Search

Abstract Background Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists. Methods We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation. It comprises two parts: (1) Feature Extraction Network (FEN) for brain tumor feature extraction at different levels and (2) Multi-scale Feature Fusing Network (MSFFN) for merge all different scale features in a parallel manner. In addition, we use two hybrid loss functions to optimize the proposed network for the class imbalance issue. Results We validate our method on BRATS 2015, with 0.86, 0.73 and 0.61 in Dice for the three tumor regions (complete, core and enhancing), and the model parameter size is only 6.3 MB. Without any post-processing operations, our method still outperforms published state-of-the-arts methods on the segmentation results of complete tumor regions and obtains competitive performance in another two regions. Conclusions The proposed parallel structure can effectively fuse multi-level features to generate rich feature representation for high-resolution results. Moreover, the hybrid loss functions can alleviate the class imbalance issue and guide the training process. The proposed method can be used in other medical segmentation tasks.

BackgroundThe incidence of perioperative neurocognitive disorders (PNDs) is reportedly higher in older patients. Mitochondrial and synaptic dysfunctions have consistently been demonstrated in models of aging and neurodegenerative diseases; nonetheless, their role in PND is not well understood.MethodsThe Morris water maze and elevated plus maze tests were used to assess the learning and memory abilities of both C57BL/6 and 3×Tg-AD mice of different ages (8 and 18 months). PND was induced by laparotomy in C57BL/6 mice and 3×Tg-AD mice (8 months old). Markers associated with neuroinflammation, mitochondrial function, synaptic function, and autophagy were assessed postoperatively. The roles of protein kinase C (PKC) and double-stranded RNA-dependent protein kinase (PKR) were further demonstrated by using PKC-sensitive inhibitor bisindolylmaleimide X (BIMX) or PKR−/− mice.ResultsSignificant cognitive impairment was accompanied by mitochondrial dysfunction and autophagy inactivation in both aged C57BL/6 and 3×Tg-AD mice. Laparotomy induced a significant neuroinflammatory response and synaptic protein loss in the hippocampus. Cognitive and neuropathological changes induced by aging or laparotomy were further exacerbated in 3×Tg-AD mice. Deficits in postoperative cognition, hippocampal mitochondria, autophagy, and synapse were significantly attenuated after pharmacological inhibition of PKC or genetic deletion of PKR.ConclusionsOur findings suggest similar pathogenic features in aging, Alzheimer's disease, and PND, including altered mitochondrial homeostasis and autophagy dysregulation. In addition, laparotomy may exacerbate cognitive deficits associated with distinct neuronal inflammation, mitochondrial dysfunction, and neuronal loss independent of genetic background. The dysregulation of PKC/PKR activity may participate in the pathogenesis of these neurodegenerative diseases.

Background: Renal fibrosis is a key pathological change that occurs in the progression of almost all chronic kidney diseases . CKD has the characteristics of high morbidity and mortality. Its prevalence is increasing each year on a global scale, which seriously affects people’s health and quality of life. Natural products have been used for new drug development and disease treatment for many years. The abundant natural products in R. ribes L. can intervene in the process of renal fibrosis in different ways and have considerable therapeutic prospects.Purpose: The etiology and pathology of renal fibrosis were analyzed, and the different ways in which the natural components of R. ribes L. can intervene and provide curative effects on the process of renal fibrosis were summarized. Methods: Electronic databases, such as PubMed, Life Science, MEDLINE, and Web of Science, were searched using the keywords ‘R. ribes L.’, ‘kidney fibrosis’, ‘emodin’ and ‘rhein’, and the various ways in which the natural ingredients protect against renal fibrosis were collected and sorted out.Results: We analyzed several factors that play a leading role in the pathogenesis of renal fibrosis, such as the mechanism of the TGF-β/Smad and Wnt/β-catenin signaling pathways. Additionally, we reviewed the progress of the treatment of renal fibrosis with natural components in R. ribes L. and the intervention mechanism of the crucial therapeutic targets.Conclusion: The natural components of R. ribes L. have a wide range of intervention effects on renal fibrosis targets, which provides new ideas for the development of new anti-kidney fibrosis drugs.

To date, zinc-ion batteries (ZIBs) have been attracting extensive attention due to their outstanding properties and the potential to be the solution for next-generation energy storage systems. However, the uncontrollable growth of zinc dendrites and water-splitting issues seriously restrict their further scalable application. Over the past few years, solid polymer electrolytes (SPEs) have been regarded as a promising alternative to address these challenges and facilitate the practical advancement of zinc batteries. In this review, we revisit the research progress of SPEs applied in zinc batteries in the past few years and focus on introducing cutting-edge polymer science and technologies that can be utilised to prepare advanced SPEs for high-performance zinc batteries. The operating mechanism of SPEs and the functions of polymers are summarised. To highlight the polymer’s functions, SPEs are categorised into three types, homogenous polymer SPEs, hybrids polymer SPEs, and nanocomposites SPEs, which are expected to reveal the roles and principles of various polymers in zinc batteries. This review presents the current research progress and fundamental mechanisms of polymer-based SPEs in zinc batteries, outlines the challenging issues encountered, and proposes potential solutions for future endeavours.

In the construction process of large-scale bridges, there are uncertainties and time-varying factors in the environment and construction loads. It is difficult to make accurate estimates of the theoretical calculation models of construction control in advance. In view of this situation, Bayesian dynamic updating method is introduced to re-estimate the predicted results of the theoretical model. When applying this method, first, the finite element calculation model is determined based on the response surface method, and its calculation results are used as prior information. Then, combined with the actual detection data during the construction process, the Bayesian update formula is derived based on the conjugate prior distribution to correct the theoretical prediction results of bridge construction monitoring. Finally, the actual stress detection data of the control section of high-pier and large-span continuous rigid frame bridges during the construction process illustrate the application process of Bayesian updating in improving the theoretical prediction model. Results indicate that the internal force of the bridge control section obtained by re-evaluating by Bayesian theory not only incorporates the priori information models but also actual monitors sample information during the construction process. The predicted results reflect the true deformation and stress state of the bridge during the bridge construction process and improve the precision of construction monitoring.

Micro-cooling systems are compact refrigeration systems widely applicable in microchemical analysis, biomedicine, and microelectromechanical systems (MEMS). These systems rely on the use of micro-ejectors to achieve precise, fast, and reliable flow and temperature control. However, the efficiency of micro-cooling systems is hindered by spontaneous condensation occurring downstream of the nozzle throat and within the nozzle itself, impacting the performance of the micro-ejector. A micro-scale ejector mathematical model describing wet steam flow was simulated to investigate the steam condensation phenomenon and its influence on flow, incorporating equations for liquid phase mass fraction and droplet number density transfer. The simulation results of wet vapor flow and ideal gas flow were compared and analyzed. The findings revealed that the pressure at the micro-nozzle outlet exceeded predictions based on the ideal gas assumption, while the velocity fell below it. These discrepancies indicated that condensation of the working fluid reduces the pumping capacity and the efficiency of the micro-cooling system. Furthermore, simulations explored the impact of inlet pressure and temperature conditions on spontaneous condensation within the nozzle. The results demonstrated that the properties of the working fluid directly influence transonic flow condensation, underscoring the importance of selecting appropriate working fluid parameters for nozzle design to ensure nozzle stability and optimal micro-ejector operation.

