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This laboratory experiment visually demonstrates the advanced scientific concept of aggregation-induced emission (AIE) using hands-on learning methods. The experiment is divided into two parts: the first period consists of synthesis and isolation of the organic compound 2(TPA-O)-TPE using typical organic chemistry lab techniques; the second period focuses on visual demonstration of AIE properties based on micelle formation and thin-layer chromatography (TLC). By introducing a scientific concept through a classic technology, students can build on the foundation of prior understandings and experiences toward engaging new ideas. This experiment is suitable for many areas of chemistry, especially for organic chemistry, analytical chemistry, and materials chemistry.

Super-resolution imaging techniques have broken the diffraction-limited resolution of light microscopy. However, acquiring three-dimensional (3D) super-resolution information about structures and dynamic processes in live cells at high speed remains challenging. Recently, the development of high-speed single-point edge-excitation subdiffraction (SPEED) microscopy, along with its 2D-to-3D transformation algorithm, provides a practical and effective approach to achieving 3D subdiffraction-limit information in subcellular structures and organelles with rotational symmetry. One of the major benefits of SPEED microscopy is that it does not rely on complex optical components and can be implemented on a standard, inverted epifluorescence microscope, simplifying the process of sample preparation and the expertise requirement. SPEED microscopy is specifically designed to obtain 2D spatial locations of individual immobile or moving fluorescent molecules inside submicrometer biological channels or cavities at high spatiotemporal resolution. The collected data are then subjected to postlocalization 2D-to-3D transformation to obtain 3D super-resolution structural and dynamic information. In recent years, SPEED microscopy has provided significant insights into nucleocytoplasmic transport across the nuclear pore complex (NPC) and cytoplasm-cilium trafficking through the ciliary transition zone. This Review focuses on the applications of SPEED microscopy in studying the structure and function of nuclear pores.

Semen Cuscutae (S) is an effective traditional Chinese medicine (TCM) in clinical practice for tonifying the kidney and calming the fetus. The effects of S on benefiting the kidney, fixing essence and calming the fetus were enhanced after salt processed. However, its bioactive substance and relevant mechanism have not been clearly yet. This study aims to explore the effective components of Semen Cuscutae processed with salt solution (Y) and its potential mechanism on the treatment of kidney deficiency miscarriage (KDM) with multi-components and multi-targets. In this study, serum pharmacochemistry and network pharmacology were used to investigate the active ingredients and molecular mechanism of Y in improving kidney deficiency abortion. A rat model of kidney deficiency miscarriage was established using hydroxyurea combine with mifepristone to assess the effect of S on tonifying the kidney and calming the fetus. The results show that 13 active ingredients derived from Y were identified by serum pharmacochemistry analysis. 92 common targets of active ingredients of Y and diseases were screened by network pharmacology, including 18 key targets (TP53, AKT1, TNF, IL6, VEGFA, CASP3, JUN, EGFR, etc). The KEGG pathway enrichment analysis showed that it was mainly concentrated in PI3K/Akt signaling pathway, and it was speculated that Y may protect pregnancy through PI3K/AKT and other signaling pathways with multi-component, multi-target and multi-pathway action characteristics. Animal pharmacodynamic analysis suggested that Y showed the better effect on the hydroxyurea combine with mifepristone induced abortion compared to the raw products of S. This study laid the foundation to identify the active components and potential mechanisms of Y in improving KDM by combining the in-tegrated methods of serum pharmacochemistry and network pharmacology.

Abstract We developed an inductively coupled plasma mass spectrometry method for testing 23 elements, namely, Mg, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Sn, Sb, Ba, W, Tl, Pb, and U, in human serum. The serum samples were analyzed after diluting 1/25 with 0.5% nitric acid, 0.02% Triton-X-100, and 2% methanol. Sc, In, Y, Tb, and Bi were assigned internal standards to correct the baseline drift and matrix interference. The kinetic energy discrimination mode of the instrument with helium gas as the collision gas eliminated polyatomic interference. All 23 elements exhibited excellent linearity in their testing range, with a coefficient of determination ≥ 0.9996. The limits of detection of the 23 elements were within the range of 0.0004–0.2232 µg/L. The intra- and inter-day precision (relative standard deviation) were

Introduction: Ginger (Zingiber officinale Roce.) is a widely consumed food item and a prominent traditional Chinese medicinal herb. The intrinsic quality of ginger may differ due to variations in its origin and processing techniques. To evaluate the quality of ginger, a straightforward and efficient discriminatory approach has been devised, utilizing 6-gingerol, 8-gingerol, and 10-gingerol as benchmarks.Methods: In order to categorize ginger samples according to their cultivated origins with different longitude and latitude (Shandong, Anhui, and Yunnan provinces in China) and processing methods (liquid nitrogen pulverization, ultra-micro grinding, and mortar grinding), similarity analysis (SA), hierarchical cluster analysis (HCA), and principal component analysis (PCA) were employed. Furthermore, there was a quantitative determination of the significant marker compounds gingerols, which has considerable impact on maintaining quality control and distinguishing ginger products accurately. Moreover, discrimination analysis (DA) was utilized to further distinguish and classify samples with unknown membership degrees based on the eigenvalues, with the aim of achieving optimal discrimination between groups.Results: The findings obtained from the high-performance liquid chromatography (HPLC) data revealed that the levels of various gingerols present in all samples exhibited significant variations. The study confirmed that the quality of ginger was primarily influenced by its origin and processing method, with the former being the dominant factor. Notably, the sample obtained from Anhui province and subjected to liquid nitrogen pulverization demonstrated the highest content of gingerols.Conclusion: The results obtained from the analysis of SA, HCA, PCA, and DA were consistent and could be employed to evaluate the quality of ginger. As such, the combination of HPLC fingerprints and chemo metric techniques provided a dependable approach for comprehensively assessing the quality and processing of ginger.

