Experimental Verification Research Articles

Combining network pharmacology and experimental verification to study the anti-colon cancer effect and mechanism of sulforaphene.

Sulforaphene is a derivative of glucosinolate and a potential bioactive substance used for treating colon cancer. This study aimed to evaluate the potential inhibitory effect and mechanisms of sulforaphene in human colon cancer Caco-2 cells. Network pharmacology, molecular docking, and experimental verification were performed to elucidate potential sulforaphene mechanisms in the treatment of this condition. Network pharmacology predicted 27 intersection target genes between sulforaphene and colon cancer cell inhibition. Key sulforaphene targets associated with colon cancer cell inhibition were identified as EGFR, MAPK14, MCL1, GSK3B, PARP1, PTPRC, NOS2, CTSS, TLR9, and CTSK. Gene ontology functional enrichment analysis revealed that the above genes were primarily related to the positive regulation of peptidase activity, cytokine production in the inflammatory response, and the cell receptor signaling pathway. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that sulforaphene mainly inhibited the proliferation of cancer cells by affecting apoptosis as well as the signaling pathways of PD-1, Toll-like receptor, T cell receptor, and P13k-Akt. Molecular docking results further confirmed that CTSS, GSK3B, and NOS2 were significantly up-regulated and had good binding affinity with sulforaphene. In vitro experiments also indicated that sulforaphene had a significant inhibitory effect on human colon cancer Caco-2 cells. This paper revealed the pharmacodynamic mechanism of sulforaphene in the treatment of colon cancer for the first time. It provides scientific insight into the development of sulforaphene as a medicinal resource. © 2024 Society of Chemical Industry.

A novel splice-altering TNC variant (c.5247A > T, p.Gly1749Gly) in an Chinese family with autosomal dominant non-syndromic hearing loss

BackgroundThis study aims to analyze the pathogenic gene in a Chinese family with non-syndromic hearing loss and identify a novel mutation site in the TNC gene.MethodsA five-generation Chinese family from Anhui Province, presenting with autosomal dominant non-syndromic hearing loss, was recruited for this study. By analyzing the family history, conducting clinical examinations, and performing genetic analysis, we have thoroughly investigated potential pathogenic factors in this family. The peripheral blood samples were obtained from 20 family members, and the pathogenic genes were identified through whole exome sequencing. Subsequently, the mutation of gene locus was confirmed using Sanger sequencing. The conservation of TNC mutation sites was assessed using Clustal Omega software. We utilized functional prediction software including dbscSNV_AdaBoost, dbscSNV_RandomForest, NNSplice, NetGene2, and Mutation Taster to accurately predict the pathogenicity of these mutations. Furthermore, exon deletions were validated through RT-PCR analysis.ResultsThe family exhibited autosomal dominant, progressive, post-lingual, non-syndromic hearing loss. A novel synonymous variant (c.5247A > T, p.Gly1749Gly) in TNC was identified in affected members. This variant is situated at the exon–intron junction boundary towards the end of exon 18. Notably, glycine residue at position 1749 is highly conserved across various species. Bioinformatics analysis indicates that this synonymous mutation leads to the disruption of the 5' end donor splicing site in the 18th intron of the TNC gene. Meanwhile, verification experiments have demonstrated that this synonymous mutation disrupts the splicing process of exon 18, leading to complete exon 18 skipping and direct splicing between exons 17 and 19.ConclusionThis novel splice-altering variant (c.5247A > T, p.Gly1749Gly) in exon 18 of the TNC gene disrupts normal gene splicing and causes hearing loss among HBD families.

Investigation of intra-fractionated range guided adaptive proton therapy (RGAPT): II. Range-shift compensated on-line treatment adaptation and verification

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3 cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and −6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers.

AOP-Anchored Transcriptome Analysis Catalogue Accelerates the Discovery of Environmental Toxicants in Zebrafish.