In plant cells, multiple paralogs from ribosomal protein (RP) families are always synchronously expressed, which is likely contributing to ribosome heterogeneity or functional specialization. However, previous studies have shown that most RP mutants share common phenotypes. Consequently, it is difficult to distinguish whether the phenotypes of the mutants have resulted from the loss of specific genes or a global ribosome deficiency. Here, to investigate the role of a specific RP gene, we employed a gene overexpression strategy. We found that Arabidopsis lines overexpressing RPL16D (L16D-OEs) display short and curled rosette leaves. Microscopic observations reveal that both the cell size and cell arrangement are affected in L16D-OEs. The severity of the defect is positively correlated with RPL16D dosage. By combining transcriptomic and proteomic profiling, we found that overexpressing RPL16D decreases the expression of genes involved in plant growth, but increases the expression of genes involved in immune response. Overall, our results suggest that RPL16D is involved in the balance between plant growth and immune response.

In order to improve the force performance of traditional anti-buckling energy dissipation bracing with excessive non-recoverable deformation caused by strong seismic action, this paper presents a prestress-braced frame structure system with shape memory alloy (SMA) and investigates its deformation characteristics under a horizontal load. Firstly, this paper establishes a theoretical analysis model by analyzing the geometric relationship between the deformation of SMA cables and inter-story displacement based on the internal force balance equation. The model is used to solve the anti-lateral displacement stiffness of the SMA cable-supported frame structure and to derive a reasonable formula for calculating the initial prestress and cross-sectional area of SMA cables. Then, the mechanical behavior of the SMA cable-supported frame structure system under an equivalent horizontal load is simulated using ABAQUS software and compared with the calculated results of conventional tie-supported and non-dissipative-supported frame structures. The results show that the force performance of the frame structure system determined by the SMA cable design method proposed in this paper is significantly improved under the horizontal load. Furthermore, it can ensure a certain ductility requirement of the frame structure system, which verifies the effectiveness of the design method of the SMA cable frame structure system proposed in this paper.

Abstract Background To observe the outcome of isolated calf muscle vein thrombosis (ICMVT) undergoing open reduction and internal fixation (ORIF) for closed intra-articular distal femur fractures (DFFs) and to analyze related factors. Methods The study was designed as a prospective clinical cohort study at our hospital. From August 2018 to August 2020,a total of 140 patients with flesh ICMVT after ORIF for closed intra-articular DFFs were collected during hospitalization. After the administration of antithrombotic agents immediately after diagnosis, the location and prognosis of postoperative ICMVT were examined by Duplex ultrasonography (DUS) with a three-month follow-up. There were 29 males and 111 females with the average age of 70.16 ± 8.75 years old. Sonography was used to evaluate the resolution of muscular vein thrombosis at the time point of the third month postoperatively and the results were compared between the two time points. Multivariable analysis was performed to evaluate the relationship between the resolution of ICMVT three months postoperatively and risk factors including age, Body Mass Index (BMI), gender, thrombosis length (> 5 / ≤5 cm), thrombosis diameter(> 0.6/≤0.6 cm), and thrombosis-related biochemistry indices. Results The postoperative ICMVTs was diagnosed at 5.47 ± 2.46 days after ORIF for closed intra-articular DFFs. At the follow up of 3 months,120 cases was tending to disappear with 88 cases(62.9 %) completely dissolved and 32 cases(22.9 %) partly dissolved. There existed 14 cases (10.0 %) without change on the size and 6 cases (4.2 %) with proximal propagation. Multivariate analysis revealed that thrombus diameter over 0.6 cm (odds ratio [OR], 8.900; 95 % confidence interval [CI]: 3.623–21.865), thrombus length over 5.0 cm (OR, 3.904; 95 % CI, 1.121–13.603), FIB over 3.0 g/L (OR, 3.627; 95 % CI, 1.356–9.689), and D-dimer over 1.0 mg/L (OR, 2.602; 95 % CI, 1.075–6.296) were four independent risk factors of non-completely dissolved ICMVTs. Conclusions 85.8 % of ICMVT was tending to disappear at the third months after ORIF for closed intra-articular DFFs. Thrombus diameter, thrombus length, FIB, and D-dimer were four independent risk factors of non-completely dissolved ICMVTs. The Thrombus diameter has a significant effect on the natural course of ICMVTs, especially with diameter larger than 0.6 cm.

When fish are under oxidative stress, levels of reactive oxygen species (ROS) are chronically elevated, which play a crucial role in fish innate immunity. In the present study, the mechanism by which betaine regulates ROS production via Wnt10b/β-catenin signaling was investigated in zebrafish liver. Our results showed that betaine enrichment of diet at 0.1, 0.2 and 0.4 g/kg induced Wnt10b and β-catenin gene expression, but suppressed GSK-3β expression in zebrafish liver. In addition, the content of superoxide anion (O2·−), hydrogen peroxide (H2O2) and hydroxyl radical (·OH) was decreased by all of the experimental betaine treatments. However, betaine enrichment of diet at 0.1, 0.2 and 0.4 g/kg enhanced gene expression and activity of superoxide dismutase (SOD), glutathione peroxidase (GSH-PX) and catalase (CAT) in zebrafish liver. In addition, Wnt10b RNA was further interfered in zebrafish, and the results of Wnt10b RNAi indicated that Wnt10b plays a key role in regulating ROS production and antioxidant enzyme activity. In conclusion, betaine can inhibit ROS production in zebrafish liver through the Wnt10b/β-catenin signaling pathway.

ObjectiveTo assess the diagnostic performance of US and MRI in predicting malignancy of soft tissue masses by using a scoring system.MethodsA total of 120 cases of pathologically confirmed soft tissue masses (71 cases of malignant lesions and 49 cases of benign lesions) were enrolled. All patients underwent ultrasound and MRI examination prior to biopsy or surgical excision. A scoring system based on the parameters of conventional US and MRI to distinguish malignant and benign masses was established by the regression model. The receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of US and MRI.ResultsMultivariate analysis showed that margin, maximum diameter, and vascular density were independent predictors for malignancy found by US, while maximum diameter, margin, and affected peripheral soft tissue were independent predictors for malignancy found by MRI. The mean scores of the benign and malignant groups were 2.8 ± 1.6, 5.1 ± 1.1 on US and 1.3 ± 1.2, 3.5 ± 0.9 on MRI. Based on the cut-off score of 3.5 and 2.5 calculated by ROC analysis, US and MRI had 92% and 87% sensitivity, 72% and 76% specificity, 86% and 89% accuracy, respectively. The combination of these two modalities achieved the sensitivity of 91%, specificity of 82%, and accuracy of 93%.ConclusionsBoth US and MRI can provide valuable information about the differential diagnosis between benign and malignant soft tissue masses. The combination of the two imaging-based scoring systems can increase the diagnostic performance, especially in specificity.

