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Spondyloarthritis is a prevalent and persistent condition that significantly impacts the quality of life. Its intricate pathological mechanisms have led to a scarcity of animal models capable of replicating the disease progression in humans, making it a prominent area of research interest in the field. To delve into the pathological and physiological traits of spontaneous non-human primate spondyloarthritis, this study meticulously examined the disease features of this natural disease model through an array of techniques including X-ray imaging, MRI imaging, blood biochemistry, markers of bone metabolism, transcriptomics, proteomics, and metabolomics. X-ray imaging results revealed that crab-eating monkeys (Macaca fascicularis) with spontaneous spondyloarthritis developed bone spurs in the spine and experienced bone destruction in peripheral joints. MRI imaging further confirmed inflammatory changes in the spine and facet joints of these monkeys, along with inflammation and bone destruction in peripheral joints. Blood biochemistry analysis showed abnormalities in liver function and kidney function indicators in crab-eating monkeys with spontaneous spondyloarthritis. Analysis of bone metabolism-related markers showed a decrease in bone formation (BGP) and bone resorption (β-CTx). A thorough correlation analysis was conducted on the transcriptome and proteome expression data, revealing a significant positive correlation between the two datasets. A total of eight genes and proteins with high expression levels were identified as common to both the transcriptome and proteome. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on these co-expressed genes and proteins, indicating a predominant enrichment in the IL-17 signaling pathway, with S100A8 and S100A9 identified as the core proteins. Further analysis using clinical data in conjunction with proteome data through Weighted Gene Co-expression Network Analysis (WGCNA) demonstrated a significant positive correlation between the high expression of S100A8 and S100A9 and the clinical phenotypes of spinal abnormalities, thereby corroborating the close association of S100A8 and S100A9 with the phenotype of spondyloarthritis. Human clinical studies have already established a link between S100A8 and S100A9 and autoimmune-related arthritic diseases, suggesting that the spontaneous spondyloarthritis observed in crab-eating macaques may be related to autoimmune diseases. It is hypothesized that S100A8 and S100A9 could serve as potential predictive biomarkers for spondyloarthritis in non-human primates.

The preparation of carbon nanotubes (CNTs) from plastics is of great significance for realizing high value utilization of waste and reducing carbon emission. Here, several kinds of real waste plastics were introduced into catalytic pyrolysis, and the process was also optimized. The results showed that the presence of impurities (e.g., adhesive labels) reduced the initial activation energy of the pyrolysis reaction, and the pyrolysis process was extended and could be divided into two stages. During the catalytic process, impurities play a toxic role on the catalyst at higher temperatures and result in the agglomeration of catalyst particles and a decrease in catalytic activity. Less than 10 wt.% carbon fibers were collected from milk cup waste. However, after experimental optimization, the influence of the impurity component was greatly reduced. The toxic effect of organic impurity volatiles on a catalyst was avoided by employing a segmented catalytic pyrolysis process, which led to an increase in solid carbon content of more than 20 % for express package waste. Simultaneously, more uniform and smoother CNTs can be found in the obtained solid carbon. The process of preparing carbon nanotubes with higher yield and better quality is feasible and has important application prospects in the utilization of waste plastics.

Chalcogen bonds (ChBs) involving selenium have attracted substantial scholarly interest in past years owing to their fundamental roles in various chemical and biological fields. However, the effect of the valency state of the electron-deficient selenium atom on the characteristics of such ChBs remains unexplored. Herein, we comparatively studied the σ-hole-type Se∙∙∙O ChBs between SeF2/SeF4 and a series of oxygen-bearing Lewis bases, including water, methanol, dimethyl ether, ethylene oxide, formaldehyde, acetaldehyde, acetone, and formic acid, using ab initio computations. The interaction energies of these chalcogen-bonded heterodimers vary from −5.25 to −11.16 kcal/mol. SeF2 participates in a shorter and stronger ChB than SeF4 for all the examined heterodimers. Such Se∙∙∙O ChBs are closed-shell interactions, exhibiting some covalent character for all the examined heterodimers, except for SeF4∙∙∙water. Most of these chalcogen-bonded heterodimers are predominantly stabilized through orbital interactions between the lone pair of the O atom in Lewis bases and the σ*(Se–F) antibonding orbitals of Lewis acids. The back-transfer of charge from the lone pair of selenium into the σ* or π* antibonding orbitals of Lewis bases is also observed for all systems. Energy decomposition analysis reveals that the electrostatic component significantly stabilizes the targeted heterodimers, while the induction and dispersion contributions cannot be ignored.

