning model to alleviate the aforementioned shortcomings. Different from existing methods, our primary idea is to accurately identify all categories of traffic objects and understand traffic scenes by making full use of all information, and making the suggestions or strategy for driving operation in natural language by using Long Short Term Memory network (LSTM) rather than keywords. The proposed solution naturally solves the problems of feature fusion, general object recognition, and low-level semantic understanding. We tested the solution on our created traffic scene image dataset for evaluation of image captioning. Extensive experiments including quantitative and qualitative comparisons demonstrate that the proposed solution can identify more objects and produce higher-level semantic information than the state-of-the-arts.

In view of the fact that on flat panel display industrial production line, the power consumption is very important and determined by their different driving modes and light‐emitting principles. Based on the industrial control systems, this paper analyses the power consumption of flat panel display, studies the main factors affecting its power consumption and analyses the main methods to improve light efficiency and reduce power consumption. This paper also discusses the focus issues in the current standard of flat panel display power measurement at home and abroad. According to the characteristics of different flat panel displays, this paper discusses the dual indexes of power consumption and efficiency in power measurement, and how to consider the influence of screen brightness and angle of view in the measurement.

There is a very important index in the influence of human visual perception, that is, the degree of the motion blur about the moving image on display device. In this paper, a new kind of improved method is proposed to calculate the response time of liquid crystal under the new backlight driving and modulation, which is more accurate than the traditional moving window integration method. This paper also discusses the eye tracking movement of the human visual system, including studying the influence of image content, size, motion speed and motion direction, based on the human vision property study and the analyzes of the simulation model of image motion artifacts perceived by human eyes. Experiments are performed, using the human eye vision perception measurement system. The experiment data shows that the edge blur time can be reduced to 10.5 ms, compared with the 16.5 ms in traditional drive, reduced by 36.4%, and the human perception of motion blur can be markedly reduced. Also, the results show that the simulation results are in good agreement with the actual measurement data by modifying the proportion coefficient of the eye tracking object model to 0.9. At the same time, through the improved human eye tracking model of smooth moving objects, the actual motion blur phenomenon perceived by the human eye can be calculated more accurately, and the results can be used in image processing algorithms to improve the quality of the moving image on the display.

Automatic image annotation plays a significant role in image understanding, retrieval, classification, and indexing. Today, it is becoming increasingly important in order to annotate large-scale social media images from content-sharing websites and social networks. These social images are usually annotated by user-provided low-quality tags. The topic model is considered as a promising method to describe these weak-labeling images by learning latent representations of training samples. The recent annotation methods based on topic models have two shortcomings. First, they are difficult to scale to a large-scale image dataset. Second, they can not be used to online image repository because of continuous addition of new images and new tags. In this paper, we propose a novel annotation method based on topic model, namely local learning-based probabilistic latent semantic analysis (LL-PLSA), to solve the above problems. The key idea is to train a weighted topic model for a given test image on its semantic neighborhood consisting of a fixed number of semantically and visually similar images. This method can scale to a large-scale image database, as training samples involved in modeling are a few nearest neighbors rather than the entire database. Moreover, this proposed topic model, online customized for the test image, naturally addresses the issue of continuous addition of new images and new tags in a database. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art especially in terms of overall metrics.

Recent efforts in biomaterial-assisted brain tissue engineering suggest that match of mechanical properties of biomaterials to those of native brain tissue may be crucial for brain regeneration. In particular, the mechanical properties of native brain tissue vary as a function of age. To date, detailed characterization of age-dependent viscoelastic properties of brain tissue throughout the postnatal development to adulthood is only available at sparse age points in animal studies. To fill this gap, we have characterized the linear viscoelastic properties of the cerebral cortex in rats at well-spaced ages from postnatal day 4 to 4 months old, the age range that is widely used in neural regeneration studies. Using an oscillatory rheometer, the viscoelastic properties of rat cortical slices were measured independently by storage moduli (G′) and loss moduli (G″). The data demonstrated increases in both the storage moduli and the loss moduli of cortex tissue over post-natal age in rats. At all ages, the damping factor (G″/G′ ratio) remained constant at low oscillatory strain frequencies (