A wideband vertically-polarized end-fire magneto-electric (ME) dipole antenna fed by an asymmetrical substrate integrated coaxial line (ASICL) is proposed for millimeter-wave (mm-wave) applications. The proposed ME dipole antenna has an impedance bandwidth of 60% and produces an end-fire radiation with a stable gain between 5.8 and 6.9 dBi in simulation. To verify the design concept and its application in arrays, a 1 × 8 linear array with a fixed beam and a 1 × 4 linear array with multiple beams are designed, fabricated and measured. The fabricated fixed-beam array prototype exhibits an impedance bandwidth of 62%, a peak gain of 15.9 dBi and a radiation efficiency of 79%. The multi-beam array is fed by a 4 × 4 ASICL Butler matrix. Experimentally, excellent beam scanning radiation patterns with reasonably low side lobes and low cross polarizations are demonstrated at 24, 30 and 36 GHz. And an overlapped operating bandwidth of 28.6% is achieved by the fabricated prototype.

As a core step in clustering analysis, distance measurement results can influence clustering accuracy. Existing measurement methods are mostly based on cluster feature information. However, these cluster features may be insufficient and result in losing data information for clusters containing a number of objects. To improve measurement accuracy, we make full use of the distribution characteristics of objects in clusters, i.e., we use descriptive statistics and the Wilcoxon-Mann-Whitney rank sum test in nonparametric statistics to measure distances during clustering. Furthermore, we propose a two-stage clustering algorithm to improve clustering analysis performance. In terms of avoiding preliminarily assuming the number of clusters, with the proposed distance measurement method, the clustering algorithm can discover clusters with arbitrary shapes and improve clustering accuracy. Experiments with multiple datasets compared with other clustering algorithms illustrate the accuracy and efficiency of the proposed clustering algorithm.

In this paper, based on the classic Kermack-McKendrick SIR model, we propose an ordinary differential equation model to re-examine the COVID-19 epidemics in Wuhan where this disease initially broke out. The focus is on the impact of all those major non-pharmaceutical interventions (NPIs) implemented by the local public healthy authorities and government during the epidemics. We use the data publicly available and the nonlinear least-squares solver lsqnonlin built in MATLAB to estimate the model parameters. Then we explore the impact of those NPIs, particularly the timings of these interventions, on the epidemics. The results can help people review the responses to the outbreak of the COVID-19 in Wuhan, while the proposed model also offers a framework for studying epidemics of COVID-19 and/or other similar diseases in other places, and accordingly helping people better prepare for possible future outbreaks of similar diseases.

Compared with single or twin tunnels, the pressure arch effect of deeply buried, symmetrically distributed triple tunnels are more complex and less studied. In this paper, the arching responses are in-situ measured in the deeply buried, symmetrically distributed triple tunnels of Badaling Great Wall station. Numerical research is subsequently conducted to investigate the formation and development of the pressure arch of triple tunnels. Then, the influencing law of buried depth on pressure arch behavior is systematically studied. Based on monitoring data, the rock pressure distribution is asymmetric about the axis of the triple tunnels, and the arching response of the middle tunnels is more significant than that of the left and right tunnels. According to numerical analysis, a combined large pressure arch may be easily formed across the triple tunnels. The pre-arching and double-arching effects are also numerically observed during triple tunnel excavations. The inner boundary of the pressure arch of the middle tunnel is 14.0 m, nearly two times higher than those of the left and right tunnels. This simulation result indicates that the mechanical state of the middle tunnel is the least desirable. Moreover, the critical arching depth of closely spaced tunnels is 1.75 times that of a single tunnel. Compared with a single tunnel, the support of triple tunnels should be additionally strengthened.

In this work, Sn-doped Ga2O3 films fabricated using plasma-enhanced atomic layer deposition were treated by rapid thermal annealing (RTA). The RTA influence on the chemical state, surface morphology, energy band alignment, and electrical properties of Sn-doped Ga2O3 films were thoroughly investigated. The results of X-ray photoelectron spectroscopy (XPS) demonstrated that Sn atoms were successfully doped into these films. Moreover, energy band alignments were obtained by the energy-loss peak of the O 1s spectrum and valence band spectra and thoroughly discussed. X-ray reflectivity (XRR) and atomic force microscope (AFM) measurements indicated that the Sn-doping level affects the interfacial microstructure and surface morphology. As the Sn content increases, the film thickness decreases while the roughness increases. Finally, the leakage current-voltage (I-V) characteristics proved that the Sn-doped Ga2O3 films have a large breakdown field. In I-V tests, all metal oxide semiconductor (MOS) capacitors exhibited a hard breakdown. This research demonstrates a method for manufacturing high-performance optoelectronic devices with desired properties.

Aiming at the impact of the uncertainty of source load on the optimal scheduling in an integrated energy system (IES), in this paper, based on hybrid resolution modeling and hybrid instruction cycle scheduling technology, three time scales of day-ahead, intra-day rolling and real-time feedback optimization scheduling models are established, respectively, with the objectives of the economic optimal daily operation of the system, the minimum sum of the operation cost of energy purchase and wind curtailment penalty cost in the rolling control time domain, and the minimum adjustment amount of equipment output power. Then, the chaotic gravitational search algorithm (CGSA) is used to solve the problem, and the composite coordination optimization operation strategy of IES with mixed time scales based on CGSA is proposed. In the example, the comparison between the multi-timescale scheduling plan and the actual output, the comparison of the system scheduling results under different strategies and the comparison of different optimization algorithms show that the proposed optimization operation strategy is beneficial to optimize the energy flow distribution, reduce the system operation cost, improve the IES economy and optimization speed.

Abstract Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which provides a powerful tool for improving phospho-variant prediction based on protein sequence information. In the study, we present PhosVarDeep, a novel unified deep-learning framework for phospho-variant prediction. PhosVarDeep takes reference and variant sequences as inputs and adopts a Siamese-like CNN architecture containing two identical subnetworks and a prediction module. In each subnetwork, general phosphorylation sequence features are extracted by a pre-trained sequence feature encoding network and then fed into a CNN module for capturing variant-aware phosphorylation sequence features. After that, a prediction module is introduced to integrate the outputs of the two subnetworks and generate the prediction results of phospho-variants. Comprehensive experimental results on phospho-variant data demonstrates that our method significantly improves the prediction performance of phospho-variants and compares favorably with existing conventional machine learning methods.

For improving solubility and bioaccessibility of phytosterols (PS), phytosterol nanoparticles (PNPs) were prepared by emulsification–evaporation combined high-pressure homogenization method. The organic phase was formed with the dissolved PS and soybean lecithin (SL) in anhydrous ethanol, then mixed with soy protein isolate (SPI) solution, and homogenized into nanoparticles, followed by the evaporation of ethanol. The optimum fabrication conditions were determined as PS (1%, w/v): SL of 1:4, SPI content of 0.75% (w/v), and ethanol volume of 16 ml. PNPs were characterized to have average particle size 93.35 nm, polydispersity index (PDI) 0.179, zeta potential −29.3 mV, and encapsulation efficiency (EE) 97.3%. The impact of temperature, pH, and ionic strength on the stability of fabricated PNPs was determined. After 3-h in vitro digestion, the bioaccessibility of PS in nanoparticles reached 70.8%, significantly higher than the 18.2% of raw PS. Upon freeze-drying, the particle size of PNPs increased to 199.1 nm, resulting in a bimodal distribution. The solubility of PS in water could reach up to 2.122 mg/ml, ~155 times higher than that of raw PS. Therefore, this study contributes to the development of functional PS-food ingredients.

