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Abstract In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

Abstract Weathering features of sandstone heritage can be recognized by using artificial intelligence (AI) based surrogate models, and most models perform classification tasks for types based on precise labels. But there are lack of prior validated knowledge of the weathering or untagged historical data for complex weathering conditions in many cases. To this aim, a unsupervised graph neural network (GNN) based on the statistical features of the acoustic emission (AE) signals is constructed. Firstly, taking unweathered sandstone as a reference, we define 4 weathering levels of sandstone ranging from I to IV based on pore indicators. We selected 11 statistical features that are high correlated with pore of sandstone. Then, this GNN is constructed and trained by 2880 sets of statistical measured AE signals. Compared with AEs, LOF and IF models, GNN achieves the best identification performance among the four evaluation criteria. Each iteration of the GNN network is fitting the feature information of the signals and their neighbors. By data dimensionality reduction techniques, when the GNN stops iterating, it will be easy to distinguish unweathered AE signals from weathered one by comparing the reconstruction error of each signal. Furthermore, when the nearest neighbor’s k gradually increases, the AUC of GNN also gradually increases and then tend to stable when k equals to 50–100. While the hidden layers of the network aggregates less information about the neighborhood features of the signals and cannot distinguish significantly between unweathered and weathered signals when the value of k is small. As the depth of the network deepens, the feature values between signals become more and more similar, their reconstruction errors in the output layer of the network to become more similar, making it difficult to distinguish unweathered AE signals from weathered AE signals via GNN. Meanwhile, GNN adopts more AE features and considers the similarity between each features. This can greatly eliminate various errors caused by wave velocity measurement, greatly improving the robustness of AE detection. Hence, the GNN model presented addresses the limitations of relying solely on P-wave velocity measurements to assess the degree of sandstone weathering at stone cultural heritage.

High-temperature-resistant adhesives are critical materials in the aerospace field. The zirconium-modified aluminum phosphate-based adhesives developed in this work had the advantage of adjustable thermal expansibility, achieving a high matching of coefficient of thermal expansion (CTE) with alumina. The introduction of zirconium can significantly improve the thermal stability of the adhesive matrix, and the Zr/Al ratio substantially affects the various reaction processes inside the adhesive, especially the types of zirconium-containing compounds. Most of the zirconium-containing compounds in the A7Z3 adhesive were ZrO2 only when the mass ratio of zirconium hydroxide to aluminum hydroxide was 3 : 7, which was the key reason why it had the highest CTE. The room-temperature bonding strength of A7Z3 after heat treatment at 1500 °C reached 67.2 MPa. After pretreatment at 1500 °C, the high-temperature bonding strength of A7Z3 was greater than 50 MPa in the range of (room temperature) RT–1000 °C. After 40 thermal cycles between RT and 1500 °C, the bonding strength still reached 10 MPa. Physical bonding occurred at temperatures below 1000 °C, while chemical bonding dominated above 1000 °C based on the generation of Al5BO9 and mullite at the interfaces.

Abstract Background At present, the most effective treatment for symptomatic moyamoya disease (MMD) is surgery. However, the high incidence of postoperative complications is a serious problem plaguing the surgical treatment of MMD, especially the acute cerebral infarction. Decreased cerebrovascular reserve is an independent risk factor for ischemic infarction, and the pulsatility index (PI) of transcranial Doppler (TCD) is a common intuitive index for evaluating intracranial vascular compliance. However, the relationship between PI and the occurrence of ischemic stroke after operation is unclear. Objective To explore whether the PI in the middle cerebral artery (MCA) could serve as a potential predictor for the occurrence of ischemic infarction after bypass surgery in MMD. Methods We performed a retrospective analysis of data from 71 patients who underwent combined revascularization surgery, including superficial temporal artery-middle cerebral artery (STA-MCA) anastomosis and encephalo-duro-myo-synangiosis (EDMS). The patients were divided into two groups according to the median of ipsilateral MCA-PI before operation, low PI group (MCA-PI