As a new type of agricultural waste block substrate utilization, the initial wet base state of the substrate culture block needs to be dried. Therefore, studying the drying mechanism of substrate culture block is critical. In this study, the substrate culture block in a dry state was taken as the research object. Based on physical and chemical properties, the internal section of the substrate culture block was characterized by scanning electron microscopy and the pore condition of the particles was quantified. The results showed that the internal pore structure was uniform and favorable for plant root growth. Based on the pore structure, pore channel modeling was constructed to investigate the distribution of the internal multiphase medium and to distinguish between channels and pore-blind channels. The applicability of the modeling was verified and discussed. By measuring the drying rate of the substrate culture block and classifying its drying stages as fast speed, constant speed, and slow speed, it is clarified that the forms of moisture existence are bound-state water and free-state water, and the moisture migration is prioritized as surface adsorption water, interparticle water, particle attached water, and capillary water. Innovate a method to quantify the change of pore space in the drying process by pore coefficient ratio to evaluate the drying quality. The results show that when the pore coefficient ratio is about 40 %, its moisture content is 20 %∼30 %, and the drying effect is best at this time. The physical drying test further confirmed the correctness of the conclusion of the drying stage division and water loss law. This study can provide a theoretical reference for the modeling study of the pore structure of the block matrix and the exploration of its drying mechanism.

Pulmonary artery stenosis represents a group of disorders involving main, branch or peripheral pulmonary arteries with pain, dyspnea, hemoptysis or even no symptoms. Early diagnosis and timely intervention are crucial for reducing mortality, but timely diagnosis is challenging due to the non-specific symptoms. Computed tomography pulmonary angiography (CTPA) is useful in the diagnosis because it can provide more details about abnormal changes in the lumen, vessel wall and adjacent mediastinal structures. Congenital and acquired pulmonary artery anomalies have some characteristics on CTPA, which can be useful for differential diagnosis. Awareness of these conditions is important for radiologists. This pictorial review provides an overview of CTPA imaging features of pulmonary artery stenosis.

Abstract Background Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. Methods A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. Results The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p

Abstract Introduction Cervical hybrid surgery (HS) combines anterior cervical discectomy and fusion (ACDF) and cervical disc arthroplasty (CDA) to establish an individualized surgical plan for patients with multiple cervical disc degenerative diseases. In order to maintain the stability of the spine after HS, an external cervical collar is often used. However, there is still controversy regarding the importance of a cervical collar following surgery. This study aims to determine whether the cervical collar is effective and how long it should be worn after surgery. Methods This is a randomized, single-center, prospective, parallel-controlled trial. Eligible participants will be selected according to the inclusion and exclusion criteria. The primary outcome is the neck disability index, which will be evaluated before surgery and at one week, 3 weeks, 6 weeks, 3 months, 6 months, and 12 months following surgery. The secondary outcomes consist of the Japanese Orthopedic Association Scores, MOS 36-item short-form health survey (SF-36), visual analog scale, Pittsburgh Sleep Quality Index (PSQI), Bazaz dysphagia scoring system, Falls Efficacy Scale, cervical collar satisfaction score, neck soft tissue assessment, and Braden Scale, as well as radiologic assessments for cervical lordosis, disc height of the operative levels, fusion rate, range of motion (ROM), and complications including anterior bone loss, prosthesis migration, and heterotopic ossification. The clinical and radiologic examinations were performed by investigators with no therapeutic relationship with the individual patient. All radiographs were examined by one independent radiologist. Ethics and dissemination The results of this study will be published in peer-reviewed journals and presented at conferences. Upon completion of this trial, our findings could provide an appropriate cervical collar-wearing guideline for patients receiving HS. Trial registration ChiCTR.org.cn ChiCTR2000033002. Registered on 2020–05-17.

Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy of face recognition is greatly reduced due to the obscuring of objects, such as masks and sunglasses. Wearing masks in public has been a crucial approach to preventing illness, especially since the Covid-19 outbreak. This poses challenges to applications such as face recognition. Therefore, the removal of masks via image inpainting has become a hot topic in the field of computer vision. Deep learning-based image inpainting techniques have taken observable results, but the restored images still have problems such as blurring and inconsistency. To address such problems, this paper proposes an improved inpainting model based on generative adversarial network: the model adds attention mechanisms to the sampling module based on pix2pix network; the residual module is improved by adding convolutional branches. The improved inpainting model can not only effectively restore faces obscured by face masks, but also realize the inpainting of randomly obscured images of human faces. To further validate the generality of the inpainting model, tests are conducted on the datasets of CelebA, Paris Street and Place2, and the experimental results show that both SSIM and PSNR have improved significantly.