The structure of thermoelectric materials largely determines the thermoelectric characteristics. Hence, a better understanding of the details of the structural transformation process/conditions can open doors for new applications. In this study, the structural transformation of PbTe (a typical thermoelectric material) is studied at the atomic scale, and both nucleation and growth are analyzed. We found that the phase transition mainly occurs at the surface of the material, and it is mainly determined by the surface energy and the degree of freedom the atoms have. After exposure to an electron beam and high temperature, high-density crystal-nuclei appear on the surface, which continue to grow into large particles. The particle formation is consistent with the known oriented-attachment growth mode. In addition, the geometric structure changes during the transformation process. The growth of nanoparticles is largely determined by the van der Waals force, due to which adjacent particles gradually move closer. During this movement, as the relative position of the particles changes, the direction of the interaction force changes too, which causes the particles to rotate by a certain angle.

Purpose: The purpose of this study is to compare the therapeutic effects of the lumbopelvic technique and a novel adjustable plate for sacral fractures. Materials and Methods: Patients with unilateral sacral fractures fixed via the lumbopelvic technique or using a novel adjustable plate from June 2011 to June 2017 were recruited into this study and were divided into two groups: group A (lumbopelvic fixation) and group B (novel adjustable plate). Surgical time, intraoperative blood loss, frequency of intraoperative fluoroscopy, reduction quality, and related complications were reviewed. Fracture healing was assessed by the radiographs conducted at follow-up. Functional outcome was evaluated according to the Majeed score at the final follow-up. Results: The mean blood loss and frequency of intraoperative fluoroscopy in group B were reduced compared with group A (both P 0.05) in this study. The complication rate was 22.22% (4/18) in group A and 4.16% (1/24) in group B (P = 0.191). Conclusion: For patients with unilateral sacral fractures, satisfactory radiographic appearance and functional outcome could be obtained by both the lumbopelvic technique and a novel adjustable plate. However, the novel adjustable plate represents a less invasive technique with lower radiograph exposure.

Abstract Background To explore the feasibility to identify malignant musculoskeletal soft tissue tumors using real-time shear wave elastography (rtSWE). Methods One hundred fifteen musculoskeletal soft tissue tumors in 92 consecutive patients were examined using both conventional ultrasonography (US) and rtSWE. For each patient, the rtSWE parameters including maximum elasticity (E max), mean elasticity (E mean), minimum elasticity (E min), standard deviation of the elasticity (E sd), and rtSWE image pattern were obtained. Eighty-one histopathologically confirmed tumors from 73 patients were subjected to analysis. Results The 81 lesions included in the study were histopathologically classified as malignant (n = 21) or benign (n = 60). The statistically significant differences between benign and malignant lesions were found in conventional US characters including size, depth, margin, echogenicity, mass texture, and power Doppler signal. Meanwhile, the significant differences were also found in quantitative rtSWE findings including E max, E mean, E min, and E sd values and in qualitative rtSWE parameter named rtSWE image pattern. Multivariate analysis showed that infiltrative margin (OR, 4.470), and size (OR, 1.046) were independent predictors for malignancy in US findings, while E sd value (OR, 9.047) was independent predictors for malignancy in quantitative rtSWE parameters. Areas under the ROC curve (Azs) for US features, Esd value, and rtSWE image pattern were 0.851, 0.795, and 0.792, respectively. Conclusions Conventional US and quantitative and qualitative rtSWE parameters are useful for malignancy prediction of musculoskeletal soft tissue tumors. rtSWE can be used to supplement conventional US to diagnose musculoskeletal soft tissue tumors.

Accurate protein contact map prediction is essential for de novo protein structure prediction. Over the past few years, deep learning has brought a significant breakthrough in protein contact map prediction and optimized deep learning architectures are highly desired for performance improvement. As an emerging deep learning architecture, the generative adversarial network (GAN) has shown the powerful capability of learning intrinsic patterns, which inspires us to comprehensively exploit GAN for predicting accurate protein contact maps. In this study, we present GANcon, a novel GAN-based deep learning architecture for protein contact map prediction, which to the best of our knowledge is the first GAN-based approach in this field. Instead of using a single neural network, GANcon is composed of two competitive networks that are evolving through adversarial learning. The generator network employs a dedicated encoder-decoder architecture that can efficiently capture the underlying contact information from versatile protein features to generate contact maps, while the discriminator network learns the differences between generated contact maps and real ones and promotes the generator network to produce more accurate contact maps. Moreover, to deal with the imbalance problem and take into account the symmetry of contact maps, we also propose a novel symmetrical focal loss, which can further enhance the effectiveness of adversarial learning for better performance. The experimental results on several datasets demonstrate that GANcon outperforms many state-of-the-art methods, indicating the effectiveness of our method for predicting protein contact maps. GANcon is freely available at https://github.com/melissaya/GANcon.

Recent advances in evolutionary algorithms based on membrane computing have shown that the mechanism of membrane computing is an effective way to solve optimization problems. In this work, we propose a new evolutionary multi-objective algorithm that uses membrane systems to solve multi-objective optimization problems. Based on the mechanism of living cell structure and function, the algorithm introduces three factors, including membrane structure, multiset and reaction rules. The membrane structure of the proposed algorithm is inspired by the structure of the membrane system, which has multiple layers and nested structures in the skin membrane. Two special symbol-objects are designed to improve the search efficiency of the algorithm. In addition, some reaction rules are used to evolve the symbol-objects of multiset in the inner region of the membrane. In addition, the proposed method combines external archive to maintain the diversity of non-dominated solutions and enhance the search capabilities of the solutions. Our proposed method is compared to five state-of-the-art multi-objective heuristic algorithms. For comparison, six different criteria were used: the quality of the resulting approximate set, the diversity of candidate solutions, and the rate of convergence to the Pareto front. Experimental results show that the proposed method is competitive in performance in qualitative and quantitative measurements of selected test functions. Therefore, the algorithm is feasible and effective for solving multi-objective optimization problems.

This paper proposes an optimal generation scheduling approach based on linear relaxation and mixed integer programming, which is used to solve the generation dispatch problem. The quadratic transmission loss constraint of each transmission line is converted into linear constraints by using the linear relaxation and mixed integer programming technique. Consequently, the original optimal generation scheduling problem is formulated as a quadratic programming or mixed integer quadratic programming problem that can be solved by commercial optimization solver. In order to improve the efficiency of algorithm, this paper further analyses the generation scheduling model and deletes the redundant variables and constraints. Three test systems, including IEEE 30-node system, IEEE 118-node system, and Polish 2746-node system, are employed to examine the effectiveness of the proposed method. The comparative results obtained by the proposed method, quadratically constrained quadratic programming method (QCQP), and solving constraint integer programs solver (SCIP) verify the effectiveness of the proposed method in solving the optimal generation scheduling problem.

