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A two-year long-term workshop centered around a phenotypic whole-cell screening strategy for integrating cutting-edge knowledge and discovery-based laboratory experiences was developed to serve as a preparatory course for enhancing the teaching quality of the upper-level discovery-based research internship curriculum for pharmacy major undergraduates. The workshop comprises four modules that integrate lessons and experiments into a genuine research project, which initiates with a medicinal-chemistry course, run concurrently in the second year and culminates with the discovery-based research internship curriculum for pharmacy major students as a corequisite in the last year. The effective implementation of the featured pedagogical approaches in the modules is detailed as described. A group of students (N = 4) passed the instructor-student two-way choice and were enrolled in the senior-level phenotypic hit discovery laboratory project. The students synthesized a small library of molecules, tested them in an antibacterial assay, and further designed new compounds to analyze structure-activity relationships. Furthermore, positive feedback from students revealed that the phenotypic hit discovery oriented workshop enabled them to gain in-depth cutting-edge knowledge and provided an understanding of how medicinal chemists engage in new drug discovery research. The student's early exposure to troubleshooting or exploring unexpected results in an authentic scientific environment inspired them with ambition and confidence to engage with new drug discovery. We hope that this workshop will serve as a springboard for those wishing to educate medicinal chemists at an undergraduate level with modern strategies that are relevant to the current and future drug discovery landscape.

Interdisciplinary experiments within chemistry are very important for the improvements in the overall quality of the education received by students. In this work, we describe an interdisciplinary experiment that was successfully completed by undergraduate students. This training integrates multiple disciplines including natural product medicinal chemistry, organic synthesis, and instrumental analysis. The experiment involved the extraction of crude berberine hydrochloride (BBH) from "Coptidis rhizoma," recrystallization for purification, and qualitative and quantitative analysis of purified BBH using the "Brick-L" miniature mass spectrometer (mini-MS). The use of mini-MS analysis is the highlight of this work. Mini-MS offers an unprecedented opportunity to incorporate MS analysis into regular undergraduate teaching due to its small size, low power consumption, and portability. The modular design of the mini-MS instrument used in this work allows instructors to disassemble the instrument on-site in the laboratory to show and introduce the actual structure and components inside the instrument to students. Students not only gain hands-on experience with technology but also have the opportunity to see some of the "inner workings" rather than simply treating the mass spectrometer as "a black box". This greatly helped students understand the construction and analysis principles of MS and motivated students to learn about scientific instruments. This comprehensive experiment can help undergraduate students to improve their overall ability and get in touch with the frontiers of analytical chemistry. This teaching practice can also provide a viable case for comprehensive chemistry experiments in other related disciplines.

Here, we present an interdisciplinary educational project intended for high school chemistry that is focused on the establishment of a mathematical model to describe the relationship between conductivity and concentration, while linking chemistry to mathematics with the help of computer software. The project was designed to help high school students engage in methods and comprehend advanced topics beyond the typical high school chemistry curriculum, as well as to resolve a problem that no single disciplinary perspective is able to solve. This project provided students with a simple method to build mathematical models, which could be used to help them discover the relationship between discrete experimental data and mathematical equations. A postinstructional survey showed that, after completing the project, more students knew how to construct mathematical equations that fit discrete data.

This study explores the feasibility of using Cu/AZO thin films as low-emissivity materials with antibacterial properties, fabricated using the linear sputtering method. The linear sputtering technique deposits thin films onto continuous substrates, offering high throughput, uniform coatings, and precise control over film properties. In this research, Cu/AZO thin films underwent either vacuum annealing or hydrogen plasma annealing treatments. The Cu layer imparts antibacterial properties, while the AZO layer primarily provides thermal insulation. Experimental results show that annealing treatments enhance both photoelectric performance and antibacterial capability. Annealed Cu/AZO films exhibit lower resistivity and emissivity. Among the samples, those subjected to vacuum annealing at 400 °C are most suitable for low-emissivity applications, with an average visible light transmittance of 60%, an emissivity of 0.16, and an antibacterial activity value of 8.8. The Cu/AZO films proposed in this study effectively combine antibacterial and thermal insulation properties, making them relevant for the field of green materials.

