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Lipidomics is an emerging discipline that systematically studies the various types, functions, and metabolic pathways of lipids within living organisms. This field compares changes in diseases or drug impact, identifying biomarkers and molecular mechanisms present in lipid metabolic networks across different physiological or pathological states. Through employing analytical chemistry within the realm of lipidomics, researchers analyze traditional Chinese medicine (TCM). This analysis aids in uncovering potential mechanisms for treating diverse physiopathological conditions, assessing drug efficacy, understanding mechanisms of action and toxicity, and generating innovative ideas for disease prevention and treatment. This manuscript assesses recent literature, summarizing existing lipidomics technologies and their applications in TCM research. It delineates the efficacy, mechanisms, and toxicity research related to lipidomics in Chinese medicine. Additionally, it explores the utilization of lipidomics in quality control research for Chinese medicine, aiming to expand the application of lipidomics within this field. Ultimately, this initiative seeks to foster the integration of traditional medicine theory with modern science and technology, promoting an organic fusion between the two domains.

Many existing control techniques proposed in the literature tend to overlook faults and physical limitations in the systems, which significantly restricts their applicability to practical, real-world systems. Consequently, there is an urgent necessity to advance the control and synchronization of such systems in real-world scenarios, specifically when faced with the challenges posed by faults and physical limitations in their control actuators. Motivated by this, our study unveils an innovative control approach that combines a neural network-based sliding mode algorithm with fuzzy logic systems to handle nonlinear systems. This proposed controller is further enhanced with an intelligent observer that takes into account potential faults and limitations in the control actuator, and it integrates a fuzzy logic engine to regulate its operations, thus reducing system chatter and increasing its adaptability. This strategy enables the system to maintain regulation in the face of control input constraints and faults and ensures that the closed-loop system will achieve convergence within a finite-time frame. The detailed explanation of the control design confirms its finite-time stability. The robust performance of the proposed controller applied to autonomous and non-autonomous systems grappling with control input limitations and faults demonstrates its effectiveness.

Underwater vehicles are crucial for advancing underwater geological exploration, military training, and the marine industry. However, the accuracy of lithium-ion battery SOC estimation algorithms for these vehicles deteriorates with depth changes due to variations in seawater temperature, leading to limited generalization. This paper proposes a SOC estimation framework that combines the UKF with the MLP-Mixer neural network for lithium-ion batteries in underwater vehicles. The proposed framework employs the MLP-Mixer neural network to predict battery terminal voltage, incorporating hierarchical temperature compensation based on temperature data from the BMS. This straightforward and efficient compensation method enhances prediction accuracy. Subsequently, the framework combines the MLP-Mixer neural network with the UKF to form a new estimator, MM-UKF. The MM-UKF estimator’s prediction accuracy, temperature adaptability, and generalization ability underwent comprehensive examination and evaluation using a public dataset under varying temperatures and complex operating conditions. Results indicate that: 1) The superior ability of the MLP-Mixer neural network to observe battery terminal voltage plays a significant role in the precise SOC estimation of MM-UKF; 2) The proposed method shows robustness and performs well under different driving cycles. The RMSE of SOC estimation does not exceed 0.01 under intense driving cycles, showcasing superior generalization ability; 3) While the estimator’s prediction accuracy deviates slightly from the reference value at low temperatures and SOC levels, it outperforms prediction methods based on MLP and LSTM neural networks in various temperatures and low SOC scenarios. Overall, the proposed SOC estimation method based on MM-UKF holds significant practical value for underwater vehicles operating under complex conditions.

Change detection is an essential fundamental task in remote sensing image analysis. Owing to powerful deep abstract feature extraction ability, many deep learning-based change detection methods have emerged recently. Although previous works have recognized the characteristics and advantages of multilevel deep features and attempted to integrate them, the utilization process lacks a clear emphasis on the advantages aspect of different-level features. One-size-fits-all fusion treats all levels equally, neglecting the spatial advantage and semantic advantage of low and high-level features, respectively. This leads to deficiencies in the integrity and edge accuracy of the final change predictions. To address these issues, we propose a multitask level-specific constraint network, named MLCNet, which addresses the issues by optimizing the advantages of features at different levels. MLCNet comprises a Siamese encoder, fusion modules tailored for features at different levels, and a decoding mechanism that effectively combines semantic and spatial information of features at adjacent levels. In addition, by reconstructing the original ground truths (GTs) into the semantic, binary, and edge GTs, multitask learning constraints are established during network training, compelling the network to enhance targeted emphasis on the characteristics of features at different levels. Experimental results on the three building change detection datasets validate the practicality of MLCNet.