Context Xiaoyaosan (XYS), a traditional Chinese medicine (TCM), has been widely used to relieve a variety of disorders caused by depression. Objective This study evaluates the effect of XYS against tumour metastasis in a chronic restraint stress mouse model. Methods and materials Forty C57BL/6J mice were randomly divided into four groups, including blank-control (BC), blank-stress (BS), XYS-control (XC) and XYS-stress (XS). BS and XS groups were exposed to immobilization stress for 2 h per day for 28 days commencing seven days before tumour cell injection. XC and XS groups were given a gavage of XYS (1516.67 mg/kg) before chronic immobilization stress. Mice were injected with HT-29 colon cancer cells in the spleen to produce liver metastasis. After 28 days of injecting with HT-29 cells, flow cytometry, western blot, PCR and immunohistochemical staining were performed to uncover the role of chronic restraint stress and XYS in the liver metastasis of colon cancer. Results Metastatic liver weight of mice in XS group (3.33 ± 0.18 g) was significantly lower than BS group (4.01 ± 0.27 g). Chronic restraint stress significantly increased CD11b+F4/80+ and CD11b+GrloLy6Chi cell infiltration. XYS treatment significantly decreased the CD11b+F4/80+ tumour-associated macrophage (TAM) population and CD11b+GrloLy6Chi myeloid-derived suppressor cell (MDSC). TGF-β, IL-6, MMP-9 and VEGF in spleen tumours significantly decreased in XYS group. XYS also reduced VEGF and CD31 in hepatic metastatic tissue, which were elevated by chronic restraint stress. Conclusions XYS may successfully inhibit chronic-stress-induced liver metastasis. Results suggest that XYS may have therapeutic value for colorectal cancer treatment.

The cross-view multimedia are widely existed and attract many attentions in recent years. Nevertheless, it is noted that the phenomenon, that data in different classes from same view are more similar than that in same class from different views, is usually presented for cross-view multimedia data. The intrinsic imperfection leads disappointing performance for cross-view multimedia recognition or classification. To solve this problem, in this paper, we propose a novel discriminative learning framework with low-rank constraint, which can be applied for view-invariant low-dimensional subspace learning. The advantages of our framework include three aspects. Firstly, to unlock the latent class structure and view structure, a self-expressed model by dual low-rank constraints are presented, which can separate the two manifold structures in the learned subspace. Secondly, two effective discriminative graphs are constructed to guide the affinity relationship of data in the above two low-dimensional projected subspaces respectively. Finally, the joint semantic consensus constraint is designed to be integrated into the learning framework, which can explore the shared and view-specific information for enforcing the view-invariant character in semantic space. Experimental results on several public cross-view multimedia datasets demonstrate that our proposed method outperforms existing excellent subspace learning approaches.

Understanding the genetic basis of trait variations and their coordination between relative species or populations distributing in different environmental conditions is important in evolutionary biology. In marine ectotherms, growth-defense trade-offs are a common ecological and evolutionary phenomenon. However, the biochemical and molecular mechanisms that govern these trade-offs in marine ectotherms in the evolutionary perspective remain poorly investigated. Oysters are among the most important species in global aquaculture. Crassostrea gigas (C. gigas) and Crassostrea angulata (C. angulata) are two allopatric congeneric dominant oyster species that inhabit the northern and southern intertidal areas of China. Wild C. gigas and C. angulata were spawned, and their F1 progeny were cultured in the same sites to reduce the environmental effects. Untargeted metabolomics and transcriptomics, together with phenotypic parameters including morphological traits (growth performance), nutritional content (glycogen, crude fat, and fatty acid content), physiology (normalized oxygen consumption rate and total antioxidant capacity) were applied to assess metabolic and transcript divergences between C. gigas and C. angulata. Integrated analyses of metabolites and transcriptomes showed that C. gigas allocated more energy to storage and defense by suppressing glycolysis, fatty acid oxidation and by upregulating fatty acid synthesis, antioxidant gene expression, and related metabolites. The metabolic and transcript results were further confirmed by the phenotypic data that C. gigas has higher glycogen and crude fat content and fatty acid unsaturation and stronger antioxidant capacity than C. angulata. In contrast, C. angulata exhibited better growth performance and a higher oxygen consumption rate. These findings suggest that C. angulata allocates more energy to growth, which is embodied in its stronger aerobic capacity and higher levels of protein synthesis genes, metabolites, and growth-related biomarkers. This study will help to enlighten the evolutionary patterns and genetic basis of growth-defense trade-offs in marine ectotherms and the biochemical and molecular mechanisms underlying energy allocation. Also, the key genes and metabolites of glycogen and fatty acids pathway identified in this study will be applied for meat quality improvement in the oyster industry.

The wildlife re-identification recognition methods based on the camera trap were used to identify different individuals of the same species using the fur, stripes, facial features and other features of the animal body surfaces in the images, which is an important way to count the individual number of a species. Re-identification of wild animals can provide solid technical support for the in-depth study of the number of individuals and living conditions of rare wild animals, as well as provide accurate and timely data support for population ecology and conservation biology research. However, due to the difficulty of recording the shy wild animals and distinguishing the similar fur of different individuals, only a few papers have focused on the re-identification recognition of wild animals. In order to fill this gap, we improved the locally aware transformer (LA transformer) network structure for the re-identification recognition of wild terrestrial animals. First of all, at the stage of feature extraction, we replaced the self-attention module of the LA transformer with a cross-attention block (CAB) in order to calculate the inner-patch attention and cross-patch attention, so that we could efficiently capture the global information of the animal body’s surface and local feature differences of fur, colors, textures, or faces. Then, the locally aware network of the LA transformer was used to fuse the local and global features. Finally, the classification layer of the network realized wildlife individual recognition. In order to evaluate the performance of the model, we tested it on a dataset of Amur tiger torsos and the face datasets of six different species, including lions, golden monkeys, meerkats, red pandas, tigers, and chimpanzees. The experimental results showed that our wildlife re-identification model has good generalization ability and is superior to the existing methods in mAP (mean average precision), and obtained comparable results in the metrics Rank 1 and Rank 5.

With the rapid increase of smart Internet of Things (IoT) devices, edge networks generate a large number of computing tasks, which require edge-computing resource devices to complete the calculations. However, unreasonable edge-computing resource allocation suffers from high-power consumption and resource waste. Therefore, when user tasks are offloaded to the edge-computing system, reasonable resource allocation is an important issue. Thus, this paper proposes a digital-twin-(DT)-assisted edge-computing resource-allocation model and establishes a joint-optimization function of power consumption, delay, and unbalanced resource-allocation rate. Then, we develop a solution based on the improved whale optimization scheme. Specifically, we propose an improved whale optimization algorithm and design a greedy initialization strategy to improve the convergence speed for the DT-assisted edge-computing resource-allocation problem. Additionally, we redesign the whale search strategy to improve the allocation results. Several simulation experiments demonstrate that the improved whale optimization algorithm reduces the resource allocation and allocation objective function value, the power consumption, and the average resource allocation imbalance rate by 12.6%, 15.2%, and 15.6%, respectively. Overall, the power consumption with the assistance of the DT is reduced to 89.6% of the power required without DT assistance, thus, improving the efficiency of the edge-computing resource allocation.