To achieve rapid and accurate identification of valuable wood products, an analytical method was developed by combining electric soldering iron cauterization with soft ionization by chemical reaction in transfer-mass spectrometry (SICRIT-MS). SICRIT is a flow-through dielectric barrier discharge ionization technique pioneered by Zenobi et al. in 2016. The electric soldering iron cauterization-SICRIT-MS method requires no sample pretreatment, easy operation and a single analysis in less than 5 s, meeting the demands of rapid analysis. Operating parameters for the soldering iron and SICRIT ion source were optimized to achieve maximum total ion current intensity under soldering iron temperature of 450 ℃, ion source AC voltage amplitude of 2 000 V, and sample transfer line temperature of 150 ℃. With the optimized parameters, the SICRIT-MS method was applied to analyze valuable wood samples, including 29 certified standard wood samples and 6 online-purchased real samples, resulting in a dataset of 210 sets of mass spectral fingerprint data. Based on the mass spectral fingerprint data acquired under positive ion mode, a predictive model was trained using the random forest algorithm. The random forest model underwent optimization for the number of decision trees, max feature algorithm, and feature selection criteria, was evaluated through out-of-bag and 10-fold cross-validation. The results showed the error rates of out-of-bag and 10-fold cross-validation are 4.76% and 4.74%, respectively. The established random forest model can accurately distinguish wood samples from the genera Dalbergia, Guibourtia, and Pterocarpus with a classification accuracy of larger than 95%. The importance of features in distinguishing the three wood genera was investigated through binary classification modeling, revealing features 269.1, 270.1, 255.1, 159.0, 182.1, 102.1 and 83.1 as crucial in classification. These features may correspond to characteristic compounds in different wood species or differences in the content of the same compound across species. The predictive model was successfully applied to rapid identification of genera in valuable wood products sold online. Three purchased Guibourtia samples are confirmed as authentic, while the other three are not identified as the claimed genera. This method provides a scientific basis and experimental reference for authenticity identification and quality evaluation.

The supercooled liquid fraction (SLF) in mixed-phase clouds (MPCs) is an essential variable of cloud microphysical processes and climate sensitivity. However, the SLF is currently calculated in spaceborne remote sensing only as the cloud phase–frequency ratio of adjacent pixels, which results in a loss of the original resolution in observations of cloud liquid or ice content within MPCs. Here, we present a novel method for retrieving the SLF in MPCs based on the differences in radiative properties of supercooled liquid droplets and ice particles at visible (VIS) and shortwave infrared (SWI) channels of the geostationary Himawari-8. Liquid and ice water paths are inferred by assuming that clouds are composed of only liquid or ice, with the real cloud water path (CWP) expressed as a combination of these two water paths (SLF and 1-SLF as coefficients), and the SLF is determined by referring to the CWP from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The statistically relatively small cloud phase spatial inhomogeneity at a Himawari-8 pixel level indicates an optimal scene for cloud retrieval. The SLF results are comparable to global SLF distributions observed by active instruments, particularly for single-layered cloud systems. While accessing the method's feasibility, SLF averages are estimated between 74 % and 78 % in Southern Ocean (SO) stratocumulus across seasons, contrasting with a range of 29 % to 32 % in northeastern Asia. The former exhibits a minimum SLF around midday in summer and a maximum in winter, while the latter trend differs. This novel algorithm will be valuable for research to track the evolution of MPCs and constrain the related climate impact.

Abstract Genetically encoded libraries play a crucial role in discovering structurally rigid, high-affinity macrocyclic peptide ligands for therapeutic applications. Bicyclic peptides with metal centres like bismuth were recently developed as a new type of constrained peptide with notable affinity, stability and membrane permeability. This study represents the genetic encoding of peptide-bismuth and peptide-arsenic bicycles in phage display. We introduce bismuth tripotassium dicitrate (gastrodenol) as a water-soluble bismuth(III) reagent for phage library modification and in situ bicyclic peptide preparation, eliminating the need for organic co-solvents. Additionally, we explore arsenic(III) as an alternative thiophilic element that is used analogously to our previously introduced bicyclic peptides with a bismuth core. The modification of phage libraries and peptides with these elements is instantaneous and entirely biocompatible, offering an advantage over conventional alkylation-based methods. In a pilot display screening campaign aimed at identifying ligands for the biotin-binding protein streptavidin, we demonstrate the enrichment of bicyclic peptides with dissociation constants two orders of magnitude lower than those of their linear counterparts, underscoring the impact of structural constraint on binding affinity.