Current toxicity screening approaches to evaluate the vast number of environmental chemicals that require assessment are hampered due to their significant costs, time requirements, and reliance on live animal testing. The aim of the present study was to develop an adverse outcome pathway (AOP)-anchored transcriptome analysis (AATA) catalogue to expedite the discovery of environmental toxicants. 437 AOPs from the AOPwiki (https://aopwiki.org/) and 2280 transcriptomics data sets from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI ArrayExpress (AE) repositories were comprehensively reviewed and analyzed. By using the differentially expressed molecular key event (mKE) genes as connection nodes, we created a large-scale environmental substance─target gene (mKE)─predicted adverse outcomes (SGAs) network that included 78 substances, 1099 genes, and 354 adverse outcomes (AOs). To validate the reliability of the network, comprehensive literature verification was conducted. We demonstrated that 164 of the 354 AOs identified have been previously characterized in the literature. The results for 136 of these AOs were consistent with the predictions of the AATA catalogue, representing an accuracy rate of 82.9%. Besides, distinct patterns in molecular KEs and AOs among categories of substances, such as biocides and metals, were demonstrated. Some representative substances, including atrazine and copper, pose significant risks to fish at various levels of biological organization. Moreover, experimental verification of the AATA predictions was conducted, including exposures of zebrafish to perfluorooctanesulfonate, cresyl diphenyl phosphate, and lanthanum. Results demonstrated consistency with predictions of the AATA catalogue, with an accuracy rate of 92.3%. Collectively, the present findings support the AATA catalogue as an efficient and promising platform for identifying environmental toxicants to fish and thereby provide novel insights into the understanding of potential risks of environmental contaminants.

ANCF-based dynamic modeling of variable curvature soft pneumatic actuators with experimental verifications

Abstract Accurate and computationally efficient models of soft pneumatic actuators are crucial for utilizing their compliance in various fields. However, existing research primarily relies on the piecewise constant curvature (PCC) assumption or the quasi-static assumption, only valid in limited situations. In this paper, we present a dynamic model based on absolute nodal coordinate formulation (ANCF) that simultaneously accounts for variable curvature deformation and dynamic properties. To this end, deformed configurations of soft pneumatic actuators are firstly discretized into ANCF-based beam elements. Based on this parameterization method, the dynamic model is derived by the principle of virtual work. After identifying model parameters, Newmark algorithm is utilized to solve the dynamic model in real-time, averagely consuming 6.76 s of a 10 s simulation. The derived dynamic model is experimental verified using a soft pneumatic actuator. The experimental results demonstrate that the maximum simulation errors of the tip remain below 2.5 % of the actuator’s length when the actuator is subjected to various pressure and tip loads. In addition, the overshoot behavior and period of vibration in the oscillations are also predicted by the dynamic model. Moreover, the dynamic model exhibits an average 46.53 % reduction in simulation error compared with the static ANCF-based model. Overall, this work paves the way to a deeper insight to dynamic motion analysis of soft pneumatic actuators.

Network pharmacology analysis and molecular mechanism of paeoniflorin and its metabolite in prolactinoma cells.

Prolactinoma was the most common functionalpituitary neuroendocrine tumor tissue type, which was caused by excessive proliferation of pituitary prolactin (PRL) cells. Drug therapy of dopamine receptoragonists was generally considered as the prior treatment for prolactinoma patients. However, there were still prolactinoma patients who were resistant to dopamine agonists. Studies have been reported that paeoniflorin can inhibit the secretion of PRL in prolactinoma cells lacking dopamine D2 receptor (D2R) expression, and paeoniflorin can be metabolized into albiflorin by intestinal flora in rats. The effect of albiflorin on prolactinoma has not been reported yet. In this study, network pharmacology was used to analyze the mechanism of paeoniflorin and its metabolite albiflorin as multi-target therapy for prolactinoma, and the experimental verification was carried out. In order to clarify the complex relationship among paeoniflorin, albiflorin and prolactinoma, we constructed a component-target-disease network, and further constructed interaction network, MMP9, EGFR, FGF2, FGFR1 and LGALS3 were screened as the core targets. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that paeoniflorin and albiflorin may be involved in various pathways in the treatment of prolactinoma, included relaxin signaling pathway and PI3K-Akt signaling pathway. Molecular docking analysis showed that paeoniflorin and albiflorin had good binding activity with MMP9. Western blotting results showed that paeoniflorin and albiflorin could significantly reduce the expression of MMP9, and ELISA results showed that paeoniflorin and albiflorin could significantly reduce the concentration of PRL in GH3 cells, and the reduce degree of albiflorin was stronger than paeoniflorin at 50μM, which indicated that albiflorin might be a potential drug to treat prolactinoma, which can regulate prolactinoma through MMP9 and reduce the concentration of PRL. Our study provided a new therapeutic strategy for prolactinoma.

