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In the past few years, due to the growth of the open-source community and the popularity of perceptual computing resources, the ROS (Robotic Operating System)Ecosystem has been widely shared and used in academia, industrial applications, and service fields. With the advantages of reusability of algorithms and system modularity, service robot applications are flourishing via the released ROS navigation framework. In the ROS navigation framework, the grid cost maps are majorly designed for path planning and obstacle avoidance with range sensors. However, the robot will often collide with dynamic obstacles since the velocity information is not considered within the navigation framework in time. This study aims to improve the feasibility of high-speed dynamic obstacle avoidance for an ROS-based mobile robot. In order to enable the robot to detect and estimate dynamic obstacles from a first-person perspective, vision tracking and a laser ranger with an Extend Kalman Filter (EKF) have been applied. In addition, an innovative velocity obstacle layer with truncated distance is implemented for the path planner to analyze the performances between the simulated and actual avoidance behavior. Finally, via the velocity obstacle layer, as the robot faces the high-speed obstacle, safe navigation can be achieved. [ABSTRACT FROM AUTHOR]

Glutamate transporters (GLTs) are integral to the glutamatergic system, modulating glutamate homeostasis to enhance resilience and resistance against environmental stress. There are six GLTs identified in the Pacific oyster ( Crassostrea gigas ), which were categorized into two subfamilies: excitatory amino acid transporters ( Cg EAATs) and vesicular glutamate transporters ( Cg VGLUTs). The Cg EAATs harbor a GltP domain, while Cg VGLUTs feature an MFS domain, both with conserved sequence and structural characteristics. The expression of Cg GLTs is elevated during the planktonic larval stage compared to the fertilized egg stage and is constitutively expressed in various tissues of adult oysters, suggesting its critical role in both larval development and the physiological processes of adult oysters. Transcriptomic analysis revealed diverse expression patterns of GLTs in oyster gills after 7 days of high-temperature stress, with CgEAAT3 showing a significant upregulation. A KEGG pathway enrichment analysis identified glutathione metabolism and ferroptosis as prominently enriched pathways. At 48 h after high-temperature stress, the expression levels of Glutathione Peroxidase 4 ( CgGPX4 ) and CgEAAT3 , along with elevated Fe content in the gills, significantly increased. Moreover, the RNAi-mediated the inhibition of CgEAAT3 expression under high-temperature stress, resulting in a significant reduction in CgGPX4 expression and a further increase in Fe accumulation in oyster gills. These results indicate that CgEAAT3 contributes to the regulation of ferroptosis and redox homeostasis by modulating CgGPX4 expression. This study provides new insights into the adaptive mechanisms of bivalves to environmental stress.

This paper presents a three-channel reconfigurable step attenuator based on radio frequency (RF) microelectromechanical system (MEMS) switches, in response to the current issues of high insertion loss and low attenuation accuracy of attenuators. The coplanar waveguide (CPW), cross-shaped power dividers, RF MEMS switches, and π-type attenuation resistor networks are designed as a basic unit of the attenuator. The attenuator implemented attenuation of 0~30 dB at 5 dB intervals in the frequency range of 1~25 GHz through two basic units. The results show that the insertion loss is less than 1.41 dB, the attenuation accuracy is better than 2.48 dB, and the geometric size is 2.4 mm × 4.0 mm × 0.7 mm. The attenuator can be applied to numerous fields such as radar, satellites, aerospace, electronic communication, and so on.

Background: Acute myeloid leukemia (AML) is the most common type of acute leukemia among adults with the recommend therapy of combination of cytarabine and idarubicin in the induction phase. The uncoordinated pharmacokinetics prevent adequate control of drug ratio following systemic administration. Therefore, the dual-loaded liposomes containing cytarabine and idarubicin for synergistic effects were proposed and investigated. Methods: The molar ratio of cytarabine and idarubicin for synergistic effects was investigated. The dual-loaded liposomes were prepared and characterized by particle size, zeta potential, encapsulation efficiency, cryo-Transmission electron microscopy (cryo-TEM), and in vitro stability. The in vitro cytotoxicity and cell uptake of liposomes were determined within CCRF-CEM cells. The PK experiments was carried out in male SD rats. The in vivo antitumor effect was carried out within CD-1 nude female mice. The antitumor mechanism of liposomes was investigated. Results: The synergistic molar ratios were found to be in the range of 20:1~40:1. The size distribution of the dual-loaded liposomes was approximately 100 nm with PDI ≤ 0.1, a zeta potential of approximately -30 mV, an entrapment efficiency of cytarabine and idarubicin of >95% with spherical structure and uniform distribution, and in vitro stability for 21 d. The drugs in the liposomes can be quickly uptaken by the leukemia cells. The PK experiments showed that the molar ratio of cytarabine to idarubicin in plasma was maintained at 30:1 within 4 h. The efficacy of liposomes was significantly enhanced. Conclusions: The dual-loaded liposomes containing cytarabine and idarubicin showed enhanced antitumor efficacy.

China's rail transit system is developing rapidly, but achieving seamless high-precision localization of trains throughout the entire route in closed environments such as tunnels and culverts still faces significant challenges. Traditional localization technologies cannot meet current demands, and the present paper proposes an autonomous localization method for trains based on pulse observation in a tunnel environment. First, the Letts criterion is used to eliminate abnormal gyro data, the CEEMDAN method is employed for signal decomposition, and the decomposed signals are classified using the continuous mean square error and norm method. Noise reduction is performed using forward linear filtering and dynamic threshold filtering, respectively, maximizing the retention of its effective signal components. A SINS/OD integrated localization model is established, and an observation equation is constructed based on velocity matching, resulting in an 18-dimensional complex state space model. Finally, the EM algorithm is used to address Non-Line-Of-Sight and multipath effect errors. The optimized model is then applied in the Kalman filter to better adapt to the system's observation conditions. By dynamically adjusting the noise covariance, the localization system can continue to maintain continuous high-precision position information output in a tunnel environment.

Insomnia is the second most prevalent mental illness worldwide. Periostracum cicadae (PC), as an animal traditional Chinese medicine with rich pharmacological effects, has been documented as a treatment for children's night cries, and later extended to treat insomnia. This study aimed to investigate the effects of PC extract and N-acetyldopamine compounds in ameliorating insomnia. The UPLC-ESI-QTOF-MS analysis determined that PC extract mainly contained N-acetyldopamine components. Previously, we also isolated some acetyldopamine polymers from PC extract, among which acetyldopamine dimer A (NADA) was present in high content. Molecular docking and molecular dynamic simulations demonstrated that NADA could form stable complexes with 5-HT1A, BDNF, and D2R proteins, respectively. The effects of PC extract and NADA on insomnia were evaluated in the PCPA-induced insomnia model. The results indicated that PC extract and NADA could effectively ameliorate hypothalamic pathology of insomnia rats, increase the levels of 5-HT, GABA, and BDNF, and decrease the levels of DA, DOPAC, and HVA. Meanwhile, the PC extract and NADA also could significantly affect the expression of 5-HT1A, BDNF, and DARPP-32 proteins. This study proved that PC extract and acetyldopamine dimer A could effectively improve PCPA-induced insomnia in rats. It is speculated that the main pharmacological substances of PC were acetyldopamine components.