This paper addressed three major challenges in the customization of mixed propellant charges: optimal design, performance simulation, and manufacturing. Based on parametric modeling software Grasshopper, parametric models of propellants with highly complex geometries were endowed with combustion properties of multiple formulations, coupled with rapidly changing burning rate-pressure field during iterative combustion to achieve visual and integrated simulation of burning surface regression, form function, and combustion performance of mixed charges, providing convenient theoretical guidance for structural optimization design of customized strategies. Customized zones designed with complex geometries and multiple formulations co-constructed in one propellant were prepared by multi-material additive manufacturing techniques. Different customized zones of one or more propellants were superimposed in the design sequence to customize the combustion performance. Mixed charges for three cases of burning surface, burning rate, and joint control were discussed. The simulation results matched well with the experimental results, including the progressively random perforated combustion, variation of burning rate pressure exponent, and superimposed combustion of single and multiple charges with different customized zones. As an advanced engineering strategy urgently needed in chemical engineering, this work not only opens the door to matching performance simulation-driven design with MM-AM technologies but also serves as an efficient and common customization strategy from design to manufacture.

In this study, a new series of quinoxalinone derivatives (5a–5p, 6a–6n) was designed and its hypoglycemic activity was evaluated. The results showed that compounds 5i and 6b exhibited stronger hypoglycemic effects than the lead compounds and were comparable to the positive control Pioglitazone. 5i and 6b may exert hypoglycemic effects by alleviating cellular OS and modulating the interactions among GLUT4, SGLT2, and GLUT1 proteins. The alleviating cellular OS of compound 6b was better than that of 5i, and 6b was found to bind better than 5i for most of the screening targets. In summary, compound 6b is a potential lead compound with hypoglycaemic activity.3

Abstract Atmospheric Water Harvesting (AWH) using porous adsorbents is emerging as a promising solution to combat water shortage. Thus, a clearer understanding of the developing trends and optimization strategies of different porous adsorbents can be extremely helpful. Therefore, in this concept, the different types of porous adsorbents and AWH devices are briefly introduced with a focus on the factors that influence the static and kinetic properties of porous adsorbents and their respective optimization strategies. In addition, the fast transport characteristics of water molecules in micropores are studied from the perspective of superfluidity as part of the analysis of the kinetic properties of porous adsorbents. Finally, the future development of porous materials for AWH and the accompanying challenges are summarized.

O/W emulsions stabilized by polyphenol/amylose (AM) complexes with several polyphenol/AM mass ratios and different polyphenols (gallic acid (GA), epigallocatechin gallate (EGCG) and tannic acid (TA)) were prepared by a high-intensity ultrasound emulsification technique. The effect of the pyrogallol group number of polyphenols and the mass ratio of polyphenols/AM on polyphenol/AM complexes and emulsions was studied. The soluble and/or insoluble complexes gradually formed upon adding polyphenols into the AM system. However, insoluble complexes were not formed in the GA/AM systems because GA has only one pyrogallol group. In addition, the hydrophobicity of AM could also be improved by forming polyphenol/AM complexes. The emulsion size decreased with increasing pyrogallol group number on the polyphenol molecules at a fixed ratio, and the size could also be controlled by the polyphenol/AM ratio. Moreover, all emulsions presented various degrees of creaming, which was restrained by decreasing emulsion size or the formation of a thick complex network. The complex network was enhanced by increasing the ratio or pyrogallol group number on the polyphenol molecules, which was because the increasing number of complexes was adsorbed onto the interface. Altogether, compared to GA/AM and EGCG/AM, the TA/AM complex emulsifier had the best hydrophobicity and emulsifying properties, and the TA/AM emulsion had the best emulsion stability.

Co-pyrolysis technology containing biomass offers remarkable advantages in reducing NOx emissions economically and efficiently. In this work, it was innovatively introduced to solve the problem of excessive NOx emission during the incineration of waste energetic materials (EMs). The kinetics and NOx emission characteristics of waste double-base propellant (DP), pine sawdust (PS), and their mixtures with different ratios during pyrolysis were investigated by thermogravimetric analysis and fixed-bed experiments. The results showed that there was a significant interaction between DP and PS. Kinetic analysis by Friedman and Kissinger-Akahira-Sunose (KAS) methods demonstrated that the average activation energies of the mixtures with different ratios were smaller than that of DP, indicating that the addition of PS improved the reactivity of co-pyrolysis. In addition, the fixed-bed experiment determined that the lowest NOx emission was achieved during DP pyrolysis alone at 900 ℃. Co-pyrolysis at this temperature was found to have synergistic effects of reduced NOx emissions for different ratios of mixtures. The best synergistic effect was achieved at the mixing ratio of 60 wt% DP and 40 wt% PS, resulting in a 72.11 % reduction in actual NOx emissions compared to the expected value. This study provides a new direction and powerful data support for the clean, efficient and economic treatment of waste EMs, especially for practical engineering strategies.