Needle-type sensor, characterized by its slender, elongated shape, is a promising sensing method due to its rapid response, high sensitivity, and portability. Recently, the needle-type sensor technology has garnered increasing attention, leading to its accelerated development and extensive use in medical and healthcare, environmental monitoring, and geosciences. However, there remains a need for a comprehensive review of existing research. Here, we utilize scientometric analysis, which is booming recently, to conduct a comprehensive investigation of the needle-type sensor field. This analysis covers various aspects, including annual trends, journals, institutions, countries, disciplines, authors, references, and keywords of 136,667 publications from the Web of Science Core Collection (WoSCC) database spanning from January 1, 2004, to January 1, 2024. Additionally, we identify current hotspots, frontiers, and predict future trends. Eventually, three research hotspots are refined: multidisciplinary materials science, sensor miniaturization and integration, and biomedical engineering, indicating that further investigations may focus on creating biocompatible materials to enhance sensing properties, optimizing sensor structure through miniaturization and integration methods, and improving clinical applications in biomedical engineering. This work may facilitate the development of needle-type sensors.

Land-cover mapping is important for urban planning and management, and current land-cover mapping products are unable to meet the needs of cities due to frequent land surface changes. In this study, based on the low-to-high network (L2HNet) network, we generate a high-resolution land-cover mapping product for Wuhan and its surrounding areas. In this article, we adopt a simplified L2HNet by removing the confident area selection and the L2H loss module to shorten the cycle time of the entire mapping process. The mapping process used ESA LandCover (2021) as low-resolution labels and Google Maps as high-resolution remote sensing images. In the course of the experiment, we also calculate the four indicators mean intersection over union (MIoU), overall accuracy (OA), frequency weighted intersection over union (FWIoU), and Kappa, evaluate the accuracy of our product in predicting fine feature structure using a point-based test method, and compare it with six mainstream land-cover mapping products. The product achieves a 1m-resolution land-cover product in study areas while maintaining an over 75.21% MIoU. OA, FWIoU, and Kappa all maintain values above 85.00%, showing excellent prediction results. In quantitative analysis, compared to ESA LandCover(2021), the L2HNet product has a significant improvement in mapping accuracy for build-up and permanent water, including an exciting 21.08% improvement in permanent water accuracy and an amazing improvement in build-up. The comparison with mainstream products also shows the credibility and practicality of the product. The end result of this research fills a gap in Wuhan and its surrounding areas' 1m-resolution land-cover mapping product. While significantly improving the product's resolution, L2HNet makes time- and labor-saving periodic mapping a reality.

In the field of hyperspectral and multispectral (HS–MS) image fusion, convolutional neural network (CNN) has achieved promising performance. Nevertheless, most networks cannot fully extract the spatial–spectral features from 3-D HS–MS images via vanilla convolution. 3-D-CNN, treating the spatial and spectral dimension at the same weight maybe a solution to this problem. However, this approach ignores the distinctiveness of spatial–spectral features and suffers from high computational complexity. To meet these challenges, a novel architecture called four-stream network (FS-Net) is proposed, which integrates the spatial–spectral feature extraction, and multiscale and multilevel feature fusion into a united framework. First, the simple but effective 2-D spatial feature extractor and spectral feature extractor are designed, which encode the spatial and spectral features of HS–MS images into four groups of feature maps. Next, these four groups of feature maps are fused in pairs, where the output can be seen as the comprehensive representation of input images. Finally, multilevel and multiscale feature fusion strategies helps to recover the HS image at high-spatial-resolution, and thus, simultaneously capture the detail texture and contextual information of the different and complex ground objects. Extensive experiments were conducted on three datasets with various experimental configurations to demonstrate that the proposed FS-Net achieves superior results quantitatively and visually when compared with the state-of-the-art networks.