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.

This paper introduces a broadband triple-pole triple-throw (3P3T) RF MEMS switch with a frequency range from DC to 380 GHz. The switch achieves precise signal control and efficient modulation through its six-port design. It achieves an insertion loss of -0.66 dB across its frequency range, with isolation and return loss metrics of -32 dB and -15 dB, respectively. With its low actuation voltage of 6.8 V and rapid response time of 2.28 μs, the switch exemplifies power-efficient and prompt switching performance. The compact design is ideal for integration into space-conscious systems. This switch is pivotal for 6G research and has potential applications in satellite communications, military radar systems, and next-generation radio applications that require multi-antenna access.

This paper presents the design of a 60 GHz millimeter-wave (MMW) slot array horn antenna based on the substrate-integrated waveguide (SIW) structure. The novelty of this device resides in the achievement of a broad impedance bandwidth and high gain performance by meticulously engineering the radiation band structure and slot array. The antenna demonstrates an impressive impedance bandwidth of 14.96 GHz (24.93%), accompanied by a remarkable maximum reflection coefficient of -39.47 dB. Furthermore, the antenna boasts a gain of 10.01 dBi, showcasing its outstanding performance as a high-frequency antenna with a wide bandwidth and high gain. To validate its capabilities, we fabricated and experimentally characterized a prototype of the antenna using a probe test structure. The measurement results closely align with the simulation results, affirming the suitability of the designed antenna for radar sensing applications in future global industrial scenarios.

Accelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations. With rapid advancements in computer vision and deep learning, research into tracking the motion of individual pixels with vision cameras has increased. The recently developed CoTracker, a transformer-based model, has demonstrated excellence in motion tracking, yet its performance in measuring structural vibrations has not been fully explored. This paper investigates the efficacy of the CoTracker model in extracting full-field structural vibrations using cameras. It is initially applied to capture the dense point movements in video sequences of a cantilever beam recorded using a high-speed camera. Subsequently, modal analysis using delay-embedding dynamic mode decomposition (DMD) is conducted to extract modal parameters including natural frequencies, damping ratios, and mode shapes. The results, benchmarked against those from a reference accelerometer and the Finite Element Method (FEM) result, demonstrate CoTracker's high potential for general applicability in structural vibration measurements., Competing Interests: The authors declare no conflicts of interest.

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves ACCrobust scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate.

Extensive industrial activities and anthropogenic agricultural practices have led to substantial ammonia release to the environment. Although croplands can act as ammonia sinks, reduced crop production under high concentrations of ammonium has been documented. Alpha-ketoglutarate (AKG) is a critical carbon source, displaying pleiotropic physiological functions. The objective of the present study is to disclose the potential of AKG to enhance ammonium assimilation in poplars. It showed that AKG application substantially boosted the height, biomass, and photosynthesis activity of poplars exposed to excessive ammonium. AKG also enhanced the activities of key enzymes involved in nitrogen assimilation: glutamine synthetase (GS) and glutamate synthase (GOGAT), elevating the content of amino acids, sucrose, and the tricarboxylic acid cycle (TCA) metabolites. Furthermore, AKG positively modulated key genes tied to glucose metabolism and ATP synthesis, while suppressing ATP-depleting genes. Correspondingly, both H + -ATPase activity and ATP content increased. These findings demonstrate that exogenously applying AKG improves poplar growth under a high level of ammonium treatment. AKG might function through sufficient carbon investment, which enhances the carbon-nitrogen balance and energy stability in poplars, promoting ammonium assimilation at high doses of ammonium. Our study provides novel insight into AKG's role in improving poplar growth in response to excess ammonia exposure.

The extracellular matrix (ECM) within the brain possesses a distinctive composition and functionality, influencing a spectrum of physiological and pathological states. Among its constituents, perineuronal nets (PNNs) are unique ECM structures that wrap around the cell body of many neurons and extend along their dendrites within the central nervous system (CNS). PNNs are pivotal regulators of plasticity in CNS, both during development and adulthood stages. Characterized by their condensed glycosaminoglycan-rich structures and heterogeneous molecular composition, PNNs not only offer neuroprotection but also participate in signal transduction, orchestrating neuronal activity and plasticity. Interfering with the PNNs in adult animals induces the reactivation of critical period plasticity, permitting modifications in neuronal connections and promoting the recovery of neuroplasticity following spinal cord damage. Interestingly, in the adult brain, PNN expression is dynamic, potentially modulating plasticity-associated states. Given their multifaceted roles, PNNs have emerged as regulators in the domains of learning, memory, addiction behaviors, and other neuropsychiatric disorders. In this review, we aimed to address how PNNs contribute to the memory processes in physiological and pathological conditions.

A Ag@AuNP-functionalized capillary-based surface-enhanced Raman scattering (SERS) sensing platform for the interference-free detection of glucose using SERS tags with a built-in nitrile signal has been proposed in this work. Capillary-based SERS capture substrates were prepared by connecting 4-mercaptophenylboronic acid (MBA) to the surface of the Ag@AuNP layer anchored on the inner wall of the capillaries. The SERS tags with a built-in interference-free signal could then be fixed onto the Ag@AuNP layer of the capillary-based capture substrate based on the distinguished feature of glucose, which can form a bidentate glucose-boronic complex. Thus, many "hot spots" were formed, which produced an improved SERS signal. The quantitative analysis of glucose levels was realized using the interference-free SERS intensity of nitrile at 2222 cm -1 , with a detection limit of about 0.059 mM. Additionally, the capillary-based disposable SERS sensing platform was successfully employed to detect glucose in artificial urine, and the new strategy has great potential to be further applied in the diagnosis and control of diabetes.