We report enzymes from the berberine bridge enzyme (BBE) superfamily that catalyze the oxidative cyclization of the monoterpene moiety in cannabigerolic acid (CBGA) to form cannabielsoin (CBE). The enzymes are from a variety of organisms and are previously uncharacterized. Out of 232 homologues chosen from the enzyme superfamily, four orthologues were shown to accept CBGA as a substrate and catalyze the biosynthesis of CBE. The four enzymes discovered in this study were recombinantly expressed and purified in Pichia pastoris. These enzymes are the first report of heterologous expression of BBEs that did not originate from the Cannabis plant that catalyze the production of cannabinoids using CBGA as substrate. This study details a new avenue for discovering and producing natural and unnatural cannabinoids.

In the present paper, we introduce a 4 × 4 matrix spectral problem and derive a four-component Fokas–Lenells equation. N-fold Darboux transformations for the four-component Fokas–Lenells equation are constructed by using the gauge transformation between spectral problems and an iterative procedure. Resorting to the limit technique and Taylor series, we obtain the N-fold generalized Darboux transformation. As an illustrative example, by utilizing the resulting Darboux transformations and Mathematica software, some exact solutions for the four-component Fokas–Lenells equation are obtained, including soliton, breather solution and rogue wave solution. Moreover, the dynamical behaviour of these solutions is analysed in detail.

Due to the introduction of silicon carbide reinforcement, the physical and cutting properties of SiCp/Mg composites are very different from those of metal composites. Nanosecond pulse laser processing is more efficient than traditional processing for SiCp/Mg composites. A low-power pulsed fiber laser was used to etch 3.0 mm thick SiCp/Mg composites. The effect of low laser power (0~50 W) on the morphology and heat-affected zone of the SiCp/Mg composite after etching was studied. The results show that when the laser power increases, the material accumulation at the ablation end of the machining surface becomes more and more serious. With the increase in power, the differences in ablation width and ablation depth on the surface of composite materials do not increase proportionally. When the laser power increases gradually, the width of the heat-affected zone increases in the direction of the perpendicular laser beam and reaches the maximum value at the etched end.

The effects of laser welding parameters on the interface microstructure of AZ31B magnesium alloy and 304 stainless steel were investigated. After welding, a scanning electron microscope and ultra-depth of field microscope were used to observe the microstructure of the welded material, to analyze the effects of power on the interface morphology. The simulation of laser welding of magnesium and steel was carried out by the COMSOL software. The results showed that when the power was 15 W–20 W, the temperature did not reach the melting point of magnesium alloy, there was MgO at the welding, and the interface had poor connection strength. When the power was 35 W–50 W, the temperature reached or even exceeded the boiling point of magnesium alloy, and the interface formed hot cracks, pores, and oxides and had poor joint strength. When the power was 25 W–30 W, the temperature was between the melting point and boiling point of magnesium, and the interface had excellent connection strength.

Abstract For wireless sensor networks (WSNs) based on ZigBee technology, the network topology plays an important role for improving the energy efficiency and the network lifetime. An appropriate construction method of network topology should be designed for saving the energy of the battery in each network node so as to prolong the lifetime of ZigBee WSNs. In this paper, a novel cluster tree topology construction method based on particle swarm optimization (PSO) algorithm is proposed. In order to transform the network topology construction problem into an energy consumption optimization problem, an evaluation function reflecting the network energy consumption is designed, and the network topology is mapped into a particle population individual suitable for the PSO algorithm. In order to prolong the network lifetime as long as possible, two network topology reconstruction methods with the PSO algorithm based on fixed and variable energy thresholds are further proposed, respectively. The simulation results show that the proposed methods can prolong the lifetime of ZigBee WSNs effectively.

Node localization in wireless sensor networks (WSNs) has received a considerable amount of attention. In this paper, using the natural low-rank properties of the Euclidean distance matrix (EDM), we formulate the node location finding problem from only a small fraction of random entries of the EDM as a low-rank matrix recovery problem. A Bayesian-learning-based method is utilized to recover the original EDM, based on which the relative positions of all the sensor nodes in WSNs are accurately estimated by applying classical multi-dimensional scaling (MDS). In addition, with the location knowledge of anchor nodes, we transform the relative positions into absolute positions. The simulation results illustrate that our proposed approach leads to superior performance over various other methods.

