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Lithium-sulfur (Li-S) batteries offer a high theoretical energy density but suffer from poor cycling stability and polysulfide shuttling, which limits their practical application. To address these challenges, we developed a PANI-modified MoS 2 -NG composite, where MoS 2 nanoflowers were uniformly grown on graphene oxide (GO) through PANI modification, resulting in an increased interlayer spacing of MoS 2 . This expanded spacing exposed more active sites, enhancing polysulfide adsorption and catalytic conversion. The composite was used to prepare MoS 2 -NG/PP separators for Li-S batteries, which achieved a high specific capacity of 714 mAh g -1 at a 3 C rate and maintained a low capacity decay rate of 0.085% per cycle after 500 cycles at 0.5 C. The larger MoS 2 interlayer spacing was key to improving redox reaction kinetics and suppressing the shuttle effect, making the MoS 2 -NG composite a promising material for enhancing the performance and stability of Li-S batteries.

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries (LIBs) due to the abundant availability of sodium, lower costs, and comparable electrochemical performance characteristics. A thorough understanding of their performance features is essential for the widespread adoption and application of SIBs. Therefore, in this study, we investigate the output characteristics and electrochemical impedance spectroscopy (EIS) features of sodium-ion batteries (SIBs) under various states. The research results show that, unlike conventional lithium iron phosphate (LFP) batteries, SIBs exhibit a strong linear relationship between state of charge (SOC) and open-circuit voltage (OCV) across various SOC and temperature conditions. Additionally, the discharge capacity of the battery remains relatively stable within a temperature range of 15 °C to 35 °C; when the temperatures are outside this range, the available capacity of the sodium-ion battery reduces significantly. Moreover, the EIS profiles in the high-frequency region are predominantly influenced by the ohmic internal resistance, which remains largely unaffected by SOC variations. In contrast, the low-frequency region demonstrates a significant correlation between SOC and impedance, with higher SOC values resulting in reduced impedance, indicated by smaller semicircle radii in the EIS curves. This finds highlights that EIS profiling can effectively monitor SOC and state of health (SOH) in SIBs, offering a clear correlation between impedance parameters and the battery's operational state. The research not only advances our understanding of the electrochemical properties of SIBs but also provides a valuable reference for the design and application of sodium-ion battery systems in various scenarios.

The substitution of ingredients with Salmonidae, particularly Salmo salar , has led to widespread reports of financial losses and health risks globally, emphasizing the urgent need for the development of a rapid and precise method for species identification. The aim of the present study was to develop a novel method for the rapid screening of Salmonidae ingredients and the accurate detection of S. salar simultaneously using multiplex real-time PCR coupled with melting curve analysis. Specifically, primer sets specific for S. salar and Salmonidae were cross-confirmed. Moreover, the reaction system and conditions of a real-time duplex PCR were optimized, and the proposed methodology was verified, proving that the assay has good specificity and sensitivity. Clear and distinguishable melting peaks, with expected Tm values of around 80 °C ( S. salar ) and 84 °C (Salmonidae), were observed for twelve products, proving the presence of S. salar . However, four products were not derived from S. salar , but they could have belonged to another species within the Salmonidae family due to the presence of only one specific melting peak at a Tm value of about 84 °C. Therefore, the novel assay in the present study allows for the fast and accurate screening of Salmonidae ingredients and the detection of S. salar simultaneously.

In chromatic confocal line sensors, calibration is usually divided into peak extraction and wavelength calibration. In previous research, the focus was mainly on peak extraction. In this paper, a kernel-based algorithm is proposed to deal with wavelength calibration, which corresponds to the mapping relationship between peaks (i.e., the wavelengths) in image space and profiles in physical space. The primary component of the mapping function is depicted using polynomial basis functions, which are distinguished along various dispersion axes. Considering the unknown distortions resulting from field curvature, sensor fabrication and assembly, and even the inherent complexity of dispersion, a typical kernel trick-based nonparametric function element is introduced here, predicated on the notion that similar processes conducted on the same sensor yield comparable distortions.To ascertain the performance with and without the kernel trick, we carried out wavelength calibration and groove fitting on a standard groove sample processed via glass grinding and with a reference depth of 66.14 μm. The experimental results show that depths calculated by the kernel-based calibration algorithm have higher accuracy and lower uncertainty than those ascertained using the conventional polynomial algorithm. As such, this indicates that the proposed algorithm provides effective improvements.

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from "0" to "9". These results show the potential of the capacitive sensor array for advanced human-computer interaction applications.

The digital transformation of metrology is one of the most active activities in the field of metrology at present, where the digital calibration certificate (DCC) being developed is the topic receiving the most concern in the current digital transformation. In practical industrial applications, the issue of storage carrier for the DCC plays a crucial role in its promotion and implementation. To address this issue, a DCC meta-model called DCC-Lite schema has been designed along with a corresponding processing method. This solution involves compressing and segmenting the DCC to make it suitable for storage using RFID tags. These RFID tags are affixed to the instruments and accompany them throughout their usage. Additionally, the DCC-RFID processing system has been developed to validate the effectiveness of the DCC meta-model and its corresponding processing method within a wireless temperature acquisition system. Experimental results demonstrate that the system successfully reads and writes the DCC stored within the RFID tag group. Furthermore, it enables automated parsing of the DCC calibration data by the machine and real-time compensation of measurement data to reduce measurement errors.