Abnormal Behavior Recognition Based on 3D Dense Connections.

Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.

An energy concisions analytical modelling approach with experimental verification for cutting performance assessment in EDM of Ti-based superalloy

Abstract Electric discharge machining (EDM) is a stochastic process which is commonly engaged for cutting of Ti-based difficult-to-cut alloys. Experimental investigation in EDM is costly and requires significant amount of time due to the complex nature of the process. Moreover, the energy intensive nature is another criticism associated to this technique. Therefore, this research is focused on developing an energy conscious mathematical model of the process while considering the categorical parameters like type of dielectric, electrode and nano-powder along with pulse time ratio. Taguchi design of experiment (DOE) has been executed for the experimentation. The comprehensive analysis of the findings depicts transformer oil (TO) delivers a remarkable improvement in MRR and SEC in comparison to other dielectric choices. Furthermore, brass electrode stands out for achieving the best surface finish. When it comes to micro-additives, SiC exhibits substantial potential in increasing MRR, and graphite gives a better surface finish. The process has also been effectively modeled which helps to predict material removal rate (MRR), surface roughness (SR) and specific energy consumption with decent precision which is the key contribution of this study. 3D simulation for EDM illustrates that the features of the crater are defined by the liquefied region, whilst the dynamics of the mushy zone play a crucial role in controlling and characterizing the microstructure size progression. The confirmatory experimental results revealed that the use of optimized parametric combinations demonstrate 90.87% improvement in MRR, 52.07% reduction in SR and 96.19% decrease in SEC in contest to the responses’ values obtained at non-optimal settings.

The stray light induced redshift of absorption peaks of inorganic anions in the far ultraviolet region-an "artifact".

In the analytical process of spectrophotometry, the prerequisite for accurate qualitative and quantitative analysis is obtaining the intrinsic spectra of the analyte. However, the intrinsic properties of spectra can sometimes be masked by easily overlooked non-intrinsic factors, such as those from measuring instruments, leading to erroneous spectral identification. In this study, we documented an unusual redshift phenomenon in the far ultraviolet spectral region. With a spectrophotometer under the nitrogen atmosphere, we selected 14 representative inorganic anions and investigated their absorption spectral behaviors at different optical pathlengths and concentrations. It was intriguing to observe that the absorption peaks with maximum absorption wavelengths below a watershed wavelength of 200 nm underwent a redshift as pathlength and concentration increased, while those above 200 nm did not exhibit a significant redshift phenomenon. In-depth formula simulations and experimental verifications demonstrated that this peculiar spectral behavior was caused by unavoidable stray light in the spectrophotometer. Some methodological and instrumental recommendations are given in the paper. Our study results may serve as a reminder to carefully identify non-intrinsic phenomena when studying absorption spectra in the far ultraviolet region, and provide guidance on spectral corrections in scientific research and practical applications.

Unveiling the distinctive mechanical and thermal properties of γ-GeSe

γ-GeSe is a newly identified polymorph among group-IV monochalcogenides, characterized by a distinctive interatomic bonding configuration. Despite its promising applications in electrical and thermal domains, the experimental verification of its mechanical and thermal properties remains unreported. Here, we experimentally characterize the in-plane Young’s modulus (E) and thermal conductivity (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\kappa\\:$$\\end{document}) of γ-GeSe. The mechanical vibrational modes of freestanding γ-GeSe flakes are measured using optical interferometry. Nano-indentation via atomic force microscopy is also conducted to induce mechanical deformation and to extract the E. Comparison with finite-element simulations reveals that the E is 97.3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\pm\\:$$\\end{document}7.5 GPa as determined by optical interferometry and 109.4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\pm\\:$$\\end{document}13.5 GPa as established through the nano-indentation method. Additionally, optothermal Raman spectroscopy reveals that γ-GeSe has a lattice thermal conductivity of 2.3 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\pm\\:$$\\end{document} 0.4 Wm−1K−1 and a total thermal conductivity of 7.5 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\pm\\:$$\\end{document} 0.4 Wm−1K−1 in the in-plane direction at room temperature. The notably high \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:E/\\kappa\\:$$\\end{document} ratio in γ-GeSe, compared to other layered materials, underscores its distinctive structural and dynamic characteristics.

