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AbstractTricyrtis xianjuensis Li, Chen & Ma 2014 is a rare and endangered species endemic to Zhejiang province, with fewer than 200 individuals in the wild. In our present study, the complete chloroplast genome of T. xianjuensis was assembled by using high-throughput sequencing data, and its genomic features were described and comparative genomic analyses within Liliaceae family were performed. The complete chloroplast genome of T. xianjuensis was 155,748 bp in length, exhibiting a GC content of 37.3%. This genome structure contained two inverted repeats (IRs), as well as a small single-copy (SSC) and a large single-copy (LSC) region. The IR region measured 26,371 bp, while the SSC and LSC regions were 17,729 bp and 85,277 bp in length, respectively. A total of 137 genes were identified, including 85 protein-coding genes, 38 tRNA genes, eight rRNA genes, and six pseudogenes. Phylogenic analysis revealed T. xianjuensis shared a clade with T. formosana Baker 1879 and T. macropoda Miq. 1867, with a support rate of 100%. The assembly and analysis of T. xianjuensis chloroplast genome provided an insight into further studies on the conservation genetics of this endangered species.

Abstract During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed.

AbstractLuisia hancockii Rolfe 1896 is an epiphytic orchid species. In our present study, the whole chloroplast genome sequence of L. hancockii was de novo assembled by using high-throughput Illumina reads, and phylogenetic analysis was conducted within species of subtribe Aeridinae. The complete chloroplast genome sequence of L. hancockii was 146,243 bp in length, with a typical quadripartite structure, and its large single-copy, small single-copy, and inverted repeat were 84,441 bp, 11,412 bp, and 25,195 bp long, respectively. The GC content of the whole chloroplast genome was 36.6%, while the GC contents of LSC, SSC, and IR were 33.8%, 27.5%, and 43.3%, respectively. The chloroplast genome consisted of 129 genes, including 74 protein-coding genes, eight rRNAs, 38 tRNAs, and nine pseudogenes. Phylogenic tree was generated using the best model GTR + R, and the results showed that L. hancockii was sister to Holcoglossum and Vanda species, with a support of 100%.

Abstract Background Osteosarcoma (OS) is a highly invasive primary malignancy of the bone that is common in children and adolescents. MicroRNAs (miRNAs) are novel diagnostic and predictive biomarkers for cancers. The miRNA miR-3195 is aberrantly expressed in multiple types of tumors. However, the expression levels and biological functions of miR-3195 in OS remain unclear. Methods Two Gene Expression Omnibus (GEO) datasets (GSE69470 and GSE16088) were used to analyze differentially expressed miRNAs and mRNAs in osteosarcoma cell lines and OS tissues. Quantitative RT-PCR was used to detect the expression levels of miR-3195 and the SRY-box transcription factor 4 (SOX4) mRNA in OS tissues and cell lines. The relationship between miR-3195 and the 3’-upstream region (3’-UTR) in the SOX4 mRNA (predicted through bioinformatics) was analyzed using Pearson's correlation analysis and confirmed by a dual-luciferase reporter gene experiment. Cell counting kit-8 assays, colony formation assays, flow cytometry, wound healing assays, transwell assays, and western blotting were performed to explore the effects of miR-3195 levels on SOX4 affected OS cell biological behavior. Results Our results revealed that miR-3195 was the most down-regulated miRNA and SOX4 was the most up-regulated mRNA by Bioinformatic analysis. It was further confirmed miR-3195 had low expression, and SOX4 had high expression levels in clinical OS tissue samples; the expression levels of both genes were negatively correlated with each other in OS tissues. Overexpression of miR-3195 in OS cell lines significantly inhibited cell proliferation, migration, and invasiveness, while promoting apoptosis; all these effects were reversed by increasing SOX4 expression levels. We also found that miR-3195 could directly bind with the SOX4 gene and down-regulate SOX4 expression. Conclusions miR-3195 can modulate proliferation, migration, invasiveness, and apoptosis in OS cells by regulating the SOX4 gene. Thus, the miR-3195/SOX4 signaling may be a novel therapeutic target in OS treatment.

Abstract To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms.

Objectives: This experiment investigated the role of the FAD-dependent oxidoreductase domain-containing 2 (FOXRED2) in the development of cutaneous malignant melanoma. Methods: We explored the expression and prognostic effects of FOXRED2 in cutaneous malignant melanoma by performing bioinformatics analyses and immunohistochemical staining experiments and verified the biological influence of FOXRED2 on human melanoma cells using in vitro experiments. Results: FOXRED2 expression was significantly higher in cutaneous malignant melanoma compared to normal skin and nevus tissues and closely associated with prognosis. The expression levels of FOXRED2 mRNA and protein were significantly upregulated in human melanoma cell lines, and knocking down FOXRED2 expression inhibits proliferation, invasion, and migration, promotes apoptosis, and alters tumor cell biology in A2058 and A375 cells. Conclusion: FOXRED2 may play a crucial role in the development and progression of cutaneous malignant melanoma.