Current calculation methods for the carbon content as received ( C ar ) of coal rely on multiple instruments, leading to high costs for enterprises. There is a need for a cost-effective model that maintains accuracy in CO 2 emission accounting. This study introduces an MISM model using key parameters identified through correlation and ablation analyses. An Improved State-Space Model (ISSM) and an IS-Mamba module are integrated into a Multi-Layer Perceptron (MLP) framework, enhancing information flow and regression accuracy. The MISM model demonstrates superior performance over traditional methods, reducing the Root Mean Square Error (RMSE) by 22.36% compared to MLP, and by 9.65% compared to Mamba. Using only six selected parameters, the MISM model achieves a precision of 0.27% for the discrepancy between the calculated CO 2 emissions and the actual measurements. An ablation analysis confirms the importance of certain parameters and the effectiveness of the IS-Mamba module at improving model performance. This paper offers an innovative solution for accurate and cost-effective carbon accounting in the thermal power sector, supporting China's carbon peaking and carbon neutrality goals.

Cardiac remodeling, a critical process that can lead to heart failure, is primarily characterized by cardiac hypertrophy. Studies have shown that transgenic mice with Gαq receptor blockade exhibit reduced hypertrophy under induced pressure overload. GQ262, a novel Gαq/11 inhibitor, has demonstrated good biocompatibility and specific inhibitory effects on Gαq/11 compared to other inhibitors. However, its role in cardiac remodeling remains unclear. This study aims to explore the anti-cardiac remodeling effects and mechanisms of GQ262 both in vitro and in vivo, providing data and theoretical support for its potential use in treating cardiac remodeling diseases. Cardiac hypertrophy was induced in mice via transverse aortic constriction (TAC) for 4 weeks and in H9C2 cells through phenylephrine (PE) induction, confirmed with WGA and H&E staining. We found that GQ262 improved cardiac function, inhibited the protein and mRNA expression of hypertrophy markers, and reduced the levels of apoptosis and fibrosis. Furthermore, GQ262 inhibited the Akt/mTOR signaling pathway activation induced by TAC or PE, with its therapeutic effects disappearing upon the addition of the Akt inhibitor ARQ092. These findings reveal that GQ262 inhibits cardiomyocyte hypertrophy and apoptosis through the Akt/mTOR signaling pathway, thereby reducing fibrosis levels and mitigating cardiac remodeling.

Trigger-action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as "IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed". As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user-rule bipartite graph. Then, we design a user-user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods.

Lead-free Mn 2+ -based metal halide materials are now being considered as clean candidates for backlight displays and lights due to the d-d transition, diverse components, and environmental friendliness. Therefore, efficient and stable Mn 2+ -based metal halide phosphors are in great demand for practical applications. In this work, adopting the mixed-ligand strategy, a series of [(CH 3 ) 4 N] 2- x [(C 2 H 5 ) 4 N] x MnCl 4 phosphors were synthesized by mechanochemical process. With the increase molar ratio of (CH 3 ) 4 N/(C 2 H 5 ) 4 N, the phase of phosphors is transformed from orthorhombic to tetragonal. Compared to [(CH 3 ) 4 N] 2 MnCl 4 and [(C 2 H 5 ) 4 N] 2 MnCl 4 phosphors, the mixed-ligand strategy significantly boosts the green emission intensity of Mn 2+ -based metal halide phosphors. The obtained [(CH 3 ) 4 N][(C 2 H 5 ) 4 N]MnCl 4 phosphors exhibit a high photoluminescence quantum yield (PLQY) of 83.78% under 450 nm excitation, which is attributed to the modulation of the adjacent [MnCl 4 ] 2 - distance by using the different chain length of organic cations, effectively suppressing non-radiative recombination. Additionally, the [(CH 3 ) 4 N][(C 2 H 5 ) 4 N]MnCl 4 phosphors exhibit a green emission at 516 nm, narrow full width at half-maximum (FWHM) of 45.53 nm, and good thermal stability. The constructed white light-emitting diode (WLED) device exhibits a wide color gamut of 108.3% National Television System Committee, demonstrating the suitability of the [(CH 3 ) 4 N][(C 2 H 5 ) 4 N]MnCl 4 phosphors as a green emitter for WLED displays and lightings. This work provides a new way to modulate the PL performance of manganese-based metal halides for application in the optoelectronic field.

To solve the problems on resource utilization and environmental pollution of waste concrete and waste polypropylene (PP) plastics, the recycling of them into asphalt pavement is a feasible approach. Considering the high melting temperature of waste PP, this study adopted a thermal-and-mechanochemical method to convert waste PP into high-performance warm-mix asphalt modifiers (PPMs) through the hybrid use of dicumyl peroxide (DCP), maleic anhydride (MAH), and epoxidized soybean oil (ESO) for preparing an asphalt mixture (RCAAM) containing recycled concrete aggregate (RCA). For the prepared RCAAM containing PPMs, the mixing temperature was about 30 °C lower than that of the hot-mix RCAAM containing untreated PP. Further, the high-temperature property, low-temperature crack resistance, moisture-induced damage resistance, and fatigue resistance of the RCAAM were characterized. The results indicated that the maximum flexural strain of the RCAAM increased by 7.8~21.4% after using PPMs, while the sectional fractures of the asphalt binder were reduced after damaging at low temperature. The use of ESO in PPMs can promote the cohesion enhancement of the asphalt binder and also improve the high-temperature deformation resistance and fatigue performance of the RCAAM. Notably, the warm-mix epoxidized PPMA mixture worked better close to the hot-mix untreated PPMA mixture, even after the mixing temperature was reduced by 30 °C.

Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%.

Respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and coronavirus pneumonia, present a major global health challenge. Current diagnostic and therapeutic options for these diseases are limited, necessitating the urgent development of novel biomarkers and therapeutic strategies. In recent years, microRNAs (miRNAs) within extracellular vesicles (EVs) have received considerable attention due to their crucial role in intercellular communication and disease progression. EVs are membrane-bound structures released by cells into the extracellular environment, encapsulating a variety of biomolecules such as DNA, RNA, lipids, and proteins. Specifically, miRNAs within EVs, known as EV-miRNAs, facilitate intercellular communication by regulating gene expression. The expression levels of these miRNAs can reflect distinct disease states and significantly influence immune cell function, chronic airway inflammation, airway remodeling, cell proliferation, angiogenesis, epithelial-mesenchymal transition, and other pathological processes. Consequently, EV-miRNAs have a profound impact on the onset, progression, and therapeutic responses of respiratory diseases, with great potential for disease management. Synthesizing the current understanding of EV-miRNAs in respiratory diseases such as COPD, asthma, lung cancer, and novel coronavirus pneumonia, this review aims to explore the potential of EV-miRNAs as biomarkers and therapeutic targets and examine their prospects in the diagnosis and treatment of these respiratory diseases.

Considering the dilemma of obtaining economic and high-performance composites based on non-polar and main-chain-saturated ethylene propylene diene monomer rubber (EPDM), we proposed an effective and universal filler modification and nanocomposite preparation method. Specifically, the montmorillonite (MMT) surface was coated with polydopamine (PDA) to obtain DMMT, which was confirmed by XRD, XPS, FTIR, and TGA. After compounding DMMT gel with the solid EPDM via the gel compounding method, a silane coupling agent, vinyltrimethoxysilane, was introduced to construct covalent interactions between rubber and filler. Compared with the unmodified MMT filler EPDM, the EPDM/DMMT nanocomposite showed much fewer filler aggregates in the matrix. The highest tensile strength of the composites reached 6.5 MPa with 10 phr DMMT, almost 200% higher than that of pure EPDM.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model's performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model's mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification and 97.69% and 98.05% in ternary classification, and on the SEED dataset, we achieved an accuracy of 98.63%, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Fumonisin B1 (FB1), a mycotoxin produced by Fusarium species, is prevalent in crops and animal feed, posing significant health risks to livestock and humans. FB1 induces oxidative stress in Sertoli cells, destroys testicular structure, and affects spermatogenesis. However, methods to mitigate the reproductive toxicity of FB1 in testes remain unknown. Quercetin, a natural flavonoid antioxidant, may offer protective benefits. This study investigated the protective effects and mechanisms of quercetin against FB1-induced reproductive toxicity in TM4 cells (a Sertoli cell line). The results indicated that 40 μM quercetin improved cell viability, reduced apoptosis, and preserved cell functions. Quercetin also decreased reactive oxygen species (ROS) levels in TM4 cells exposed to FB1, enhanced the expression of antioxidant genes, and improved mitochondrial membrane potential. Compared with FB1 alone, the combination of quercetin and FB1 increased ATP levels, as well as pyruvate and lactic acid, the key glycolysis products. Furthermore, this combination elevated the mRNA and protein expression of glycolysis-related genes, including glucose-6-phosphate isomerase 1 ( Gpi1 ), hexokinase 2 ( Hk2 ), aldolase ( Aldoa ), pyruvate kinase, muscle ( Pkm ), lactate dehydrogenase A ( Ldha ) and phosphofructokinase, liver, B-type ( Pfkl ). Quercetin also boosted the activity of PKM and LDHA, two crucial glycolytic enzymes. In summary, quercetin mitigates FB1-induced toxicity in TM4 cells by reducing ROS levels and enhancing glycolysis. This study offers new insights into preventing and treating FB1-induced toxic damage to the male reproductive system and highlights the potential application of quercetin.

Direct ink writing (DIW) of high-temperature thin-film sensors holds significant potential for monitoring extreme environments. However, existing high-temperature inks face a trade-off between cost and performance. This study proposes a SiCN/RuO 2 /TiB 2 composite ceramic ink. The added TiB 2 , after annealing in a high-temperature atmospheric environment, forms B 2 O 3 glass, which synergizes with the SiO 2 glass phase formed from the SiCN precursor to effectively encapsulate RuO 2 particles. This enhances the film's density and adhesion to the substrate, preventing RuO 2 volatilization at high temperatures. Additionally, the high conductivity of TiB 2 improves the film's overall conductivity. Test results indicate that the SiCN/RuO 2 /TiB 2 film exhibits high linearity from room temperature to 900 °C, high stability (resistance drift rate of 0.1%/h at 800 °C), and high conductivity (4410 S/m). As a proof of concept, temperature sensors and a heat flux sensor were successfully fabricated on a metallic hemispherical surface. Performance tests in extreme environments using high-power lasers and flame guns verified that the conformal thin-film sensor can accurately measure spherical temperature and heat flux, with a heat flux sensor response time of 53 ms. In conclusion, the SiCN/RuO 2 /TiB 2 composite ceramic ink developed in this study offers a high-performance and cost-effective solution for high-temperature conformal thin-film sensors in extreme environments.

Timely and accurate detection of viruses is crucial for infection diagnosis and treatment. However, it remains a challenge to develop a portable device that meets the requirement of being portable, powerless, user-friendly, reusable, and low-cost. This work reports a compact ∅30 × 48 mm portable powerless isothermal amplification detection device (material cost ∼$1 USD) relying on LAMP (Loop-Mediated Isothermal Amplification). We have proposed chromatographic-strip-based microporous permeation technology which can precisely control the water flow rate to regulate the exothermic reaction. This powerless heating combined with phase-change materials can maintain a constant temperature between 50 and 70 °C for a duration of up to 49.8 min. Compared with the conventional methods, it avoids the use of an additional insulation layer for heat preservation, greatly reducing the size and cost. We have also deployed a color card and a corresponding algorithm to facilitate color recognition, data analysis, and storage using a mobile phone. The experimental results demonstrate that our device exhibits the same limit of detection (LOD) as the ProFlex PCR for SARS-CoV-2 pseudovirus samples, with that for both being 103 copies/μL, verifying its effectiveness and reliability. This work offers a timely, low-cost, and easy way for respiratory infectious disease detection, which could provide support in curbing virus transmission and protecting the health of humans and animals, especially in remote mountainous areas without access to electricity or trained professionals.