Capturing CO2 and converting it into valuable chemicals and fuels have been regarded as a pivotal strategy in addressing the environmental challenges of ever-growing CO2 emissions. Combining CO2 capture and conversion through material or process integration can eliminate the energy-intensive steps such as separation, compression, and transportation across a wide range of space and temperatures. The flue gas at high temperatures > 300 °C can be handled with dual-function materials consisting of sorbents and catalysts. The dual-function materials combine CO2 capture and conversion through material integration, converting CO2 with reactions such as methanation, reverse water-gas shift, dry reforming of CH4, and oxidative dehydrogenation of propane. On the other hand, capturing CO2 from air directly requires a long time to collect enough CO2 for the subsequent conversion reaction. Consequently, direct air capture will likely combine with the conversion reactions in stepwise operations. The low latent heat in CO2 from direct air capture makes it more suitable for reactions at a mild condition (< 250 °C), and stepwise operation allows the separate control of the capture and conversion conditions. Herein, we reviewed recent advancements in coupling CO2 capture from flue gas and ambient air with thermal catalytic conversion. We discussed the requirements for materials, reactor configuration, and process operation for capturing and converting CO2 from these sources and proposed that future research should focus on enhancing the efficiency, scalability, and sustainability of CO2 capture and conversion technologies and optimizing the process design.

ABSTRACT This study systematically analyzes the relationship of vitamin A on the neonatal respiratory diseases. An extensive literature search for relevant studies was conducted on PubMed, Web of Science, and so on. After screening in strict accordance with the inclusion and exclusion criteria, 12 articles on vitamin A deficiency and 12 articles on vitamin A supplementation were included. Stata 17.0 software was used to perform meta‐analysis, heterogeneity test, and sensitivity analysis, and the corresponding mathematical model was used to merge the data. The meta‐analysis results of the relationship between vitamin A deficiency and neonatal respiratory diseases indicated that compared with the neonates with normal vitamin A, the neonates with vitamin A deficiency had adverse health outcomes of neonatal respiratory diseases (OR = 4.86, 95% CI: 2.68–8.84), of which neonatal respiratory distress syndrome (NRDS) (OR = 4.10, 95% CI: 2.32–7.23) and neonatal pneumonia (OR = 3.22, 95% CI: 2.18–4.77) were analyzed by subgroup analysis. The meta‐analysis of the relationship between vitamin A supplementation therapy and neonatal respiratory diseases showed that vitamin A supplementation was an effective therapeutic measure for neonatal respiratory diseases (RR = 1.06, 95% CI: 1.04–1.07): NRDS (RR = 1.03, 95% CI: 1.02–1.05) and NBPD (RR = 1.08, 95% CI: 1.01–1.15). The funnel chart method results show that there was publication bias in studies on vitamin A deficiency induced to and vitamin A supplementation therapy for neonatal respiratory diseases. The sensitivity analysis results showed that excluding some special article had some effect on the final pooled effect. But generally speaking, the result of meta‐analysis was stable. There is a statistical correlation of vitamin A on the neonatal respiratory diseases from two aspects of etiological exploration and effect evaluation of treatment.

Abstract Dyslexia and developmental language disorders are important learning difficulties. However, their genetic basis remains poorly understood, and most genetic studies were performed on Europeans. There is a lack of genome-wide association studies (GWAS) on literacy phenotypes of Chinese as a native language and English as a second language (ESL) in a Chinese population. In this study, we conducted GWAS on 34 reading/language-related phenotypes in Hong Kong Chinese bilingual children (including both twins and singletons; total N = 1046). We performed association tests at the single-variant, gene, and pathway levels. In addition, we tested genetic overlap of these phenotypes with other neuropsychiatric disorders, as well as cognitive performance (CP) and educational attainment (EA) using polygenic risk score (PRS) analysis. Totally 5 independent loci (LD-clumped at r2 = 0.01; MAF > 0.05) reached genome-wide significance (p 0.3 and having at least 2 correlated SNPs (r2 > 0.5) with p