The closed-form solution of each rotation joint angle is the most direct and convenient method for humanoid robot arms to imitate the real-time motion of the human arm. Occasionally, the human structure differs from that of a robot, and sensor information is inadequate. Therefore, it is difficult to obtain a closed-form solution for the inverse kinematics (IK) of a robot when using the Denavit–Hartenberg (D–H) method or screw theory alone. This study proposes combining the screw theory and the D–H method to solve the closed-form solution of the IK of a robotic arm with two unique structures: the elbow joint offset and the non-vertical state at the shoulder joint of the robotic arm. The proposed combination method first solves the elbow joint angle using screw theory. Then, the other rotation joint angles are solved using the D–H method. With the proposed method, human arm motion is successfully mapped to the corresponding joint of the robotic arm, and the closed-form solution allows the robotic arm to imitate human arm motion in real–time. Simulation results show that IK can be used to obtain high–precision angles and positions. Experiments show that the robotic arm has an accurate imitation performance, the accuracy of the $z$ -axis of the manipulator is significantly improved by 73.6%, and that of the $x$ -axis and $y$ -axis is also improved by 37.36% and 21.89%, respectively.

In order to reduce the influence of temperature deformation of large-size body-mounted radiators on the observation accuracy of space station telescopes and adapt to launch vibration loads, this paper proposes a floating combination stress release mechanism. Firstly, based on the dimensions, operating conditions, and stress directions of the radiator, an “orthogonal + parallel” layout of the radiator stress release mechanism is designed. To verify whether the layout design complies with the fully constrained theory, the layout design model is simplified into a four-point model, including one fixed support, two line-degree-of-freedom release mechanisms, and one plane-degree-of-freedom release mechanism. Degree-of-freedom analysis is conducted, and the statically determinate support goal is successfully achieved. Then, through the layout design of the stress release mechanism, specific modeling designs are carried out for the fixed support, line-degree-of-freedom release mechanisms, and plane-degree-of-freedom release mechanism using three-dimensional modeling software. Finally, the feasibility of the design scheme is verified through finite element simulation and mechanical testing. The results show that under sine and random vibrations, the maximum response amplification factor is 4.2, which meets the design requirement of being less than five. The maximum stress is 192 MPa, which is lower than the material’s yield limit. Under the application of an 80 °C temperature difference, the displacement response value of the radiator is 3.28 mm. This value falls within the allowable movement range of the stress release mechanism and meets the design criteria.

Cutaneous squamous cell carcinoma (cSCC) is one of the most common types of skin cancer. The incidence of the skin cancer is increasing year by year and seriously affects the quality of life of patients. Therefore, finding new targeted markers is of great significance for diagnosis and treatment. In recent years, the role of long non-coding RNA in diseases has been paid more attention. It is involved in the development of diseases through influencing transcription, epigenetics, and regulating competitive endogenous RNA networks. In cSCC, its related LncRNAs are abnormally expressed and affect tumor phenotypes through molecular, gene, signal pathway and other ways. In this review, we summarize studies on the role of seven common long non-coding RNA in cutaneous squamous cell carcinoma.

NLRP3 inflammasome activates IL-1β and IL-18. In recent years, a large number of in vitro and in vivo studies showed elevations in expression levels of NLRP3 inflammasome, IL-1β and IL-18 in psoriasis. The involvement of NLRP3 inflammasome in the pathogenesis of psoriasis may be related to the activation of NF-κB, P2X7, S1PR1 and miRNA155. Blocking NLRP3 inflammasome may affect AMPK and SIRT1 molecules as well as T cell signaling pathways in psoriasis models. Here we review the association of NLRP3 and psoriasis, the possible mechanisms by which NLRP3 regulates the progress of psoriasis and the changes in inflammatory pathway after blockade of NLRP3.

The health of humans has been negatively impacted by PM2.5 exposure, but the chemical composition and toxicity of PM2.5 might vary depending on its source. To investigate the toxic effects of particulate matter from different sources on lung epithelial cells (A549), PM2.5 samples were collected from residential, industrial, and transportation areas in Nanjing, China. The chemical composition of PM2.5 was analyzed, and toxicological experiments were conducted. The A549 cells were exposed using an air–liquid interface (ALI) exposure system, and the cytotoxic indicators of the cells were detected. The research results indicated that acute exposure to different sources of particulate matter at the air–liquid interface caused damage to the cells, induced the production of ROS, caused apoptosis, inflammatory damage, and DNA damage, with a dose–effect relationship. The content of heavy metals and PAHs in PM2.5 from the traffic source was relatively high, and the toxic effect of the traffic–source samples on the cells was higher than that of the industrial– and residential–source samples. The cytotoxicity of particulate matter was mostly associated with water–soluble ions, carbon components, heavy metals, PAHs, and endotoxin, based on the analysis of the Pearson correlation. Oxidative stress played an important role in PM2.5–induced biological toxicity.