The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. However, designing a continuous-scale super-resolution algorithm with excellent performance is still a difficult problem to solve. In order to solve this problem, we propose a continuous-scale SR algorithm based on a Transformer, which is called residual dense Swin Transformer (RDST). Firstly, we design a residual dense Transformer block (RDTB) to enhance the information flow before and after the network and extract local fusion features. Then, we use multilevel feature fusion to obtain richer feature information. Finally, we use the upsampling module based on the local implicit image function (LIIF) to obtain continuous-scale super-resolution results. We test RDST on multiple benchmarks. The experimental results show that RDST achieves SOTA performance in the fixed scale of super-resolution tasks in the distribution, and significantly improves (0.1∼0.6 dB) the arbitrary scale of super-resolution tasks out of distribution. Sufficient experiments show that our RDST can use fewer parameters, and its performance is better than the SOTA SR method.

Objective: This review assessed the effects of reflexology on symptoms in pregnancy. Methods and analysis: PubMed, Embase, Springer, Web of Science, the Cochrane Library, and reference lists of previous systematic reviews were searched for the eligible randomized controlled trials (RCT) from the inception date of each predefined database up to May 31st, 2023. Data were extracted, and methodological quality was evaluated by the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2). The efficacy of treatment was assessed using pooled effect sizes (Hedges’ g) and 95% confidence intervals (CI). Meta-analysis was performed using the RevMan 5.4 manager, and publication bias was evaluated by Begg's test. Results: The included a total of 13 RCTs in this review, of eleven was high risk of bias and two were low, reported the effects of reflexology on low back and/or pelvic pain (LBPP), labor pain, duration of labor, anxiety, fatigue, sleep quality, constipation symptoms, and ankle and foot edema in pregnancy. The effect sizes (Hedges’ g) for reflexology in labor pain, duration of labor, anxiety, fatigue, and sleep quality showed statistical significance, which the meta-analysis also confirmed except for fatigue and sleep quality due to insufficient studies. Conclusion: Reflexology is probably effective and safe for labor pain, duration of labor, and anxiety in pregnancy, while the evidences for reflexology in LBPP, fatigue, sleep quality, constipation symptoms, and ankle and foot edema during pregnancy were insufficient. Based on the low to high quality of included studies, strong supportive evidence is not yet available. Rigorous-design and large-scale clinical trials should be conducted to provide higher-quality, reliable evidence.

Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform’s architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers’ burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries.

Abstract Background The dose perturbation effect of immobilization devices is often overlooked in intensity-modulated radiation therapy (IMRT) for breast cancer (BC). This retrospective study assessed the dosimetric effects of supine immobilization devices on the skin using a commercial treatment planning system. Methods Forty women with BC were divided into four groups according to the type of primary surgery: groups A and B included patients with left and right BC, respectively, who received 50 Gy radiotherapy in 25 fractions after radical mastectomy, while groups C and D included patients with left and right BC, respectively, who received breast-conservation surgery (BCS) and 40.05 Gy in 15 fractions as well as a tumor bed simultaneous integrated boost to 45 Gy. A 0.2-cm thick skin contour and two sets of body contours were outlined for each patient. Dose calculations were conducted for the two sets of contours using the same plan. The dose differences were assessed by comparing the dose-volume histogram parameter results and by plan subtraction. Results The supine immobilization devices for BC resulted in significantly increased skin doses, which may ultimately lead to skin toxicity. The mean dose increased by approximately 0.5 and 0.45 Gy in groups A and B after radical mastectomy and by 2.7 and 3.25 Gy in groups C and D after BCS; in groups A–D, the percentages of total normal skin volume receiving equal to or greater than 5 Gy (V5) increased by 0.54, 1.15, 2.67, and 1.94%, respectively, while the V10 increased by 1.27, 1.83, 1.36, and 2.88%; the V20 by 0.85, 1.87, 2.76, and 4.86%; the V30 by 1.3, 1.24, 10.58, and 11.91%; and the V40 by 1.29, 0.65, 10, and 10.51%. The dose encompassing the planning target volume and other organs at risk, showed little distinction between IMRT plans without and with consideration of immobilization devices. Conclusions The supine immobilization devices significantly increased the dose to the skin, especially for patients with BCS. Thus, immobilization devices should be included in the external contour to account for dose attenuation and skin dose increment. Trial registration This study does not report on interventions in human health care.