This study examined the longitudinal associations between parental feeding practices and child weight status, and their potential modification effects by child sex, age, and maternal and paternal educations among children. Data were collected from 2015 to 2017 of 2139 children aged 6–17 years and their parents in five Chinese mega-cities. Parental feeding practices were assessed using 11-items from Child Feeding Questionnaire. Waist-to-height ratio (WHtR), body mass index (BMI), and general and central obesity were measured and analyzed using a mixed-effects model. Three parental feeding patterns were identified by factor analysis including “concern”, “pressure to eat”, and “control”. Concern was associated with higher BMI z-score, WHtR (βs ranged from 0.01 to 0.16), and general obesity (ORs ranged from 1.29 to 6.41) among children aged ≤12 years and >12 years, regardless of child sex and parental educations. Pressure to eat was associated with lower BMI z-score (β = −0.08, p < 0.001), WHtR (β = −0.004, p < 0.01), and general (OR = 0.53, 95%CI = 0.42, 0.66) and central obesity (OR = 0.72, 95%CI = 0.58, 0.90) among children aged ≤12 years. Further analyses showed that significant associations were found for children with maternal or paternal education of college and above. Control was associated with increased risk of general and central obesity among children with maternal education of college and above, regardless of age. Our study indicates that higher concern and lower pressure to eat were associated with increased risk of obesity among children. Control was associated with increased risk of obesity among children with maternal education of college and above. Future childhood obesity preventions may optimize parental feeding practices.

Background: This study aimed to develop an ultrathin nanofibrous membrane able to, firstly, mimic the natural fibrous architecture of human Bruch’s membrane (BM) and, secondly, promote survival of retinal pigment epithelial (RPE) cells after surface functionalization of fibrous membranes. Methods: Integrin-binding peptides (IBPs) that specifically interact with appropriate adhesion receptors on RPEs were immobilized on Bruch’s-mimetic membranes to promote coverage of RPEs. Surface morphologies, Fourier-transform infrared spectroscopy spectra, contact angle analysis, Alamar Blue assay, live/dead assay, immunofluorescence staining, and scanning electron microscopy were used to evaluate the outcome. Results: Results showed that coated membranes maintained the original morphology of nanofibers. After coating with IBPs, the water contact angle of the membrane surfaces varied from 92.38 ± 0.67 degrees to 20.16 ± 0.81 degrees. RPE cells seeded on IBP-coated membranes showed the highest viability at all time points (Day 1, p < 0.05; Day 3, p < 0.01; Days 7 and 14, p < 0.001). The proliferation rate of RPE cells on uncoated poly(ε-caprolactone) (PCL) membranes was significantly lower than that of IBP-coated membranes (p < 0.001). SEM images showed a well-organized hexa/polygonal monolayer of RPE cells on IBP-coated membranes. RPE cells proliferated rapidly, contacted, and became confluent. RPE cells formed a tight adhesion with nanofibers under high-magnification SEM. Our findings confirmed that the IBP-coated PCL membrane improved the attachment, proliferation, and viability of RPE cells. In addition, in this study, we used serum-free culture for RPE cells and short IBPs without immunogenicity to prevent graft rejection and immunogenicity during transplantation. Conclusions: These results indicated that the biomimic BM-IBP-RPE nanofibrous graft might be a new, practicable approach to increase the success rate of RPE cell transplantation.

Medium-chain triglycerides (MCTs) are an emerging choice to treat neurodegenerative disorders such as Alzheimer’s disease. They are triesters of glycerol and three medium-chain fatty acids, such as capric (C8) and caprylic (C10) acids. The availability of C8–C10 methyl esters (C8–C10 ME) from vegetable oil processes has presented an opportunity to use methyl esters as raw materials for the synthesis of MCTs. However, there are few reports on enzymes that can efficiently hydrolyse C8–C10 ME to industrial specifications. Here, we report the discovery and identification of a novel lipase from Lasiodiplodia theobromae fungus (LTL1), which hydrolyses C8–C10 ME efficiently. LTL1 can perform hydrolysis over pH ranges from 3.0 to 9.0 and maintain thermotolerance up to 70 °C. It has high selectivity for monoesters over triesters and displays higher activity over commercially available lipases for C8–C10 ME to achieve 96.17% hydrolysis within 31 h. Structural analysis by protein X-ray crystallography revealed LTL1’s well-conserved lipase core domain, together with a partially resolved N-terminal subdomain and an inserted loop, which may suggest its hydrolytic preference for monoesters. In conclusion, our results suggest that LTL1 provides a tractable route towards to production of C8–C10 fatty acids from methyl esters for the synthesis of MCTs.