The technologies and models based on machine vision are widely used for early wildfire detection. Due to the broadness of wild scene and the occlusion of the vegetation, smoke is more easily detected than flame. However, the shapes of the smoke blown by the wind change constantly and the smoke colors from different combustors vary greatly. Therefore, the existing target detection networks have limitations in detecting wildland fire smoke, such as low detection accuracy and high false alarm rate. This paper designs the attention model Recursive Bidirectional Feature Pyramid Network (RBiFPN for short) for the fusion and enhancement of smoke features. We introduce RBiFPN into the backbone network of YOLOV5 frame to better distinguish the subtle difference between clouds and smoke. In addition, we replace the classification head of YOLOV5 with Swin Transformer, which helps to change the receptive fields of the network with the size of smoke regions and enhance the capability of modeling local features and global features. We tested the proposed model on the dataset containing a large number of interference objects such as clouds and fog. The experimental results show that our model can detect wildfire smoke with a higher performance than the state-of-the-art methods.

The development of stimuli-responsive controlled release formulations is a potential method of improving pesticide utilization efficiency and alleviating current pesticide-related environmental pollution. In this study, a self-destruction redox-responsive pesticide delivery system using biodegradable disulfide-bond-bridged mesoporous organosilica (DMON) nanoparticles as the porous carriers and coordination complexes of gallic acid (GA) and Fe(III) ions as the capping agents were established for controlling prochloraz (PRO) release. The GA–Fe(III) complexes deposited onto the surface of DMON nanoparticles could effectively improve the light stability of prochloraz. Due to the decomposition of GA–Fe(III) complexes, the nano-vehicles had excellent redox-responsive performance under the reducing environments generated by the fungus. The spreadability of PRO@DMON–GA–Fe(III) nanoparticles on the rice leaves was increased due to the hydrogen bonds between GA and rice leaves. Compared with prochloraz emulsifiable concentrate, PRO@DMON–GA–Fe(III) nanoparticles showed better fungicidal activity against Magnaporthe oryzae with a longer duration under the same concentration of prochloraz. More importantly, DMON–GA–Fe(III) nanocarriers did not observe obvious toxicity to the growth of rice seedlings. Considering non-toxic organic solvents and excellent antifungal activity, redox-responsive pesticide controlled release systems with self-destruction properties have great application prospects in the field of plant disease management.

We explored the related metabolites produced by different quality semi-glutinous japonica rice varieties and the modulatory role of key metabolites in metabolic mechanisms. In this study, three high-quality edible semi-glutinous rice varieties were employed as investigational materials, the metabolites of the three varieties were detected using LC–MS metabolomics technology, and the rice quality traits of the three rice varieties were determined. The taste value (TV) of Yangnongxiang 28 (YNX28H) was substantially higher than that of Hongyang 5 hao (HY5H) and Nanjing 5718 (NJ5718), and the hardness (HA) of YNX28H was significantly lower than that of HY5H and NJ5718. The HA was significantly negatively correlated with the TV. The highest chalkiness rate (CR) and chalkiness degree (CD) were observed for NJ5718, and the lowest CR and CD were observed for HY5H. HY5H had a substantially lower protein content (PC) than YNX28H and NJ5718 and a markedly higher amylose content (AC) than those two varieties. Overall, 188 differential metabolites (DMs) were recognized between HY5H and NJ5718. A total of 136 DMs were detected between YNX28H and NJ5718, and 198 DMs were recognized between HY5H and YNX28H. The metabolites with a strong correlation with rice quality were mainly associated with amino acid metabolism, lipid metabolism and the citrate cycle. The key metabolites in the metabolic pathway include lipid metabolites (sagittariol, glycerophosphocholine, gamma-eudesmol rhamnoside, goshonoside F1, diosbulbinoside F, and corchorifatty acid F), amino acid metabolites (pantothenic acid, L-serine, L-proline, L-aspartic acid, L-glutamate, L-asparagine, and glutathione) and carbohydrate metabolites (sucrose, levan, D-maltose, and amylose). These key metabolites play important regulatory roles in metabolic mechanisms, providing a theoretical basis for breeding new high-quality edible rice varieties.

With the increasing demand for energy capacity and power density in battery systems, the thermal safety of lithium-ion batteries has become a major challenge for the upcoming decade. The heat transfer during the battery thermal runaway provides insight into thermal propagation. A better understanding of the heat exchange process improves a safer design and enhances battery thermal management performance. This work proposes a three-dimensional thermal model for the battery pack simulation by applying an in-house model to study the internal battery thermal propagation effect under the computational fluid dynamics (CFD) simulation framework. The simulation results were validated with the experimental data. The detailed temperature distribution and heat transfer behaviour were simulated and analyzed. The thermal behaviour and cooling performance were compared by changing the abnormal heat generation locations inside the battery pack. The results indicated that various abnormal heat locations disperse heat to the surrounding coolant and other cells. According to the current battery pack setups, the maximum temperature of Row 2 cases can be increased by 2.93%, and the temperature difference was also increased. Overall, a new analytical approach has been demonstrated to investigate several stipulating battery thermal propagation scenarios for enhancing battery thermal performances.

In the present study, the non-blue laccase Melac13220 from Methylobacterium extorquens was immobilized using three methods to overcome problems related to the stability and reusability of the free enzyme: entrapment of the enzyme with sodium alginate, crosslinking of the enzyme with glutaraldehyde and chitosan-, and site-specific covalent immobilization of the enzyme on Fe3O4 nanoparticles by an aldehyde tag. The site-specific covalent immobilization method showed the highest immobilization efficiency and vitality recovery. The optimum temperature of Melac13220 was increased from 65 °C to 80 °C. Immobilized Melac13220 showed significant tolerance to some organic solvents and maintained approximately 80% activity after 10 cycles of use. Differential scanning calorimetry (DSC) indicated that the melting temperature of the enzyme was increased (from 57 °C to 79 °C). Immobilization of Melac13220 also led to improvement in dye decolorization such that Congo Red was completely decolorized within 10 h. The immobilized enzyme can be easily prepared without purification, demonstrating the advantages of using the aldehyde tag strategy and providing a reference for the practical application of different immobilized laccase methods in the industrial field.

The steam ejector is valuable and efficient in the fire suppression field due to its strong fluid-carrying capacity and mixing ability. It utilizes pressurized steam droplets generated at the exit to extinguish the fire quickly and the steam droplet strategy allows for an expressive decrease in water consumption. In this regard, the fire suppression process is influenced by the steam ejector efficiency, the performance of the pressurized steam, and the ejector core geometry, which controls the quality of the extinguishing mechanisms. This study investigated the impact of different mixing section diameters on the pumping performance of the ejector. The results showed that change in the diffuser throat diameter was susceptible to the entrainment ratio, which significantly increased, by 4 mm, by increasing the throat diameter of the diffuser and improved the pumping efficiency. Still, the critical back pressure of the ejector reduced. In addition, the diameter effect was studied and analyzed to evaluate the ejector performance under different operating parameters. The results revealed a rise in the entrainment ratio, then it diminished with increasing primary fluid pressure. The highest entrainment ratio recorded was 0.5 when the pressure reached 0.36 MPa at the critical range of back pressure, where the entrainment ratio remained constant until a certain back pressure value. Exceeding the critical pressure by increasing the back pressure to 7000 Pa permitted the entrainment ratio to reduce to zero. An optimum constant diameter maximized the ejector pumping efficiency under certain operating parameters. In actual design and production, it is necessary to consider both the exhaust efficiency and the ultimate exhaust capacity of the ejector.