Abstract Communications industry has remarkably changed with the development of fifth-generation cellular networks. Image, as an indispensable component of communication, has attracted wide attention. Thus, finding a suitable approach to assess image quality is important. Therefore, we propose a deep learning model for image quality assessment (IQA) based on explicit-implicit dual stream network. We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features. Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features. On this basis, the number of network layers of VGGNet is reduced by adding the parallel wavelet kurtosis value frequency domain features. Thus, the training parameters and the sample requirements decline. We verified, by cross-validation of different databases, that the wavelet kurtosis feature fusion method based on deep learning has a more complete feature extraction effect and a better generalisation ability. Thus, the method can simulate the human visual perception system better, and subjective feelings become closer to the human eye. The source code about the proposed EI-IQA model is available on github https://github.com/jacob6/EI-IQA .

Recently, miRNAs have become a promising biomarker for disease diagnostics. miRNA-145 is closely related to strokes. The accuracy determination of miRNA-145 (miR-145) in stroke patients still remains challenging due to its heterogeneity and low abundance, as well as the complexity of the blood matrix. In this work, we developed a novel electrochemical miRNA-145 biosensor via subtly coupling the cascade strand displacement reaction (CSDR), exonuclease III (Exo III), and magnetic nanoparticles (MNPs). The developed electrochemical biosensor can quantitatively detect miRNA-145 ranging from 1 × 102 to 1 × 106 aM with a detection limit as low down as 100 aM. This biosensor also exhibits excellent specificity to distinguish similar miRNA sequences even with single-base differences. It has been successfully applied to distinguish healthy people from stroke patients. The results of this biosensor are consistent with the results of the reverse transcription quantitative polymerase chain reaction (RT-qPCR). The proposed electrochemical biosensor has great potential applications for biomedical research on and clinical diagnosis of strokes.

Abstract This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA.

Abstract Image quality assessment is an important topic in the field of digital image processing. In this study, a full-reference image quality assessment method called Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM) is proposed. More precisely, a Log-Gabor filter is first used to decompose reference and distorted images, and Riesz transform is performed on the decomposed images on the basis of monogenic signal theory. Then, the monogenic signal similarity matrix is obtained by calculating the similarity of the local amplitude/phase/direction characteristics of monogenic signal. Next, we weight the summation of these characteristics with visual contrast sensitivity. Since the first-order Riesz transform cannot clearly express the corners and intersection points in the image, we calculate the gradient magnitude similarity between the reference and distorted images as a feature, which is combined with monogenic signal similarity to obtain a local quality map. Finally, we conduct the monogenic phase congruency using the Riesz transform feature matrix from the reference image and utilize it as a weighted function to derive the similarity index. Extensive experiments on five benchmark IQA databases, namely, LIVE, CSIQ, TID2008, TID2013, and Waterloo Exploration, indicate that RVSIM is a robust IQA method.

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.

A series of novel podophyllotoxin derivatives obtained by 4β-N-acetylamino substitution at C-4 position was designed, synthesized, and evaluated for in vitro cytotoxicity against four human cancer cell lines (EC-9706, HeLA, T-24 and H460) and a normal human epidermal cell line (HaCaT). The cytotoxicity test indicated that most of the derivatives displayed potent anticancer activities. In particular, compound 12h showed high activity with IC50 values ranging from 1.2 to 22.8 μM, with much better cytotoxic activity than the control drug etoposide (IC50: 8.4 to 78.2 μM). Compound 12j exhibited a promising cytotoxicity and selectivity profile against T24 and HaCaT cell lines with IC50 values of 2.7 and 49.1 μM, respectively. Compound 12g displayed potent cytotoxicity against HeLA and T24 cells with low activity against HaCaT cells. According to the results of fluorescence-activated cell sorting (FACS) analysis, 12g induced cell cycle arrest in the G2/M phase accompanied by apoptosis in T24 and HeLA cells. Furthermore, the docking studies showed possible interactions between human DNA topoisomerase IIα and 12g. These results suggest that 12g merits further optimization and development as a new podophyllotoxin-derived lead compound.