Wireless charger production is critical to energy storage, and effective fault diagnosis of bearings and gears is essential to ensure wireless charging performance with high efficiency, high tolerance to misalignment, and thermal safety. As minor faults are usually difficult to detect, timely diagnosis and detection of minor faults can prevent the fault from worsening and ensure the safety of wireless charging systems. Diagnosing minor faults in bearings and gears with data is a useful but difficult task. To achieve a satisfactory diagnosis of minor faults in the production of wireless charging systems related to the mechanical system that produces wireless charging devices, such as robot arms, this paper proposes a deep learning network based on CNN and LSTM (DTLCL). The method uses deep learning network, model-based transfer learning and range adaptation technology. First, a deep neural network is built to extract significant fault features. Second, the deep transfer network is initialised using model-based transfer learning with a good starting point. Finally, range adaptation using the maximum mean discrepancy between the features learned from the source and target ranges is realised by a multi-layer adaptive technology. The effectiveness of the method was verified using actual measurement data. The training time is 19 s, and the accuracy exceeds 94.5%. The explanation results show that the proposed DTLCL method provides higher accuracy and robust identification of smaller errors compared to the current combination of integrated and single non-transmission models. Due to its data-driven nature, the DTLCL method could be used for fault diagnosis of bearings and gears, which would further promote the application process of wireless charging.

Chronic apical periodontitis (CAP) is a disease with characteristics of inflammation and bone loss. In this study, our objective was to examine the function of small extracellular vesicles (sEVs) obtained from milk in encouraging osteogenic differentiation and inhibiting inflammation by miR-21 in CAP. The expression of miR-21 was detected using qRT-PCR in human CAP samples. The impact of miR-21 on the process of osteogenic differentiation was investigated using CCK-8, qRT-PCR, immunofluorescence staining, and Western blot analysis. The evaluation of RAW 264.7 cell polarization and the assessment of inflammatory factor expression were conducted through qRT-PCR. The influence of sEVs on MC3T3-E1 cells and RAW 264.7 cells was examined, with a particular emphasis on the involvement of miR-21. In human CAP samples, a decrease in miR-21 expression was observed. MiR-21 increased the expression of osteogenesis-related genes and M2 polarization genes while decreasing the expression of M1 polarization genes and inflammatory cytokines. Treatment with milk-derived sEVs also promoted osteogenesis and M2 polarization while inhibiting M1 polarization and inflammation. Conversely, the addition of miR-21 inhibitors resulted in opposite effects. Our results indicated that sEVs derived from milk had a positive effect on bone formation and activation of anti-inflammatory (M2) macrophages and simultaneously reduced inflammation by regulating miR-21 in CAP.

For the synergistic catalysis of toluene and NOx, CeTiOx catalysts with metal organic frameworks as sacrificial templates, were synthesized using a hydrothermal method. The activity results show that use of the Ce1TiOx catalyst affords a synergistic catalytic efficiency of more than 75% between 250 and 400 °C. Characterizations confirmed that the Ce1TiOx catalyst balanced the catalytic reduction reaction by reducing the concentration of Ce3+ and increasing the Lewis acid content, resulting in, an excellent synergistic catalytic activity. The mechanistic study revealed that NH3 and toluene exhibited competitive adsorption and competed with toluene intermediates for reactive oxygen species, inhibiting the synergistic catalytic reaction.

An effective hybrid system was applied as the first report for the successful treatment of key pollutants (hydroxypropyl guar gum, HPG) in fracturing flowback fluid, and the synergistic index of the hybrid system was 20.45. In this regard, chemical oxygen demand (COD) removal ratio was evaluated with various influencing operating factors including reaction time, H2O2 concentration, Fe2+ concentration, ultrasonic power, initial pH, and temperature. The optimal operating parameters by single-factor analysis method were: the pH of 3.0, the H2O2 concentration of 80 mM, the Fe2+ concentration of 5 mM, the ultrasonic power of 180 W, the ultrasonic frequency of 20–25 kHz, the temperature of 39 ℃, the reaction time of 30 min, and the COD removal rate reached 81.15 %, which was permissible to discharge surface water sources based on the environmental standards. A possible mechanism for HPG degradation and the generation of reactive species was proposed. Results of quenching tests showed that various impacts of the decomposition rate by addition of scavengers had followed the order of EDTA-2Na

Hepatocellular carcinoma (HCC), the most common malignancy of the liver, exhibits high recurrence and metastasis. Structural modifications of natural products are crucial resources of antitumor drugs. This study aimed to synthesize C-14 derivatives of tetrandrine and evaluate their effects on HCC. Forty C-14 sulfonate tetrandrine derivatives were synthesized and their in vitro antiproliferative was evaluated against four hepatoma (HepG-2, SMMC-7721, QGY-7701, and SK-Hep-1) cell lines. For all tested cells, most of the modified compounds were more active than the lead compound, tetrandrine. In particular, 14-O-(5-chlorothiophene-2-sulfonyl)-tetrandrine (33) exhibited the strongest antiproliferative effect, with half-maximal inhibitory concentration values of 1.65, 2.89, 1.77, and 2.41 μM for the four hepatoma cell lines, respectively. Moreover, 33 was found to induce apoptosis via a mitochondria-mediated intrinsic pathway via flow cytometry and western blotting analysis. In addition, colony formation, wound healing, and transwell assays demonstrated that 33 significantly inhibited HepG-2 and SMMC-7721 cell proliferation, migration, and invasion, indicating that it might potentially be a candidate for an anti-HCC therapy in the future.