PURPOSETo investigate the value of tumor morphologic features of pT1-2 gastric cancer (GC) on contrast-enhanced computed tomography (CT) in assessing lymph node metastasis (LNM) with reference to histopathological results.METHODSEighty-six patients seen from October 2017 to April 2019 with pT1‐2 GC proven by histopathology were included. Tumor volume and CT densities were measured in the plain scan and the portal-venous phase (PVP), and the percent enhancement was calculated. The correlations between tumor morphologic features and the N stages were analyzed. The diagnostic capability of tumor volume and enhancement features in predicting the LN status of pT1-2 GCs was further investigated using receiver operating characteristic (ROC) analysis.RESULTSTumor volume, CT density in the PVP, and tumor percent enhancement in the PVP correlated significantly with the N stage (rho: 0.307, 0.558, and 0.586, respectively). Tumor volumes were significantly lower in the LNM− group than in the LNM+ group (14.4 mm3 vs. 22.6 mm3, P = 0.004). The differences between the LNM− and LNM+ groups in the CT density in the PVP and the percent enhancement in the PVP were also statistically significant (68.00 HU vs. 87.50 HU, P < 0.001; and 103.06% vs. 179.19%, P < 0.001, respectively). The area under the ROC curves for identifying the LNM+ group was 0.69 for tumor volume and 0.88 for percent enhancement in the PVP, respectively. The percent enhancement in the PVP of 145.2% and tumor volume of 17.4 mL achieved good diagnostic performance in determining LNM+ (sensitivity: 71.4%, 82.1%; specificity: 91.4%, 58.6%; and accuracy: 84.9%, 66.3%, respectively).CONCLUSIONTumor volume and percent enhancement in the PVP of pT1-2 GC could improve the diagnostic accuracy of LNM and would be helpful in image surveillance of these patients.

Abstract Background The extent of pancreatoduodenectomy for pancreatic head cancer remains controversial, and more high‐level clinical evidence is needed. This study aimed to evaluate the outcome of extended pancreatoduodenectomy (EPD) with retroperitoneal nerve resection in pancreatic head cancer. Methods This multicenter randomized trial was performed at 6 Chinese high‐volume hospitals that enrolled patients between October 3, 2012, and September 21, 2017. Four hundred patients with stage I or II pancreatic head cancer and without specific pancreatic cancer treatments (preoperative chemotherapy or chemoradiation) within three months were randomly assigned to undergo standard pancreatoduodenectomy (SPD) or EPD, with the latter followed by dissection of additional lymph nodes (LNs), nerves and soft tissues 270° on the right side surrounding the superior mesenteric artery and celiac axis. The primary endpoint was overall survival (OS) by intention‐to‐treat (ITT). The secondary endpoints were disease‐free survival (DFS), mortality, morbidity, and postoperative pain intensity. Results The R1 rate was slightly lower with EPD (8.46%) than with SPD (12.56%). The morbidity and mortality rates were similar between the two groups. The median OS was similar in the EPD and SPD groups by ITT in the whole study cohort (23.0 vs. 20.2 months, P = 0.100), while the median DFS was superior in the EPD group (16.1 vs. 13.2 months, P = 0.031). Patients with preoperative CA19–9 < 200.0 U/mL had significantly improved OS and DFS with EPD (EPD vs. SPD, 30.8 vs. 20.9 months, P = 0.009; 23.4 vs. 13.5 months, P < 0.001). The EPD group exhibited significantly lower locoregional (16.48% vs. 35.20%, P < 0.001) and mesenteric LN recurrence rates (3.98% vs. 10.06%, P = 0.022). The EPD group exhibited less back pain 6 months postoperation than the SPD group. Conclusions EPD for pancreatic head cancer did not significantly improve OS, but patients with EPD treatment had significantly improved DFS. In the subgroup analysis, improvements in both OS and DFS in the EPD arm were observed in patients with preoperative CA19–9 < 200.0 U/mL. EPD could be used as an effective surgical procedure for patients with pancreatic head cancer, especially those with preoperative CA19–9 < 200.0 U/mL.

Quadruped robots interact with the ground with discrete foot points during locomotion, which makes them gain an advantage in obstacle crossing compared with the wheeled and tracked robots. Quadruped robots can jump from current position to one position a certain distance ahead to negotiate the obstacles between them, for example. However, current quadruped control strategies usually assume that the landing area is large enough, and thus jumping distance control of quadruped robots had not yet been studied sufficiently. This paper proposes a method for controlling the distance of quadruped robot jumps based on deep reinforcement learning (DRL). In the method, kinematic parameters in the control module are optimized to achieve the quadruped jumping tasks. Based on the understanding of the kinematics and dynamics of quadruped robot jumping, an initial jumping is realized by controlling the robot foot moving along a carefully designed parameterized trajectory. This initial trajectory is then used to train a set of jumping parameters using a deep reinforcement learning (DRL) algorithm. Through thousands of jumping trials in the Gazebo simulation environment, the optimal parameters were acquired. Our proposed method allows for accurate jumping within the 0.5 m to 0.8 m range. Additionally, the controller has been successfully implemented on a real quadruped robot.

Introduction: In recent years, with the rapid development of renewable energy generation, the stability of the power grid has been greatly reduced. In response to this problem, integrating the user side transferable load into the power market has become the key to the development of future power grid. At present, large transferable loads have entered the electricity market in some pilot areas of China, but the relevant research on small and medium-sized transferable users entering the electricity market is still few.Methods: This paper proposes the concept of generation load aggregators. A two-stage generation load aggregator robust optimization model is developed to obtain the scheduling scheme with the lowest operating cost under the worst scenario. The model consists of distributed renewable power, transferable load, self-provided power, energy storage, etc. Uncertainties of renewable energy and load are introduced in the model. By using the column constraint generation algorithm and strong pairwise theory, the original problem is decomposed into the main problem and sub-problems to be solved alternately, so as to obtain the scheduling scheme with the lowest operating cost in the worst scenario under different conservatism.Results: The solved results are compared with those without the generation load aggregator, illustrating the role of the generation load aggregator in relieving peak and valley pressure on the grid from the load side, reducing the cost of electricity for loads, and promoting the consumption of renewable energy. The comparison with the deterministic optimization algorithm shows a significant decrease in the total cost and validates the performance of the selected solution algorithm. The boundary conditions for the use of energy storage by generation load aggregators for peak and valley reduction under the time-sharing tariff mechanism are also derived.Discussion: This study can provide reference for the investors of generation load aggregators when planning whether to install energy storage or the scale of energy storage, and also help the power market management department to design a reasonable incentive mechanism.

