Tumor-derived extracellular vesicles: how they mediate glioma immunosuppression.

Gliomas, the most common malignant brain tumor, present a grim prognosis despite available treatments such as surgical resection, temozolomide (TMZ) therapy, and radiation therapy. This is due to their aggressive growth, high level of immunosuppression, and the blood-brain barrier (BBB), which obstruct the effective exchange of therapeutic drugs. Gliomas can significantly affect differentiation and function of immune cells by releasing extracellular vesicles (EVs), resulting in a systemic immunosuppressive state and a highly immunosuppressive microenvironment. In the tumor immune microenvironment (TIME), the primary immune cells are regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs). In particular, glioma-associated TAMs are chiefly composed of monocyte-derived macrophages and brain-resident microglia. These cells partially exhibit characteristics of a pro-tumorigenic, anti-inflammatory M2-type. Glioma-derived EVs can hijack TAMs to differentiate into tumor-supporting phenotypes or directly affect the maturation of peripheral blood monocytes (PBMCs) and promote the activation of MDSCs. In addition, EVs impair the ability of dendritic cells (DCs) to process antigens, subsequently hindering the activation of lymphocytes. EVs also impact the proliferation, differentiation, and activation of lymphocytes. This is primarily evident in the overall reduction of CD4 + helper T cells and CD8 + T cells, coupled with a relative increase in Tregs, which possess immunosuppressive characteristics. This study investigates thoroughly how tumor-derived EVs impair the function of immune cells and enhance immunosuppression in gliomas, shedding light on their potential implications for immunotherapy strategies in glioma treatment.

Analysis of differentially expressed proteins after EHP-infection and characterization of caspase 3 protein in the whiteleg shrimp (Litopenaeus vannamei).

Whiteleg shrimp (Litopenaeus vannamei) is the most important species of shrimp farmed worldwide in terms of its economic value. Enterocytozoon hepatopenaei (EHP) infects the hepatopancreas, resulting in the hepatopancreatic microsporidiosis (HPM) of the host, which causes slow growth of the shrimp and poses a threat to the farming industry. In this study, differentially expressed proteins (DEPs) between EHP-infected and uninfected shrimp were investigated through proteomics sequencing. A total of 9908 peptides and 2092 proteins were identified. A total of 69 DEPs were identified in the hepatopancreas (HP), of which, 28 were upregulated and 41 were downregulated. Our results showed that the differences among the level of multiple proteins involved in the apoptosis were significant after the EHP infection, which indicated that the apoptosis pathway was activated in whiteleg shrimp. In addition, expression leve of caspase 3 gene were identified related to the EHP infection. Furthermore, predictions of spatial structure, analysis of phylogeny and chromosome-level linearity of the caspase 3 protein were performed as well. In conclusion, a relatively complete proteomic data set of hepatopancreas tissues in whiteleg shrimp were established in this study. Findings about genes involved in the apoptosis here will provide a further understanding of the molecular mechanism of EHP infection in the internal immunity of whiteleg shrimp.

Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.

BACKGROUND: Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. METHODS: Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. RESULTS: Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. CONCLUSIONS: Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.

Gaseous air pollutants and hospitalizations for mental disorders in 17 Chinese cities: Association, morbidity burden and economic costs.

The short-term morbidity effects of gaseous air pollutants on mental disorders (MDs), and the corresponding morbidity and economic burdens have not been well studied. We aimed to explore the associations of ambient sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO) with MDs hospitalizations in 17 Chinese cities during 2015-2018, and estimate the attributable risk and economic costs of MDs hospitalizations associated with gaseous pollutants. City-specific relationships between gaseous pollutants and MDs hospitalizations were evaluated using over-dispersed generalized additive models, then combined to obtain the pooled effect. Concentration-response (C-R) curves of gaseous pollutants with MDs from each city were pooled to allow regional estimates to be derived. The morbidity and economic burdens of MDs hospitalizations attributable to gaseous pollutants were further assessed. A total of 171,939 MDs hospitalizations were included. We observed insignificant association of O3 with MDs. An interquartile range increase in SO2 at lag0 (9.1 µg/m³), NO2 at lag0 (16.7 µg/m³) and CO at lag2 (0.4 mg/m³) corresponded to a 3.02% (95%CI: 0.72%, 5.38%), 5.03% (95%CI: 1.84%, 8.32%) and 2.18% (95%CI: 0.40%, 4.00%) increase in daily MDs hospitalizations, respectively. These effects were modified by sex, season and cause-specific MDs. The C-R curves of SO2 and NO2 with MDs indicated nonlinearity and the slops were steeper at lower concentrations. Overall, using current standards as reference concentrations, 0.27% (95%CI: 0.07%, 0.48%) and 0.06% (95%CI: 0.02%, 0.10%) of MDs hospitalizations could be attributable to extra SO2 and NO2 exposures, and the corresponding economic costs accounted for 0.34% (95%CI: 0.08%, 0.60%) and 0.07% (95%CI: 0.03%, 0.11%) of hospitalization expenses, respectively. Moreover, using threshold values detected from C-R curves as reference concentrations, the above mentioned morbidity and economic burdens increased a lot. These findings suggest more strict emission control regulations are needed to protect mental health from gaseous pollutants.