In this work, a porous tobacco straw-based polyacrylic acid hydrogel STS-PAA with high adsorption performance was prepared by polymerizing pretreated waste tobacco straw (TS) with acrylic acid/potassium acrylate by UV radiation initiation. The adsorption performance of metal ions was investigated. The effects of different temperatures (25°C, 35°C, and 45°C), adsorption times (1–420 min), pH values (2.0–6.0) and initial concentrations (0.25–4.0 mmol L−1) of metal ions on the adsorption amount of heavy metal ions were investigated. The results showed that the hydrogel had a high removal rate of Pb2+, Cd2+ and Hg2+ in aqueous solution. The adsorption of Pb2+ was particularly effective. When C0 = 4.0 mmol L−1, pH = 6, the equilibrium adsorption amount of Pb2+, Cd2+ and Hg2+ reached 1.49 mmol g−1, 1.02 mmol L−1 and 0.94 mmol g−1, respectively. The chemical structure and morphology of the hydrogels were characterized by FT-IR, EDS, SEM and XPS. The Langmuir model fits well with the adsorption system. The kinetic data suggest the adsorption of Pb2+, Cd2+ and Hg2+ follow the pseudo-first-order model. This indicates that STS-PAA adsorption of three heavy metal ions is monolayer physical adsorption. Thermodynamic analysis shows that the adsorption of Pb2+, Cd2+ and Hg2+ by STS-PAA is an endothermic (ΔH>0) entropy increase (ΔS>0) non-spontaneous reaction.

Summary: Preparation of highly efficient and stable perovskite light-emitting diodes (PeLEDs) with reproducible device performance is challenging. This protocol describes steps for fabrication of high-performance and self-healing PeLEDs. These include instructions for synthesis of charge-transporting zinc oxide (ZnO) nanocrystals, step-by-step device fabrication, and control over self-healing of the degraded devices.For complete details on the use and execution of this protocol, please refer to Teng et al. (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Extracellular vesicles (EVs) exhibiting versatile biological functions provide promising prospects as natural therapeutic agents and drug delivery vehicles. For future clinical translation, revealing the fate of EVs in vivo, especially their accumulation at lesion sites, is very important. The continuous development of in vivo imaging technology has made it possible to track the real‐time distribution of EVs. This article reviews the applications of mammal‐, plant‐, and bacteria‐derived EVs in tumor therapy, the labeling methods of EVs for in vivo imaging, the advantages and disadvantages of different imaging techniques, and possible improvements for future work.

In order to accurately obtain photometric information of high concentration SO42− and other substances in the process industry, the spectroscopy behavior of SO42−, S2−, Ni2+ and Cu2+ in air and nitrogen atmosphere was compared based on the UV-visible spectrophotometer with a nitrogen replacing the oxygen. Different from Ni2+ and Cu2+, the accuracy of SO42− and S2− in the ultraviolet region was effectively improved by using a nitrogen atmosphere (P detection results were regressed within the limited standard range, RE < 5%). The nitrogen atmosphere suppressed the additional light attenuation caused by its absorption of ultraviolet rays by isolating oxygen and was also reflected in the decrease in the degree of red shift of the characteristic wavelength for SO42− with increasing concentration. Therefore, the detection results of SO42− showed an effective improvement in sensitivity. Nevertheless, according to the complementary experimental results and theoretical calculations, in addition to oxygen absorption, the low detection accuracy of SO42− high concentration is also attributed to the reduction of the energy required for electronic excitation per unit group caused by the interaction between SO42− groups, resulting in a deviation of the C-A curve from linearity at high concentrations. The influence of this intermolecular force on the detection results is far more important than oxygen absorption. The research can provide reliable theoretical guidance and technical support for the pollution-free direct measurement of high-concentration solutions in the process industry and promote the sustainable development of the process industry.

Soft robots, which exhibit distinguishing features in terms of compliance, adaptability, and safety, have been expansively adopted in various niche applications. For soft robots, innovative actuators have been designed based on smart materials enabling the robots to perform flexible and versatile functions, whereas extra spaces and accessories to accommodate motors and power devices have been eliminated to achieve structural optimisation. Herein, different types of actuation mechanisms for soft robots are summarised to reflect the state-of-the-art research and applications. Major characteristics of the actuation mechanisms are updated. Design methodologies of the actuation mechanisms are discussed in detail. Furthermore, their advantages, disadvantages, and application potential are compared and summarised. In the end, based on our knowledge and understanding, new thoughts and recommendations to further develop the actuation mechanisms are put forward. This review is useful to support the conclusion that, through incorporating actuation mechanisms and advanced intelligent technologies, soft robots tend to create disruptive innovations in applications.

Technological advancements have revolutionized the space industry, facilitating deep space exploration using CubeSats. One objective is to locate potential life-support elements, such as water, on extraterrestrial planets. Water possesses a distinct spectral signature at 183 GHz, useful in remote sensing and environmental monitoring applications. Detecting this signature provides crucial information about water and ice presence and distribution on celestial bodies, aiding future exploration and colonization efforts. Mostly in space remote sensing uses corrugated horn antennae due to high gain and radiation patterns but fabrication of corrugated antenna is very challenging or even impossible in some cases. To ease this challenge, in our research we propose ideas to transform a corrugated horn antenna into a smooth-walled design by using MATLAB Cubic smoothing Splines algorithms. We compare simulation results between smooth-walled and corrugated antennas, and we can see some improvements in insertion losses, Voltage Standing Wave ratio (VSWR), and gain. We also manufactured this 183 GHz antenna using a commercially available 3D printer by utilizing Acrylonitrile Butadiene Styrene (ABS) material. The antenna surface was then coated with a thin layer of copper using conductive paint. In the end, we practically evaluate smooth-walled antenna functionality and compare it with the theriacal results. Validating the antenna’s functionality proposes a cost-effective and accessible production method to be used in a CubeSat engineering model or university students’ project.