Quantitative real-time PCR (qRT-PCR) is a widely applied technique for accurately assessing the expression of target genes. In practice, the evaluation of gene expression requires appropriate reference genes. To screen reliable reference genes for evaluating gene expression via qRT-PCR in Mythimna loreyi, a notorious migratory pest across Asia, Africa, Europe, and Australia, we assessed the expression stability of 13 candidate reference genes in M. loreyi using the ΔCt method, BestKeeper, Normfinder, GeNorm, and the web-based comprehensive platform RefFinder. These reference genes include RPL10, RPL27, RPL32, RPS3, TATA−box, GAPDH, AK, Actin, EF, α−tubulin, SOD, 18S rRNA, and FTZ−F1, which is frequently employed in Lepidoptera insects. Our findings revealed that the performance of the candidate reference gene depended on experimental conditions. Specifically, RPL27 and RPL10 were the most suitable for evaluating expression changes across developmental stages, tissues, and adult ages. The optimal reference genes were recommended in specific experiment conditions, for instance, EF and RPS3 were recommended for mating status, AK and RPL10 were recommended for temperature treatments, RPL27 and FTZ−F1 were recommended for larva diet, and EF and RPL27 were recommended for adult diet treatments. Additionally, expression profiles of pheromone-binding protein 2 (MlorPBP2) and glutathione S-transferase (MlorGST1) were used to validate the reference genes. This study provides reference genes for the accurate normalization of qRT-PCR data, laying the groundwork for studying the expression of target genes in M. loreyi.

Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining polarization information causes it to be more sensitive to noise; serious noise reduces the quality of polarized images and subsequent performance in advanced visual tasks. Unfortunately, the flourishing low-light image enhancement methods applied to intensity images have not demonstrated satisfactory performance when transferred to polarized images. In this paper, we propose a low-light image enhancement paradigm based on the antagonistic properties of polarization parameters. Furthermore, we develop a dual-branch network that relies on a gradient residual dense feature extraction module (GRD) designed for polarized image characteristics and polarization loss, effectively avoiding noise introduced during the direct amplification of brightness, and capable of restoring target contour details. To facilitate a data-driven learning method, we propose a simulation method for underwater low-light polarized images. Extensive experimental results on real-world datasets demonstrate the effectiveness of our proposed approach and its superiority against other state-of-the-art methods.

Abstract Purpose Cortical visual impairment (CVI) is the common cause of pediatric visual impairment in cerebral palsy (CP) while exotropia is the most common strabismus associated with CP. We aim to observe the strabismic surgery outcomes in pediatric patients with CP and CVI. Method Our medical records were collected from pediatric patients treated in our hospital from May 1, 2017 to Jan 1, 2022. With normal intelligence assessment and diagnosis of exotropia in children with CP and CVI, microsurgeries were performed under intravenous combined inhalation anesthesia. The strabismus was examined by the prism test under best vision correction and the contrast sensitivity testing (CST) was measured at five levels of spatial frequencies. Result A total of 38 exotropia patients with CP and CVI were identified and included for analysis during the study period with age ranged from 5 to 12 years (mean 8.45 years) and mean follow up duration was 8.7 months (6–42 months). After bilateral lateral rectus recession (with/without medial rectus resection or inferior oblique transposition), the exotropia amount of participants were obviously revealed from − 30 ~ − 140 (median, IQR: − 50, 40) prism diopters (PD) preoperatively to 0 ~ − 15 (0, 5) PD postoperatively. Statistically significantly improvements were observed at all levels of spatial frequency on CST postoperatively, especially at high spatial frequency areas (p

Abstract The purpose of this study was to compare the differences of V-pattern exotropia in craniosynostosis and normal children. 39 children were included in this study, 19 craniosynostosis and 20 children in control group. They underwent comprehensive ocular examinations and received strabismus surgery. The extraocular muscle samples were analysed. Compared with the control group, craniosynostosis group had larger deviation in primary and up gaze, larger V pattern, and more severe inferior oblique overaction. For 20–40, and 50–60 prism diopter exotropia, the lateral recession in the craniosynostosis group was larger than that in the control group, 7.13 ± 0.44 mm vs 6.71 ± 0.47 mm, 8.90 ± 0.21 mm vs 7.75 ± 0.46 mm (p = 0.025, 0.000). The anterior transposition of craniosynostosis group was more anterior than that of control group, posterior 1.03 ± 1.24 vs 2.68 ± 0.94 mm (p = 0.000). Compared with the control group, the extraocular muscle abnormality in craniosynostosis was significant, 32% vs 5% (p = 0.031). There were 40 proteins in craniosynostosis group, which were different from those in control group. A larger V pattern and larger deviation is common in craniosynostosis children. For the same PD of deviation, it usually needs more recession in craniosynostosis because of the thinner and weaker extraocular muscles. Collagen related proteins were increased in craniosynostosis, and decreased contraction related protein tropomodulin might play key role for the weakness of EOMs.