Carbamate is a key structural motif in the development of fungicidal compounds, which is still promising and robust in the discovery of green pesticides. Herein, we report the synthesis and evaluation of the fungicidal activity of 35 carbamate derivatives, among which 19 compounds were synthesized in our previous report. These derivatives were synthesized from aromatic amides in a single step, which was a green oxidation process for Hofmann rearrangement using oxone, KCl and NaOH. Their chemical structures were characterized by 1 H NMR, 13 C NMR and high-resolution mass spectrometry. Their antifungal activity was tested against seven plant fungal pathogens. Many of the compounds exhibited good antifungal activity in vitro (inhibitory rate > 60% at 50 μg/mL). Compound 1ag exhibited excellent broad-spectrum antifungal activities with inhibition rates close to or higher than 70% at 50 μg/mL. Notably, compound 1af demonstrated the most potent inhibition against F. graminearum , with an EC 50 value of 12.50 μg/mL, while compound 1z was the most promising candidate fungicide against F. oxysporum (EC 50 = 16.65 μg/mL). The structure-activity relationships are also discussed in this paper. These results suggest that the N -aryl carbamate derivatives secured by our green protocol warrant further investigation as potential lead compounds for novel antifungal agents.

Bio-inspired hydrogel robots have become promising due to their advantage of the interaction safety and comfort between robots and humans, while current hydrogel robots mainly focus on underwater movement due to the hydration-dehydration process of thermo-responsive hydrogels, which greatly limits their practical applications. To expand the motion of the thermo-responsive hydrogel robot to the ground, we constructed a hydrogel robot inspired by a caterpillar, which has an anisotropic double-layered structure by the interfacial diffusion polymerization method. Adding PVA and SA to PNIPAm will cause different conformation transitions. Therefore, sticking the two layers of hydrogel together will form a double-layer anisotropic structure. The ultra-high hydrophilicity of PVA and SA significantly reduces the contact angle of the hydrogel from 53.1° to about 10° and reduces its hydration time. The responsive time for bending 30° of the hydrogel robot has been greatly reduced from 1 h to half an hour through the enhancement of photo-thermal conversion and thermal conductivity via the addition of Fe 3 O 4 nanoparticles. As a result, the fabricated hydrogel robot can achieve a high moving speed of 54.5 mm·h -1 on the ground. Additionally, the fabricated hydrogel has excellent mechanical strength and can endure significant flexibility tests. This work may pave the road for the development of soft robots and expand their applications in industry.

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency-space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency-space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence-arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence-arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.

Alginate oligosaccharides (AOSs), which are an attractive feed additive for animal production, exhibit pleiotropic bioactivities. In the present study, we investigated graded doses of AOS-mediated alterations in the physiological responses of piglets by determining the intestinal architecture, barrier function, and microbiota. A total of 144 weaned piglets were allocated into four dietary treatments in a completely random design, which included a control diet (CON) and three treated diets formulated with 250 mg/kg (AOS250), 500 mg/kg (AOS500), and 1000 mg/kg AOS (AOS1000), respectively. The trial was carried out for 28 days. Our results showed that AOS treatment reinforced the intestinal barrier function by increasing the ileal villus height, density, and fold, as well as the expression of tight junction proteins, especially at the dose of 500 mg/kg AOS. Meanwhile, supplementations with AOSs showed positive effects on enhancing antioxidant capacity and alleviating intestinal inflammation by elevating the levels of antioxidant enzymes and inhibiting excessive inflammatory cytokines. The DESeq2 analysis showed that AOS supplementation inhibited the growth of harmful bacteria Helicobacter and Escherichia_Shigella and enhanced the relative abundance of Faecalibacterium and Veillonella . Collectively, these findings suggested that AOSs have beneficial effects on growth performance, antioxidant capacity, and gut health in piglets.

The carbon content as received ( C ar ) of coal is essential for the emission factor method in IPCC methodology. The traditional carbon measurement mechanism relies on detection equipment, resulting in significant detection costs. To reduce detection costs and provide precise predictions of C ar s even in the absence of measurements, this paper proposes a neural network combining MLP with an attention mechanism (MSA-Net). In this model, the Attention Module is proposed to extract important and potential features. The Skip-Connections are utilized for feature reuse. The Huber loss is used to reduce the error between predicted C ar values and actual values. The experimental results show that when the input includes eight measured parameters, the MAPE of MSA-Net is only 0.83%, which is better than the state-of-the-art Gaussian Process Regression (GPR) method. MSA-Net exhibits better predictive performance compared to MLP, RNN, LSTM, and Transformer. Moreover, this article provides two measurement solutions for thermal power enterprises to reduce detection costs.