Towards Using Digital Technologies to Balance Conservation and Fire Mitigation in Building Heritage Hosting Vulnerable Occupants: Rapid Evacuation Simulator Verification for the “Omero Museum” (Ancona, Italy)

Digital technologies can support designers in balancing architectural heritage conservation and performances using multidisciplinary approaches. Fire safety represents a challenging issue, especially in public historical buildings hosting vulnerable occupants, since heavy modifications are often required to facilitate their evacuation. Digital tools based on evacuation simulation are able to verify the impact of other sustainable, compatible evacuation management and planning approaches, especially considering the use of generic software, which can be used by low-trained technicians according to rapid setups. Nevertheless, simulator reliability should be experimentally verified through case study applications. This work thus offers the experimental verification of a rapid setup-based generic evacuation simulator in the context of a significant case study (the “Omero Museum”, Ancona, Italy), placed in a historic building hosting vulnerable occupants (disabled, elderly, children), thanks to a full-scale evacuation drill. The rapid setup described different vulnerable occupants’ categories according to literature data. Comparisons between drill and simulation results, using consolidated verification indicators, showed the overall reliability of the proposed approach, and thus encourage additional tests in historical buildings. The proposed setup-based simulator could be combined with other digital tools (virtual reality, BIM-related) to provide full support to fire risk and evacuation assessments when vulnerable occupants are present.

Towards a practical usage for the Sleuth Kit supporting file system add-ons

Most modern digital devices with storage utilize a file system to manage files and directories. Consequently, when digital forensic investigators derive evidence from such devices, they collect and analyze data stored on them through file system analysis. However, there are numerous types of file systems, with new ones continually being developed. Each file system possesses a distinct metadata structure and file management system. Therefore, investigators must possess prior knowledge of the specific file system being examined. Nevertheless, it is challenging for practitioners to be knowledgeable about all existing file systems. To address this issue, forensic software such as The Sleuth Kit (TSK), an open-source forensic tool, is employed for investigations. However, even these tools may not offer complete support for relatively recent file systems.Hence, we propose a structure for integrating a new file system into the open-source forensic tool TSK. Additionally, to validate this proposed structure, we demonstrate that support for five file systems (Ext4, XFS, Btrfs, F2FS, and Hikvision) can be added following this framework. To achieve this, we conducted an analysis of the metadata and file management scheme for these five file systems. Furthermore, we examined the operational procedures of the TSK framework. Based on these analyses, investigation capabilities for the five file systems have been incorporated into TSK. Moreover, reliability verification experiments were conducted on the developed tools; and performance evaluation was carried out in comparison with other commercial digital forensic tools. The findings of this study can serve as a foundation for future forensic studies based on file systems. Additionally, the TSK developed based on the proposed structure can assist investigators in conducting digital forensics effectively.

SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking

Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements.

Intelligent Acoustic and Optical Anomaly Monitoring System for Expressway Tunnels Based on the Internet of Things

A smart sound and light anomaly monitoring system for highway tunnels based on Internet of Things technology was studied to address the issues of highway tunnel lighting systems. By utilizing Internet of Things technology, the tunnel lighting system is combined with abnormal sound recognition. Through the design of algorithm models, the recognition of abnormal sound inside the tunnel and the intelligent control of the lighting system is achieved. By pruning and validating the hidden layer nodes of the model, a more streamlined abnormal sound recognition model is obtained. Through experimental verification, this model has the highest recognition accuracy among all models, with a recognition rate of 91.75% at a compression rate of 20%. Compared with Average Percentage of Zeros (APoZ), Random Pruning and Mean Activation, the recognition rate is increased by 2.64%, 1.47% and 1.40%, respectively. In the design of tunnel lighting, fuzzy control is applied to the lighting inside the tunnel to improve the driving safety of drivers and further reduce the power consumption of excessive lighting in the tunnel. Through experiments, it has been proven that the system can work well, saving up to 727[Formula: see text] of energy per day.