The challenge of meat quality degradation due to transportation difficulties in high-altitude plateaus underscores the importance of an efficient thawing process for Tibetan pork to ensure its quality. This study compared four thawing methods ultrasound thawing (UT), refrigerator thawing (RT), hydrostatic thawing (HT), and microwave thawing (MT) to assess their impact on the quality of Tibetan pork, focusing on thawing loss, tenderness, color variation, and alterations in protein secondary structure and moisture content. Additionally, the study examined the impact of thawing on the metabolites of Tibetan pork using metabolomics techniques. The results indicated that UT yielded the highest quality samples. UT significantly accelerated the thawing rate and had minimal impact on tenderness compared to traditional thawing methods. Moreover, protein and lipid oxidation levels were reduced by UT treatment. Furthermore, it enhanced the binding capacity of protein and water molecules, reduced drip loss, and maintained meat color stability. What’s more, amino acid metabolites such as l-glutamic acid, l-proline, oxidized glutathione, and 1-methylhistidine played a significant role in thawing oxidation in Tibetan pork, exhibiting a positive correlation with protein oxidation. UT resulted in a notable decrease in the levels of hypoxanthine and 2-aminomethylpyrimidine, contributing to the reduction of bitterness in the thawed meat and consequently enhancing the freshness of Tibetan pork. This study offers novel insights into understanding the biological changes occurring during the thawing process, while also furnishing a theoretical framework and technical assistance to improve the quality of Tibetan pork and propel advancements in food processing technology.

Abstract The development of tumor drug microcarriers has attracted considerable interest due to their distinctive therapeutic performances. Current attempts tend to elaborate on the micro/nano‐structure design of the microcarriers to achieve multiple drug delivery and spatiotemporal responsive features. Here, the desired hydrogel microspheres are presented with spatiotemporal responsiveness for the treatment of gastric cancer. The microspheres are generated based on inverse opals, their skeleton is fabricated by biofriendly hyaluronic acid methacrylate (HAMA) and gelatin methacrylate (GelMA), and is then filled with a phase‐changing hydrogel composed of fish gelatin and agarose. Besides, the incorporated black phosphorus quantum dots (BPQDs) within the filling hydrogel endow the microspheres with outstanding photothermal responsiveness. Two antitumor drugs, sorafenib (SOR) and doxorubicin (DOX), are loaded in the skeleton and filling hydrogel, respectively. It is found that the drugs show different release profiles upon near‐infrared (NIR) irradiation, which exerts distinct performances in a controlled manner. Through both in vitro and in vivo experiments, it is demonstrated that such microspheres can significantly reduce tumor cell viability and enhance the efficiency in treating gastric cancer, indicating a promising stratagem in the field of drug delivery and tumor therapy.

Eliciting anti-tumor immune responses and improving the tumor microenvironment crucial for boosting the effectiveness of anti-PD-1 immunotherapy. Tumor-associated macrophages (TAMs), the primary types of immune cells infiltrating tumors, play a critical role in the formation of an immunosuppressive microenvironment. In this study, we constructed a novel Evans Blue (EB)-based in vivo self-assembled nanocarrier system, mUNO-EB-ICG-Fc@Alb nanoparticles (designated as MA NPs), for targeted imaging and clearance of M2-TAMs to elicit antitumor immunotherapy of PD-1 inhibitor. In vitro experiments demonstrated the specific fluorescence imaging and killing effect of MA NPs on M2-TAMs. In vivo experiments shown that MA NPs-induced chemodynamic therapy (CDT) successfully reversed the tumor immunosuppressive microenvironment (ITM), promoted intratumoral infiltration of T lymphocytes, and ultimately enhancing the anti-tumor immunotherapy effect of PD-1 inhibitors. This study might provide good inspiration for improving the therapeutic efficacy of cancer immunotherapy.