Objectives: To explore whether COVID-19 vaccination protects against hospital admission by preventing infections and severe disease. Methods: We leveraged the UK Biobank and studied associations of COVID-19 vaccination (BioNTech-BNT162b2 or Oxford-AstraZeneca-ChAdOx1) with hospitalizations from cardiovascular and other selected diseases (N = 393,544; median follow-up = 54 days among vaccinated individuals). Multivariable Cox, Poisson regression, propensity score matching, and inverse probability treatment weighting analyses were performed. We also performed adjustment using prescription-time distribution matching, and prior event rate ratio. Results: We observed that COVID-19 vaccination (at least one dose), compared with no vaccination, was associated with reduced short-term risks of hospitalizations from stroke (hazard ratio [HR] = 0.178, 95% confidence interval [CI]: 0.127-0.250, P = 1.50e-23), venous thromboembolism (HR = 0.426, CI: 0.270-0.673, P = 2.51e-4), dementia (HR = 0.114, CI: 0.060-0.216; P = 2.24e-11), non-COVID-19 pneumonia (HR = 0.108, CI: 0.080-0.145; P = 2.20e-49), coronary artery disease (HR = 0.563, CI: 0.416-0.762; P = 2.05e-4), chronic obstructive pulmonary disease (HR = 0.212, CI: 0.126-0.357; P = 4.92e-9), type 2 diabetes (HR = 0.216, CI: 0.096-0.486, P = 2.12e-4), heart failure (HR = 0.174, CI: 0.118-0.256, P = 1.34e-18), and renal failure (HR = 0.415, CI: 0.255-0.677, P = 4.19e-4), based on standard Cox regression models. Among the previously mentioned results, reduced hospitalizations for stroke, heart failure, non-COVID-19 pneumonia, and dementia were consistently observed across regression, propensity score matching/inverse probability treatment weighting, prescription-time distribution matching, and prior event rate ratio. The results for two-dose vaccination were similar. Conclusions: Taken together, this study provides further support to the safety and benefits of COVID-19 vaccination, and such benefits may extend beyond reduction of infection risk or severity per se. However, causal relationship cannot be concluded and further studies are required.

Summary: Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.

BackgroundSubways are typical congregate settings and may facilitate aerosol transmission of viruses. However, quantified transmission probability estimates are lacking. PurposeTo model spread and diffusion of respiratory aerosols in subways by simulation and calculation of infection probabilities. MethodsThe internal environment of carriages of Shanghai Metro Line 10 was used to establish a study scene. The movement of tiny particles was simulated using the turbulent model. Trend analysis of infection probabilities and viral quantum doses was conducted in a closed subway carriage scene by a quantum emission-infection probability model. ResultsUnder a typical twelve-vent air conditioning configuration, respiratory droplet aerosols within a subway carriage dispersed rapidly throughout various regions due to airflow, with limited short-term diffusion to other carriages. Concurrently, owing to the uncertainty of airflow patterns, the airflow might circulate and converge within carriages, causing delayed outward dispersion or hindered dispersion of droplet aerosols upon entry into these zones. Passengers boarding the carriage could exacerbate the formation of these zones. When the air conditioning system functioned adequately (air exchange rate=23.21 h−1), the probability of a virus carrier transmitting the virus to other passengers within the same carriage via aerosol transmission was approximately 3.8%. However, in the event of air conditioning system failure (air exchange rate=0.5 h−1), this probability escalated dramatically to 30%. Furthermore, a super-spreader (with virus spreading exceeding 90% of the average) elevated the infection probability to 14.9%. Additionally, due to the complexity of turbulence within the carriage, if local diffusion occurred in 1/2 zones of a carriage, the anticipated infection probability would increase to 8.9%, or during the morning or evening rush hours leading to elevated aerosol concentrations, the infection probability would rise to 4.7%. The subway transmission probability for common coronaviruses diminished to as low as 0.9%. ConclusionCombined computational fluid dynamics and infection probability analysis reveals that in the prevalent twelve-vent air conditioning configurations, despite being a major transportation hub with substantial spatial-temporal overlap, the internal space of subway carriages exhibits a certain level of resistance to virus aerosol transmission owing to built-in ventilation capabilities. However, turbulence and passenger positioning may lead to localized hovering of droplet aerosols, thereby increase the risk of virus transmission. Furthermore, super-spreaders, poor operational status of built-in air conditioning system, and high passenger volume at morning or evening peak hours exert profound effects on virus transmission and infection probability.

Abstract High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks.

Generalized frequency division multiplexing (GFDM) system always faces challenges related to out-of-band (OOB) power leakage and a high peak-to-average power ratio (PAPR).Through an in-depth analysis of power spectrum characteristics of spectrally precoded GFDM systems, a universal spectral precoding (SP) framework based on the weighted fractional Fourier transform (WFRFT) was established.A two-dimensional parameter-adjustable WFRFT-SP-GFDM system was introduced.By flexibly configuring the parameters, the WFRFT spectral precoder could be made to approximate the weighted fractional Fourier transform matrix, achieve precise control over OOB power and PAPR.Simulation results indicate that, when compared to conventional GFDM and WFRFT-GFDM waveforms, the WFRFT-SP-GFDM waveform effectively suppresses OOB power leakage, while maintaining favorable PAPR and bit error rate (BER) performance.