Weak doping can broaden, shift, and quench plasmon peaks in nanoparticles, but the mechanistic intricacies of the diverse responses to doping remain unclear. In this study, we used the time-dependent density functional theory (TD-DFT) to compute the excitation properties of transition-metal Pd- or Pt-doped gold and silver atomic arrays and investigate the evolution characteristics and response mechanisms of their plasmon peaks. The results demonstrated that the Pd or Pt doping of the off-centered 10 × 2 atomic arrays broadened or shifted the plasmon peaks to varying degrees. In particular, for Pd-doped 10 × 2 Au atomic arrays, the broadened plasmon peak significantly blueshifted, whereas a slight red shift was observed for Pt-doped arrays. For the 10 × 2 Ag atomic arrays, Pd doping caused almost no shift in the plasmon peak, whereas Pt doping caused a substantial red shift in the broadened plasmon peak. The analysis revealed that the diversity in these doping responses was related to the energy positions of the d electrons in the gold and silver atomic clusters and the positions of the doping atomic orbitals in the energy bands. The introduction of doping atoms altered the symmetry and gap size of the occupied and unoccupied orbitals, so multiple modes of single-particle transitions were involved in the excitation. An electron transfer analysis indicated a close correlation between excitation energy and the electron transfer of doping atoms. Finally, the differences in the symmetrically centered 11 × 2 doped atomic array were discussed using electron transfer analysis to validate the reliability of this analytical method. These findings elucidate the microscopic mechanisms of the evolution of plasmon peaks in doped atomic clusters and provide new insights into the rational control and application of plasmons in low-dimensional nanostructures.

Currently, the intelligent defect detection of massive grid transmission line inspection pictures using AI image recognition technology is an efficient and popular method. Usually, there are two technical routes for the construction of defect detection algorithm models: one is to use a lightweight network, which improves the efficiency, but it can generally only target a few types of defects and may reduce the detection accuracy; the other is to use a complex network model, which improves the accuracy, and can identify multiple types of defects at the same time, but it has a large computational volume and low efficiency. To maintain the model's high detection accuracy as well as its lightweight structure, this paper proposes a lightweight and efficient multi type defect detection method for transmission lines based on DCP-YOLOv8. The method employs deformable convolution (C2f_DCNv3) to enhance the defect feature extraction capability, and designs a re-parameterized cross phase feature fusion structure (RCSP) to optimize and fuse high-level semantic features with low level spatial features, thus improving the capability of the model to recognize defects at different scales while significantly reducing the model parameters; additionally, it combines the dynamic detection head and deformable convolutional v3's detection head (DCNv3-Dyhead) to enhance the feature expression capability and the utilization of contextual information to further improve the detection accuracy. Experimental results show that on a dataset containing 20 real transmission line defects, the method increases the average accuracy (mAP@0.5) to 72.2%, an increase of 4.3%, compared with the lightest baseline YOLOv8n model; the number of model parameters is only 2.8 M, a reduction of 9.15%, and the number of processed frames per second (FPS) reaches 103, which meets the real time detection demand. In the scenario of multi type defect detection, it effectively balances detection accuracy and performance with quantitative generalizability.

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates trend towards secondary generation during extraction and transportation, leading to safety issues such as pipeline blockages. Consequently, developing new and efficient natural gas hydrate inhibitors has become a focal point in hydrate research. Kinetic hydrate inhibitors (KHIs) offer an effective solution by disrupting the nucleation and growth processes of hydrates without altering their thermodynamic equilibrium conditions. This paper systematically reviews the latest research progress and development trends in KHIs for natural gas hydrates, covering their development history, classification, and inhibition mechanisms. It particularly focuses on the chemical properties, inhibition effects, and mechanisms of polymer inhibitors such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap). Studies indicate that these polymer inhibitors provide an economical and efficient solution due to their low dosage and environmental friendliness. Additionally, this paper explores the environmental impact and biodegradability of these inhibitors, offering guidance for future research, including the development, optimization, and environmental assessment of new inhibitors. Through a comprehensive analysis of existing research, this work aims to provide a theoretical foundation and technical reference for the commercial development of natural gas hydrates, promoting their safe and efficient use as a clean energy resource.

Natural rubber (NR) composites have been widely applied in damping products to reduce harmful vibrations, while rubber with only a single composition barely meets performance requirements. In this study, rubber blend composites including various ratios of NR and styrene butadiene rubber (SBR) were prepared via the conventional mechanical blending method. The effects of the rubber components on the compression set, compression fatigue temperature rising and the thermal oxidative aging properties of the NR/SBR blend composites were investigated. Meanwhile, the dynamic mechanical thermal analyzer and rubber processing analyzer were used to characterize the dynamic viscoelasticity of the NR/SBR blend composites. It was shown that, with the increase in the SBR ratio, the vulcanization rate of the composites increased significantly, while the compression fatigue temperature rising of the composites decreased gradually from 47 °C (0% SBR ratio) to 31 °C (50% SBR ratio). The compression set of the composites remained at ~33% when the SBR ratio was no more than 20%, and increased gradually when the SBR ratio was more than 20%.

Digital Calibration Certificates (DCCs) are a key focus in metrology digitalization, necessitating that they satisfy the criteria for machine readability and understandability. Current DCCs are machine-readable, but they are still missing the essential semantic information required for machine understandability. This shortfall is particularly notable in the lack of a dedicated semantic ontology for measurement terminologies. This paper proposes a domain ontology for measurement terminologies named the OMT (Ontology for Measurement Terminology), using a foundation of metrological terms from standards like the International Vocabulary of Metrology (VIM), the Guide to the Expression of Uncertainty in Measurement (GUM), and JJF1001. It also incorporates insights from models such as the SI Reference Point, the Simple Knowledge Organization System (SKOS), and the DCC Schema. The methodology was guided by Stanford's Seven-Step Method, ensuring a systematic development process tailored to the needs of metrological semantics. Through semantic expression capability verification and SPARQL query validations, the OMT has been confirmed to possess essential machine readability and understandability features. It has been successfully integrated into version 3.2.1 of DCCs across ten representative domains. This integration demonstrates an effective method for ensuring that DCCs are machine-readable and capable of interoperating within digital environments, thereby advancing the research in metrology digitization., Competing Interests: The authors declare no conflicts of interest.