Background: Osteoarthritis (OA) is a chronic joint disease characterized by degeneration of the articular cartilage or inflamed joints. Britanin is a guaiacyl-type sesquiterpene lactone possessing anti-inflammatory and antioxidant activities. Methods: In this study, western blotting, Reverse-transcription PCR (RT-PCR), and immunofluorescent staining method were used to clarifying the molecular mechanisms of britanin in alleviating chondrocyte ferroptosis. The effect of britanin on reactive oxygen species (ROS) and lipid peroxidation levels in chondrocytes was examined by Dihydroethidium (DHE) stain and fluorescent dye BODIPY581 / 591 C11. The destabilized medial meniscus (DMM) model was used to mimic OA, then britanin was injected into the knee articular cavity. The morphological analysis was performed by hematoxylin and eosin (H&E) staining, Safranin O-Fast green staining and Micro-computed tomography (Micro-CT) analysis. Immunohistochemical analysis and Immunofluorescence assay was conducted to verify the effect of britanin in vivo. Results: The results revealed that britanin treatment regulates the expression of chondrocyte extracellular matrix–related factors, such as ADAMTS5, MMP13, SOX9, aggrecan, and COL2A1. Britanin also significantly reversed the influence of interleukin (IL)-1β on the levels of reactive oxygen species and lipid peroxidation and upregulated glutathione peroxidase 4 (GPX4) expression in IL-1β-induced chondrocytes. Erastin could alleviate the effects of britanin on chondrocytes. Moreover, britanin could prevent IL-1β-induced oxidative stress and ferroptosis in chondrocytes. Britanin also upregulated the expression of nuclear factor E2–related factor 2 (Nrf2) in IL-1β-induced chondrocytes, and Nrf2 activator played a similar role as that of britanin in IL-1β-induced chondrocytes. Furthermore, injecting britanin into the knee joints of OA rats alleviated cartilage destruction caused by surgical resection. Conclusions: These findings confirmed that britanin can inhibit ferroptosis in OA chondrocytes through the Nrf2-GPX4 pathway. Thus, britanin has the potential to be developed as a drug for OA therapy.

Data independent acquisition tensor (DIAT), a specialized mass spectrometry data format, is intricately crafted for acquiring the meticulous processing and analysis of proteomics data through the advanced methodology of data independent acquisition (DIA). Compared to conventional approaches, DIAT technique has the advantages of the seamless processing of data visualization and efficient deep learning model training, ensuring a convenient user experience. Despite these merits, the DIAT mass spectrometry data format remains a relatively recent introduction. As a consequence, comprehensive principles and methodologies elucidating the nuances of this data format are predominantly found within the confines of specialized literatures. Furthermore, the absence of robust software platform supporting for the utilization of DIAT format data is a key impediment, hindering the widespread application in various scientific domains. In response to these challenges, a meticulously designed software solution for the comprehensive handling and analyzing of DIAT data was introduced in this study. Leveraging the robust capabilities of the PyQt framework, the software embodied a spectrum of functionalities intricately tied to DIAT technique, including DIAT data format conversion, the intuitive visualization of DIAT data, the training of classification models using DIAT data, and the accurate prediction of DIAT data labels through the utilization of trained models. Crucially, each of these functionalities underwent rigorous test using authentic mass spectrometry data. The test results unequivocally showcased the prowess of the DIAT computational software, empowering users to engage effortlessly in complex DIAT data analysis. This not only lowered the entry barriers for users, but also injected a renewed vigor into the field of DIA mass spectrometry data analysis. In summary, DIAT emerged as a trailblazing mass spectrometry data format meticulously tailored for the expeditious processing and analysis of proteomics data acquired through the sophisticated techniques of DIA. This study introduced a groundbreaking software solution, which was developed on the robust PyQt framework, aimed at surmounting the existing limitations associated with the nascent DIAT format. The implemented software was validated through rigorous real-world data testing, not only facilitated sophisticated DIAT data analysis but also significantly contributed to enhance the accessibility and dynamism of DIA mass spectrometry data analysis. This innovative approach will hold immense promise for advancing research and applications in the ever-evolving field of proteomics.