Gaussia luciferase (Gluc) is currently known as the smallest naturally secreted luciferase. Due to its small molecular size, high sensitivity, short half-life, and high secretion efficiency, it has become an ideal reporter gene and is widely used in monitoring promoter activity, studying protein-protein interactions, protein localization, high-throughput drug screening, and real-time monitoring of tumor occurrence and development. Although studies have shown that different Gluc mutations exhibit different bioluminescent properties, their mechanisms have not been further investigated. The purpose of this study is to reveal the relationship between the conformational changes of Gluc mutants and their bioluminescent properties through molecular dynamics simulation combined with neural relationship inference (NRI) and Markov models. Our results indicate that, after binding to the luciferin coelenterazine (CTZ), the α-helices of the 109–119 residues of the Gluc Mutant2 (GlucM2, the flash-type mutant) are partially unraveled, while the α-helices of the same part of the Gluc Mutant1 (GlucM1, the glow-type mutant) are clearly formed. The results of Markov flux analysis indicate that the conformational differences between glow-type and flash-type mutants when combined with luciferin substrate CTZ mainly involve the helicity change of α7. The most representative conformation and active pocket distance analysis indicate that compared to the flash-type mutant GlucM2, the glow-type mutant GlucM1 has a higher degree of active site closure and tighter binding. In summary, we provide a theoretical basis for exploring the relationship between the conformational changes of Gluc mutants and their bioluminescent properties, which can serve as a reference for the modification and evolution of luciferases.

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film’s semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing.

ObjectivesThis study seeks to correct the distortion of results caused by the inviscid-flow assumption when potential flow theory is used to calculate a two-ship floating system with a small gap, and analyze the motion response characteristics of the gap water.MethodsA CFD numerical model of a ship-to-ship transfer system is established and the water response characteristics and mechanism in the gap are analyzed. The CFD calculation results are then compared with those of potential flow theory in order to obtain the accurate damping coefficient of the two-ship gap and correct the results of potential flow theory.ResultsThe results show that the trend of wave elevation is different when waves of different frequencies pass through the gap. For low frequency waves, the incident wave cannot pass through the gap between the two ships, so the gap wave elevation is smaller than the wave amplitude of the incident wave. For high frequency waves, the incident wave can pass through the gap, so the gap wave elevation is greater than the wave amplitude of the incident wave. There is a high-speed area in the gap between the two ships which decreases the hydrodynamic pressure on the inner sides of the two vessels, resulting in greater suction which may adversely affect transfer operations. ConclusionsThe correction method proposed herein can obtain the damping coefficient of gap water more accurately, providing guidance for the high-precision and rapid hydrodynamic and motion calculation of ship-to-ship transfer systems.

ABSTRACTBackground: Posterior oropharyngeal saliva (POS) is increasingly recognized as an alternative specimen for detecting respiratory pathogens. The accuracy of Xpert® MTB/RIF Ultra (X-Ultra), when performed on POS obtained from patients with paucibacillary pulmonary tuberculosis (TB) is unclear. Methods: We consecutively recruited adults with symptoms suggestive of pulmonary TB who were negative by both smear microscopy and Xpert MTB/RIF (X-Classic). Each participant was required to provide one bronchoalveolar lavage fluid (BALF) and one POS specimen, respectively. Diagnostic performances of X-Ultra and X-Classic on POS were compared against clinical and mycobacterial reference standards. Findings: 686 participants meeting inclusion criteria were consecutively enrolled into the study. The overall diagnostic sensitivities of X-Ultra and X-Classic on POS samples were 78.9% [95% confidence interval (CI): 72.8–83.8] and 56.4% (95% CI: 49.7–62.9), respectively; the specificities were 96.6% (95% CI: 94.3–98.1) for X-Ultra and 97.6 (95CI: 95.5–98.8) for X-Classic in POS specimens. Notably, the sensitivity of X-Ultra on POS was as sensitive as X-Classic on BALF against microbiological reference standard (78.9% VS 73.1%). Against clinical diagnosis as a reference standard, the sensitivities of X-Ultra and X-Classic on POS were 55.9% (95% CI: 50.5–61.2; 193/345) and 40.0% (95% CI: 34.8–45.4; 138/345), respectively. The risk of negative results with POS was dramatically increased with decreasing bacterial loads. Conclusions: The testing of POS using X-Ultra shows promise as a tool to identify patients with paucibacillary TB. Considering that bronchoscopy is a semi-invasive procedure, POS testing ahead of bronchoscopy, may decrease the need for bronchoscopic procedures, and the cost of care.