Enzyme engineering is an indispensable tool in the field of synthetic biology, where enzymes are challenged to carry out novel or improved functions. Achieving these goals sometimes goes beyond modifying the primary sequence of the enzyme itself. The use of protein or nucleic acid scaffolds to enhance enzyme properties has been reported for applications such as microbial production of chemicals, biosensor development and bioremediation. Key advantages of using these assemblies include optimizing reaction conditions, improving metabolic flux and increasing enzyme stability. This review summarizes recent trends in utilizing genetically encodable scaffolds, developed in line with synthetic biology methodologies, to complement the purposeful deployment of enzymes. Current molecular tools for constructing these synthetic enzyme-scaffold systems are also highlighted.

In this paper, a 3 × 3 spectral problem is proposed and a five-component equation that consists of two different mKdV equations is derived. A Darboux transformation of the five-component equation is presented relating to the gauge transformations between the Lax pairs. As applications of the Darboux transformations, interesting exact solutions, including soliton-like solutions and a solution that consists of rational functions of e x and t, for the five-component equation are obtained.

Gelsolin is a severing and capping protein that targets filamentous actin and regulates filament lengths near plasma membranes, contributing to cell movement and plasma membrane morphology. Gelsolin binds to the plasma membrane via phosphatidylinositol 4,5-bisphosphate (PIP2) in a state that cannot cap F-actin, and gelsolin-capped actin filaments are uncapped by PIP2 leading to filament elongation. The process by which gelsolin is removed from PIP2 at the plasma membrane is currently unknown. Gelsolin also binds ATP with unknown function. Here we characterize the role of ATP on PIP2-gelsolin complex dynamics. Fluorophore-labeled PIP2 and ATP were used to study their interactions with gelsolin using steady-state fluorescence anisotropy, and Alexa488-labeled gelsolin was utilized to reconstitute the regulation of gelsolin binding to PIP2-containing phospholipid vesicles by ATP. Under physiological salt conditions ATP competes with PIP2 for binding to gelsolin, while calcium causes the release of ATP from gelsolin. These data suggest a cycle for gelsolin activity. Firstly, calcium activates ATP-bound gelsolin allowing it to sever and cap F-actin. Secondly, PIP2-binding removes the gelsolin cap from F-actin at low calcium levels, leading to filament elongation. Finally, ATP competes with PIP2 to release the calcium-free ATP-bound gelsolin, allowing it to undergo a further round of severing.

This paper analyzes the influence of laser linewidth on the measurement accuracy of a frequency-scanning Brillouin optical time domain reflectometer (FS-BOTDR), allowing for both the width of Brillouin gain spectrum and the signal-to-noise ratio (SNR) of the BOTDR system. The measurement accuracy of the Brillouin frequency shift (BFS) is theoretically investigated versus the duration of the probe pulse and the linewidth of the laser source, by numerically simulating how a FS-BOTDR works and evaluating the Brillouin gain spectrum (BGS) width and the system SNR. The simulation results show that the BFS accuracy is improved as the laser linewidth becomes narrower when the probe pulse width is fixed. We utilize five types of lasers with respective linewidths of 1.05 MHz, 101 kHz, 10.2 kHz, 3.1 kHz, and 98 Hz to compare the BFS measurement accuracy over a ~10 km optical sensing fiber. The experimental results demonstrate that the root-mean-square error (RMSE) of BFS decreases with the laser linewidth narrowing from 1.05 MHz to 3.1 kHz, which is in good agreement with the numerical simulation. However, the RMSE of BFS increases when the laser linewidth is less than 3.1 kHz, which may arise from the coherent Rayleigh noise due to a too narrow laser linewidth. The results can provide a theoretical basis and experimental guidance for choosing the appropriate laser linewidth in BOTDR.