Composite coating technology was used to prepare a modified polyamide 6 (PA6) polymer material masterbatch with low mutual interference and good spinnability using molybdenum oxide, tungsten trioxide, graphene oxide, etc., as modifiers. Experiment shows that using nanoscale composite-coated modified masterbatches capable of spectral heating and heat storage in the preparation of nylon 6 fiber, adding modified masterbatches with a mass ratio of 6% online, and controlling various process parameters can result in good functionality and usability as well as enable consistent production. Optimal production conditions are achieved by utilizing pre-dried PA6 chips with a moisture content of 420 ppm. The master batch is subjected to drying at a temperature of 95°C for 12 h. During this process, the master batch is introduced at an addition ratio of 6%. Subsequent spinning is conducted at a speed of 4,300 m·min−1 and a temperature of 255°C. Cooling is facilitated by air set at a temperature of 17°C, flowing at a speed of 0.45 m·min−1. In the production line, one roller operates without heating, while the second roller is maintained at a temperature range of 160°C. The stretching process is performed at a ratio of 1.3. To ensure proper lubrication, an oil rate of 1.3% is applied. These meticulously controlled process parameters collectively contribute to the most favorable production status.

Abstract Immobilization of graphene quantum dots (GQDs) on a solid support is crucial to prevent GQDs from aggregation in the form of solid powder and facilitate the separation and recycling of GQDs after use. Herein, spatially dispersed GQDs are post-synthetically coordinated within a two-dimensional (2D) and water-stable zirconium-based metal–organic framework (MOF). Unlike pristine GQDs, the obtained GQDs immobilized on 2D MOF sheets show photoluminescence in both suspension and dry powder. Chemical and photoluminescent stabilities of MOF-immobilized GQDs in water are investigated, and the use of immobilized GQDs in the photoluminescent detection of copper ions is demonstrated. Findings here shed the light on the use of 2D MOFs as a platform to further immobilize GQDs with various sizes and distinct chemical functionalities for a range of applications.

Abstract Adsorbents with high adsorption efficiency and excellent biosafety for biomedical applications are highly required. MXene is a promising candidate owning these advantages, yet pristine MXene faces dilemmas including insufficient utility of surface site as well as limited processibility. Here, we develop MXene‐encapsulated porous microcapsules via microfluidics. The microcapsules have a biomass hydrogel shell that provides robust support for MXene in the core, by which the microcapsules are endowed with high MXene dosage and remarkable biosafety. Additionally, the MXene nanoflakes assemble into a three‐dimensional network via metal ion‐induced gelation, thereby avoiding restacking and significantly improving surface utilization. Moreover, a freeze‐pretreatment of the microcapsules during preparation results in the formation of a macroporous structure in the shell, which can facilitate the diffusion of the target molecules. These features, combined with additional magneto‐responsiveness rendered by the incorporation of magnetic nanoparticles, contribute to prominent performances of the microcapsules in cleaning uremia toxins including creatinine, urea, and uric acid. Thus, it is anticipated that the MXene‐encapsulated microcapsules will be promising adsorbents in dialysis‐related applications, and the combination of microfluidic encapsulation with metal ion gelation will provide a novel approach for construction of hybrid MXene materials with desired functions.

Summary: C3N4 is widely applied in the synthesis of single-atom catalysts. However, understanding on the active site and the reaction mechanism is not fully in consensus. Especially, bare studies have considered the coordination environment of the single-atomic dopant and the effect of nitrogen vacancy on C3N4. In this study, we found that the presence of nitrogen vacancies promotes the activation of water and reduces the activation energy barrier for hydrogen generation. The results show that a synergistic effect between single-atom Pt and nitrogen vacancies enables the catalyst to achieve a superior hydrogen production rate of 3,890 μmol/g/h, which is 16.8 times higher than that of pristine C3N4. Moreover, the catalyst is also applicable for photocatalytic hydrogen production from seawater without significantly decreased hydrogen production rate. This study paves the way for the rational design and optimization of next-generation photocatalysts for sustainable energy applications, particularly in solar-driven hydrogen production.