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately. METHODS: In this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance. RESULTS: The experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions. CONCLUSION: Our predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.

Rapid Seismic Evaluation of Continuous Girder Bridges with Localized Plastic Hinges.

In seismic assessment of continuous girder bridges, plastic hinges form in bridge piers to dissipate seismic energy through nonlinear restoring forces. Considering temporal and spatial variations of ground motions, seismic evaluation of the bridges involves nonlinear stochastic vibration and expensive computation. This paper presents an approach to significantly increase the efficiency of seismic evaluation for continuous girder bridges with plastic hinges. The proposed approach converts nonlinear motion equations into quasi-linear state equations, solves the equations using an explicit time-domain dimension-reduced iterative method, and incorporates a stochastic sampling method to statistically analyze the seismic response of bridges under earthquake excitation. Taking a 3 × 30 m continuous girder bridge as an example, fiber beam-column elements are used to simulate the elastic-plastic components of the continuous girder bridge, and the elastic-plastic time history analysis of the continuous girder bridge under non-uniform seismic excitation is carried out. Results show that the computation time is only 5% of the time of the nonlinear time history approach while retaining the accuracy. This study advances the capability of rapid seismic assessment and design for bridges with localized nonlinear behaviors such as plastic hinges.

Lateral Vibration Control of Long-Span Small-Radius Curved Steel Box Girder Pedestrian Bridge with Distributed Multiple Tuned Mass Dampers.

Curved pedestrian bridges are important urban infrastructure with the desired adaptability to the landscape constraints and with aesthetic benefits. Pedestrian bridges feature thin cross-sections, which provide sufficient load capacities but lead to low natural frequencies that make the bridges susceptible to vibration under pedestrian excitation. This study investigates the lateral vibration of a curved bridge with a small radius down to 20 m, proposes an approach to mitigate the lateral vibration of bridges with large curvatures using distributed multiple tuned mass dampers (MTMD), and conducts in-situ bridge tests to evaluate the vibration mitigation performance. The lateral vibration was investigated through in-situ tests and finite element analysis as well as the code requirements. The key parameters of the distributed MTMD system were improved by strategically selecting the mass ratio, bandwidth, center frequency ratio, and damper number. The results showed that the curved bridge was subjected to significant lateral vibration due to the coupling of torque and moment, and the recommended design parameters for the studied bridge were derived, i.e., the total mass ratio is 0.02, bandwidth is 0.15, center frequency ratio is 1.0, and damper number is 3. The proposed approach effectively improves the deployment of MTMD for lateral vibration control of the curved bridge. The field tests showed that the vibration was reduced by up to 82% by using the proposed approach.

Rapid Hemostatic Biomaterial from a Natural Bath Sponge Skeleton.

Uncontrolled bleeding is the main cause of mortality from trauma. Collagen has been developed as an important hemostatic material due to its platelet affinity function. A bath sponge skeleton is rich in collagen, also known as spongin. To understand the hemostatic effect of spongin, spongin materials, SX, SFM and SR were prepared from the bath sponge Spongia officinalis, and hemostatic experiments were performed. The SX, SFM and SR were significantly better than the positive control, type I collagen, in shortening the whole blood clotting time in vitro and hemostasis upon rat tail amputation. In a hemostatic experiment of rabbit common carotid artery injury, the hemostatic time and 3 h survival rate of the SFM group were 3.00 ± 1.53 min and 100%, respectively, which are significantly better than those of the commercial hemostat CELOX-A (10.33 ± 1.37 min and 67%, respectively). Additionally, the SFM showed good coagulation effects in platelet-deficient blood and defibrinated blood, while also showing good biocompatibility. Through a variety of tests, we speculated that the hemostatic activity of the SFM is mainly caused by its hyperabsorbency, high affinity to platelets and high effective concentration. Overall, the SFM and spongin derivates could be potential hemostatic agents for uncontrolled bleeding and hemorrhagic diseases caused by deficiency or dysfunction of coagulation factors.