Quantitative real-time PCR (qRT-PCR) is widely accepted as a precise and convenient method for quantitatively analyzing the expression of functional genes. The data normalization strongly depends upon stable reference genes. The bean bug, Riptortus pedestris (Hemiptera: Alydidae), is a significant pest of leguminous crops and broadly distributed across Southeast Asia. In this study, a total of 16 candidate reference genes (RPL32, RPS23, SDHA, UBQ, UCCR, GST, TATA−box, HSP70, GAPDH, RPL7A, SOD, RPS3, Actin, α−tubulin, AK, and EF1) were carefully chosen in R. pedestris, and their expression levels were assessed across various conditions, including different developmental stages, diverse tissues, temperature treatments, adult age, molting time, and mating status. Following this, the stability of these reference genes was evaluated using four algorithms (ΔCt, GeNorm, NormFinder, and BestKeeper). Ultimately, the comprehensive rankings were determined using the online tool RefFinder. Our results demonstrate that the reference gene for qRT-PCR analysis in R. pedestris is contingent upon the specific experimental conditions. RPL7A and EF1 are optimal reference genes for developmental stages. Furthermore, α−tubulin and EF1 exhibit the most stable expression across various adult tissues. RPL32 and RPL7A exhibit the most stable expression for adult age. For nymph age, RPL32 and SOD display the most stable expression. For temperature conditions, RPS23 and RPL7A were identified as the most suitable for monitoring gene expression. Lastly, we verified the practicability of evaluating expression levels of odorant-binding protein 37 (RpedOBP37) and cytochrome P450 6a2 (RpedCYP6) throughout developmental stages, tissues, and temperature conditions. These findings are a significant addition to the qRT-PCR analysis studies on R. pedestris, serving as a fundamental groundwork for future investigations on stable reference genes in R. pedestris as well as other organisms.

The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.

With the acceleration of the pace of urban life and the development of information technology, the takeout industry has emerged as the times require, which obtains intermediate costs by distributing goods to consumers. People pay more and more attention to public health, which requires takeout workers to drive as fast as possible to ensure the quality and safety of goods, but it also makes takeout workers suffer from various occupational injuries, such as car accidents, stomach diseases caused by eating disorders and long-term psychological pressure. This paper optimized the employment protection of takeout workers in combination with their professional characteristics. This paper used the analytic hierarchy process (AHP) to analyze the indicators that can evaluate the optimization effect of employment protection for takeout workers, and compared the occupation of takeout workers before and after employment protection. The experimental results showed that in Meituan takeout, the rationality of the average delivery management system before and after the optimization of employment protection was 47.2 and 64.4%, respectively; in ELEME takeout, the rationality of the average takeout distribution management system before and after the optimization of employment protection was 55.0% and 69.8%, respectively. Therefore, in the context of public health, the implementation of social security, employment relationship and optimization of service evaluation mechanism for outbound sales personnel can effectively improve the rationality of the delivery management system.

In this paper, a novel two-dimensional direction of arrival (2D-DOA) estimation method with total variation regularization is proposed to deal with the problem of sparse DOA estimation for spatially extended sources. In a general sparse framework, the sparse 2D-DOA estimation problem is formulated with the regularization of extended source characteristics including spatial position grouping, acoustic signal block sparse, and correlation features. An extended sources acoustic model, two-dimensional array manifold and its complete representation, total variation regularization penalty term, and the regularization equation are built, and are utilized to seek the solutions where the non-zero coefficients are grouped together with optimum sparseness. A total variation sparse 2D-DOA estimation model is constructed by combining total variation regularization with LASSO. The model can be easily solved by the convex optimization algorithm, and the solving process can promote the sparsity of the solution on the spatial derivatives and the solution itself. The theoretical analysis results show that the steps of decorrelation processing and angle matching of traditional 2D-DOA estimation methods could be avoided when adopting the proposed method. The proposed method has better robustness to noise, better sparsity, and faster estimation speed with higher resolution than traditional methods. It is promising to provide a coherent sources sparse representation of a non-strictly sparse field.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.