After years of service, bridges could lose their expected functions. Considering the significant number of bridges and the adverse inspecting environment, the urgent requirement for timely and efficient inspection solutions, such as computer vision techniques, have been attractive in recent years, especially for those bridge components with poor accessibility. In this paper, a lightweight procedure for bridge apparent-defect detection is proposed, including a crack annotation method and crack detection. First of all, in order to save computational costs and improve generalization performance, we propose herein a relative-breakpoint annotation method to build a crack instance segmentation dataset, a critical process for a supervised vision-based crack detection method. Then, the trained models based on classic Mask RCNN and Yolact are transferred to evaluate the effectiveness of the proposed method. To verify the correctness, universality and generality of the proposed crack-detection framework, approximately 800 images are used for model training, while nearly 100 images are saved for validation. Results show that the crack instance segmentation model can achieve a level of 90% for both accuracy and recall values, with a limited dataset.

Question Classification (QC) is the fundamental task for Question Answering Systems (QASs) implementation, and is a vital task, as it helps in identifying the question category. It plays a big role in predicting the answer to a question while building a QAS. However, classifying medical questions is still a challenging task due to the complexity of medical terms. Many researchers have proposed different techniques to solve these problems, but some of these problems remain partially solved or unsolved. With the help of deep learning technology, various text-processing problems have become much easier to solve. In this paper, an improved deep learning-based model for Medical Forum Question Classification (MFQC) is proposed to classify medical questions. In the proposed model, feature representation is performed using Word2Vec, which is a word embedding model. Additionally, the features are extracted from the word embedding layer based on Convolutional Neural Networks (CNNs). Finally, a Bidirectional Long Short Term Memory (BiLSTM) network is used to classify the extracted features. The BiLSTM model analyzes the target information of the representation and then outputs the question category via a SoftMax layer. Our model achieves state-of-the-art performance by effectively capturing semantic and syntactic features from the input questions. We evaluate the proposed CNN-BiLSTM model on two benchmark datasets and compare its performance with existing methods, demonstrating its superiority in accurately categorizing medical forum questions.

Bile acids (BAs) are well known to facilitate the absorption of dietary fat and fat-soluble molecules. These unique steroids also function by binding to the ubiquitous cell membranes and nuclear receptors. As chemical signals in gut–liver axis, the presence of metabolic disorders such as nonalcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM), and even tumors have been reported to be closely related to abnormal levels of BAs in the blood and fecal metabolites of patients. Thus, the gut microbiota interacting with BAs and altering BA metabolism are critical in the pathogenesis of numerous chronic diseases. This review intends to summarize the mechanistic links between metabolic disorders and BAs in gut–liver axis, and such stage-specific BA perturbation patterns may provide clues for developing new auxiliary diagnostic means.

1,3,4-Oxadiazole derivatives are among the most studied anticancer drugs. Previous studies have analyzed the action of different 1,3,4-oxadiazole derivatives and their effects on cancer cells. This study investigated the characterization of two new compounds named 6 and 14 on HeLa and PC-3 cancer cell lines. Based on the previously obtained IC50, cell cycle effects were monitored by flow cytometry. RNA sequencing (RNAseq) was performed to identify differentially expressed genes, followed by functional annotation using gene ontology (GO), KEGG signaling pathway enrichment, and protein–protein interaction (PPI) network analyses. The tubulin polymerization assay was used to analyze the interaction of both compounds with tubulin. The results showed that 6 and 14 strongly inhibited the proliferation of cancer cells by arresting them in the G2/M phase of the cell cycle. Transcriptome analysis showed that exposure of HeLa and PC-3 cells to the compounds caused a marked reprograming of gene expression. Functional enrichment analysis indicated that differentially expressed genes were significantly enriched throughout the cell cycle and cancer-related biological processes. Furthermore, PPI network, hub gene, and CMap analyses revealed that compounds 14 and 6 shared target genes with established microtubule inhibitors, indicating points of similarity between the two molecules and microtubule inhibitors in terms of the mechanism of action. They were also able to influence the polymerization process of tubulin, suggesting the potential of these new compounds to be used as efficient chemotherapeutic agents.

Low-temperature stress limits the growth and development of foxtail millet. Freezing stress caused by sudden temperature drops, such as late-spring coldness, often occurs in the seedling stage of foxtail millet. However, the ability and coping strategies of foxtail millet to cope with such stress are not clear. In the present study, we analyzed the self-regulatory mechanisms of freezing stress in foxtail millet. We conducted a physiological study on foxtail millet leaves at −4 °C for seven different durations (0, 2, 4, 6, 8, 10, and 12 h). Longer freezing time increased cell-membrane damage, relative conductance, and malondialdehyde content. This led to osmotic stress in the leaves, which triggered an increase in free proline, soluble sugar, and soluble protein contents. The increases in these substances helped to reduce the damage caused by stress. The activities of superoxide dismutase, peroxidase, and catalase increased reactive oxygen species (ROS) content. The optimal time point for the response to freezing stress was 8 h after exposure. The transcriptome analysis of samples held for 8 h at −4 °C revealed 6862 differentially expressed genes (DEGs), among which the majority are implicated in various pathways, including the starch and sucrose metabolic pathways, antioxidant enzyme pathways, brassinolide (BR) signaling pathway, and transcription factors, according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. We investigated possible crosstalk between BR signals and other pathways and found that BR signaling molecules were induced in response to freezing stress. The beta-amylase (BAM) starch hydrolase signal was enhanced by the BR signal, resulting in the accelerated degradation of starch and the formation of sugars, which served as emerging ROS scavengers and osmoregulators to resist freezing stress. In conclusion, crosstalk between BR signal transduction, and both starch and sucrose metabolism under freezing stress provides a new perspective for improving freezing resistance in foxtail millet.