A Magnetically Coupled Piezoelectric Ocean Wave Energy Harvester for Self-Powered Low-Power Appliances

The ocean contains abundant wave energy, which can be used as a new regenerative energy for power generation and provide energy for self-powered low-power appliances, realizing in situ energy utilization. Then, it can effectively promote the sustainable development strategy of the ocean and bring huge energy and social and environmental benefits. This paper proposes a magnetically coupled piezoelectric ocean wave energy harvester (MPWEH) system for wave energy harvesting. The MPWEH system includes four modules: energy harvesting, force magnification, piezoelectric power generation, and energy storage module. The energy harvesting module moves up and down in the tube body with the wave, effectively capturing and absorbing the wave energy and transforming the harvesting module’s motion into the interaction repulsive force between magnets. Then, the force magnification module amplified the magnetic force and applied it to the polarization direction of the piezoelectric ceramic to self-power the low-power appliances. This research carried out the feasibility verification experiment with the MPWEH prototype, and the results show that when the period is 3[Formula: see text]s, and the amplitude is 2[Formula: see text]m, the maximum voltage can reach 15[Formula: see text]V, and the maximum power can reach 12.85[Formula: see text]mW. Take Jiaozhou Bay Bridge as an example. The MPWEH system’s energy harvesting potential is studied, and the annual power generation of all the acquisition devices can reach 55.72[Formula: see text]kWh, which can be enough energy to supply the sensors on the cross-sea bridge.

Isolation and anti-neuroinflammation activity of sesquiterpenoids from Artemisia argyi: computational simulation and experimental verification

BackgroundArtemisia argyi is a traditional herbal medicine belonging to the genus Artemisia that plays an important role in suppressing inflammation. However, the chemical constituents and underlying mechanisms of its therapeutic potential in neuroinflammation are still incompletely understood, and warrant further investigation.MethodsSeveral column chromatography were employed to isolate and purify chemical constituents from Artemisia argyi, and modern spectroscopy techniques were used to elucidate their chemical structures. The screening of monomeric compounds with nitric oxide inhibition led to the identification of the most effective bioactive compound, which was subsequently confirmed for its anti-inflammatory capability through qRT‒PCR. Predictions of compound-target interactions were made using the PharmMapper webserver and the TargetNet database, and an integrative protein-protein interaction network was constructed by intersecting the predicted targets with neuroinflammation-related targets. Topological analysis was performed to identify core targets, and molecular docking and molecular dynamics simulations were utilized to validate the findings. The result of the molecular simulations was experimentally validated through drug affinity responsive target stability (DARTS) and Western blot experiments.ResultsSeventeen sesquiterpenoids, including fifteen known sesquiterpenoids and two newly discovered guaiane-type sesquiterpenoids (argyinolide S and argyinolide T) were isolated from Artemisia argyi. Bioactivity screening revealed that argyinolide S (AS) possessed the most potent anti-inflammatory activity. However, argyinolide T (AT) showed weak anti-inflammatory activity, so AS was the target compound for further study. AS may regulate neuroinflammation through its modulation of eleven core targets: protein kinase B 1 (AKT1), epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein Kinase (FYN), Janus Kinase (JAK) 1, mitogen-activated protein (MAP) Kinase 1,8 and 14, matrix metalloproteinase 9 (MMP9), ras-related C3 botulinum toxin substrate 1 (RAC1), nuclear factor kappa-B p65 (RELA), and retinoid X receptor alpha (RXRA). Molecular dynamics simulations and DARTS experiments confirmed the stable binding of AS to JAK1, and Western blot experiments demonstrated the ability of AS to inhibit the phosphorylation of downstream Signal transducer and activator of transcription 3 (STAT3) mediated by JAK1.ConclusionsThe sesquiterpenoid compounds isolated from Artemisia argyi, exhibit significant inhibitory effects on inflammation in C57BL/6 murine microglia cells (BV-2). Among these compounds, AS, a newly discovered guaiane-type sesquiterpenoid in Artemisia argyi, has been demonstrated to effectively inhibit the occurrence of neuroinflammation by targeting JAK1.

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Abstract Considering the issue of capturing the local and global contextual information and enhancing the parallel capability of bearing fault diagnosis in variable load and noise environments, a fault diagnosis method of rolling bearing based on PE-DCM and ViT is proposed. Firstly, the one-dimensional vibration signal is converted into a two-dimensional time-frequency diagram by continuous wavelet transform in the data processing module, and the model can understand the characteristics of the vibration signal more comprehensively. Secondly, a pyramid exponential expansion convolution module is established to extract the local features of fault information. Then, the global features of the fault information are learnt through the ViT (Vision Transformer) network, and the adaptive multi-attention is used to dynamically adjust the attention weights according to the features of the input data so as to inhibit noise or unimportant information. Finally, the experimental verification is carried out by using Case Western Reserve University and self-made MFS-bearing data set. The experimental results show that the method can better reflect the powerful image classification ability of the ViT network and has better noise resistance and generalization compared with other fault diagnosis methods.