Summary: Low-temperature solid oxide fuel cells (LT-SOFCs) are a promising next-generation fuel cell due to their low cost and rapid start-up, posing a significant challenge to electrode materials with high electrocatalytic activity. Herein, we reported the bimetallic nanoparticles encapsulated in carbon nanotubes (NiFe@CNTs) prepared by carefully controlling catalytic pyrolysis of waste plastics. Results showed that plenty of multi-walled CNTs with outer diameters (14.38 ± 3.84 nm) were observed due to the smallest crystalline size of Ni-Fe alloy nanoparticles. SOFCs with such NiFe@CNTs blended in anode exhibited remarkable performances, reaching a maximum power density of 885 mW cm−2 at 500°C. This could be attributed to the well-dispersed alloy nanoparticles and high graphitization degree of NiFe@CNTs to improve HOR activity. Our strategy could upcycle waste plastics to produce nanocomposites and demonstrate a high-performance LT-SOFCs system, addressing the challenges of sustainable waste management and guaranteeing global energy safety simultaneously.

Foot contact detection is critical for legged robot running control using state machine, in which the controller uses different control modules in the leg flight phase and landing phase. This paper presents an online learning framework to improve the rapidity of foot contact detection in legged robot running. In this framework, the Gaussian mixture model with three sub-components is adopted to learn the contact data vectors corresponding to running on flat ground, running upstairs, and running downstairs. An online data stream learning algorithm is used to update the model. To deal with the difficulty in obtaining contact data at landing moment online, a “trace back” module is designed to trace back the contact data in the memory stack until the data meet with the probability contact criterion. To test if the foot is in contact with the ground, a projection method is proposed. The acquiring data vector during the leg flight phase is projected onto an independent random vector space, and the contact event is triggered if all projected random variables fall within 1.5σ of the corresponding Gaussian distribution. Experiments on a legged robot show that the presented algorithm can predict the foot contact 16 ms in advance compared with the prediction using only leg force, which will ease the controller design and enhance the stability of legged robot control.

Emerging evidence indicates that circRNA can regulate various diseases. However, the mechanisms of circRNA in these diseases have not been fully understood. Therefore, detecting potential circRNA–disease associations has far-reaching significance for pathological development and treatment of these diseases. In recent years, deep learning models are used in association analysis of circRNA–disease, but a lack of circRNA–disease association data limits further improvement. Therefore, there is an urgent need to mine more semantic information from data. In this paper, we propose a novel method called Semantic Association Analysis by Embedding and Deep learning (SAAED), which consists of two parts, a neural network embedding model called Entity Relation Network (ERN) and a Pseudo-Siamese network (PSN) for analysis. ERN can fuse multiple sources of data and express the information with low-dimensional embedding vectors. PSN can extract the feature between circRNA and disease for the association analysis. CircRNA–disease, circRNA–miRNA, disease–gene, disease–miRNA, disease–lncRNA, and disease–drug association information are used in this paper. More association data can be introduced for analysis without restriction. Based on the CircR2Disease benchmark dataset for evaluation, a fivefold cross-validation experiment showed an AUC of 98.92%, an accuracy of 95.39%, and a sensitivity of 93.06%. Compared with other state-of-the-art models, SAAED achieves the best overall performance. SAAED can expand the expression of the biological related information and is an efficient method for predicting potential circRNA–disease association.

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.

Biogas slurry, a byproduct of biogas plants, is considered a high-quality bio-organic fertilizer. Despite providing nutrients to crops, biogas slurry may contain a high concentration of heavy metals, leading to food safety problems and endangering human health if such metals are absorbed by plants. Therefore, biogas slurry should undergo systematic risk assessment prior to direct use on farmland to ensure its safety for soils and crops. In this study, the risk of applying biogas slurry in peanut cultivation was comprehensively evaluated. Based on nitrogen contents, different concentrations of biogas slurry were applied in peanut cultivation. The results achieved herein showed that the application of biogas slurry as a nutrient supplier in peanut cultivation would significantly affect the physical and chemical properties of soil and characteristics of the plant and the quality of peanuts. Although the heavy metal content of biogas slurry was within the permitted range, it had potential risks to human health and the environment. Principal component analysis (PCA) showed that biogas slurry was the primary source of heavy metals in soil. After the application of biogas slurry, the contents of As and Hg in the soil increased significantly, which were 11.12 and 26.67 times higher than those in the control soil. The contents of Cu, Zn, Pb, Cd, and As in peanut kernel samples under different levels of biogas slurry application were all lower than the maximum permissible limit set by the Standardization Administration of China. In contrast, the content of Hg in peanut kernels was higher than the maximum permissible limit value of 0.02 mg/kg. Peanut had a higher enrichment capacity of Cd and Zn and a higher migration capacity of Pb. The health risk assessment showed that the long-term consumption of peanuts grown with a high dosage of biogas slurry would be harmful to the health of children aged 2–6 years with a large consumption level.