Warm deformation is a plastic-forming process that differs from traditional cold and hot forming techniques. At the macro level, it can effectively reduce the problem of high deformation resistance in cold deformation and improve the surface decarburization issues during the hot deformation process. Microscopically, it has significant advantages in controlling product structure, refining grain size, and enhancing product mechanical properties. The Gleeble-1500D thermal-mechanical physical simulation system was used to conduct isothermal compression tests on GCr15 bearing steel. The tests were conducted at temperatures of 600-1050 °C and strain rates of 0.01-5 s -1 . Based on the experimental data, the critical strain model and dynamic recrystallization model for the warm-hot forming of GCr15 bearing steel were established in this paper. The model accuracy is evaluated using statistical indicators such as the correlation coefficient (R). The dynamic recrystallization model exhibits high predictive accuracy, as indicated by an R-value of 0.986. The established dynamic recrystallization model for GCr15 bearing steel was integrated into the Forge ® 3.2 numerical simulation software through secondary program development to simulate the compression process of GCr15 warm-hot forming. The dynamic recrystallization fraction was analyzed in various deformation regions. The grain size of the severe deformation zone, small deformation zone, and difficult deformation zone was compared based on simulated compression specimens under the conditions of 1050 °C and 0.1 s -1 with the corresponding grain size obtained with measurement based on metallographic photos; the relative error between the two is 5.75%. This verifies the accuracy of the established dynamic recrystallization and critical strain models for warm-hot deformation of GCr15 bearing steel. These models provide a theoretical basis for the finite element method analysis and microstructure control of the warm-hot forming process in bearing races.

(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.

Background: Breast cancer (BRCA) is one of the most common cancers in women worldwide and a leading cause of death from malignancy. This study was designed to identify a novel biomarker for prognosticating the survival of BRCA patients., Methods: The prognostic potential of eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) was assessed using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) as training cohort and validation set, respectively. The functional enrichment analysis of differentially expressed genes (DEGs) was performed. The relationship between EIF4G1 and tumor microenvironment (TME) was analyzed. Immunotherapy responses were explored by the immunophenoscores (IPS) and tumor immune dysfunction and exclusion (TIDE) score. The Connectivity Map (CMap) was used to discover potentially effective therapeutic molecules against BRCA. Immunohistochemistry (IHC) was applied to compare the protein levels of EIF4G1 in normal and cancer tissues and to verify the prognostic value of EIF4G1., Results: BRCA patients with increased expression of EIF4G1 had a shorter overall survival (OS) in all cohorts and results from IHC. EIF4G1-related genes were mainly involved in DNA replication, BRCA metastasis, and the MAPK signaling pathway. Infiltration levels of CD4 + -activated memory T cells, macrophages M0, macrophages M1, and neutrophils were higher in the EIF4G1 high-expression group than those in the EIF4G1 low-expression group. EIF4G1 was positively correlated with T cell exhaustion. Lower IPS was revealed in high EIF4G1 expression patients. Five potential groups of drugs against BRCA were identified., Conclusion: EIF4G1 might regulate the TME and affect BRCA metastasis, and it is a potential prognostic biomarker and therapeutic target for BRCA.

L-Asparagine (Asn) has been regarded as one of the most economical molecules for nitrogen (N) storage and transport in plants due to its relatively high N-to-carbon (C) ratio (2:4) and stability. Although its internal function has been addressed, the biological role of exogenous Asn in plants remains elusive. In this study, different concentrations (0.5, 1, 2, or 5 mM) of Asn were added to the N-deficient hydroponic solution for poplar 'Nanlin895'. Morphometric analyses showed that poplar height, biomass, and photosynthesis activities were significantly promoted by Asn treatment compared with the N-free control. Moreover, the amino acid content, total N and C content, and nitrate and ammonia content were dramatically altered by Asn treatment. Moreover, exogenous Asn elicited root growth inhibition, accompanied by complex changes in the transcriptional pattern of genes and activities of enzymes associated with N and C metabolism. Combined with the plant phenotype and the physiological and biochemical indexes, our data suggest that poplar is competent to take up and utilize exogenous Asn dose-dependently. It provides valuable information and insight on how different forms of N and concentrations of Asn influence poplar root and shoot growth and function, and roles of Asn engaged in protein homeostasis regulation.

Glycerol is seen in biological systems as an intermediate in lipid metabolism. In recent years, glycerol has been reported to act as a chemical chaperone to correct the conformation of proteins. Here, we investigate the role of glycerol in galectin-7 (Gal-7). The thermal shift and CD assays showed that the thermal stability of Gal-7 increased with glycerol concentration but with little secondary structure changes induced by glycerol. In addition, glycerol can inhibit Gal-7-mediated erythrocyte agglutination. We also solved the crystal structures of human Gal-7 in complex with glycerol in two different conditions. Glycerol binds at the carbohydrate-recognition binding sites of Gal-7, which indicates glycerol as a small ligand for Gal-7. Surprisingly, glycerol can bind a new pocket near the N-terminus of Gal-7, which can greatly reduce the flexibility and improve the stability of this region. Moreover, overexpression of Gal-7 decreased the intracellular triglyceride levels and increased mRNA expression of aquaporin-3 (AQP-3) when HeLa cells were incubated with glycerol. These findings indicate that Gal-7 might regulate glycerol metabolism. Overall, our results on human Gal-7 raise the perspective to systematically explore this so far unrecognized phenomenon for Gal-7 in glycerol metabolism.

At present, the detection accuracy of bolt-loosening diagnoses is still not high. In order to improve the detection accuracy, this paper proposes a fault diagnosis model based on the TSCNN model, which can simultaneously extract fault features from vibration signals and time-frequency images and can precisely detect the bolt-loosening states. In this paper, the LeNet-5 network is improved by adjusting the size and number of the convolution kernels, introducing the dropout operation, and building a two-dimensional convolutional neural network (2DCNN) model. Combining the advantages of a one-dimensional convolutional neural network (1DCNN) with wide first-layer kernels to suppress high-frequency noise, a two-stream convolutional neural network (TSCNN) is proposed based on 1D and 2D input data. The proposed model uses raw vibration signals and time-frequency images as input and automatically extracts sensitive features and representative information. Finally, the effectiveness and superiority of the proposed approach are verified by practical experiments that are carried out on a machine tool guideway. The experimental results show that the proposed approach can effectively achieve end-to-end bolt-loosening fault diagnoses, with an average recognition accuracy of 99.58%. In addition, the method can easily achieve over 93% accuracy when the SNR is over 0 dB without any denoising preprocessing. The results show that the proposed approach not only achieves high classification accuracy but also has good noise immunity.

The improved performance of recycled aggregate has an important impact on its use in engineering. In this study, to improve the weak surface properties, recycled aggregates were treated by nano-silica slurry and applied to concrete beam specimens. Under the action of cracks caused by continuous load and drying-wetting cycles with chloride ingress, the effects of different recycled aggregate additions, nano-silica contents and crack widths on the self-healing performance of cracks and the resistance to chloride ingress of the recycled concrete beams were investigated. It was found that the self-healing rate of cracks increased first and then decreased with increased nano-silica content, reaching a maximum when the content reached 0.4%. Greater amounts of additive in the recycled aggregate increased the concentration of free chloride ions in cracks. However, this concentration was found to be weakened in nano-reinforced aggregate. From a comprehensive perspective, the relative chloride ion concentration can be effectively reduced by controlling the crack width to be smaller than 0.12 mm and using improved recycled aggregates treated with 0.2% nano-silica material. This study provides a reference for the application of recycled aggregate concrete under severe environmental and load conditions.