Laser-Assisted Nanowetting: Selective Fabrication of Polymer/Gold Nanorod Arrays Using Anodic Aluminum Oxide Templates.

1D polymer nanomaterials have attracted significant interest in recent years because of their unique properties and promising applications in various fields. It is, however, still a challenge to fabricate polymer nanoarrays with desired sizes and controlled morphologies. Here, an unprecedented approach, the laser-assisted nanowetting (LAN) method, to selectively fabricate polymer nanoarrays is presented. Polystyrene (PS) is blended with gold nanorods (AuNRs), which are used to absorb the energy from the laser. After the blend films are brought in contact with AAO templates, the AuNRs at regions shone by the laser beams absorb the energy and heat the surrounding polymer chains, resulting in the formation of PS/AuNRs arrays in selected areas. This work paves a new research direction for developing template-based polymer nanomaterials.

Reproducible and Bendable SERS Substrates with Tailored Wettability Using Block Copolymers and Anodic Aluminum Oxide Templates.

Surface properties are essential for substrates exhibiting high sensitivity in surface-enhanced Raman scattering (SERS) applications. In this work, novel SERS hybrid substrates using polystyrene-block-poly(methyl methacrylate) and anodic aluminum oxide templates is presented. The hybrid substrates not only possess hierarchical porous nanostructures but also exhibit superhydrophilic surface properties with the water contact angle ≈0°. Such surfaces play an important role in providing uniform enhanced intensities over large areas (relative standard deviation ≈10%); moreover, these substrates are found to be highly sensitive (limit of detection ≈10-12 m for rhodamine 6G (R6G)). The results show that the hybrid SERS substrates can achieve the simultaneous detection of multicomponent mixtures of different target molecules, such as R6G, crystal violet, and methylene blue. Furthermore, the bending experiments show that about 70% of the SERS intensities are maintained after bending from ≈30° to 150°.

Seven Hormonal Biomarkers for Diagnosing Endometriosis: Meta-Analysis and Adjusted Indirect Comparison of Diagnostic Test Accuracy.

OBJECTIVE: To compare the diagnostic accuracy of different hormonal biomarkers and to find the most effective hormonal biomarker for the diagnosis of endometriosis. DATA SOURCES: We conducted a systematic search using PubMed, EMBASE, Cochrane Library, and China Biomedical Literature to identify relevant studies from the first day of databases to August 2018. METHODS OF STUDY SELECTION: Two independent reviewers screened for study eligibility and extracted data. Random controlled trials, cross-sectional studies, case-control studies, and cohort studies evaluating the diagnostic accuracy of hormonal markers for endometriosis were included. TABULATION, INTEGRATION, AND RESULTS: We included 17 studies that involved 1279 participants and evaluated 7 hormonal biomarkers. The pooled sensitivity and specificity in endometriosis were .79 (.71, .86) and .89 (.82, .94) for aromatase, .30 (.18, .46) and .80 (.65, .90) for human chorionic gonadotropin/luteinizing hormone receptor, .75 (.66, .83) and .47 (.34, .60) for estrogen receptor (ER)-α, .65 (.56, .74) and .68 (.55, .80) for ER-ß, .45 (.38-.52) and .92 (.85-.97) for serum prolactin, .69 (.51, .83) and .30 (.16, .49) for estrogen sulfotransferase, and .73 (.60-.84) and .48 (.33-.63) for 17ß-hydroxysteroid dehydrogenase type 2 (17ßHSD2). Compared with human chorionic gonadotropin/luteinizing hormone receptor, ER-α, ER-ß, estrogen sulfotransferase, and 17ßHSD2, aromatase had a higher sensitivity, specificity, positive likelihood ratio, and diagnostic odds ratio. The specificities of aromatase and serum prolactin were comparable, but the sensitivity, positive likelihood ratio, and positive likelihood ratio of serum prolactin were much lower than that of aromatase. CONCLUSION: Aromatase may be an excellent diagnostic test for endometriosis. However, because of the moderate quality of the included studies and the limited sample size, this result requires more research to validate. (PROSPERO registration number: PROSPERO 2018 CRD42018105126.).

Human-like acceleration and deceleration control of a robot astronaut floating in a space station.

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot's contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability.

Bibliometric analysis of the research hotspots and trends of circular RNAs.