Propulsion mechanism of artificial flagellated micro-swimmers actuated by acoustic waves - theory and experimental verification.

This work examines the acoustically actuated motions of artificial flagellated micro-swimmers (AFMSs) and compares the motility of these micro-swimmers with the predictions based on the corrected resistive force theory (CRFT) and the bar-joint model proposed in our previous work. The key ingredient in the theory is the introduction of a correction factorKin drag coefficients to correct the conventional resistive force theory so that the dynamics of an acoustically actuated AFMS with rectangular cross-sections can be accurately modeled. Experimentally, such AFMSs can be easily manufactured based on digital light processing (DLP) of UV-curable resins. We first determined the viscoelastic properties of a UV-cured resin through dynamic mechanical analysis (DMA). In particular, the high-frequency storage moduli and loss factors were obtained based on the assumption of time-temperature superposition (TTS), which were then applied in theoretical calculations. Though the extrapolation based on the TTS implied the uncertainty of high-frequency material response and there is limited accuracy in determining head oscillation amplitude, the differences between the measured terminal velocities of the AFMSs and the predicted ones are less than 50%, which, to us, is well acceptable. These results indicate that the motions of acoustic AFMS can be predicted, and thus, designed, which pave the way for their long-awaited applications in targeted therapy.

Micro-expression recognition based on a novel GCN-transformer cooperation model for IoT-eHealth

If a person is truly healthy, his/her well-being encompasses both physical and psychological health. However, the existing IoT-eHealth system typically focus only on monitoring the user’s physical data through various sensors, neglecting their mental state. To enhance the intelligence level of IoT-eHealth system and enable it to have the psychological monitoring ability, a novel collaborative model based on Graph Convolutional Network (GCN) and Transformer is designed for Micro-Expression (ME) recognition in this paper. Firstly, facial information within each frame is transformed into a Spatial Topological Relationship Graph (STRG) by using facial landmarks detection and psychological relationship of local patches. Then, in order to automatically aggregate the key information on facial patches that contribute to ME recognition from the structured graph data, a Hierarchical Adaptive Graph Pooling (HAGP) module is designed for obtaining discriminative frame-level feature based on GCN utilizing graph structure and vertex global dependencies. Finally, in order to model the long-term dependencies among frames and capture the key frame-level features that are beneficial for ME recognition, a Temporal Sensitive Self-Attention (TSSA) mechanism is designed, and a novel Temporal Sensitive Transformer (TST) encoder is constructed based on TSSA to explore the evolution law of facial patterns and obtain discriminative video-level features that are helpful for ME recognition. In the comparative experiments of standard dataset verification and practical dataset testing, designed collaborative model is superior to other methods and can achieve the highest recognition accuracy, which almost can meet the application requirements of IoT-eHealth system.

Regulation of MicroRNAs After Spinal Cord Injury in Adult Zebrafish.

Spinal cord injury (SCI) is a central nerve injury that often leads to loss of motor and sensory functions at or below the level of the injury. Zebrafish have a strong ability to repair after SCI, but the role of microRNAs (miRNAs) after SCI remains unclear. Locomotor behavior analysis showed that adult zebrafish recovered about 30% of their motor ability at 2 weeks and 55% at 3 weeks after SCI, reflecting their strong ability to repair SCI. Through miRNA sequencing, mRNA sequencing, RT-qPCR experiment verification, and bioinformatics predictive analysis, the key miRNAs and related genes in the repair of SCI were screened. A total of 38 miRNAs were significantly different, the top ten miRNAs were verified by RT-qPCR. The prediction target genes were verified by the mRNAs sequencing results at the same time point. Finally, 182 target genes were identified as likely to be networked regulated by the 38 different miRNAs. GO and KEGG enrichment analysis found that miRNAs targeted gene regulation of many key pathways, such as membrane tissue transport, ribosome function, lipid binding, and peroxidase activity. The PPI network analysis showed that miRNAs were involved in SCI repair through complex network regulation, among which dre-miR-21 may enhance cell reversibility through nop56, and that dre-miR-125c regulates axon growth through kpnb1 to repair SCI.