The cytochrome P450 monooxygenases (CYP450) are the largest enzyme family in plant metabolism and widely involved in the biosynthesis of primary and secondary metabolites. Foxtail millet (Setaria italica (L.) P. Beauv) can respond to abiotic stress through a highly complex polygene regulatory network, in which the SiCYP450 family is also involved. Although the CYP450 superfamily has been systematically studied in a few species, the research on the CYP450 superfamily in foxtail millet has not been completed. In this study, three hundred and thirty-one SiCYP450 genes were identified in the foxtail millet genome by bioinformatics methods, which were divided into four groups, including forty-six subgroups. One hundred and sixteen genes were distributed in thirty-three tandem duplicated gene clusters. Chromosome mapping showed that SiCYP450 was distributed on seven chromosomes. In the SiCYP450 family of foxtail millet, 20 conserved motifs were identified. Cis-acting elements in the promoter region of SiCYP450 genes showed that hormone response elements were found in all SiCYP450 genes. Of the three hundred and thirty-one SiCYP450 genes, nine genes were colinear with the Arabidopsis thaliana genes. Two hundred SiCYP450 genes were colinear with the Setaria viridis genes, including two hundred and forty-five gene duplication events. The expression profiles of SiCYP450 genes in different organs and developmental stages showed that SiCYP450 was preferentially expressed in specific tissues, and many tissue-specific genes were identified, such as SiCYP75B6, SiCYP96A7, SiCYP71A55, SiCYP71A61, and SiCYP71A62 in the root, SiCYP78A1 and SiCYP94D9 in leaves, and SiCYP78A6 in the ear. The RT-PCR data showed that SiCYP450 could respond to abiotic stresses, ABA, and herbicides in foxtail millet. Among them, the expression levels of SiCYP709B4, SiCYP71A11, SiCYP71A14, SiCYP78A1, SiCYP94C3, and SiCYP94C4 were significantly increased under the treatment of mesotrione, florasulam, nicosulfuron, fluroxypyr, and sethoxydim, indicating that the same gene might respond to multiple herbicides. The results of this study will help reveal the biological functions of the SiCYP450 family in development regulation and stress response and provide a basis for molecular breeding of foxtail millet.

Many organisms can sense and respond to magnetic fields (MFs), with migratory species in particular utilizing geomagnetic field information for long-distance migration. Cryptochrome proteins (Crys) along with a highly conserved Iron-sulfur cluster assembly protein (i.e., MagR) have garnered significant attention for their involvement in magnetoresponse (including magnetoreception). However, in vivo investigations of potential transcriptional crosstalk between Crys and MagR genes have been limited. The brown planthopper, Nilaparvata lugens, is a major migratory pest insect and an emerging model for studying MF intensity-related magnetoresponse. Here, we explored in vivo transcriptional crosstalk between Crys (Cry1 and Cry2) and MagR in N. lugens. The expression of Crys and MagR were found to be sensitive to MF intensity changes as small as several micro-teslas. Knocking down MagR expression led to a significant downregulation of Cry1, but not Cry2. The knockdown of either Cry1 or Cry2 individually did not significantly affect MagR expression. However, their double knockdown resulted in significant upregulation of MagR. Our findings clearly indicate transcriptional crosstalk between MagR and Crys known to be involved in magnetoresponse. This work advances the understanding of magnetoresponse signaling and represents a key initial step towards elucidating the functional consequences of these novel in vivo interactions.

Iron-based materials are widely applied in Fenton chemistry, and they have promising prospects in the processing of wastewater. The composition complexity and rich chemistry of iron and/or oxides, however, hamper the precise understanding of the active sites and the working mechanism, which still remain highly controversial. Herein, iron oxides of four different model systems are designed through a conventional precipitation method plus H2 reduction treatment. These systems feature Fe@Fe3O4 with abundant oxygen vacancy, Fe0 and Fe3O4 particles with interface structures, and Fe3O4-dominated nanoparticles of different sizes. These materials are applied in the decomposition of methyl orange as a model reaction to assess the Fenton chemistry. The Fe@Fe3O4 with core–shell structures exhibits significantly higher decomposition activity than the other Fe3O4-rich nanoparticles. A thin Fe3O4 layer formed by auto-oxidation of iron particles when exposed to air can boost the activity as compared with the Fe0 and Fe3O4 particles with interface structures but poor oxygen vacancy. The unique hetero-structure with the co-existence of both metallic iron and oxygen vacancy displays excellent redox propensity, which might account for the superior Fenton activity. This finding provides a new perspective to understand and design highly efficient iron-based Fenton catalysts.

Blood pressure monitoring is a significant concern in the field of healthcare, and the utilization of flexible encapsulated sensors presents a promising solution for achieving noninvasive and comfortable monitoring. This paper presents a study on the flexible encapsulation of MEMS pressure sensors and the development of an enhanced arterial tonometry method for blood pressure measurement, ultimately leading to the realization of a blood pressure monitoring system based on flexible encapsulated sensors. To improve wearer comfort and acquire reliable pulse signals, a flexible encapsulation sensor combining parylene and PDMS materials was fabricated. Additionally, to address the issue of low accuracy in blood pressure measurement, various machine learning algorithms were compared and analyzed, leading to the identification of the random forest model as the optimal regressor. Consequently, a blood pressure monitoring system based on the improved arterial tension method was designed and implemented. The experimental results demonstrate that the proposed system achieved a significant enhancement of 31.4% and 21% in the accuracy of systolic and diastolic blood pressure measurements, respectively, compared with the arterial tension method.