Nowadays, hydraulic quadruped robot shows high power density, good impact resistance and robustness in the research. The controller is the key to realize these features. This paper shows the design of an open-source single-leg controller for the hydraulic quadruped robot Spurlos using a distributed control scheme. The single-leg system of the hydraulic quadruped robot Spurlos contains three angle encoders, three servo valves and six pressure sensors, which has the same components as most single-leg systems. Through the chips designed in the controller, the signal can be received from the encoders and the sensors, meanwhile the signal can be delivered to the servo valves. The software part of the controller adopts the MBD (Model-Based Design) method, which can greatly improve the development efficiency. According to the experiments, the controller design is reasonable, stable operation, and can satisfy the requirements of the hydraulic quadruped robot for leg motion control. The controller designed in this paper provides a solution to the problem that there is no ready-made control board for hydraulic quadruped robot which have three degrees of freedom for each leg. It enables the control researches for hydraulic quadruped robots to be more easily implemented.

Mixed-lineage leukemia 1 (MLL1) introduces 1-, 2- and 3-methylation into histone H3K4 through the evolutionarily conserved set domain. In this study, bovine embryonic stem cells (bESCs, known as bESCs-F7) were established from in vitro-fertilized (IVF) embryos via Wnt signaling inhibition; however, their contribution to the endoderm in vivo is limited. To improve the quality of bESCs, MM-102, an inhibitor of MLL1, was applied to the culture. The results showed that MLL1 inhibition along with GSK3 and MAP2K inhibition (3i) at the embryonic stage did not affect bESCs’ establishment and pluripotency. MLL1 inhibition improved the pluripotency and differentiation potential of bESCs via the up-regulation of stem cell signaling pathways such as PI3K-Akt and WNT. MLL1 inhibition decreased H3K4me1 modification at the promoters and altered the distribution of DNA methylation in bESCs. In summary, MLL1 inhibition gives bESCs better pluripotency, and its application may provide high-quality pluripotent stem cells for domestic animals.

Although numerous studies have investigated that atmospheric fine particulate matter (PM2.5) can be toxic to environmental organisms, the research on the reproductive toxicity of PM2.5 is limited, and the key toxic components and underlying mechanisms remain unknown. In this work, PM2.5 samples of four seasons in Nanjing from March 1, 2021, to February 28, 2022 were collected and the chemical components were analyzed. Caenorhabditis elegans (C. elegans) was employed to conduct the toxicological testing. The reproductive toxicity of PM2.5 to C. elegans in different seasons was evaluated by multiple reproductive endpoints. Exposure to high concentrations of PM2.5 significantly decreased the brood size and the number of fertilized eggs in utero. PM2.5 exposure also increased the number of germ cell corpses and caused abnormal expression of apoptosis-related genes (ced-9, ced-4, and ced-3), which confirmed that PM2.5 induced germline apoptosis. In addition, PM2.5 exposure significantly increased the production of reactive oxygen species (ROS) in C. elegans and the fluorescence intensity of HUS-1 protein in of transgenic strain WS1433. Meanwhile, the expression of genes related to DNA damage (cep-1, clk-2, egl-1, and hus-1) and oxidative stress (mev-1, isp-1, and gas-1) also significantly altered in C. elegans, suggesting induction of DNA damage and oxidative stress. According to Pearson correlation analyses, DNA damage and oxidative stress were significantly correlated with multiple reproductive endpoints in C. elegans. Thus, it was speculated that PM2.5 caused reproductive dysfunction and germ cell apoptosis in C. elegans may be by inducing ROS and DNA damage. In addition, heavy metals in PM2.5 were significantly correlated with multiple endpoints at physiological and biochemical, suggesting that the heavy metals might be an important contributor to the reproductive toxicity induced by PM2.5.

Abstract Accurate precipitation prediction can help decision makers judge the trend of climate change and formulate more effective measures, and prevent flood and drought disasters. In this paper, we propose a short‐term regional precipitation prediction model based on wind‐improved spatiotemporal convolutional network. Among them, the improved graph convolution network integrates the effects of wind direction and geographic location at past moments to capture the spatial dependence, whilst the gated recurrent unit captures the temporal dependence by learning the dynamic changes of data. The spatio‐temporal memory flow module and attention module are added to capture spatial deformation and temporal variation more accurately, thereby better matching the physical properties of precipitation. The proposed model achieves better prediction results on real data sets. Experiments show that our method is better at extracting the spatio‐temporal information of precipitation data and capturing its time dependence and spatial correlation.