Devising cooperative or competitive situations is an important teaching strategy in educational practices. Nevertheless, there is still controversy regarding which situation is better for learning. This study was conducted to explore the effects of cooperative and competitive situations on statistical learning, through the alternating serial reaction time (ASRT) task. Individual cooperative and competitive situations were devised in this study, in which individual situation served as the control condition. Ninety recruited participants were randomly assigned to a cooperative, competitive, or individual group to perform the ASRT task. For general learning, cooperative and competitive situations could indeed make learners respond faster, and there was no significant difference in the RT between the cooperative and competitive groups. Moreover, statistical learning was observed in all three groups. An additional analysis of the early stage of the experiment showed that the learning effect of the competitive group was greater than those of the cooperative and individual groups, in terms of statistical learning. However, the final learning effect was not significantly different among the three groups. Overall, the cooperative and competitive situations had a positive impact on learning and enabled the students to acquire approximately the same learning effect in a shorter time period, compared with the individual situation. Specifically, the competitive situation accelerated the statistical learning process but not the general learning process.

Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework's effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.

This study constructed a two-dimensional alkaline water electrolyzer model based on the two-phase flow Euler-Euler model. In the model, the micro-nano surface electrodes with different structure types and graphic parameters (distance, height, and width) were used and compared with the vertical flat electrode to evaluate their influence on electrolysis performance. The simulation results show that the performance of the micro-nano surface electrode is much better than that of the vertical flat electrode. The total length of micro-nano structural units relates to the contact area between the electrode and the electrolyte and affects the cell voltage, overpotential, and void fraction. When rectangular structural units with a distance, height, and width of 0.5 µm, 0.5 µm, and 1 µm are used, the total length of the corresponding micro-nano surface electrode is three times that of the vertical flat electrode, and the cathode overpotential decreases by 65.31% and the void fraction increases by 54.53% when it replaces the vertical flat electrode.

Microstructure is an important factor that affects the mechanical properties and service life of forgings. Through the full study of the formability of the material, the internal microstructure of the material can be effectively controlled. In order to accurately describe the formability of materials during thermal processing, 3D hot processing maps containing strains were established in this paper, and the 3D hot processing maps were coupled with the finite element method for simulation calculation. The Cr5 alloy steel was subjected to unidirectional thermal compression at a strain rate of 0.005-5 s -1 and temperature range of 900-1200 °C on a Gleeble-1500D thermal simulation machine, in order to obtain the date of true stress and strain. Based on the dynamic material model (DMM), the 3D processing maps of Cr5 alloy steel was established, and the 3D processing maps were associated with the analysis of microstructure evolution during hot deformation. The results show that the optimum thermal deformation conditions are as follows: temperature of 1000-1125 °C, strain rate of 0.01-0.2 s -1 , and peak power dissipation of 0.41. The 3D processing maps were coupled with the finite element software FORGE ® to simulate the hot working process, and the distribution and change of power dissipation and flow instability domain on the metal deformation under different thermal deformation conditions were obtained. The comparison between the simulation results and metallographic images of typical regions of metal deformation shows that they are in good agreement. This method can effectively predict and analyze the formability of materials during hot processing and provide guidance for practical industrial production.

Duchenne muscular dystrophy (DMD) is an X-linked recessive disease characterized by skeletal muscle instability, progressive muscle wasting, and fibrosis. A major driver of DMD pathology stems from aberrant upregulation of transforming growth factor β (TGFβ) signaling. In this report, we investigated the major transducers of TGFβ signaling, i.e., receptor Smads (R-Smads), in DMD patient skeletal muscle and observed a 48-fold increase in Smad8 mRNA. Smad1 , Smad2 , Smad3 , and Smad5 mRNA were only minimally increased. A similar pattern was observed in the muscle from the mdx 5cv mouse. Western blot analysis showed upregulation of phosphorylated Smad1, Smad5, and Smad8 compared to total Smad indicating activation of this pathway. In parallel, we observed a profound diminishment of muscle-enriched microRNAs (myomiRs): miR-1, miR-133a, and miR-133b. The pattern of Smad8 induction and myomiR suppression was recapitulated in C2C12 muscle cells after stimulation with bone morphogenetic protein 4 (BMP4), a signaling factor that we found upregulated in DMD muscle. Silencing Smad8 in C2C12 myoblasts derepressed myomiRs and promoted myoblast differentiation; there was also a concomitant upregulation of myogenic regulatory factors (myogenin and myocyte enhancer factor 2D) and suppression of a pro-inflammatory cytokine (interleukin-6). Our data suggest that Smad8 is a negative regulator of miR-1, miR-133a, and miR-133b in muscle cells and that the BMP4-Smad8 axis is a driver of dystrophic pathology in DMD.

Recently, MnO 2 -coated polymeric filters have shown promising performance in room-temperature formaldehyde abatement. However, a commonly known concern of MnO 2 /polymer composites is either MnO 2 crystal encapsulation or weak adhesion. This work reports a low-cost high-throughput and green strategy to produce binder-free MnO 2 -nonwoven composite air filters. The production approach is energy saving and environmentally friendly, which combines MnO 2 crystal coating on bicomponent polyolefin spunbond nonwovens and subsequent heat immobilizing of crystals, followed by the removal of weakly bonded MnO 2 . The binder-free MnO 2 -nonwoven composites show firm catalyst-fiber adhesion, a gradient porous structure, and excellent formaldehyde removal capability (94.5% ± 0.4%) at room temperature, and the reaction rate constant is 0.040 min -1 . In contrast to the MnO 2 -nonwoven composites containing organic binders, the HCHO removal of binder-free filters increased by over 4%. This study proposes an alternative solution in producing catalyst/fabric composite filters for formaldehyde removal.

Japanese encephalitis virus (JEV) is an important zoonotic pathogen, which causes central nervous system symptoms in humans and reproductive disorders in swine. It has led to severe impacts on human health and the swine industry; however, there is no medicine available for treating yet. Therefore, vaccination is the best preventive measure for this disease. In the study, a modified mRNA vaccine expressing the prM and E proteins of the JEV P3 strain was manufactured, and a mouse model was used to assess its efficacy. The mRNA encoding prM and E proteins showed a high level of protein expression in vitro and were encapsulated into a lipid nanoparticle (LNP). Effective neutralizing antibodies and CD8+ T-lymphocytes-mediated immune responses were observed in vaccinated mice. Furthermore, the modified mRNA can protect mice from a lethal challenge with JEV and reduce neuroinflammation caused by JEV. This study provides a new option for the JE vaccine and lays a foundation for the subsequent development of a more efficient and safer JEV mRNA vaccine.