The difficulty of exposing active sites and easy recombination of photogenerated carriers have always been two critical problems restricting the photocatalytic activity of g-C3N4. Herein, a simple (NH4)2MoO4-induced one-step calcination method was successfully introduced to transform bulk g-C3N4 into g-C3N4/MoO2 composites with a large specific surface area. During the calcination, with the assistance of NH3 and water vapor produced by ammonium molybdate, the pyrolytical oxidation and depolymerization of a g-C3N4 interlayer were accelerated, finally realizing the exfoliation of the g-C3N4. Furthermore, another pyrolytical product of ammonium molybdate was transformed into MoO2 under an NH3 atmosphere, which was in situ loaded on the surface of a g-C3N4 nanosheet. Additionally, the results of photocatalytic hydrogen evolution under visible light show that the optimal g-C3N4/MoO2 composite has a high specific surface area and much improved performance, which is 4.1 times that of pure bulk g-C3N4. Such performance improvement can be attributed to the full exposure of active sites and the formation of abundant heterojunctions. However, with an increasing feed amount of ammonium molybdate, the oxidation degree of g-C3N4 was enhanced, which would widen the band gap of g-C3N4, leading to a weaker response ability to visible light. The present strategy will provide a new idea for the simple realization of exfoliation and constructing a heterojunction for g-C3N4 simultaneously.

In this article, a collective database from validated numerical simulation has been established to study the suppression effects of water-based suppression systems under a single-compartment fire scenario at various suppression configurations and fire locations. Five fuel locations along the axis between the centre and corner of the room were configurated to dynamically analyse how the horizontal distance between the nozzle and fuel pan affects the heat release rate (HRR), temperature cooling phenomena at different heights and also the velocity profile. Throughout the fuel pan relocations, the water-mist system has achieved an average suppression time of 25 s for all the locations, it was found that the water mist system can effectively control the fire under 200 °C that is distanced over 2 m spanwise displacement from the nozzle against the fire, while the sprinkler has exhibited an excellent fuel surface cooling effect due to large momentum and heat capacity within the coverage area with an average suppression time of 50 s. The results of this study have further explored the spray coverage and droplet penetrability of different suppression systems at different locations corresponding to the fire source, and the quantitative assessment of fuel locations could also contribute to the future development of performance-based fire safety designs.

Neomycin is a common ototoxic aminoglycoside antibiotic that causes sensory hearing disorders worldwide, and monosialotetrahexosylganglioside (GM1) is reported to have antioxidant effects that protect various cells. However, little is known about the effect of GM1 on neomycin-induced hair cell (HC) ototoxic damage and related mechanism. In this study, cochlear HC-like HEI-OC-1 cells along with whole-organ explant cultures were used to establish an in vitro neomycin-induced HC damage model, and then the apoptosis rate, the balance of oxidative and antioxidant gene expression, reactive oxygen species (ROS) levels and mitochondrial membrane potential (MMP) were measured. GM1 could maintain the balance of oxidative and antioxidant gene expression, inhibit the accumulation of ROS and proapoptotic gene expression, promoted antioxidant gene expression, and reduce apoptosis after neomycin exposure in HEI-OC-1 cells and cultured cochlear HCs. These results suggested that GM1 could reduce ROS aggregation, maintain mitochondrial function, and improve HC viability in the presence of neomycin, possibly through mitochondrial antioxidation. Hence, GM1 may have potential clinical value in protecting against aminoglycoside-induced HC injury.

Sensorineural hearing loss (SNHL) affects approximately 466 million people worldwide, which is projected to reach 900 million by 2050. Its histological characteristics are lesions in cochlear hair cells, supporting cells, and auditory nerve endings. Neurological disorders cover a wide range of diseases affecting the nervous system, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), autism spectrum disorder (ASD), etc. Many studies have revealed that neurological disorders manifest with hearing loss, in addition to typical nervous symptoms. The prevalence, manifestations, and neuropathological mechanisms underlying vary among different diseases. In this review, we discuss the relevant literature, from clinical trials to research mice models, to provide an overview of auditory dysfunctions in the most common neurological disorders, particularly those associated with hearing loss, and to explain their underlying pathological and molecular mechanisms.

Background: Consensus is lacking with regard to whether hearing loss is an independent risk factor for dementia. We therefore conducted a meta-analysis to clarify the relationship of hearing loss and dementia.Methods: Prospective cohort studies investigating the association between hearing loss and the incidence of dementia in a community-derived population were included by searching electronic databases that included PubMed, Embase, and Cochrane's Library. A random-effects model was adopted to combine the results.Results: Fourteen cohorts including 726,900 participants were analyzed. It was shown that hearing loss was independently associated with dementia [adjusted hazard ratio (HR): 1.59, 95% confidence interval (CI): 1.37 to 1.86, p < 0.001; I2 = 86%]. Sensitivity analysis sequentially excluding any of the individual studies included showed similar results. Subgroup analysis according to the diagnostic methods for hearing loss, validation strategy for dementia, follow-up duration, and adjustment of apolipoprotein E genotype also showed consistent results (p-values for subgroup differences all > 0.05). Meta-analysis with five studies showed that hearing loss was also connected to higher risk of Alzheimer's disease (adjusted HR: 2.24, 95% CI: 1.32 to 3.79, p = 0.003; I2 = 2%).Conclusions: Hearing loss may increase the risk of dementia in the adult population. Whether effective treatment for hearing loss could reduce the incidence of dementia should be explored in the future.

In the navigation of underwater robots, large ocean current disturbance often causes significant tracking errors. To better resist ocean current disturbance, the hydrodynamic characteristics of the spherical underwater robot are studied, and a model predictive control strategy based on adaptive model parameters is proposed, according to these characteristics. Firstly, the hydrodynamic characteristics of the robot under static water and constant flow disturbance were obtained and analyzed by the computational fluid dynamics method. Then, the dynamic models of the robot under different disturbances could be calculated from the data obtained, based on the least square method. Finally, an adaptive model predictive control (AMPC) strategy, with an ocean current observer, was designed, based on the dynamic models. When the current disturbance velocity was twice the robot velocity, the proposed strategy reduced the tracking error by 39% and 42% in X and Y directions, respectively. In addition, the hydrodynamic characteristics were verified by experiments.