ObjectiveTo explore the association of the trajectory of serum Cystatin C (Cysc) with diabetic kidney disease (DKD), a retrospective cohort study of Chinese subjects was carried out.MethodA review of 2,928 diabetes mellitus (DM) patients admitted to the clinic and ward of the Endocrinology Department, Shengjing Hospital of China Medical University from January 1, 2014 to December 31, 2014 was performed. Subsequent visits to the hospital were followed until December 31, 2020. The primary endpoint was the incidence of DKD as diagnosed by urinary albumin/creatinine ratio ≥30 mg/g and/or estimated glomerular filtration rate

Food safety is a key issue in promoting human health and sustaining life. Food analysis is essential to prevent food components or contaminants causing foodborne-related illnesses to consumers. Electrochemical sensors have become a desirable method for food safety analysis due to their simple, accurate and rapid response. The low sensitivity and poor selectivity of electrochemical sensors working in complex food sample matrices can be overcome by coupling them with covalent organic frameworks (COFs). COFs are a kind of novel porous organic polymer formed by light elements, such as C, H, N and B, via covalent bonds. This review focuses on the recent progress in COF-based electrochemical sensors for food safety analysis. Firstly, the synthesis methods of COFs are summarized. Then, a discussion of the strategies is given to improve the electrochemistry performance of COFs. There follows a summary of the recently developed COF-based electrochemical sensors for the determination of food contaminants, including bisphenols, antibiotics, pesticides, heavy metal ions, fungal toxin and bacterium. Finally, the challenges and the future directions in this field are discussed.

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and to accelerate the development of new robotic grasping tasks. A well-designed demonstration device can effectively represent human grasping motion to transfer grasping skills to robots. In this paper, an improved gripper-like exoskeleton with a data collection system is proposed. First, we present the mechatronic details of the exoskeleton and its motion-tracking system, considering the manipulation flexibility and data acquisition requirements. We then present the capabilities of the device and its data collection system, which collects the position, pose and displacement of the gripper on the exoskeleton. The collected data is further processed by the data acquisition and processing software. Next, we describe the principles of Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) in robot skill learning, which are used to transfer the raw data from demonstrations to robot motions. In the experiment, an optimized trajectory was learned from multiple demonstrations and reproduced on a robot. The results show that the GMR complemented with GMM is able to learn a smooth trajectory from demonstration trajectories with noise.

Abstract Correction method can reduce the high deviation between the prediction results of numerical model and the observation results and improve the prediction accuracy. Based on the numerical models, including Rapid Refresh Multi‐scale Analysis and Prediction System‐CHEM and CMA Unified Atmospheric Chemistry Environment, and combined with European Centre for Medium‐Range Weather Forecasts meteorological field model data, a correction method of environmental meteorological model based on Long‐Short‐Term Memory (LSTM) neural network is proposed in this paper. The method mainly includes the following steps: First, the meteorological factors that have the main influence on the PM2.5 concentration are selected by the correlation coefficient method; at the same time, the forecast results of numerical models are used as additional factors, and these factors are taken as the initial characteristics of the LSTM. Then, the network parameters of the LSTM are trained by initial characteristics and corresponding observation data, and the mapping relationship between the input factors and the output PM2.5 concentration is established. Finally, European Centre for Medium‐Range Weather Forecasts data of March 2018 are selected to test the prediction performance of LSTM correction method. Results show that compared with single environment meteorological model, the correlation coefficient, the root mean square error, and the mean absolute error between forecasted and observed PM2.5 concentration in 3–72 hr increased from 0.35–0.7 to 0.55–0.75, decreased from 45.3–67.46 to 37.74–53.7 μg/m3, and decreased by 7.86–16.52%, respectively. It indicates that the forecast performance of LSTM correction model is better than single environment meteorological model.

Short-term precipitation prediction through abundant observation data (ground observation station data, radar data, etc.) is an essential part of the contemporary meteorological prediction system. However, most current studies only use single-modal data, which leads to some problems, such as poor prediction accuracy and little prediction timeliness. This paper proposes a multimodal data fusion precipitation prediction model integrating station data and radar data. Specifically, our model consists of three parts. Firstly, the radar feature encoder comprises a shallow convolution neural network and a stacked convolutional long short term memory network (ConvLSTM), which is used to extract the spatio-temporal features of radar-echo data. The weather station data feature encoder is composed of a fully connected network and an LSTM, which is used to extract the sequential features of the weather station data. Then, the cross-modal feature encoder obtains cross-modal features by aligning and exchanging the feature information of the radar data and the weather station data through the cross-attention mechanism. Finally, the decoder outputs the quantitative short-term precipitation prediction value. Our model can integrate station and radar data characteristics and improve prediction accuracy and timeliness, and can flexibly add other modal features. We have verified our model on four short-term and impending rainfall datasets in South Eastern China, achieving the best performance among the algorithms.