An attack of congestive heart failure (CHF) can cause symptoms such as difficulty breathing, dizziness, or fatigue, which can be life-threatening in severe cases. An electrocardiogram (ECG) is a simple and economical method for diagnosing CHF. Due to the inherent complexity of ECGs and the subtle differences in the ECG waveform, misdiagnosis happens often. At present, the research on automatic CHF detection methods based on machine learning has become a research hotspot. However, the existing research focuses on an intra-patient experimental scheme and lacks the performance evaluation of working under noise, which cannot meet the application requirements. To solve the above issues, we propose a novel method to identify CHF using the ECG-Convolution-Vision Transformer Network (ECVT-Net). The algorithm combines the characteristics of a Convolutional Neural Network (CNN) and a Vision Transformer, which can automatically extract high-dimensional abstract features of ECGs with simple pre-processing. In this study, the model reached an accuracy of 98.88% for the inter-patient scheme. Furthermore, we added different degrees of noise to the original ECGs to verify the model's noise robustness. The model's performance in the above experiments proved that it could effectively identify CHF ECGs and can work under certain noise.

Urban noise causes a variety of health problems, and its prevention and control have thus become an important research topic in urban governance. Although existing literature is fairly comprehensive in revealing the physical noise patterns, it lacks the concern of people's perceived seriousness, especially at the macroscopic, i.e., citywide scale. In this paper, we borrow from the "exposure-perception-behavior" theory in environmental psychology, and propose an analytical framework for diagnosing the urban noise problem that integrates the Infrastructural and Social Sensing perspectives. Utilizing noise monitoring data that fills the spatiotemporal granularity gaps of official noise monitoring, as well as the "12345" urban complaint hotline records which serve as a proxy for residents' perceived noise levels, we empirically examine the mechanisms for physical magnitude and perceived seriousness of urban noise, respectively, by taking the Jiangbei District of Ningbo City, China as an example. Results show that the existence of perceptual bias and behavioral preference effects did shape people's perceived noise problem map that is vastly different from that of the physical noise magnitude, in which the semantics of urban places, temporal rhythms of life, and population demographics significantly influenced people's tolerance of noise. We conclude the paper with suggestions on updating the existing National Standard for urban noise regulation to reflect the perceptual aspect, and also methodological discussions on possible ways to recognize and utilize the perceptual bias in social-sensing big-data to better accommodate urban governance.

Titanium alloy is widely applied in aerospace, medical, shipping and other fields due to its high specific strength and low density. The purpose of this study was to analyze the formability of Ti6Al4V alloys at elevated temperatures. An accurate constitutive model is the basic condition for accurately simulating the plastic forming of materials, and it is an important basis for optimizing the parameters of the hot forging forming process. In this study, the optimization algorithm was used to accurately identify the high-temperature constitutive model parameters of Ti6Al4V titanium alloy, and the hot working diagram was established to optimize the hot forming process parameters. The optimal forming conditions of Ti6Al4V titanium alloy are given. Ti6Al4V alloy was subjected to high-temperature compression tests at 800-1000 °C and at strain rates of 0.01-5 s -1 on a Gleeble-1500D thermal/mechanical simulation machine. Each parameter of the Hansel-Spittel constitutive model was taken as an independent variable, and the accumulated error between the stress calculated by the constitutive model and the stress obtained by experimentation was used as an objective function. Based on response surface methodology, an inverse optimization method for identifying the parameters of the high-temperature constitutive model of Ti6Al4V alloy is proposed in this paper. An orthogonal test design was adopted to obtain sample point data, and a third-order response surface approximate model was established. The genetic algorithm (GA) was applied to reversely optimize the parameters of the constitutive model. To verify the accuracy of the optimized constitutive model, the average absolute relative error (AARE) and correlation coefficient (R) were used to evaluate the reliability of optimized constitutive model. The R value of the model was 0.999, and the AARE value was 0.048, respectively, indicating that the established high-temperature constitutive model for Ti6Al4V alloy has good calculation accuracy. The flow stress behavior of the material could be accurately delineated. Meanwhile, in order to study the formability of Ti6Al4V alloy, the hot processing map of the alloy, based on a dynamic material model, was established in this paper. The optimum hot working domains of the Ti6Al4V alloy were determined within 840-920 °C/0.01-0.049 s -1 and 940-980 °C/0.11-1.65 s -1 ; the hot processing map was verified in combination with the microstructure, and the fine and equiaxed grains and a large amount of β phase could be found at 850 °C/0.01 s -1 .

Three-dimensional graphene-based aerogels (3D GAs), combining the intrinsic properties of graphene and 3D porous structure, have attracted increasing research interest in varied fields with potential application. Some related reviews focusing on applications in photoredox catalysis, biomedicine, energy storage, supercapacitor or other single aspect have provided valuable insights into the current status of Gas. However, systematic reviews concentrating on the diverse applications of 3D GAs are still scarce. Herein, we intend to afford a comprehensive summary to the recent progress in the preparation method (template-free and template-directed method) summarized in Preparation Strategies and the application fields (absorbent, anode material, mechanical device, fire-warning material and catalyst) illustrated in Application of 3D GAs with varied morphologies, structures, and properties. Meanwhile, some unsettled issues, existing challenges, and potential opportunities have also been proposed in Future Perspectives to spur further research interest into synthesizing finer 3D GAs and exploring wider and closer practical applications.

The African swine fever virus (ASFV) is a dsDNA virus that can cause serious, highly infectious, and fatal diseases in wild boars and domestic pigs. The ASFV has brought enormous economic loss to many countries, and no effective vaccine or treatment for the ASFV is currently available. Therefore, the on-site rapid and accurate detection of the ASFV is key to the timely implementation of control. The RNA-guided, RNA-targeting CRISPR effector CRISPR-associated 13 (Cas13a; previously known as C2c2) exhibits a "collateral effect" of promiscuous RNase activity upon the target recognition. The collateral cleavage activity of LwCas13a is activated to degrade the non-targeted RNA, when the crRNA of LwCas13a binds to the target RNA. In this study, we developed a rapid and sensitive ASFV detection method based on the collateral cleavage activity of LwCas13a, which combines recombinase-aided amplification (RAA) and a lateral flow strip (named CRISPR/Cas13a-LFD). The method was an isothermal detection at 37 °C, and the detection can be used for visual readout. The detection limit of the CRISPR/Cas13a-LFD was 10 1 copies/µL of p72 gene per reaction, and the detection process can be completed within an hour. The assay showed no cross-reactivity to eight other swine viruses, including classical swine fever virus (CSFV), and has a 100% coincidence rate with real-time PCR detection of the ASFV in 83 clinical samples. Overall, this method is sensitive, specific, and practicable onsite for the ASFV detection, showing a great application potential for monitoring the ASFV in the field.