Traditional insulation material is thermally insulating and has a low thermal conductivity. The miniaturisation and higher power of electrical devices would generate lots of heat, which have created new challenges to safe and stable operation of the grid. The development of insulating materials with high thermal conductivity provides a new method to solve these problems. The improvement of thermal conductivity would increase the ability to conduct heat and greatly reduce the operating temperature of the electrical equipment, which could reduce the equipment size and extend service life. On the other hand, inorganic thermally conductive particles and the improved thermal conductivity may have great effect on thermal breakdown. In this study, the factors affecting the thermal conductivity of dielectric polymer composites were explored. Intrinsic thermal conductive polymer and particle-filled thermal conductive composites were discussed. Effect of thermal conductivity, shape, size, surface treatment of the particle and prepare process on thermal properties of the composites were illustrated. This study focused on the electrical and thermal properties of thermally conductive epoxy, polyimide and polyethylene composites. Tracking failure caused by thermal accumulation is a typical thermal breakdown phenomenon. The performance of the resistance to tracking failure was studied for these composites. The results showed that thermal conductive particles improved the resistance to tracking failure. Finally, application of thermally conductive epoxy in electrical equipment was discussed.

Self-association is a common phenomenon in biology and one that can have positive and negative impacts, from the construction of the architectural cytoskeleton of cells to the formation of fibrils in amyloid diseases. Understanding the nature and mechanisms of self-association is important for modulating these systems and in creating biologically-inspired materials. Here, we present a two-stage de novo peptide design framework that can generate novel self-associating peptide systems. The first stage uses a simulated multimeric template structure as input into the optimization-based Sequence Selection to generate low potential energy sequences. The second stage is a computational validation procedure that calculates Fold Specificity and/or Approximate Association Affinity (K*association) based on metrics that we have devised for multimeric systems. This framework was applied to the design of self-associating tripeptides using the known self-associating tripeptide, Ac-IVD, as a structural template. Six computationally predicted tripeptides (Ac-LVE, Ac-YYD, Ac-LLE, Ac-YLD, Ac-MYD, Ac-VIE) were chosen for experimental validation in order to illustrate the self-association outcomes predicted by the three metrics. Self-association and electron microscopy studies revealed that Ac-LLE formed bead-like microstructures, Ac-LVE and Ac-YYD formed fibrillar aggregates, Ac-VIE and Ac-MYD formed hydrogels, and Ac-YLD crystallized under ambient conditions. An X-ray crystallographic study was carried out on a single crystal of Ac-YLD, which revealed that each molecule adopts a β-strand conformation that stack together to form parallel β-sheets. As an additional validation of the approach, the hydrogel-forming sequences of Ac-MYD and Ac-VIE were shuffled. The shuffled sequences were computationally predicted to have lower K*association values and were experimentally verified to not form hydrogels. This illustrates the robustness of the framework in predicting self-associating tripeptides. We expect that this enhanced multimeric de novo peptide design framework will find future application in creating novel self-associating peptides based on unnatural amino acids, and inhibitor peptides of detrimental self-aggregating biological proteins.

Due to the emergence of resistance toward current antibiotics, there is a pressing need to develop the next generation of antibiotics as therapeutics against infectious and opportunistic diseases of microbial origins. The shikimate pathway is exclusive to microbes, plants and fungi, and hence is an attractive and logical target for development of antimicrobial therapeutics. The Gram-positive commensal microbe, Enterococcus faecalis, is a major human pathogen associated with nosocomial infections and resistance to vancomycin, the "drug of last resort". Here, we report the identification of several polyketide-based inhibitors against the E. faecalis shikimate pathway enzyme, 3-dehydroquinate dehydratase (DHQase). In particular, marein, a flavonoid polyketide, both inhibited DHQase and retarded the growth of Enterococcus faecalis. The purification, crystallization and structural resolution of recombinant DHQase from E. faecalis (at 2.2 Å resolution) are also reported. This study provides a route in the development of polyketide-based antimicrobial inhibitors targeting the shikimate pathway of the human pathogen E. faecalis.