A fluorescence adamantane three-dimensional porous organic framework (AdaPOF) was synthesized via a Suzuki coupling reaction. The AdaPOF showed excellent fluorescence performance with a relative high quantum yield and fluorescence stability. Due to its excellent selectivity to tetracycline (TC), a fluorescence sensor based on AdaPOF was constructed for TC determination. The selective sensing mechanism of the AdaPOF towards TC was studied by density functional theory (DFT) calculation experiments. An AdaPOF–based fluorescence method for TC determination was established, with the linear range of 0.1–9.0 μmol/L (R2 = 0.9959) and the limit of detection (S/N = 3) of 43 nmol/L. Moreover, the fluorescence method was used to the determination of TC in aquatic products and the recoveries were ranged from 94.4% to 103.8%. The results obtained by this fluorescence method were consistent with those of the high-performance liquid chromatography (HPLC) method in the TC determination.

Recently, regression-based classifiers, such as the sparse representation classifier and collaborative representation classifier, have been proposed for hyperspectral image (HSI) classification. However, HSIs are typically corrupted by noise, occlusion, or data loss. Obtaining a good performance for most regression-based methods is difficult. To address this challenge, we present a novel robust regression-based nearest regularized subspace (R2NRS) for HSI classification. In our method, each band of a pixel is assigned with a regularized regression coefficient in the NRS model to reduce the influence of those bands corrupted during classification. The reconstruction error, Markov random field, and high-confidence index next jointly generate a comprehensive spatial-spectral model to perform the HSI classification. The experimental results on two HSI data sets demonstrate the superior performance of our proposed method for HSI classification for the case when some bands of the image are corrupted by noise or data loss.

Rock penetration is an inevitable problem in the study of drilling and projectile penetration. The penetration resistance of red sandstone and limestone was investigated at projectile speeds ranging from 600–1200 m/s. The damage characteristics of these rocks were studied via experimental tests and numerical simulations. The damage condition of the target surface, internal damage state and crack distribution were obtained. It was concluded that the maximum error of the numerical simulation and experimental results was not more than 10%. The penetration resistance of limestone was approximately 23.8% stronger than that of red sandstone. However, the energy absorption effect of limestone was weaker than that of red sandstone, and large cracks can be more easily formed. The compaction area of red sandstone was softer, with obvious crack compaction in the crater area, and particle detachment can more easily occur. Red sandstone was more sensitive to the impact angle of the projectile. With oblique penetration, the projectile was more likely to deflect inside the red sandstone target.

Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.

High-dimensional optimization problems are ubiquitous in every field nowadays, which seriously challenge the optimization ability of existing optimizers. To solve this kind of optimization problems effectively, this paper proposes an elite-directed particle swarm optimization (EDPSO) with historical information to explore and exploit the high-dimensional solution space efficiently. Specifically, in EDPSO, the swarm is first separated into two exclusive sets based on the Pareto principle (80-20 rule), namely the elite set containing the top best 20% of particles and the non-elite set consisting of the remaining 80% of particles. Then, the non-elite set is further separated into two layers with the same size from the best to the worst. As a result, the swarm is divided into three layers. Subsequently, particles in the third layer learn from those in the first two layers, while particles in the second layer learn from those in the first layer, on the condition that particles in the first layer remain unchanged. In this way, the learning effectiveness and the learning diversity of particles could be largely promoted. To further enhance the learning diversity of particles, we maintain an additional archive to store obsolete elites, and use the predominant elites in the archive along with particles in the first two layers to direct the update of particles in the third layer. With these two mechanisms, the proposed EDPSO is expected to compromise search intensification and diversification well at the swarm level and the particle level, to explore and exploit the solution space. Extensive experiments are conducted on the widely used CEC’2010 and CEC’2013 high-dimensional benchmark problem sets to validate the effectiveness of the proposed EDPSO. Compared with several state-of-the-art large-scale algorithms, EDPSO is demonstrated to achieve highly competitive or even much better performance in tackling high-dimensional problems.

Evaluation of the hydrophobic/hydrophilic interaction individually between the sorbent and target compounds in sample pretreatment is a big challenge. Herein, a smart titanium substrate with switchable surface wettability was fabricated and selected as the sorbent for the solution. The titanium wires and meshes were fabricated by simple hydrothermal etching and chemical modification so as to construct the superhydrophilic and superhydrophobic surfaces. The micro/nano hierarchical structures of the formed TiO2 nanoparticles in situ on the surface of Ti substrates exhibited the switchable surface wettability. After UV irradiation for about 15.5 h, the superhydrophobic substrates became superhydrophilic. The morphologies and element composition of the wires were observed by SEM, EDS, and XRD, and their surface wettabilities were measured using the Ti mesh by contact angle goniometer. The pristine hydrophilic wire, the resulting superhydrophilic wire, superhydrophobic wire, and the UV-irradiated superhydrophilic wire were filled into a stainless tube as the sorbent instead of the sample loop of a six-port valve for on-line in-tube solid-phase microextraction. When employed in conjunction with HPLC, four kinds of wires were comparatively applied to extract six estrogens in water samples. The optimal conditions for the preconcentration and separation of target compounds were obtained with a sample volume of 60 mL, an injection rate of 2 mL/min, a desorption time of 2 min, and a mobile phase of acetonile/water (47/53, v/v). The results showed that both the superhydrophilic wire and UV-irradiated wire had the highest extraction efficiency for the polar compounds of estrogens with the enrichment factors in the range of 20–177, while the superhydrophobic wire exhibited the highest extraction efficiency for the non-polar compounds of five polycyclic aromatic hydrocarbons (PAHs). They demonstrated that extraction efficiency was mainly dependent on the surface wettability of the sorbent and the polarity of the target compounds, which was in accordance with the molecular theory of like dissolves like.