Triple-negative breast cancers (TNBCs) are heterogeneous and metastatic, and targeted therapy is highly needed for TNBC treatment. Recent studies showed that extracellular vesicles (EV) have great potential to deliver therapies to treat cancers. This study aimed to develop and evaluate a natural compound, verrucarin A (Ver-A), delivered by targeted EV, to treat TNBC. First, the surface expression of epidermal growth factor receptor (EGFR) and CD47 were confirmed with immunohistochemistry (IHC) staining of patient tissue microarray, flow cytometry and Western blotting. EVs were isolated from HEK 293F culture and surface tagged with anti-EGFR/CD47 mAbs to construct mAb-EV. The flow cytometry, confocal imaging and live-animal In Vivo Imaging System (IVIS) demonstrated that mAb-EV could effectively target TNBC and deliver the drug. The drug Ver-A, with dosage-dependent high cytotoxicity to TNBC cells, was packed in mAb-EV. The anti-TNBC efficacy study showed that Ver-A blocked tumor growth in both 4T1 xenografted immunocompetent mouse models and TNBC patient-derived xenograft models with minimal side effects. This study demonstrated that the targeted mAb-EV-Ver-A had great potential to treat TNBCs.

Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.

To study the law that governs the complex movements of the mechanism in the process of automatic weapon operation, the velocity tracking test technology of photon Doppler velocimetry is introduced to accurately measure velocity, displacement and acceleration, on the condition that there are long displacement and rapid velocity change. In the traditional way, out of interference signal time-frequency (TF) transformation draws TF distribution, and then by modulus maxima frequency extraction, comes to the law of velocity change. Due to the influence resulting from the change of fundamental signal as well as that of light intensity signal in the test, based on the TF distribution obtained by TF transformation, the traditional modulus maxima frequency extraction can extract frequency signals, but they show abnormal sudden changes at some moments, making the velocity discontinuous, unsmooth and unreal, which brings obvious errors to the subsequent calculation of acceleration and accurate displacement. Addressing the above-mentioned problems, this paper proposes a ridge extracting correction algorithm based on modulus maxima frequency extraction; this method, based on a large number of experiments where rodless cylinders are used to simulate the motion of a gun automatic mechanism, conducts a detailed calculation and analysis of the experimental results. A comparison of the two algorithms' processing results, in terms of the speed, displacement and acceleration, suggests that the ridge extracting correction algorithm successfully corrects the frequency selection error, which draws a more continuous and, therefore, effective curve of the velocity change, and by so doing, the error of the displacement test (within 1.36 m displacement) is reduced from more than 3.6% to less than 0.58%, and the uncertainty dropped 97.07%. All these show that the accurate measurement of velocity, displacement and acceleration, with sudden and rapid velocity changes considered, is realized successfully.

Neural stem cells (NSCs) exist in the central nervous system of adult animals and capable of self-replication. NSCs have two basic functions, namely the proliferation ability and the potential for multi-directional differentiation. In this study, based on the bioassay-guided fractionation, we aim to screen active components in Cuscuta chinensis to promote the proliferation of NSCs. CCK-8 assays were used as an active detection method to track the active components. On the basis of isolating active fraction and monomer compounds, the structures of these were identified by LC-MS and ( 1 H, 13 C) NMR. Moreover, active components were verified by pharmacodynamics and network pharmacology. The system solvent extraction method combined with the traditional isolation method were used to ensure that the fraction TSZE-EA-G6 of Cuscuta chinensis exhibited the highest activity. Seven chemical components were identified from the TSZE-EA-G6 fraction by UPLC-QE-Orbitrap-MS technology, which were 4- O -p-coumarinic acid, chlorogenic acid, 5- O -p-coumarinic acid, hyperoside, astragalin, isochlorogenic acid C, and quercetin-3- O -galactose-7- O -glucoside. Using different chromatographic techniques, five compounds were isolated in TSZE-EA-G6 and identified as kaempferol, kaempferol-3- O -glucoside (astragalin), quercetin-3- O -galactoside (hyperoside), chlorogenic acid, and sucrose. The activity study of these five compounds showed that the proliferation rate of kaempferol had the highest effects; at a certain concentration (25 μg/mL, 3.12 μg/mL), the proliferation rate could reach 87.44% and 59.59%, respectively. Furthermore, research results using network pharmacology techniques verified that kaempferol had an activity of promoting NSCs proliferation and the activity of flavonoid aglycones might be greater than that of flavonoid glycosides. In conclusion, this research shows that kaempferol is the active component in Cuscuta chinensis to promote the proliferation of NSCs.

Histone methyltransferase DOT1L catalyzes mono-, di- and trimethylation of histone 3 at lysine residue 79 (H3K79) and hypermethylation of H3K79 has been linked to the development of acute leukemias characterized by the MLL (mixed-lineage leukemia) rearrangements (MLLr cells). The inhibition of H3K79 methylation inhibits MLLr cells proliferation, and an inhibitor specific for DOT1L, pinometostat, was in clinical trials (Phase Ib/II). However, the compound showed poor pharmacological properties. Thus, there is a need to find new potent inhibitors of DOT1L for the treatment of rearranged leukemias. Here we present the design, synthesis, and biological evaluation of a small molecule that inhibits in the nM level the enzymatic activity of hDOT1L, H3K79 methylation in MLLr cells with comparable potency to pinometostat, associated with improved metabolic stability and a characteristic cytostatic effect.

Triple-negative breast cancers (TNBCs) are highly aggressive and recurrent. Standard cytotoxic chemotherapies are currently the main treatment options, but their clinical efficacies are limited and patients usually suffer from severe side effects. The goal of this study was to develop and evaluate targeted liposomes-delivered combined chemotherapies to treat TNBCs. Specifically, the IC 50 values of the microtubule polymerization inhibitor mertansine (DM1), mitotic spindle assembly defecting taxane (paclitaxel, PTX), DNA synthesis inhibitor gemcitabine (GC), and DNA damage inducer doxorubicin (AC) were tested in both TNBC MDA-MB-231 and MDA-MB-468 cells. Then we constructed the anti-epidermal growth factor receptor (EGFR) monoclonal antibody (mAb) tagged liposomes and confirmed its TNBC cell surface binding using flow cytometry, internalization with confocal laser scanning microscopy, and TNBC xenograft targeting in NSG female mice using In Vivo Imaging System. The safe dosage of anti-EGFR liposomal chemotherapies, i.e., <20% body weight change, was identified. Finally, the in vivo anti-tumor efficacy studies in TNBC cell line-derived xenograft and patient-derived xenograft models revealed that the targeted delivery of chemotherapies (mertansine and gemcitabine) can effectively inhibit tumor growth. This study demonstrated that the targeted liposomes enable the new formulations of combined therapies that improve anti-TNBC efficacy.