The low-energy task scheduling of cloud computing systems is a key issue in the field of cloud computing. Nevertheless, existing works on task scheduling lack consideration of the conflict relationship between tasks and focus on heuristic and other approximate algorithms. Thus, solving the problem of minimizing energy consumption with antiaffinity constraints between tasks and designing an efficient exact algorithm for task scheduling is a major challenge. This paper abstracts the problem into a multidimensional bin packing model with conflict constraints. The model is decomposed by the Lagrange relaxation principle and Dantzig–Wolfe decomposition principle. Moreover, we propose an accurate algorithm based on branch-and-price. The algorithm benefits from a new initial solution generation scheme based on maximum cliques and dominant resource proportion, and a multipattern branching strategy. The efficiency of the proposed branch-and-price algorithm is verified by a number of numerical experiments.

Co3O4 catalysts were prepared via carbonate precipitation and subsequent calcination under specific conditions. The different catalysts were characterized as received using several techniques and tested in the total oxidation of toluene or propane. Calcination at low temperature or under dynamic conditions resulted in Co3O4 catalysts with small crystallite sizes and large surface areas. The performances of the Co3O4 catalysts appeared to be closely related to the low-temperature reducibility. The best catalyst, Co-350D, showed a toluene oxidation rate of 44.5 nmol g−1 s−1 at 200 °C and a propane oxidation rate of 54.0 nmol g−1 s−1 at 150 °C. Meanwhile, Co-350D exhibited excellent cycling stability and decent long-term durability in both reactions.

Superamphiphobic coatings have widespread application potential in various fields, e.g., anti-icing, anti-corrosion and self-cleaning, but are seriously limited by poor mechanical stability. Here, mechanically stable superamphiphobic coatings were fabricated by spraying the suspension composed of phase-separated silicone-modified polyester (SPET) adhesive microspheres with fluorinated silica (FD-POS@SiO2) on them. The effects of non-solvent and SPET adhesive contents on the superamphiphobicity and mechanical stability of the coatings were studied. Due to the phase separation of SPET and the FD-POS@SiO2 nanoparticles, the coatings present a multi-scale micro-/nanostructure. Combined with the FD-POS@SiO2 nanoparticles of low surface energy, the coatings present outstanding static and dynamic superamphiphobicity. Meanwhile, the coatings present outstanding mechanical stability due to the adhesion effect of SPET. In addition, the coatings present outstanding chemical and thermal stability. Moreover, the coatings can obviously delay the water freezing time and decrease the icing adhesion strength. We trust that the superamphiphobic coatings have widespread application potential in the anti-icing field.

The photoreduction for hazardous Cr(VI) in industrial wastewater has been considered a “green” approach with low-cost and easy-to-go operation. SnS2 is a promising narrow bandgap photocatalyst, but its low charge carrier separation efficiency should be solved first. In this work, N-doped carbon quantum dots (CQDs) were prepared and loaded onto SnS2 nanoparticles via an in situ method. The resulting composite samples (NC@SnS2) were characterized, and their photocatalytic performance was discussed. SnS2 nanoparticles were obtained as hexagonal ones with a bandgap of 2.19 eV. The optimal doping level for NC@SnS2 was citric acid: urea:SnS2 = 1.2 mmol:1.8 mmol:3.0 mmol. It showed an average diameter of 40 nm and improved photocatalytic performance, compared to pure SnS2, following a pseudo-first-order reaction with a kinetic rate constant of 0.1144 min−1. Over 97% of Cr(VI) was photo-reduced after 30 min. It was confirmed that modification of SnS2 with CQDs can not only improve the light-harvesting ability but also stimulate the charge separation, which therefore can enhance the photoreactivity of SnS2 toward Cr(VI) reduction. The excellent stability of NC@SnS2 indicates that it is promising to be practically used in industrial wastewater purification.

Six new bibenzyls (three pairs of enantiomers), bletstrins D–F (1–3), were isolated from the ethyl acetate-soluble (EtOAc) extract of tubers of Bletilla striata (Thunb.) Rchb f. Their structures, including absolute configurations, were determined by 1D/2D NMR spectroscopy, optical rotation value, and experimental electronic circular dichroism (ECD) data analyses, respectively. Compounds 1–3 possess a hydroxyl-substituted chiral center on the aliphatic bibenzyl bridge, which represented the first examples of natural bibenzyl enantiomers from the genus of Bletilla. The antibacterial, antitumor necrosis factor (anti-TNF-α), and neuroprotective effects of the isolates have been evaluated. Compounds 3a and 3b were effective against three Gram-positive bacteria with minimum inhibitory concentrations (MICs) of 52–105 μg/ml. Compounds 2a and 2b exhibited significant inhibitory effects on TNF-α-mediated cytotoxicity in L929 cells with IC50 values of 25.7 ± 2.3 μM and 21.7 ± 1.7 μM, respectively. Subsequently, the possible anti-TNF-α mechanism of 2 was investigated by molecular docking simulation. Furthermore, the neuroprotective activities were tested on the H2O2-induced PC12 cell injury model, and compounds 2b, 3a, and 3b (10 μM) could obviously protect the cells with the cell viabilities of 57.86 ± 2.08%, 64.82 ± 2.84%, and 64.11 ± 2.52%, respectively.

The degeneration of midbrain dopaminergic (mDA) neurons, particularly in the substantia nigra pars compacta (SNc), is one of the most prominent pathological hallmarks of Parkinson’s disease (PD). To uncover the pathogenic mechanisms of mDA neuronal death during PD may provide therapeutic targets to prevent mDA neuronal loss and slow down the disease’s progression. Paired-like homeodomain transcription factor 3 (Pitx3) is selectively expressed in the mDA neurons as early as embryonic day 11.5 and plays a critical role in mDA neuron terminal differentiation and subset specification. Moreover, Pitx3-deficient mice exhibit some canonical PD-related features, including the profound loss of SNc mDA neurons, a dramatic decrease in striatal dopamine (DA) levels, and motor abnormalities. However, the precise role of Pitx3 in progressive PD and how this gene contributes to mDA neuronal specification during early stages remains unclear. In this review, we updated the latest findings on Pitx3 by summarizing the crosstalk between Pitx3 and its associated transcription factors in mDA neuron development. We further explored the potential benefits of Pitx3 as a therapeutic target for PD in the future. To better understand the transcriptional network of Pitx3 in mDA neuron development may provide insights into Pitx3-related clinical drug-targeting research and therapeutic approaches.