Background and objective: Circular RNAs (circRNAs) have garnered considerable attention in the study of various human diseases due to their ubiquitous expression and potential biological functions. This study conducts a bibliometric and visualization-based analysis of circRNA-related research in diseases, aiming to reveal the current status, hotspots and emerging trends within the field. Methods: Literature published between 2013 and 2022 and indexed in the Web of Science core databases was retrieved. Visualizations of publication volume, countries, authors, institutions, journals, references, and keywords were performed. Microsoft Excel (2021) was used to analyze and graph publication volume and growth trends. Additionally, CiteSpace (version 6.1.R6) and VOSviewer (version 1.6.18) were employed to visualize the bibliographic information. Results: Between 2013 and 2022, a total of 4195 relevant articles on circRNA in the context of diseases were identified. These articles covered 56 countries, 2528 institutions, 19,842 authors and 698 journals, citing 85,541 references. The annual publication volume showed an exponential growth trend, with rapid development post-2017. China, the United States and Germany emerged as the top three contributors, demonstrating high publication volume and total citations. Notably, Nanjing Medical University exhibited the highest publication volume, boasting 291 articles. Burton B. Yang and Li Yang consistently ranked among the top 10 authors in terms of publication volume and citations, emerging as core contributors in this research field. The journal Bioengineered ranked first in terms of published articles (160), with an impact factor of 6.832, while Molecular Cancer garnered the highest impact factor (41.4), solidifying its position as a top journal in this field. Furthermore, high-frequency keywords included "expression" "proliferation" "biomarker" "microRNA" "cancer", signifying the prevailing research hotspots and principal themes of this field over the past decade. As of 2022, "biomarker", "prostate cancer","drug resistance","papillary thyroid carcinoma", etc. continued as keywords during the outbreak period. At present, the value of circRNA application is mainly reflected in the two aspects of biomarkers and therapeutic targets, and the prediction of accurate diagnosis and precise treatment based on big data analysis, especially in cancer, will become a hot spot of research in the future. Conclusion: The trajectory of circRNA research from its biological origins to its applications in diseases has been delineated from 2013 to 2022. However, the transition to disease-specific applications and exploration of biological functions warrants further attention in future research endeavors.

Research progress on mechanisms of ischemic stroke: Regulatory pathways involving Microglia.

Microglia, as the intrinsic immune cells in the brain, are activated following ischemic stroke. Activated microglia participate in the pathological processes after stroke through polarization, autophagy, phagocytosis, pyroptosis, ferroptosis, apoptosis, and necrosis, thereby influencing the injury and repair following stroke. It has been established that polarized M1 and M2 microglia exhibit pro-inflammatory and anti-inflammatory effects, respectively. Autophagy and phagocytosis in microglia following ischemia are dynamic processes, where moderate levels promote cell survival, while excessive responses may exacerbate neurofunctional deficits following stroke. Additionally, pyroptosis and ferroptosis in microglia after ischemic stroke contribute to the release of harmful cytokines, further aggravating the damage to brain tissue due to ischemia. This article discusses the different functional states of microglia in ischemic stroke research, highlighting current research trends and gaps, and provides insights and guidance for further study of ischemic stroke.

Defect detection of printed circuit board assembly based on YOLOv5.

Detection of printed circuit board assembly (PCBA) defects is crucial for improving the efficiency of PCBA manufacturing. This paper proposes PCBA-YOLO, a YOLOv5-based method that can effectively increase the accuracy of PCBA defect detection. First, the spatial pyramid pooling module with cross-stage partial structure is replaced in the neck network of YOLOv5 to capture the resolution features at multiple scales. Second, large kernel convolution is introduced in the backbone network to obtain larger effective receptive fields while reducing computational overhead. Finally, an SIoU loss function that considers the angular cost is utilized to enhance the convergence speed of the model. In addition, an assembled PCBA defect detection dataset named PCBA-DET is created in this paper, containing the corresponding defect categories and annotations of defect locations. The experimental results on the PCB defect dataset demonstrate that the improved method has lower loss values and higher accuracy. Evaluated on the PCBA-DET dataset, the mean average precision reaches 97.3 % , achieving a real-time detection performance of 322.6 frames per second, which considers both the detection accuracy and the model size compared to the YOLO series of detection networks. The source code and PCBA-DET dataset can be accessed at https://github.com/ismh16/PCBA-Dataset .

Association between three genetic variants of the Perilipin Gene (PLIN) and glucose metabolism: results from a replication study among Chinese adults and a meta-analysis.