Background: The skeletal system is a common site for metastasis from breast cancer. In our prior work, we developed induced tumor-suppressing cells (iTSCs) capable of secreting a set of tumor-suppressing proteins. In this study, we examined the possibility of identifying anticancer peptides (ACPs) from trypsin-digested protein fragments derived from iTSC proteomes., Methods: The efficacy of ACPs was examined using an MTT-based cell viability assay, a Scratch-based motility assay, an EdU-based proliferation assay, and a transwell invasion assay. To evaluate the mechanism of inhibitory action, a fluorescence resonance energy transfer (FRET)-based GTPase activity assay and a molecular docking analysis were conducted. The efficacy of ACPs was also tested using an ex vivo cancer tissue assay and a bone microenvironment assay., Results: Among the 12 ACP candidates, P18 (TDYMVGSYGPR) demonstrated the most effective anticancer activity. P18 was derived from Arhgdia, a Rho GDP dissociation inhibitor alpha, and exhibited inhibitory effects on the viability, migration, and invasion of breast cancer cells. It also hindered the GTPase activity of RhoA and Cdc42 and downregulated the expression of oncoproteins such as Snail and Src. The inhibitory impact of P18 was additive when it was combined with chemotherapeutic drugs such as Cisplatin and Taxol in both breast cancer cells and patient-derived tissues. P18 had no inhibitory effect on mesenchymal stem cells but suppressed the maturation of RANKL-stimulated osteoclasts and mitigated the bone loss associated with breast cancer. Furthermore, the P18 analog modified by N-terminal acetylation and C-terminal amidation (Ac-P18-NH2) exhibited stronger tumor-suppressor effects., Conclusions: This study introduced a unique methodology for selecting an effective ACP from the iTSC secretome. P18 holds promise for the treatment of breast cancer and the prevention of bone destruction by regulating GTPase signaling.

This study introduces a novel trifluoromethylating reagent, [(bpy)Cu(O 2 CCF 2 SO 2 F) 2 ], notable for not only its practical synthesis from cost-effective starting materials and scalability but also its nonhygroscopic nature. The reagent demonstrates high efficiency in facilitating trifluoromethylation reactions with various halogenated hydrocarbons, yielding products in good yields and exhibiting broad functional group compatibility. The development of [(bpy)Cu(O 2 CCF 2 SO 2 F) 2 ] represents an advancement in the field of organic synthesis, potentially serving as a valuable addition to the arsenal of existing trifluoromethylating agents.

The electromagnetic eddy current non-destructive testing system enables the non-destructive analysis of surface defect information on tested materials. Based on the principles of eddy current detection, this paper presents a digital eddy current detection method using high-speed sampling based on STM32. A differential eddy current coil is used as the detection probe, and the combination of a differential bridge and a differential amplifier circuit helps to reduce common-mode noise interference. The detection signal is collected via an STM32-based acquisition circuit and transmitted to the host computer through Ethernet for digital demodulation processing. The host computer performs operations such as smoothing averaging, sinusoidal fitting, and outlier removal to extract the amplitude and phase of the detection signal. The system also visually displays the condition of the tested object's surface in real time through graphical visualization. Testing showed that this system can operate at frequencies up to 8.84 MHz and clearly identify defects as narrow as 1 mm on the surface of the tested steel plate.

The formation of nitrogen-containing organic interstellar molecules is of great importance to reveal chemical processes and the origin of life on Earth. Benzonitrile (BN) is one of the simplest nitrogen-containing aromatic molecules in the interstellar medium (ISM) that has been detected in recent years. Methanol (CH 3 OH) exists widely in interstellar space with high reactivity. Herein, we measured the infrared (IR) spectra of neutral and cationic BN-CH 3 OH clusters by vacuum ultraviolet (VUV) photoionization combined with time-of-flight mass spectrometry. Combining IR spectra with the density functional theory calculations, we reveal that the BN-CH 3 OH intends to form a cyclic H-bonded structure in neutral clusters. However, after the ionization of BN-CH 3 OH clusters, proton-shared N···H···O and N···H···C structures are confirmed to form between BN and CH 3 OH, with the minor coexistence of H-bond and O-π structures. The formation of the proton-shared structure expands our knowledge of the evolution of the life-related nitrogen-containing molecules in the universe and provides a possible pathway to the further study of biorelevant aromatic organic macromolecules.

As societal progress elevates living standards, the focus on meat consumption has shifted from quantity to quality. In broiler production, optimizing meat quality has become paramount, prompting efforts to refine various meat attributes. Recent advancements in sequencing technologies have revealed the genome's complexity, surpassing previous conceptions. Through experimentation, numerous genetic elements have been linked to crucial meat quality traits in broiler chickens. This review synthesizes the current understanding of genetic determinants associated with meat quality attributes in broilers. Researchers have unveiled the pivotal insights detailed herein by employing diverse genomic methodologies such as QTL-based investigations, candidate gene studies, single-nucleotide polymorphism screening, genome-wide association studies, and RNA sequencing. These studies have identified numerous genes involved in broiler meat quality traits, including meat lightness ( COL1A2 and ACAA2 ), meat yellowness ( BCMO1 and GDPD5 ), fiber diameter ( myostatin and LncIRS1 ), meat pH ( PRDX4 ), tenderness ( CAPN1 ), and intramuscular fat content ( miR-24-3p and ANXA6 ). Consequently, a comprehensive exploration of these genetic elements is imperative to devise novel molecular markers and potential targets, promising to revolutionize strategies for enhancing broiler meat quality.