Different types of buildings in different climate zones have their own design specifications and specific user populations. Generally speaking, these populations have similar sensory feedbacks in their perception of environmental thermal comfort. Existing thermal comfort models do not incorporate personal thermal comfort models for specific populations. In terms of an algorithm, the existing work constructs machine learning models based on an established human thermal comfort database with variables such as indoor temperature, clothing insulation, et al., and has achieved satisfactory classification results. More importantly, such thermal comfort models often lack scientific interpretability. Therefore, this study selected a specific population as the research object, adopted the 0-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the base training unit, and constructed a shared feature-guided new TSK fuzzy classification algorithm with extra feature compensation (SFG-TFC) to explore the perception features of the population in the thermal environment of buildings and to improve the classification performance and interpretability of the model. First, the shared features of subdatasets collected in different time periods were extracted. Second, the extra features of each subdataset were independently trained, and the rule outputs corresponding to the key shared features were reprojected into the corresponding fuzzy classifiers. This strategy not only highlights the guiding role of shared features but also considers the important compensation effect of extra features; thereby, improving the classification performance of the entire classification model. Finally, the least learning machine (LLM) was used to solve the parameters of the “then” part of each basic training unit, and these output weights were integrated to enhance the generalization performance of the model. The experimental results demonstrate that SFG-TFC has better classification performance and interpretability than the classic nonfuzzy algorithms support vector machine (SVM) and deep belief network (DBN), the 0-order TSK, and the multilevel optimization and fuzzy approximation algorithm QI-TSK.

This paper proposes a colorization algorithm for infrared images based on a Conditional Generative Adversarial Network (CGAN) with multi-scale feature fusion and attention mechanisms, aiming to address issues such as color leakage and unclear semantics in existing infrared image coloring methods. Firstly, we improved the generator of the CGAN network by incorporating a multi-scale feature extraction module into the U-Net architecture to fuse features from different scales, thereby enhancing the network’s ability to extract features and improving its semantic understanding, which improves the problems of color leakage and blurriness during colorization. Secondly, we enhanced the discriminator of the CGAN network by introducing an attention mechanism module, which includes channel attention and spatial attention modules, to better distinguish between real and generated images, thereby improving the semantic clarity of the resulting infrared images. Finally, we jointly improved the generator and discriminator of the CGAN network by incorporating both the multi-scale feature fusion module and attention mechanism module. We tested our method on a dataset containing both infrared and near-infrared images, which retains more detailed features while also preserving the advantages of existing infrared images. The experimental results show that our proposed method achieved a peak signal-to-noise ratio (PSNR) of 16.5342 dB and a structural similarity index (SSIM) of 0.6385 on an RGB-NIR (Red, Green, Blue-Near Infrared) testing dataset, representing a 5% and 13% improvement over the original CGAN network, respectively. These results demonstrate the effectiveness of our proposed algorithm in addressing the issues of color leakage and unclear semantics in the original network. The proposed method in this paper is not only applicable to infrared image colorization but can also be widely applied to the colorization of remote sensing and CT images.

Antibiotic resistance has been a serious health threat of widespread concern, as antibiotics are difficult to degrade effectively in the environment. In this study, sulfamethoxazole (SMZ), a common antibiotic in an aqueous solution, was irradiated by low-energy X-ray to investigate the effect of the absorption dose, initial concentration, initial pH, irradiation energy and other conditions on the degradation of SMZ, as well as the kinetic mechanism of SMZ degradation. The results showed that low-energy X-ray irradiation could effectively degrade SMZ in an aqueous solution at different initial concentrations and acid-base degrees, and the degradation effect of irradiation in the range of 60–80 keV is independent of the energy of X-rays. The degradation rate of the SMZ solution, with an initial concentration of 10.70 mg/L, was 94.6% at an absorbed dose of 890 Gray, with a pH of 3.5. Similar to other works on the degradation of antibiotics by ionizing irradiation, the degradation of SMZ solutions conformed to a pseudo-first-order kinetic mechanism.

The automated detection of distress such as cracks or potholes is a key basis for assessing the condition of pavements and deciding on their maintenance. A fine-grained pavement distress-detection algorithm based on integrated data using a digital twin is proposed to solve the challenges of the insufficiency of high-quality negative samples in specific scenarios An asphalt pavement background model is created based on UAV-captured images, and a lightweight physical engine is used to randomly render 5 types of distress and 3 specific scenarios to the background model, generating a digital twin model that can provide virtual distress data. The virtual data are combined with real data in different virtual-to-real ratios (0:1 to 5:1) to form an integrated dataset and used to fully train deep object detection networks for fine-grained detection. The results show that the YOLOv5 network with the virtual-to-real ratio of 3:1 achieves the best average precision for 5 types of distress (asphalt pavement MAP: 75.40%), with a 2-fold and 1.5-fold improvement compared to models developed without virtual data and with traditional data augmentation, respectively, and achieves over 40% recall in shadow, occlusion and blur. The proposed approach could provide a more reliable and refined automated method for pavement analysis in complex scenarios.