OBJECTIVE: This study was aimed to replicate the associations between three Perilipin Gene (PLIN) variants (rs894160, rs1052700, and rs2304796) and diabetes risks and to evaluate the overall effects of these variants on diabetes risk and obesity risk. METHODS: We conducted a cross-sectional study among 993 Chinese Han adults. We also made a meta-analysis to estimate associations between these variants and diabetes risk and obesity risk. RESULTS: In the sample of all participants, all three single-nucleotide polymorphisms (SNPs) were not significantly associated with diabetes risks. The PLIN polymorphisms significantly interacted with central obesity in relation to diabetes risk (P for interaction = 0.036, 0.033, and 0.042 for rs1052700, rs894160, and rs2304796, respectively). In those with allele T of rs1052700 or allele A of rs894160, fasting glucose concentration and diabetes risk increased significantly with the increment of waist circumference. Only association between rs894160 and obesity risk was available for meta-analysis. The meta-analysis indicated the overall estimation of obesity risk for rs894160 was 0.97 (0.78, 1.16) among participants with allele A versus people with genotype GG and 1.46 (0.99, 1.93) among those with genotype AA versus allele G carriers. CONCLUSION: Chinese adults with high waist circumference may have a high risk of diabetes, especially among those with allele T in rs1052700 or with allele A in rs894160. People with genotype AA (rs894160) may have a high risk of obesity.

Carboxymethyl Cellulose/Polyacrylamide/Fe3O4 Magnetic Ion Imprinting Biosorbent for Removal and Recovery of La3.

To use resources rationally, the recovery and recycling of rare earth (RE) from industrial sewage have attracted a lot of attention. Herein, a polymer adsorbent CMC/PAM/Fe3O4 (CPF) was synthesized from renewable carboxymethyl cellulose (CMC), polyacrylamide (PAM), and Fe3O4 by the template of La3+ using ion imprinting technology. The CPF was characterized with X-ray diffraction (XRD), IR, X-ray photoelectron spectroscopy (XPS), and scanning electron microscope (SEM), and results show that PAM and CMC can crosslink with each other and form copolymers with Fe3O4 particles dispersing in it. The adsorption properties for the template ions La3+ were fully studied. It is found that CPF exhibited good adsorption performance with an adsorption capacity of 34.6 mg·g-1. Cycling experiments show that CPF still has high efficiency even after 5 cycles. Meanwhile, the desorption rate can reach more than 98%. The low wastage and high adsorption/desorption efficiency would enable CPF to be a good candidate adsorbent for removal/recovery of La3+ from industrial sewage.

FC-EODR: Immersive Humanoid Dual-Arm Dexterous Explosive Ordnance Disposal Robot.

In this study, we proposes a humanoid dual-arm explosive ordnance disposal (EOD) robot design. First, a seven-degree-of-freedom high-performance collaborative and flexible manipulator is developed, aiming at the transfer and dexterous operation of dangerous objects in EOD tasks. Furthermore, an immersive operated humanoid dual-arm dexterous explosive disposal robot (FC-EODR) is designed, which has a high passability to complex terrains such as low walls, slope roads, and stairs. It can remotely detect, manipulate, and remove explosives in dangerous environments through immersive velocity teleoperation. In addition, an autonomous tool-changing system is constructed, which enables the robot to flexibly switch between different tasks. The effectiveness of the FC-EODR is finally verified through a series of experiments, including the platform performance test, manipulator load test, teleoperated wire trimming, and screw-screwing experiments. This letter provides the technical foundation for robots to replace humans in EOD tasks and emergency situations.

Spiculisporic acids B­D, three new γ-butenolide derivatives from a sea urchin-derived fungus Aspergillus sp. HDf2.

Three new γ-butenolide derivatives 1–3, named spiculisporic acids B–D, were isolated from the culture of Aspergillus sp. HDf2, a marine-derived fungus that resides in the sea urchin, Anthocidaris crassispina. The structures of 1–3 were elucidated on the basis of spectroscopic methods, including MS and 2D NMR techniques. Their in vitro cytotoxic activities against two cell lines (SGC-7901, human gastric adenocarcinoma and SPC-A-1, human lung adenocarcinoma) and inhibitory activities against Staphylococcus aureus ATCC 51650 were investigated.

Preparation and Hydrophobicity of Bionic Structures Based on Composite Infiltration Model.

The wettability, surface energy, structure, and morphology of a material's surface will affect the interaction process between the material and the organism. Moreover, these factors are not independent of each other, but will affect each other, which together determine the biological surface of the material. Although two classic theories of surface wettability control have been established, including the Wenzel model and the Cassie-Baxter model, the mechanism of the microstructure parameters on the surface wettability has not been considered. This paper established a two-dimensional mathematical model of the composite wetting pattern based on microstructure parameters, revealed the mechanism of the microstructure parameters on the surface wettability, and then used ultra-precision cutting and molding composite preparation methods to quickly and efficiently prepare bionic structures, and the hydrophobic character of the microstructure was characterized by the contact angle meter, which provides theoretical support and preparation technology for the modification of the hydrophobic character of the material.