Chilling stress is one of the main abiotic factors affecting rice growth and yield. In rice, chlorophyllide a oxygenase encoded by OsCAO1 is responsible for converting chlorophyllide a to chlorophyllide b , playing a crucial role in photosynthesis and thus rice growth. However, little is known about the function of OsCAO1 in chilling stress responses. The presence of the cis -acting element involved in low-temperature responsiveness (LTR) in the OsCAO1 promoter implied that OsCAO1 probably is a cold-responsive gene. The gene expression level of OsCAO1 was usually inhibited by low temperatures during the day and promoted by low temperatures at night. The OsCAO1 knockout mutants generated by the CRISPR-Cas9 technology in rice ( Oryza sativa L.) exhibited significantly weakened chilling tolerance at the seedling stage. OsCAO1 dysfunction led to the accumulation of reactive oxygen species and malondialdehyde, an increase in relative electrolyte leakage, and a reduction in antioxidant gene expression under chilling stress. In addition, the functional deficiency of OsCAO1 resulted in more severe damage to chloroplast morphology, such as abnormal grana thylakoid stacking, caused by low temperatures. Moreover, the rice yield was reduced in OsCAO1 knockout mutants. Therefore, the elevated expression of OsCAO1 probably has the potential to increase both rice yield and chilling tolerance simultaneously, providing a strategy to cultivate chilling-tolerant rice varieties with high yields.

Because of the absence of visual perception, visually impaired individuals encounter various difficulties in their daily lives. This paper proposes a visual aid system designed specifically for visually impaired individuals, aiming to assist and guide them in grasping target objects within a tabletop environment. The system employs a visual perception module that incorporates a semantic visual SLAM algorithm, achieved through the fusion of ORB-SLAM2 and YOLO V5s, enabling the construction of a semantic map of the environment. In the human-machine cooperation module, a depth camera is integrated into a wearable device worn on the hand, while a vibration array feedback device conveys directional information of the target to visually impaired individuals for tactile interaction. To enhance the system's versatility, a Dobot Magician manipulator is also employed to aid visually impaired individuals in grasping tasks. The performance of the semantic visual SLAM algorithm in terms of localization and semantic mapping was thoroughly tested. Additionally, several experiments were conducted to simulate visually impaired individuals' interactions in grasping target objects, effectively verifying the feasibility and effectiveness of the proposed system. Overall, this system demonstrates its capability to assist and guide visually impaired individuals in perceiving and acquiring target objects.

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

Hypopharyngeal squamous cell carcinoma (HSCC) is a kind of malignant tumor with a poor prognosis and low quality of life in the otolaryngology department. It has been found that microRNA (miRNA) plays an important role in the occurrence and development of various tumors. This study found that the expression level of miRNA-107 (miR-107) in HSCC was significantly reduced. Subsequently, we screened out the downstream direct target gene Neuronal Vesicle Trafficking Associated 1 (NSG1) related to miR-107 through bioinformatics analysis and found that the expression of NSG1 was increased in HSCC tissues. Following the overexpression of miR-107 in HSCC cells, it was observed that miR-107 directly suppressed NSG1 expression, leading to increased apoptosis, decreased proliferation, and reduced invasion capabilities of HSCC cells. Subsequent experiments involving the overexpression and knockdown of NSG1 in HSCC cells demonstrated that elevated NSG1 levels enhanced cell proliferation, migration, and invasion, while the opposite effect was observed upon NSG1 knockdown. Further investigations revealed that changes in NSG1 levels in the HSCC cells were accompanied by alterations in ERK signaling pathway proteins, suggesting a potential regulatory role of NSG1 in HSCC cell proliferation, migration, and invasion through the ERK pathway. These findings highlight the significance of miR-107 and NSG1 in hypopharyngeal cancer metastasis, offering promising targets for therapeutic interventions and prognostic evaluations for HSCC.

Vat photopolymerization (VP), as an additive manufacturing process, has experienced significant growth due to its high manufacturing precision and excellent surface quality. This method enables the fabrication of intricate shapes and structures while mitigating the machining challenges associated with non-oxide ceramics, which are known for their high hardness and brittleness. Consequently, the VP process of non-oxide ceramics has emerged as a focal point in additive manufacturing research areas. However, the absorption, refraction, and reflection of ultraviolet light by non-oxide ceramic particles can impede light penetration, leading to reduced curing thickness and posing challenges to the VP process. To enhance the efficiency and success rate of this process, researchers have explored various aspects, including the parameters of VP equipment, the composition of non-oxide VP slurries, and the surface modification of non-oxide particles. Silicon carbide and silicon nitride are examples of non-oxide ceramic particles that have been successfully employed in VP process. Nonetheless, there remains a lack of systematic induction regarding the curing mechanisms and key influencing factors of the VP process in non-oxide ceramics. This review firstly describes the curing mechanism of the non-oxide ceramic VP process, which contains the chain initiation, chain polymerization, and chain termination processes of the photosensitive resin. After that, the impact of key factors on the curing process, such as the wavelength and power of incident light, particle size, volume fraction of ceramic particles, refractive indices of photosensitive resin and ceramic particles, incident light intensity, critical light intensity, and the reactivity of photosensitive resins, are systematically discussed. Finally, this review discusses future prospects and challenges in the non-oxide ceramic VP process. Its objective is to offer valuable insights and references for further research into non-oxide ceramic VP processes.