Human embryonic stem cells (hESCs) possess the ability to differentiate into insulin-producing cells (IPCs), which can be used to treat type I diabetes. miR-375 is an essential transcriptional regulator in the development and maturation of the pancreas. In this study, we optimized a protocol to differentiate hESCs into IPCs and successfully obtained IPCs. Then, we performed overexpression and inhibition experiments of miR-375 on cells at different stages of differentiation and performed RNA-seq. The results showed that the expression of miR-375 fluctuated during hESC differentiation and was affected by miR-375 mimics and inhibitors. miR-375 influences global gene expression and the target genes of miR-375. The overexpression of miR-375 can cause changes in multiple signaling pathways during pancreatic development. miR-375 is a major participant in the differentiation of pancreatic β-cells through different target genes at different stages. This study provides new ideas for further investigation of how microRNAs affect cell fate and gene transcription.

In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale characteristic and the clustering problem of multi-polar fuzzy feature information. Additionally, the traditional classification method leads to slow speed and inaccuracy because of its difficulties. Therefore, by considering a multi-polar fuzzy feature classification of defects, a fuzzy c-means ( FCM) clustering algorithm based on multi-polar fuzzy entropy is proposed. Firstly, according to the evaluation of available characteristics, the characteristic value of clustering samples is established. Secondly, we calculated the multi-polar fuzzy entropy of clustering samples. Finally, an improved FCM clustering algorithm based on multi-polar fuzzy entropy is presented. The experimental results of the data set collected from 5 different types of weighing cheating cars demonstrate that the algorithm improves the classification accuracy of FCM with multi-polar fuzzy feature information clustering and reduces significantly both the number of iterations and the classification time.

Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.

Echography imaging is an important technique frequently used in medical diagnostics due to low-cost, non-ionising characteristics, and pragmatic convenience. Due to the shortage of skilful technicians and injuries of physicians sustained from diagnosing several patients, robot-assisted echography (RAE) system is gaining great attention in recent decades. A thorough study of the recent research advances in the field of perception, control and cognition techniques used in RAE systems is presented in this study. This survey introduces the representative system structure, applications and projects, and products. Challenges and key technological issues faced by the traditional RAE system and how the current artificial intelligence and cobots attempt to overcome these issues are summarised. Furthermore, significant future research directions in this field have been identified by this study as cognitive computing, operational skills transfer, and commercially feasible system design.

The pilot-operated proportional directional valve is a crucial component in the airplane landing gear retraction system. The time delay of the proportional valve directly affects the retraction behavior even the flight safety and needs to be analyzed. Therefore, the modeling and analysis of a typical pilot-operated proportional directional valve are presented in this paper. Substantially, the nonlinear valve system is composed of four subsystems: mechanical, electronic, electromagnetic, and fluid dynamic subsystems. The subsystems are modeled based on a lumped parameter approach. The coupling among the subsystems is analyzed. The nonlinearity is implemented using a specific set of equations in the model. Especially, the nonlinearity observed in practical application is considered in modeling the effect of the electronic anti-unloading power drive circuit on the pulse-width modulation signal. The analytical results of the model show good agreement with the experimental ones. The investigation shows that the delay of the valve results from multiple subsystems' dynamic performances which act sequentially on the main spool. The effect of the electronic part and the structural part is analyzed, and the proportion of each part is given based on the simulation results. The analysis shows that the model can provide a direct suggestion in choosing the valve part to optimize and the optimization effect limit.

Various brain tumors are very harmful to human health, and their incidence rate has risen gradually in recent years. Magnetic resonance imaging is widely used in the evaluation and treatment of brain tumors, but a radiologist's manual segmentation of massive images is time-consuming and produces some personal bias, and so a computer-aided diagnostic system could improve the working efficiency of radiologists. In recent years, a variety of automatic segmentation methods for brain tumor images has been proposed. Compared with other methods, machine-learning-based methods are able to obtain better segmentation accuracy, but such methods require the manual labeling of lots of samples to ensure the accuracy of the segmentation. In this study, semi-supervised learning theory and image spatial and clinical a priori knowledge of brain tumors are combined to propose a new brain-tumor segmentation method that can improve the segmentation accuracy with multiple classifier collaborative training (CoTraining) under the premise of fewer labeled data. In addition, according to the prior knowledge that image adjacent pixels belong to similar classes and clinical knowledge, we used superpixel graphs to construct a spatial and clinical constraint to improve brain-tumor segmentation accuracy further. The proposed method was tested on the Brain Tumor Segmentation Challenge 2012 and 2013 datasets (BRATS 2012, 2013). The experimental results demonstrate that the proposed method was able to overcome the drawbacks of fewer training samples effectively and obtained remarkable brain-tumor segmentation performance.