Isoliquiritigenin (ILQ) has a number of biological activities such as antitumor and anti-inflammatory effects. However, biomedical applications of ILQ are impeded by its poor aqueous solubility. Therefore, in this research, we prepared a novel ILQ-loaded nanoemulsion, i.e., ILQ-NE, which consisted of Labrafil® M 1944 CS (oil), Cremophor® EL (surfactant), ILQ, and phosphate-buffered saline, by employing a combined sonication (high-energy) and phase-inversion composition (low-energy) method (denoted as the SPIC method). The ILQ-NE increased the ILQ solubility ~1000 times more than its intrinsic solubility. It contained spherical droplets with a mean diameter of 44.10 ± 0.28 nm and a narrow size distribution. The ILQ loading capacity was 4%. The droplet size of ILQ-NE remained unchanged during storage at 4 °C for 56 days. Nanoemulsion encapsulation effectively prevented ILQ from degradation under ultraviolet light irradiation, and enhanced the ILQ in vitro release rate. In addition, ILQ-NE showed higher cellular uptake and superior cytotoxicity to 4T1 cancer cells compared with free ILQ formulations. In conclusion, ILQ-NE may facilitate the biomedical application of ILQ, and the SPIC method presents an attractive avenue for bridging the merits and eliminating the shortcomings of traditional high-energy methods and low-energy methods.

Circular RNA (circRNA) is a closed long non-coding RNA (lncRNA) formed by covalently closed loops through back-splicing. Emerging evidence indicates that circRNA can influence cellular physiology through various molecular mechanisms. Thus, accurate circRNA identification and prediction of its regulatory information are critical for understanding its biogenesis. Although several computational tools based on machine learning have been proposed for circRNA identification, the prediction accuracy remains to be improved. Here, first we present circLGB, a machine learning-based framework to discriminate circRNA from other lncRNAs. circLGB integrates commonly used sequence-derived features and three new features containing adenosine to inosine (A-to-I) deamination, A-to-I density and the internal ribosome entry site. circLGB categorizes circRNAs by utilizing a LightGBM classifier with feature selection. Second, we introduce circMRT, an ensemble machine learning framework to systematically predict the regulatory information for circRNA, including their interactions with microRNA, the RNA binding protein, and transcriptional regulation. Feature sets including sequence-based features, graph features, genome context, and regulatory information features were modeled in circMRT. Experiments on public and our constructed datasets show that the proposed algorithms outperform the available state-of-the-art methods. circLGB is available at http://www.circlgb.com. Source codes are available at https://github.com/Peppags/circLGB-circMRT.

ObjectiveTinnitus is a prevalent hearing disorder, which could have a devastating impact on a patient’s life. Functional studies have revealed connectivity pattern changes in the tinnitus brains that suggested a change of network dynamics as well as topological organization. However, no studies have yet provided evidence for the topological network changes in the gray matter. In this research, we aim to use the graph-theoretical approach to investigate the changes of topology in the tinnitus brain using structural MRI data, which could provide insights into the underlying anatomical basis for the neural mechanism in generating phantom sounds.MethodsWe collected 3D MRI images on 46 bilateral tinnitus patients and 46 age and gender-matched healthy controls. Brain networks were constructed with correlation matrices of the cortical thickness and subcortical volumes of 80 cortical/subcortical regions of interests. Global network properties were analyzed using local and global efficiency, clustering coefficient, and small-world coefficient, and regional network properties were evaluated using the betweenness coefficient for hub connectivity, and interregional correlations for edge properties. Between-group differences in cortical thickness and subcortical volumes were assessed using independent sample t-tests, and local efficiency, global efficiency, clustering coefficient, sigma, and interregional correlation were compared using non-parametric permutation tests.ResultsTinnitus was found to have increased global efficiency, local efficiency, and cluster coefficient, indicating generally heightened connectivity of the network. The small-world coefficient remained normal for tinnitus, indicating intact small-worldness. Betweenness centrality analysis showed that hubs in the amygdala and parahippocampus were only found for tinnitus but not controls. In contrast, hubs in the auditory cortex, insula, and thalamus were only found for controls but not tinnitus. Interregional correlation analysis further found in tinnitus enhanced connectivity between the auditory cortex and prefrontal lobe, and decreased connectivity of the insula with anterior cingulate gyrus and parahippocampus.ConclusionThese findings provided the first morphological evidence of altered topological organization of the brain networks in tinnitus. These alterations suggest that heightened efficiency of the brain network and altered auditory-limbic connection for tinnitus, which could be developed in compensation for the auditory deafferentation, leading to overcompensation and, ultimately, an emotional and cognitive burden.

Backgroud: Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness of the elder people. This research was intended to demonstrate the different expression of microRNAs (miRNA) in AMD patients and whether they can be used as biomarkers for AMD. Methods: MiRNAs expression was measured by microarray of 6 AMD cases and 6 gender matched controls. In a larger-sample case-control study with 126 AMD cases and 140 controls, whole blood samples were detected for the differences of miRNA expression. Results: A total of 216 differentially expressed miRNAs (111 increased and 105 decreased miRNAs) were detected from circulating miRNA microarray. Expanded case-control study results showed that the expression of miR-27a-3p, miR-29b-3p and miR-195-5p was increased significantly. Moreover, the level of miR-27a is higher in patients with wet AMD compared to patients with dry AMD. All 3 miRNAs showed a potential diagnostic value for AMD. Conclusion: Circulating miRNA levels were significantly varied in AMD patients. Three miRNAs, miR-27a-3p, miR-29b-3p and the miR-195-5p, might be potential diagnostic biomarkers for AMD.