Biomass burning is a major source of greenhouse gases (GHGs) and particulate matter (PM) emissions in China. Despite increasing efforts of fire monitoring, it remains challenging to quantify the variability in interannual and seasonal emissions of GHGs and PM from biomass burning. In this study, we investigated the biomass burning emissions in Northeastern China based on fire radiative power (FRP) obtained from the Visible Infrared Imaging Radiometer Suites (VIIRS) active fires datasets during the period 2012 to 2019. Our results showed that the average annual emissions from biomass burning in Northeastern China during 2012–2019 were: 81.6 Tg for CO2, 260.2 Gg for CH4, 5.5 Gg for N2O, 543.2 Gg for PM2.5 and 573.6 Gg for PM10, respectively. Higher levels of GHGs and PM emissions were concentrated in the Songnen Plain and Sanjiang Plain, the main grain producing areas in this region, and were associated with dense fire points. There were two emission peaks observed each year: after harvesting (October to November) and before planting (March to April). During this study period, the total fire emissions initially increased and then decreased in a fluctuating pattern, with emissions peaking in 2015, the year when more emission regulations were introduced. Crop straw was the major source of GHGs and PM among all kinds of biomass burning. Following more stringent controls on burning and the utilization of crop straw, the main burning season changed from autumn to spring. The proportion from spring burning increased from 20.5% in 2013 to 77.1% in 2019, with an annual growth rate of 20%. The results of this study demonstrate the effectiveness of regulatory control in reducing GHGs and PM emissions, as well as satellite fire observations as a powerful means to assess such outcomes.

In this paper, we propose a no-reference image quality assessment (IQA) metric for noisedistorted images specifically based on frequency mapping (FM), namely, FMIQA index. First, we decompose the image into intrinsic mode functions (IMFs) from small to large scale by using bidimensional empirical mode decomposition (BEMD), and perform the local feature analysis on the IMFs by Riesz transform. Considering that the combination of BEMD and Riesz transform can denoise the noise-distorted image, we use this method with appropriate application of visual contrast sensitivity function to get the denoised image. Then we calculate the similarity map of the Riesz transform feature maps from the distorted image and the denoised image to obtain the similarity indices. Finally, we combine these similarity indices to obtain the final index. Experimental results on three public databases show that the proposed FMIQA evaluates the noise-distorted image in consistency with subjective assessment and can obtain better performance in image quality prediction than other existing related methods.

Bupleuri Radix has been widely used in traditional Chinese medicine. In the current herbal market, the species Bupleurum marginatum Wall. ex DC. is the main source of Bupleuri Radix. Although Bupleuri Radix from the roots of B. marginatum grown wild in the North West of Hubei province has higher quality compared with those from other regions according to the previous investigations, the exhaustive exploitation driven by increasing demand has drastically reduced the wild resource. As a result, germplasm evaluation and quality resource exploration are important for the sustainable utilization and cultivation of B. marginatum. A preliminary study indicated differences in the tissue structure of B. marginatum grown in different areas of North Western Hubei province. In the current study, various tissues of the roots of B. marginatum grown in different areas of North Western Hubei were subjected to laser microdissection and analyzed by microscopy and ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC–Q-TOF-MS). The results show that wild plants from Maqiao Town, Baokang County contain the most saikosaponins distributed mainly in cork, cortex and phloem. This study provides key chemical information for evaluating the quality of B. marginatum roots.

The attitude control and depth tracking issue of autonomous underwater vehicle (AUV) are addressed in this paper. By introducing a nonsingular coordinate transformation, a novel nonlinear reduced-order observer (NROO) is presented to achieve an accurate estimation of AUV’s state variables. A discrete-time model predictive control with nonlinear model online linearization (MPC-NMOL) is applied to enhance the attitude control and depth tracking performance of AUV considering the wave disturbance near surface. In AUV longitudinal control simulation, the comparisons have been presented between NROO and full-order observer (FOO) and also between MPC-NMOL and traditional NMPC. Simulation results show the effectiveness of the proposed method.