Previous studies have been limited by not directly comparing the quality of public and private CHCs using a standardized patient method (SP). This study aims to evaluate and compare the quality of the primary care provided by public and private CHCs using a standardized patient method in urban China. We recruited 12 standardized patients from the local community presenting fixed cases (unstable angina and asthma), including 492 interactions between physicians and standardized patients across 63 CHCs in Xi'an, China. We measured the quality of primary care on seven criteria: (1) adherence to checklists, (2) correct diagnosis, (3) correct treatment, (4) number of unnecessary exams and drugs, (5) diagnosis time, (6) expense of visit, (7) patient-centered communication. Significant quality differences were observed between public CHCs and private CHCs. Private CHC physicians performed 4.73 percentage points lower of recommended questions and exams in the checklist. Compared with private CHCs, public CHC providers were more likely to give a higher proportion of correct diagnosis and correct treatment. Private CHCs provided 1.42 fewer items of unnecessary exams and provided 0.32 more items of unnecessary drugs. Private CHC physicians received a 9.31 lower score in patient-centered communication. There is significant quality inequality in different primary care models. Public CHC physicians might provide a higher quality of service. Creating a comprehensive, flexible, and integrated health care system should be considered an effective approach towards optimizing the management of CHC models.

Meningiomas are primary tumors of the central nervous system with high recurrence. It has been reported that somatostatin receptor 2 (SSTR2) is highly expressed in most meningiomas, but there is no effective targeted therapy approved to control meningiomas. This study aimed to develop and evaluate an anti-SSTR2 antibody-drug conjugate (ADC) to target and treat meningiomas. The meningioma targeting, circulation stability, toxicity, and anti-tumor efficacy of SSTR2 ADC were evaluated using cell lines and/or an intracranial xenograft mouse model. The flow cytometry analysis showed that the anti-SSTR2 mAb had a high binding rate of >98% to meningioma CH157-MN cells but a low binding rate of <5% to the normal arachnoidal AC07 cells. The In Vivo Imaging System (IVIS) imaging demonstrated that the Cy5.5-labeled ADC targeted and accumulated in meningioma xenograft but not in normal organs. The pharmacokinetics study and histological analysis confirmed the stability and minimal toxicity. In vitro anti-cancer cytotoxicity indicated a high potency of ADC with an IC 50 value of <10 nM. In vivo anti-tumor efficacy showed that the anti-SSTR2 ADC with doses of 8 and 16 mg/kg body weight effectively inhibited tumor growth. This study demonstrated that the anti-SSTR2 ADC can target meningioma and reduce the tumor growth.

Neuroendocrine (NE) cancers arise from cells within the neuroendocrine system. Chemotherapies and endoradiotherapy have been developed, but their clinical efficacy is limited. The objective of this study was to develop a dual-targeted extracellular vesicles (EV)-delivered combined therapies to treat NE cancer. Specifically, we produced EV in stirred-tank bioreactors and surface tagged both anti-somatostatin receptor 2 (SSTR 2) monoclonal antibody (mAb) and anti-C-X-C motif chemokine receptor 4 (CXCR4) mAb to generate mAbs-EV. Both live-cell confocal microscopy imaging and In Vivo Imaging System (IVIS) imaging confirmed that mAbs-EV specifically targeted and accumulated in NE cancer cells and NE tumor xenografts. Then the highly potent natural cytotoxic marine compound verrucarin A (Ver-A) with IC 50 of 2.2-2.8 nM and microtubule polymerization inhibitor mertansine (DM1) with IC 50 of 3.1-4.2 nM were packed into mAbs-EV. The in vivo maximum tolerated dose study performed in non-tumor-bearing mice indicated minimal systemic toxicity of mAbs-EV-Ver-A/DM1. Finally, the in vivo anticancer efficacy study demonstrated that the SSTR2/CXCR4 dual-targeted EV-Ver-A/DM1 is more effective to inhibit NE tumor growth than the single targeting and single drug. The results from this study could expand the application of EV to targeting deliver the combined potent chemotherapies for cancer treatment.

For a long time, algal chemistry from terrestrial to marine or freshwater bodies, especially chlorophytes, has fascinated numerous investigators to develop new drugs in the nutraceutical and pharmaceutical industries. As such, chlorophytes comprise a diverse structural class of secondary metabolites, having functional groups that are specific to a particular source. All bioactive compounds of chlorophyte are of great interest due to their supplemental/nutritional/pharmacological activities. In this review, a detailed description of the chemical diversity of compounds encompassing alkaloids, terpenes, steroids, fatty acids and glycerides, their subclasses and their structures are discussed. These promising natural products have efficiency in developing new drugs necessary in the treatment of various deadly pathologies (cancer, HIV, SARS-CoV-2, several inflammations, etc.). Marine chlorophyte, therefore, is portrayed as a pivotal treasure in the case of drugs having marine provenience. It is a domain of research expected to probe novel pharmaceutically or nutraceutically important secondary metabolites resulting from marine Chlorophyta . In this regard, our review aims to compile the isolated secondary metabolites having diverse chemical structures from chlorophytes (like Caulerpa ssp., Ulva ssp., Tydemania ssp., Penicillus ssp., Codium ssp., Capsosiphon ssp., Avrainvillea ssp.), their biological properties, applications and possible mode of action.

Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbeats appear much less regularly than normal heartbeats. Furthermore, most existing methods rely on data preprocessing steps, such as noise removal and R-peak location. In this study, we present a robust classification system using a multilevel discrete wavelet transform densely network (MDD-Net) for the accurate detection of normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF). First, the raw ECG signals from different databases are divided into same-size segments using an original adaptive sample frequency segmentation algorithm (ASFS). Then, the fusion features are extracted from the MDD-Net to achieve great classification performance. We evaluated the proposed method considering the intra-patient and inter-patient paradigms. The average accuracy, positive predictive value, sensitivity and specificity were 99.74%, 99.09%, 98.67% and 99.83%, respectively, under the intra-patient paradigm, and 96.92%, 92.17%, 89.18% and 97.77%, respectively, under the inter-patient paradigm. Moreover, the experimental results demonstrate that our model is robust to noise and class imbalance issues., Competing Interests: The authors declare no conflict of interest.