Antimicrobial photodynamic therapy (aPDT) has emerged as a highly promising strategy for non-antibiotic treatment of infections due to its unique advantages in efficient bactericidal action and reduction of drug resistance. The natural photosensitizing properties of curcumin (Cur) are widely acknowledged; however, its limited bioavailability has impeded its practical application. In this study, we developed a nanomaterial called Cur@ZIF-8@BA by encapsulating Cur within ZIF-8 and modifying the surface with boric acid (BA). The Cur@ZIF-8@BA exhibits pH-responsive properties and enhances bacterial binding, thereby effectively promoting photodynamic therapy. Moreover, its antibacterial activity against E. coli, Staphylococcus aureus and A. baumannii is significantly increased in the presence of light compared to a dark environment. The mechanism behind this may be that BA increases the affinity of Cur@ZIF-8@BA towards bacteria, and making released Zn2+ and BA from the nanomaterial increase bacterial cell membrane permeability. This facilitates efficient delivery of Cur into bacterial cells, resulting in generation of abundant reactive oxygen species (ROS) and subsequent bactericidal activity. In conclusion, our prepared Cur@ZIF-8@BA holds great promise as a photodynamically mediated antimicrobial strategy.

In May–June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the masses of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models: MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AODs) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas, whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of spaceborne aerosol lidars, e.g., being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space.

The giant magnetoresistance effect in two-dimensional (2D) magnetic materials has sparked substantial interest in various fields; including sensing; data storage; electronics; and spintronics. Their unique 2D layered structures allow for the manifestation of distinctive physical properties and precise performance regulation under different conditions. In this review, we present an overview of this rapidly developing research area. Firstly, these 2D magnetic materials are catalogued according to magnetic coupling types. Then, several vital effects in 2D magnets are highlighted together with theoretical investigation, such as magnetic circular dichroism, magneto-optical Kerr effect, and anomalous Hall effect. After that, we forecast the potential applications of 2D magnetic materials for spintronic devices. Lastly, research advances in the attracting magnons, skyrmions and other spin textures in 2D magnets are discussed.

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

This study compared the advantages and disadvantages of various corrosion protection methods for steel rebars and clarified the advantages of the cathodic protection (CP) method in the application of corrosion protection in marine structures. The advantages and disadvantages of sacrificial anodes and impressed current technology for the CP of steel rebars in marine structures were further discussed in detail, and the feasibility of CP applications in practical engineering was evaluated.

The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing, affecting up to 30% of the population, with approximately 20% of cases occurring in non-obese individuals. The recent shift to the term metabolic dysfunction-associated steatosis liver disease (MASLD) highlights the disease’s heterogeneity. However, there are no well-established animal models replicating non-obese NAFLD (NO-NAFLD). This study aimed to evaluate the relevance of the high-fat diet (HFD) combined with the propylthiouracil (PTU)-induced rat model in mimicking the histopathology and pathophysiology of NO-NAFLD. We first analyzed metabolic and clinical parameters between NO-NAFLD patients (Average BMI = 21.96 kg/m2) and obese NAFLD patients (Average BMI = 29.7 kg/m2). NO-NAFLD patients exhibited significantly higher levels of carnitines, phospholipids, and triglycerides. In the animal model, we examined serum lipid profiles, liver inflammation, histology, and transcriptomics. Hepatic steatosis in the HFD+PTU model at week 4 was comparable to that of the HFD model at week 8. The HFD+PTU model showed higher levels of carnitines, phospholipids, and triglycerides, supporting its relevance for NO-NAFLD. Additionally, the downregulation of lipid synthesis-related genes indicated differences in lipid accumulation between the two models. Overall, the HFD+PTU-induced rat model is a promising tool for studying the molecular mechanisms of NO-NAFLD.