Squaraine core based small molecules in bulk heterojunction organic solar cells have received extensive attentions due to their distinguished photochemical properties in far red and infrared domain. In this paper, combining theoretical simulations and experimental syntheses and characterizations, three major factors (fill factor, short circuit and open-cirvuit voltage) have been carried out together to achieve improvement of power conversion efficiencies of solar cells. As model material systems with D-A-D' framework, two asymmetric squaraines (CNSQ and CCSQ-Tol) as donor materials in bulk heterojunction organic solar cell were synthesized and characterized. Intensive density functional theory computations were applied to identify some direct connections between three factors and corresponding molecular structural properties. It then helps us to predict one new molecule of CCSQ'-Ox that matches all the requirements to improve the power conversion efficiency.

In this paper, the optimal process condition for microwave-assisted acid pretreatment of the corn stalk was investigated by Central-Composite design using Response Surface Method. Effects of microwave power, reaction time and acid concentration on reducing sugar yield were inspected. Also, analysis of corn stalk before and after the treatment was conducted with ultra depth microscope. The test result shows that, the sequence of factors is: acid concentration>reaction time>microwave power, and the interaction of microwave power and reaction time is significant; The optimal process condition is: microwave power 900W, reaction time 20min, acid concentration 2%, reducing sugar yield 230.9 mg/g. Through the microwave-assisted acid pretreatment, the fiber structures of the corn stalk were destroyed effectively, the treatment increases the degree of surface roughness, improves porosity, thus the rate of enzymolysis was enhanced. The p-value of regression model is 0.0002, and the p-value of Lack of fit is 0.1298, which indicates the regression equation is significant and the model fits well.

To further study the discharge characteristics and machining mechanism of micro-electrical discharge machining (micro-EDM), the variation trends of the discharge energy and discharge crater size with actual discharge duration are discussed based on single-pulse experiments. The polarity effect of micro-EDM was analyzed according to the motion characteristics of electrons and ions in the discharge plasma channel. The results show that the discharge current and voltage of micro-EDM were independent of the discharge width and open-circuit voltage. The energy utilization rate of the short-pulse discharge was relatively high, and the energy utilization rate decreased gradually as the discharge duration increased. Even if the mass of the positive ion was much larger than that of the electron, the kinetic energy of the positive ion was still less than that of the electron when bombarding the surface of the electrode. The acceleration and speed of electrons were very high, and the number of times that electrons bombarded the surface of positive electrode was more than 600 times that of positive ions bombarding the surface of the negative electrode during the same time.

Titanium alloys are widely used in various fields, but their machinability is poor because the chip would easily adhere to the tool surface during cutting, causing poor surface quality and tool wear. To improve the cutting performance of titanium alloy Ti-6Al-4V, experiments were conducted to investigate the effect of micro textured tool on the cutting performances. The cemented carbide tools whose rake faces were machined with line, rhombic, and sinusoidal groove textures with 10% area occupancy rates were adopted as the cutting tools. The effects of cutting depth and cutting speed on feed force and main cutting force were discussed based on experimental results. The results show that the cutting force produced by textured tools is less than that produced by non-textured tools. Under different cutting parameters, the best cutting performance can be obtained by using sinusoidal textured tools among the four types of tools. The wear of micro textured tools is significantly lower than that of non-textured tools, due to a continuous lubrication film between the chip and the rake face of the tool that can be produced because the micro texture can store and replenish lubricant. The surface roughness obtained using the textured tool is better than that using the non-textured tool. The surface roughness Ra can be reduced by 35.89% when using sinusoidal textured tools. This study is helpful for further improving the cutting performance of cemented carbide tools on titanium alloy and prolonging tool life.

The aims of this study were to evaluate the effects of CM082 on the development of choroidal neovascularization (CNV) in a laser-induced CNV rat model and to determine the drug concentration in the ocular tissues. After the laser-induced CNV model was established in rats, CM082 was orally administered. The effects of CM082 on the CNV lesions were assessed using fundus fluorescein angiography (FFA), CNV histology, and retinal pigment epithelium- (RPE-) choroid-sclera eyecup analysis. The concentrations of CM082 in the plasma and eye tissues were determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results of FFA, histology, and RPE-choroid-sclera eyecup analysis demonstrated that the CM082-treated (10 mg/kg/d or 30 mg/kg/d) rats exhibited significantly less neovascularization than did the control group. The total concentration of CM082 in the eyes (172.86 ± 57.11 ng/g) was similar to that in the plasma (196.87 ± 73.13 ng/ml). Within the eye, the concentrations of CM082 and its metabolites were highest in the retina-sclera. The orally administered CM082 thus effectively passed through the blood-retina barrier (BRB) to reach the retina in the Brown Norway rats. Therefore, at both 10 mg/kg/d and 30 mg/kg/d, CM082 was able to reduce CNV lesions in the laser-induced CNV rat model.