Sodium channels play a critical role in the generation and propagation of action potentials in excitable tissues, such as nerves, cardiac muscle, and skeletal muscle, and are the primary targets of toxins found in animal venoms. Here, two novel peptide toxins (Cl6a and Cl6b) were isolated from the venom of the spider Cyriopagopus longipes and characterized. Cl6a and Cl6b were shown to be inhibitors of tetrodotoxin-sensitive (TTX-S), but not TTX-resistant, sodium channels. Among the TTX-S channels investigated, Cl6a and Cl6b showed the highest degree of inhibition against NaV1.7 (half-maximal inhibitory concentration (IC 50 ) of 11.0 ± 2.5 nM and 18.8 ± 2.4 nM, respectively) in an irreversible manner that does not alter channel activation, inactivation, or repriming kinetics. Moreover, analysis of NaV1.7/NaV1.8 chimeric channels revealed that Cl6b is a site 4 neurotoxin. Site-directed mutagenesis analysis indicated that D816, V817, and E818 observably affected the efficacy of the Cl6b-NaV1.7 interaction, suggesting that these residues might directly affect the interaction of NaV1.7 with Cl6b. Taken together, these two novel peptide toxins act as potent and sustained NaV1.7 blockers and may have potential in the pharmacological study of sodium channels.

The comprehensive utilization technology of combined cooling, heating and power (CCHP) systems is the leading edge of renewable and sustainable energy research. In this paper, we propose a novel CCHP system based on a hybrid trigenerative compressed air energy storage system (HT-CAES), which can meet various forms of energy demand. A comprehensive thermodynamic model of the HT-CAES has been carried out, and a thermodynamic performance analysis with energy and exergy methods has been done. Furthermore, a sensitivity analysis and assessment capacity for CHP is investigated by the critical parameters effected on the performance of the HT-CAES. The results indicate that round-trip efficiency, electricity storage efficiency, and exergy efficiency can reach 73%, 53.6%, and 50.6%, respectively. Therefore, the system proposed in this paper has high efficiency and flexibility to jointly supply multiple energy to meet demands, so it has broad prospects in regions with abundant solar energy resource.

Background : China has the largest number of aging people in need of long-term care, among whom 70% have chronic diseases. For policy planners, it is necessary to understand the different levels of needs of long-term care and provide long-term care insurance to ensure the long-term care needs of all people can be met. Methods : This study combines the 2013 wave of CHARLS survey and the Life Course Survey of 2014. The combination allows us to factor in both childhood and adulthood data to provide life-course analysis. We identified 7,734 older adults with chronic diseases for analysis. The need for long-term care is defined by the presence of functional limitations based on the performance of basic activities of daily living (ADLs) and of instrumental activities of daily living (IADLs). Two dummy variables, ADLs disability and IADLs disability, and two count variables, ADLs score and IADLs score, were defined to measure incidence and severity of long-term care need, respectively. The concentration index was used to capture the inequality in long-term care need, and a decomposition method based on Probit Regression and Negative Binomial Regression was exploited to identify the contribution of each determination. Results : At least a little difficulty was reported in ADLs and IADLs in 20.44% and 19.25% of respondents, respectively. The concentration index of ADLs disability, ADLs score, IADLs disability, IADLs score were -0.085, -0.109, -0.095 and -0.120, respectively, all of which were statistically significant, indicating the pro-poor inequality in the incidence and severity of long-term care need. Decomposition analyses revealed that family income, education attainment, aging, and childhood experience played a significant role in explaining the inequalities. Conclusions : The long-term care need among older adults with chronic disease is high in China and low socioeconomic groups had a higher probability of needing long-term care or need more long-term care. It is urgent to implement long-term care insurance, especially for the individuals from lower socioeconomic groups.

Azorhizobium caulinodans is a symbiotic nitrogen-fixing bacterium that forms both root and stem nodules on Sesbania rostrata . During nodule formation, bacteria have to withstand organic peroxides that are produced by plant. Previous studies have elaborated on resistance to these oxygen radicals in several bacteria; however, to the best of our knowledge, none have investigated this process in A. caulinodans . In this study, we identified and characterised the organic hydroperoxide resistance gene ohr (AZC&#95;2977) and its regulator ohrR (AZC&#95;3555) in A. caulinodans ORS571. Hypersensitivity to organic hydroperoxide was observed in an ohr mutant. While using a lacZ -based reporter system, we revealed that OhrR repressed the expression of ohr . Moreover, electrophoretic mobility shift assays demonstrated that OhrR regulated ohr by direct binding to its promoter region. We showed that this binding was prevented by OhrR oxidation under aerobic conditions, which promoted OhrR dimerization and the activation of ohr . Furthermore, we showed that one of the two conserved cysteine residues in OhrR, Cys 11 , was critical for the sensitivity to organic hydroperoxides. Plant assays revealed that the inactivation of Ohr decreased the number of stem nodules and nitrogenase activity. Our data demonstrated that Ohr and OhrR are required for protecting A. caulinodans from organic hydroperoxide stress and play an important role in the interaction of the bacterium with plants. The results that were obtained in our study suggested that a thiol-based switch in A. caulinodans might sense host organic peroxide signals and enhance symbiosis.

Background: The aim of this study was to estimate the effects of maternal text messages on inappropriate weight for gestational age (IWGA) in newborns in rural China., Methods: Participants were pregnant women presenting for antenatal care at a Maternal and Child Health Center in Xi'an, China during the 2013-2015 period. In total, 2115 women completed the program with follow-up information included in the final analyses. All mothers were divided into four groups, including (1) a control group that received only a few "Basic" messages, (2) a Care-Seeking (CS) message group, (3) Good Household Prenatal Practices (GHPP) message group, and (4) a group receiving all 148 text messages. The primary outcome was IWGA, including small for gestational age (SGA) and macrosomia (weighing ≥4000g at birth). Multivariable logistic regression using an intent-to-treat estimate was utilized., Results: In total, 19.5% of newborns were IWGA. The risk of IWGA was 23.0% in the control group, 19.6% in the CS group, 18.9% in the GHPP group, and 16.5% in the group with All Texts. Compared to the control group, the odds ratio of IWGA was 0.65 (0.48-0.89) for the group receiving All Texts, which remained statistically significant after performing the Holm-Bonferroni correction. The odds ratio of macrosomia was 0.54 (0.34-0.87) and 0.57 (0.36-0.49) for the Care Seeking message group and the All Texts group, respectively, with statistical significance., Conclusion: A package of free informational text messages, including advice for good household prenatal practices and care seeking, may prevent the inappropriate weight for gestational age through a protective effect on macrosomia. Advice to encourage care seeking in pregnancy may prevent macrosomia among neonates in rural China as well., Competing Interests: The authors declare no conflict of interest.

The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process., Competing Interests: The authors declare no conflicts of interest.