A novel method is introduced in this study for producing ceramisite coarse aggregates that are both lightweight and possess high strength. The process involves utilizing fly ash as the primary material, along with coal ash floating beads (CAFBs) that have high softening temperature and a spherical hollow structure serving as the template for forming pores. This study examined the impact of varying particle size and quantity of floating beads on the composition and characteristics of ceramisite aggregates. Results showed that the high softening temperature of floating beads provided stability to the spherical cavity structure throughout the sintering process. Furthermore, the pore structure could be effectively tailored by manipulating the size and quantity of the floating beads in the manufacturing procedure. The obtained ceramisite aggregates feature a compact outer shell and a cellular inner core with uniformly distributed pores that are isolated from each other and mostly spherical in form. They achieve a low density ranging from 723 to 855 kg/m 3 , a high cylinder compressive strength between 8.7 and 13.5 MPa, and minimal water absorption rates of 3.00 to 4.09%. The performance metrics of these coarse aggregates significantly exceeded the parameters specified in GB/T 17431.1-2010 standards.

Previous studies have shown that feeding mice with food containing mantle tissue from Japanese scallops results in aggravated liver and kidney damage, ultimately resulting in mortality within weeks. The aim of this study is to evaluate the toxicity of scallop mantle in China's coastal areas and explore the impact of scallop mantle toxins (SMT) on intestinal barrier integrity and gut microbiota in mice. The Illumina MiSeq sequencing of V3-V4 hypervariable regions of 16S ribosomal RNA was employed to study the alterations in gut microbiota in the feces of SMT mice. The results showed that intestinal flora abundance and diversity in the SMT group were decreased. Compared with the control group, significant increases were observed in serum indexes related to liver, intestine, inflammation, and kidney functions among SMT-exposed mice. Accompanied by varying degrees of tissue damage observed within these organs, the beneficial bacteria of Muribaculaceae and Marinifilaceae significantly reduced, while the harmful bacteria of Enterobacteriaceae and Helicobacter were significantly increased. Taken together, this article elucidates the inflammation and glucose metabolism disorder caused by scallop mantle toxin in mice from the angle of gut microbiota and metabolism. SMT can destroy the equilibrium of intestinal flora and damage the intestinal mucosal barrier, which leads to glucose metabolism disorder and intestinal dysfunction and may ultimately bring about systemic toxicity.

Bacterial infection is a common complication in bone defect surgery, in which infection by clinically resistant bacteria has been a challenge for the medical community. Given this emerging problem, the discovery of novel natural-type inhibitors of drug-resistant bacteria has become imperative. Brucine, present in the traditional Chinese herb Strychnine semen , is reported to exert analgesic and anti-inflammatory effects. Brucine's clinical application was limited because of its water solubility. We extracted high-purity BS by employing reflux extraction and crystallization, greatly improved its solubility, and evaluated its antimicrobial activity against E. coli and S. aureus . Importantly, we found that BS inhibited the drug-resistant strains significantly better than standard strains and achieved sterilization by disrupting the bacterial cell wall. Considering the safety concerns associated with the narrow therapeutic window of BS, a 3D BS-PLLA/PGA bone scaffold system was constructed with SLS technology and tested for its performance, bacteriostatic behaviors, and biocompatibility. The results have shown that the drug-loaded bone scaffolds had not only long-term, slow-controlled release with good cytocompatibility but also demonstrated significant antimicrobial activity in antimicrobial testing. The above results indicated that BS may be a potential drug candidate for the treatment of antibiotic-resistant bacterial infections and that scaffolds with enhanced antibacterial activity and mechanical properties may have potential applications in bone tissue engineering.

Radiation treatment of cancers like prostate or cervix cancer requires considering nearby bone structures like vertebrae. In this work, we present and validate a novel automated method for the 3D segmentation of individual lumbar and thoracic vertebra in computed tomography (CT) scans. It is based on a single, low-complexity convolutional neural network (CNN) architecture which works well even if little application-specific training data are available. It is based on volume patch-based processing, enabling the handling of arbitrary scan sizes. For each patch, it performs segmentation and an estimation of up to three vertebrae center locations in one step, which enables utilizing an advanced post-processing scheme to achieve high segmentation accuracy, as required for clinical use. Overall, 1763 vertebrae were used for the performance assessment. On 26 CT scans acquired for standard radiation treatment planning, a Dice coefficient of 0.921 ± 0.047 (mean ± standard deviation) and a signed distance error of 0.271 ± 0.748 mm was achieved. On the large-sized publicly available VerSe2020 data set with 129 CT scans depicting lumbar and thoracic vertebrae, the overall Dice coefficient was 0.940 ± 0.065 and the signed distance error was 0.109 ± 0.301 mm. A comparison to other methods that have been validated on VerSe data showed that our approach achieved a better overall segmentation performance.

Rosa roxburghii Tratt pomace is rich in insoluble dietary fiber (IDF). This study aimed to investigate the influence of three modification methods on Rosa roxburghii Tratt pomace insoluble dietary fiber (RIDF). The three modified RIDFs, named U-RIDF, C-RIDF, and UC-RIDF, were prepared using ultrasound, cellulase, and a combination of ultrasound and cellulase methods, respectively. The structure, physicochemical characteristics, and functional properties of the raw RIDF and modified RIDF were comparatively analyzed. The results showed that all three modification methods, especially the ultrasound-cellulase combination treatment, increased the soluble dietary fiber (SDF) content of RIDF, while also causing a transition in surface morphology from smooth and dense to wrinkled and loose structures. Compared with the raw RIDF, the modified RIDF, particularly UC-RIDF, displayed significantly improved water-holding capacity (WHC), oil-binding capacity (OHC), and swelling capacity (SC), with increases of 12.0%, 84.7%, and 91.3%, respectively. Additionally, UC-RIDF demonstrated the highest nitrite ion adsorption capacity (NIAC), cholesterol adsorption capacity (CAC), and bile salt adsorption capacity (BSAC). In summary, the combination of ultrasound and cellulase treatment proved to be an efficient approach for modifying IDF from RRTP, with the potential for developing a functional food ingredient.