In view of the potential soil leaching risk of the reuse of fertilizer reclaimed from micro-flush sanitary wastewater, the batch tests of soil adsorption and degradation, soil column percolation test and multi-stage soil box percolation test were adopted. The characteristics of leachate after the interaction between reclaimed fertilizer and soil, as well as the changes of soil attributes in the soil box system, were analyzed. After obtaining the correlation coefficients of solute transport in soil through the above experiments, the HYDRUS-1D model was constructed to simulate the solute migration and transformation in the soil with a duration of 1 year and a soil thickness of 5 m. The impacts of leachate on groundwater and soil were analyzed. The results showed that the adsorption intensity of total ammonia nitrogen (TAN) (1n=0.8009) in the tested soil was lower than that of chemical oxygen demand (COD) (1n=1.1830). The HYDRUS prediction showed that concentrations of TAN and COD at the soil depth of 5 m were 0, while total nitrogen (TN) still had a concentration of 0.11 mg/L. However, the TN leaching into the deep soil mainly came from the soil itself. The TAN at 3.4 m soil showed an upward trend in the prediction period. In addition, the reuse of reclaimed fertilizer can expand the soil nutrient inventory, which is conducive to the improvement of soil fertility. It can be concluded that the soil leaching risk of reuse of reclaimed fertilizer is not significant in the short term (one year). However, the risk of fertilization on soil with high-nitrogen background value should be paid attention to in the long term.

Oxovitisins are a unique group of anthocyanin derivatives with a non-oxonium nature and α-pyranone (lactone) D ring on the structure. In this study, oxovitisin A was synthesized through the micro-oxidative reaction of carboxypyranomalvidin-3-O-glucoside (vitisin A) with water, and its thermostability, pH, and SO2 color stability were studied compared with its two precursors, malvidin-3-O-glucoside (Mv3glc) and vitisin A, as well as methylpyrano-malvidin-3-O-glucoside (Me-py). Results showed that oxovitisin A exhibited the highest stabilities, which were inseparably related to its noncharged structure and the additional carbonyl group on the D ring. Moreover, the antiproliferative capacity of oxovitisin A was comparatively evaluated against two human gastrointestinal cancer cell lines. Interestingly, oxovitisin A presented the strongest antiproliferative ability on MKN-28 (IC50 = 538.42 ± 50.06 μM) and Caco-2 cells (IC50 = 434.85 ± 11.87 μM) compared with two other pyranoanthocyanins. Therefore, we conclude that oxovitisin A as a highly stable anthocyanin derivative still exhibits a satisfactory antiproliferative ability.

The increasing popularity of lithium-ion battery systems, particularly in electric vehicles and energy storage systems, has gained broad research interest regarding performance optimization, thermal stability, and fire safety. To enhance the battery thermal management system, a comprehensive investigation of the thermal behaviour and heat exchange process of battery systems is paramount. In this paper, a three-dimensional electro-thermal model coupled with fluid dynamics module was developed to comprehensively analyze the temperature distribution of battery packs and the heat carried away. The computational fluid dynamics (CFD) simulation results of the lumped battery model were validated and verified by considering natural ventilation speed and ambient temperature. In the artificial neural networks (ANN) model, the multilayer perceptron was applied to train the numerical outputs and optimal design of the battery setup, achieving a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. The simulation results provide a practical compromise in optimizing the battery configuration and cooling efficiency, balancing the layout of the battery system, and safety performance. The present modelling framework demonstrates an innovative approach to utilizing high-fidelity electro-thermal/CFD numerical inputs for ANN optimization, potentially enhancing the state-of-art thermal management and reducing the risks of thermal runaway and fire outbreaks.

In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.

ObjectivesTo explore the relationship between ultrasound (US) features and Ki-67 labeling index (LI) of soft tissue sarcoma (STS).MethodsForty-six patients with 47 STS lesions, between September 2014 and April 2020, were enrolled in the study. Point-biserial correlation analysis and Spearman’s correlation analysis were utilized to examining the relationship between the US features and the Ki-67 LI of STS. The differences of US features between high and low Ki-67 proliferation groups were statistically analyzed by independent t test, Wilcoxon rank-sum test, and Fisher’s exact test. The optimal cut-off points of US features revealing significant differences were estimated by the maximum Youden index.ResultsA moderate correlation between the vascular density grade and the Ki-67 LI (ρ = 0.409, P = 0.004) was found in this study. In addition, other ultrasound features were irrelevant to the Ki-67 LI. The cut-off for differentiating low- and high-proliferation groups was grade II according to the best Youden index. The area under receiver operating characteristic (ROC) curve was 0.74 (p = 0.011) with a sensitivity of 60.6% and specificity of 78.6%.ConclusionsOnly the vascular density grade of STS had a weak positive correlation with Ki-67 LI, and might be capable of predicting the proliferation of STS. Other ultrasonographic features of STS such as shape and tumor margin have no correlation with Ki-67 LI.

Social stress, a common stressor, causes multiple forms of physical and mental dysfunction. Prolonged exposure to social stress is associated with a higher risk of psychological disorders, including anxiety disorders and major depressive disorder (MDD). The orexinergic system is involved in the regulation of multiple motivated behaviors. The current study examined the regulatory effect of orexinergic projections from the lateral hypothalamic area (LHA) to the lateral habenula (LHb) in depression- and anxiety-like behaviors after chronic social defeat stress. When mice were defeated during social interaction, both orexinergic neurons in the LHA and glutamatergic neurons in the LHb were strongly activated, as indicated by the FosTRAP strategy. Infusion of orexin in the LHb significantly alleviated social avoidance and depression-like behaviors induced by chronic social defeat stress. Administration of an orexin receptor 2 antagonist in the LHb further aggravated the depressive phenotype. Photoactivation of orexinergic cell bodies in the LHA or terminals in the LHb relieved anxiety-like behaviors induced by chronic social defeat stress. Collectively, we identified the antidepressant and anxiolytic effects of the circuit from LHA orexinergic neurons to the LHb in response to chronic social stress, providing new evidence of the antidepressant properties of LHA orexin circuits.

Abstract Objective This study aimed to investigate changes in the ultrastructure, apoptosis, cycle progression, and migration ability of human MDA‐MB‐231 breast cancer cells under simulated microgravity conditions. Methods MDA‐MB‐231 breast cancer cells were cultured in a rotary cell culture system bioreactor to simulate microgravity for 7 days. The cells were randomly divided into a simulated microgravity (SMG) group and normal gravity control (NG) group. Changes in apoptosis, ultrastructure, cell cycle, and migration ability were evaluated. Results Transmission electron microscopy showed that cells in the SMG group had more secondary lysosomes than the NG groups. The flow cytometry assay showed that the proportion of apoptotic cells in the SMG group was significantly increased compared with the NG group; additionally, the median and interquartile spacing of the proportion of cells in the G0/G1 phase in the SMG group was lower than in the NG group, but the proportion of cells in the S phase was increased in the SMG group compared with the NG group. The transwell migration assay showed that cell migration in the SMG group was significantly reduced compared with the NG group. The expression of BCL‐2 and MMP9 in the SMG group was decreased compared with the NG group, whereas the expression of cyclin D3 in the SMG group was increased. Conclusion Simulated microgravity through rotary cell culture system had significant effects on human MDA‐MB‐231 breast cancer cells: the cell cycle and ultrastructure were impaired, apoptosis increased, migration ability decreased, and the expression of BCL‐2, cyclin D3, and MMP9 were affected.