Micro- and nano-bubble jet stirring, as an emerging technology, shows great potential in complex mineral sorting. Flow field characteristics and structural parameters of the gas–liquid two-phase system can lead to uneven bubble distribution inside the reaction vessel. Gas–liquid mixing uniformity is crucial for evaluating stirring effects, as increasing the contact area enhances reaction efficiency. To improve flotation process efficiency and resource recovery, further investigation into flow field characteristics and structural optimization is necessary. The internal flow field of the jet-stirred tank was analyzed using computational fluid dynamics (CFDs) with the Eulerian multiphase flow model and the Renormalization Group (RNG) k − ε turbulence model. Various operating (feeding and aerating volumes) and structural parameters (nozzle direction, height, inner diameter, and radius ratio) were simulated. Dimensionless variance is a statistical metric used to assess gas–liquid mixing uniformity. The results indicated bubbles accumulated in the middle of the vessel, leading to uneven mixing. Lower velocities resulted in low gas volume fractions, while excessively high velocities increased differences between the center and near-wall regions. Optimal mixing uniformity was achieved with a circumferential nozzle direction, 80 mm height, 5.0 mm inner diameter, and 0.55 radius ratio.

The surge in ultra-high-definition video content has intensified the demand for advanced video compression techniques. Video encoding preprocessing can improve coding efficiency while ensuring a high degree of compatibility with existing codecs. Existing video encoding preprocessing methods are limited in their ability to fully exploit redundant features in video data and recover high-frequency details, and their network architectures often lack compatibility with neural video encoders. To addressing these challenges, we propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method to improve the efficiency of deep learning-based neural video encoders. Firstly, our approach integrates a degradation compensation module to mitigate encoding noise and boost feature extraction efficiency. Secondly, a lightweight fully convolutional neural network is employed, which utilizes residual learning and knowledge distillation to refine and suppress irrelevant features across spatial and channel dimensions. Furthermore, to maximize the use of redundant information, we incorporate Dense Blocks, which can enhance and reconstruct important features in the video data during preprocessing. Finally, the preprocessed frames are then mapped from pixel space to feature space through the Dense Feature-Enhanced Video Compression (DFVC) module, which improves motion estimation and compensation accuracy. The experimental results demonstrate that, compared to neural video encoders, the MDER method can reduce bits per pixel (Bpp) by 0.0714 and 0.0536 under equivalent PSNR and MS-SSIM conditions, respectively. These results demonstrate significant improvements in compression efficiency and reconstruction quality, highlighting the effectiveness of the MDER preprocessing method and its compatibility with neural video codec workflows.

The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used graph deep learning methods to identify cancer driver genes based on biological networks. However, incompleteness and the noise of the networks will weaken the performance of models. To address this, we propose a cancer driver gene identification method based on self-supervision for graph convolutional networks, which can efficiently enhance the structure of the network and further improve predictive accuracy. The reliability of SSCI is verified by the area under the receiver operating characteristic curves (AUROC), the area under the precision-recall curves (AUPRC), and the F1 score, with respective values of 0.966, 0.964, and 0.913. The results show that our method can identify cancer driver genes with strong discriminative power and biological interpretability.

The alternative splicing of pre-mRNA generates distinct mRNA variants from a pre-mRNA, thereby modulating a gene’s function. The splicing of pre-mRNA depends on splice sites and regulatory elements in pre-mRNA, as well as the snRNA and proteins that recognize these sequences. Among these, RNA-binding proteins (RBPs) are the primary regulators of pre-mRNA splicing and play a critical role in the regulation of alternative splicing by recognizing the elements in pre-mRNA. However, little is known about the function of RBPs in stress response in plants. Here, we summarized the RBPs involved in the alternative splicing of pre-mRNA and their recognizing elements in pre-mRNA, and the recent advance in the role of RBP-mediated alternative splicing in response to abiotic stresses in plants. This review proposes that the regulation of pre-mRNA alternative splicing by RBPs is an important way for plants to adapt to abiotic stresses, and the regulation of alternative splicing by RBPs is a promising direction for crop breeding.