To improve air quality and protect public health, the State Council of China issued the National Air Pollution Prevention and Control Action Plan (‘the Action Plan’) in 2013. Specific measures were designed in the Action Plan to reduce emissions of atmospheric pollutants, with core contents in energy related measures. Implementing these measures would potentially have significant co-benefits on carbon dioxide (CO2) emissions. This study quantitatively analyzes the impacts of the different measures in the Action Plan on the emissions of major air pollutants of sulfur dioxide (SO2), nitrogen oxides (NOX), particulate matter (PM) and CO2 in the Jing-Jin-Ji (JJJ, i.e., Beijing-Tianjin-Hebei) region using the Greenhouse Gas and Air Pollution Interactions and Synergies model (GAINS)-China model. The results show that the Action Plan in JJJ would reduce 47.3 ± 0.8% of SO2, 32.5 ± 2.6% of NOx, 15.2 ± 0.2% of PM2.5, and 39.9 ± 5.3% CO2 emissions, compared to the emissions in 2012. PM2.5 emission reduction has the highest co-benefit for CO2 reduction in JJJ, and also in Tianjin and Hebei. In Beijing, NOx emission reduction has high co-benefit for CO2 reduction, when considering the relative degree of co-benefit. Among the different source sectors, industrial and residential sectors are identified to be the source sectors that contribute the most to the co-benefit of CO2 emission reduction in JJJ. The results suggest that implementing energy replacement in the industrial and residential sectors in JJJ can not only generate large amounts of air pollutants emission reduction, but also can lead to significant CO2 emission reduction.

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.

The present study established an efficient genome editing approach for the construction of stable transgenic cell lines of the domestic chicken (Gallus gallus domesticus). Our objectives were to facilitate the breeding of high-yield, high-quality chicken strains, and to investigate gene function in chicken stem cells. Three guide RNA (gRNAs) were designed to knockout the C2EIP gene, and knockout efficiency was evaluated in DF-1 chicken fibroblasts and chicken ESCs using the luciferase single-strand annealing (SSA) recombination assay, T7 endonuclease I (T7EI) assay, and TA clone sequencing. In addition, the polyethylenimine-encapsulated Cas9/gRNA plasmid was injected into fresh fertilized eggs. At 4.5 d later, frozen sections of the embryos were prepared, and knockout efficiency was evaluated by the T7EI assay. SSA assay results showed that luciferase activity of the vector expressing gRNA-3 was double that of the control. Results of the T7EI assay and TA clone sequencing indicated that Cas9/gRNA vector-mediated gene knockdown efficiency was approximately 27% in both DF-1 cells and ESCs. The CRISPR/Cas9 vector was also expressed in chicken embryos, resulting in gene knockdown in three of the 20 embryos (gene knockdown efficiency 15%). Taken together, our results indicate that the CRISPR/Cas9 system can mediate stable gene knockdown at the cell and embryo levels in domestic chickens.

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

An efficient genome editing approach had been established to construct the stable transgenic cell lines in the domestic chicken (Gallus gallus domesticus) at present. Our objectives were to investigate gene function in the differentiation process of chicken embryonic stem cells (ESCs) into spermatogonial stem cells(SSCs). Three guides RNA (gRNAs) were designed to knockout the Stra8 gene, and knockout efficiency was evaluated in domestic chicken cells using cleavage activity of in vitro transcription of gRNA, Luciferase-SSA assay, T7 endonuclease I assay(T7E1) and TA clone sequence. In addition, the Cas9/gRNA plasmid was transfected into ESCs to confirm the function of Stra8. SSA assay results showed that luciferase activity of the vector expressing gRNA-1 and gRNA- 2 was higher than that of gRNA-3. TA clone sequencing showed that the knockdown efficiency was 25% (10/40) in DF-1 cells, the knockdown efficiency was 23% (9/40) in chicken ESCs. T7E1 assay indicated that there were cleavage activity for three individuals, and the knockdown efficiency was 12% (3/25). Cell morphology, qRT-PCR, immunostaining and FCS indicated that Cas9/gRNA not only resulted in the knockout of Stra8 gene, but also suggested that the generation of SSCs was blocked by the Stra8 gene knockdown in vitro. Taken together, our results indicate that the CRISPR/Cas9 system could mediate stable Stra8 gene knockdown in domestic chicken's cells and inhibit ECSs differentiation into SSCs.