Plants may encounter abiotic stresses, such as drought, flooding, salinity, and extreme temperatures, thereby negatively affecting their growth, development, and reproduction. In order to enhance their tolerance to such stresses, plants have developed intricate signaling networks that regulate stress-responsive gene expression. For example, Arabidopsis Enhanced Drought Tolerance1/HOMEODOMAIN GLABROUS 11 (AtEDT1/HDG11), one of the transcription factor genes from the group IV of homeodomain-leucine zipper (HD-ZIP) gene family, has been shown to increase drought tolerance in various transgenic plants. However, the underlying molecular mechanisms of enhanced stress tolerance remain unclear. In this study, we identified a homologous gene related to AtEDT1/HDG11, named FaTEDT1L, from the transcriptome sequencing database of cultivated strawberry. Phylogenetic analysis revealed the close relationship of FaTEDT1L with AtEDT1/HDG11, which is one of the group IV members of the HD-ZIP gene family. Yeast one-hybrid analysis showed that FaTEDT1L functions as a transcriptional activator. Transgenic Arabidopsis plants overexpressing FaTEDT1L under the control of the cauliflower mosaic virus (CaMV) 35S promoter exhibited significantly enhanced tolerance to osmotic stress (both drought and salinity) when compared to the wild-type (WT) plants. Under osmotic stress, the average root length was 3.63 ± 0.83 cm, 4.20 ± 1.03 cm, and 4.60 ± 1.14 cm for WT, 35S::FaTEDT1L T2 #3, and 35S:: FaTEDT1L T2 #5, respectively. Substantially increased root length in 35S::FaTEDT1L T2 #3 and 35S::FaTEDT1L T2 #5 was noted when compared to the WT. In addition, the average water loss rates were 64%, 57.1%, and 55.6% for WT, 35S::FaTEDT1L T2 #3, and 35S::FaTEDT1L T2 #5, respectively, after drought treatment, indicating a significant decrease in water loss rate of 35S:: FaTEDT1L T2 #3 and 35S::FaTEDT1L T2 #5 is a critical factor in enhancing plant drought resistance. These findings thus highlight the crucial role of FaTEDT1L in mitigating drought and salt stresses and regulating plant osmotic stress tolerance. Altogether, FaTEDT1L shows its potential usage as a candidate gene for strawberry breeding in improving crop resilience and increasing agricultural productivity under adverse environmental conditions.

Medical ultrasound imaging is extensively employed for diagnostic purposes. However, image quality remains a major obstacle to achieving greater accuracy. Conventional supervised deep learning denoising methods often rely on matched noise-free and noisy image pairs, which can be highly challenging in practical ultrasound applications. Moreover, due to the limitations associated with independent noise, existing unsupervised denoising methods such as Neighbor2Neighbor are unable to efficiently address correlated noise in ultrasound images. Meanwhile, these methods utilize a random neighborhood downsampling technique, frequently resulting in pixel loss. Hence, this study proposes a novel Neighbor2Neighbor algorithm, which reconstructs ultrasound images by improving the downsampling approach. Moreover, it incorporates a structural similarity index and a regularization term, thereby enhancing its ability to suppress both independent and correlated noise. Extensive experiments on an ultrasound image dataset demonstrate that the proposed Neighbor2Neighbor algorithm outperforms the state-of-the-art baseline algorithms in peak signal-to-noise ratio (PSNR), mean structural similarity index measure (MSSIM), feature similarity index measure (FSIM), and edge preservation index (EPI).

Suxiao Jiuxin pill (SX) was a commonly used traditional Chinese medical preparation for treating acute myocardial infarction. However, fewer studies have been reported for comprehensively investigating the pharmacokinetic of the non-volatile and volatile chemical constituents in SX. For current study, a comprehensive and sensitive quantitation of twenty-one compounds in rat plasma was developed and validated by integrating UPLC-QQQ-MS/MS with GC-MS after oral administration of SX. The pharmacokinetic study was performed under acceptable specificity, linearity, sensitivity, precision and accuracy, recovery, matrix effect and stability. Ultimately, borneol, isoborneol, senkyunolide A, senkyunolide Ⅰ, ligustilide, ferulic acid, neocnidilide, senkyunolide H and levistolide A were detected. Their ranges of Tmax, Cmax, T1/2z and AUC (0-∞) were 0.07 ∼ 0.33 h, 5.24 ∼ 683.35 μg/L, 0.29 ∼ 1.08 h and 6.55 ∼ 474.62 μg/L*h, respectively. This study would be meaningful in better revealing the therapeutic material basis of SX. Furthermore, it could provide reference on pharmacological effect of SX against acute myocardial infarction.