Background/Aims: Curcumin, a kind of plant polyphenolic compound, has been recently discovered to have renoprotective effects on diabetic nephropathy (DN). Podocyte can respond to various injuries including mechanical stress secondary to DN. Our previous study showed that podocyte miR-124 expression was up-regulated accompanied with podocytic adhesive capacity damage in vitro and in vivo. We hypothesized, in the present research that curcumin would ameliorate podocyte adhesion damage under mechanical stress by inhibiting miR-124 expression. Methods: Gene expression of miR-124 was measured by real-time PCR and protein expression of integrin α3 was measured by Western blotting in STZ-induced uninephrectomized diabetic rats and cultured podocytes under mechanical stress treated with curcumin respectively. Western blot and luciferase reporter assays were used to detect the effects of miR-124 overexpression on the Itga3 expression in podocytes. Results: Gene expression of miR-124 was upregulated and α3 was downregulated in renal cortex of diabetic rats and cultured podocytes under mechanical stress which were ameliorated by curcumin treatment significantly. Transient co-transfection of miR-124 mimics with luciferase expression plasmids resulted in a significant repression of luciferase activity in podocytes. Mechanistically, Itga3 may be a regulation target of miR-124. Conclusions: These results provide a novel idea that curcumin prevents against podocytic adhesive capacity damage under mechanical stress by inhibitting miR-124

Background: Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. Podocyte plays a key role in the pathogenesis of DN. Adhesive capacity damage of podocytes is characteristic in DN. Emerging evidence suggests that microRNAs (miRNAs) play crucial roles in controlling many cell adhesion molecules thus contribute to normal cell adhesion. The roles of miRNA in podocytic adhesive capacity damage in diabetic conditions remain largely unknown. Methods: Diabetes was induced by tail vein injection of streptozotocin (STZ) into uninephrectomized male Wistar rats. Comparative miRNA expression array and real-time PCR analyses were conducted in sham group at week 0 (W0, n = 3) and STZ-induced uninephrectomized diabetic rats at week 1 (W1, n = 3) and week 2 (W2, n = 3) to demonstrate the greatest increased miRNA in renal cortex. At week 2, STZ-induced uninephrectomized diabetic rats were treated with vehicle (Group U, n = 9), chemically modified antisense RNA oligonucleotide (ASO) complementary to the mature miR-124 (Group O, n = 8), miR-124 mismatch control sequence (Group M, n = 8). Urine specimens were obtained for measurement of urine albumin concentration and urinary podocyte specific protein (nephrin and podocin) quantitation. Expression of integrin α3 were detected by immunohistochemistry and western blotting. Results: MiRNAs are differentially regulated in renal cortex of STZ-induced uninephrectomized diabetic rats relative to sham rats. Among the up-regulated miRNAs, miR-124 expression demonstrated the greatest increase. Administration of miR-124 ASO for two weeks significantly reduced urinary podocytic nephrin, podocin and albumin excretion and up-regulate integrin α3 expression. Conclusion: MiR-124 is related to podocytic adhesive capacity damage and may be implicated in the pathogenesis of DN.

This paper proposes a recursive differential evolution (RDE) algorithm to identify the inertial parameters of an unknown target and simultaneously revise the friction parameters of space manipulator joints. The inertia parameters of a space manipulator, which govern the dynamic behaviours of the entire system to a significant extent, can change for many reasons during the process of on-orbit operations; consequently, it is essential to trace these changes within the control system to ensure the stability and accuracy of the entire system. RDE is inspired by a recursive least squares algorithm, using approximate gradient information to guide the mutation operation in the standard DE. A series of contrast simulations are employed to confirm the feasibility of the RDE algorithm. The simulation results show that the identification of the RDE algorithm is more precise than for a GA (genetic algorithm) and LS (least square) algorithm, and has an appropriate convergence rate. The RDE identification method is suitable for linear, nonlinear and combined systems, and can follow system dynamics exactly.

To obtain structurally novel and bioactive natural compounds from marine-derived microorganisms, the effect of high salt stress on secondary metabolite production in the marine-derived fungal strain, Spicaria elegans KLA-03, was investigated. The organism, which was isolated from marine sediment, produced different secondary metabolites when cultured in 3% and 10% saline conditions. Four characteristic metabolites, only produced in the 10% salinity culture, were isolated, and their structures were identified as (2E,2'Z)-3,3'-(6,6'-dihydroxybiphenyl-3,3'-diyl)diacrylic acid (1), aspulvinone E (2), aspochalasin E (3) and trichodermamide B (6), according to their 1D and 2D NMR spectra. Compound 1 is a new compound. High salt stress may therefore be a promising means to induce the production of new and chlorinated compounds in halotolerant fungi. Compound 1 showed moderate antibacterial activity against Pseudomonas aeruginosa and Escherichia coli with minimum inhibitory concentration (MIC) values of 0.038 and 0.767 mM, respectively.