Lightweight and high-strength insulation materials have important application prospects in the aerospace, metallurgical, and nuclear industries. In this study, a highly porous silica fiber reinforced phenolic resin matrix composite was prepared by vacuum impregnation and atmospheric drying using quartz fiber needled felt as reinforcement and anhydrous ethanol as a pore-making agent. The effects of curing agent content on the structure, composition, density, and thermal conductivity of the composite were studied. The mechanical properties of the composite in the xy direction and z direction were analyzed. The results showed that this process can also produce porous phenolic resin (PR) with a density as low as 0.291 g/cm3, where spherical phenolic resin particles are interconnected to form a porous network structure with a particle size of about 5.43 μm. The fiber-reinforced porous PR had low density (0.372~0.397 g/cm3) and low thermal conductivity (0.085~0.095 W/m·K). The spherical phenolic resin particles inside the composite were well combined with the fiber at the interface and uniformly distributed in the fiber lap network. The composite possessed enhanced mechanical properties with compressive strength of 3.5–5.1 MPa in the xy direction and appeared as gradual compaction rather than destruction as the strain reached 30% in the z direction. This research provides a lightweight and high-strength insulation material with a simple preparation process and excellent performance.

Rare ginsenosides have already been widely applied in many fields, including health food and bio-medicine. The human being can expose to rare ginsenosides directly or indirectly increasingly. However, there are few studies on the safety assessment of rare ginsenoside mixtures. In the present study, the sub-chronic toxicity of rare ginsenosides for 90 days on SD rats was performed by combining the intestinal flora analysis and urine metabonomics aiming to illustrate the safety of long-term consumption of rare ginsenosides and the potential damage for liver and intestinal. 48 adult rats were divided into four groups: control (0 mg/kg), low-dose (60 mg/kg), medium-dose (200 mg/kg), and high-dose (600 mg/kg). Rats in the high-dose group showed inflammatory changes in their livers and intestines. The strong bactericidal effect of rare ginsenosides caused intestinal flora disorder and changed the structure of intestinal flora in rats, thus inducing intestinal damage in rats. In the high-dose group, levels of alanine aminotransferase (ALT), lactate dehydrogenase (LDH), and alkaline phosphatase (AKP) increased significantly. As a result of the high-dose treatment, certain metabolic pathways were altered, such as vitamin B6 metabolism, methionine metabolism, glutathione metabolism, and others. These results indicated that high doses of rare ginsenosides induced liver injury by affecting the above metabolic pathways. Rare ginsenosides with no observed adverse effect level (NOAEL) were below 200 mg/kg/day in vivo. Thus, this present study provides insight into the rational use of rare ginsenosides.

Flour products played a vital role in the global diet structure. With the increasing demand for dietary health and food standardization, the staple food of flour products made from coarse grains due to its unique flavor and rich nutrition has become a trend and is favored by consumers. However, the lack of gluten protein in the raw materials prevented the formation of a stable gluten network structure, leading to the deterioration of the quality of flour products. Ultrasonic treatment, as an innovative food processing technology, generated energy during the action of ultrasonic waves that had a positive impact on the texture, organizational structure, or flavor characteristics of food. That was of great significance for improving food production efficiency, improving food processing quality, and extending food shelf life. This article applied ultrasonic technology to the processing of flour products from the perspective of promoting fermentation and improving production efficiency of flour products. The cavitation effect of ultrasound promoted the formation of gluten network structure, improved the rheology properties of dough and the quality of flour products by promoting protein cross-linking, improving the foaming and emulsifying stability of gluten protein, and promoting the growth and reproduction of yeast. All reviewed studies indicate that ultrasound would be a promising technology for producing high-quality surface products under appropriate conditions.

The AlGaN photocathode with a superlattice emission layer was grown by metal organic chemical vapor deposition. The existence of the superlattice structure was confirmed using a transmission electron microscope, and its optical and electrical properties were measured. The results showed that the introduction of a superlattice emission layer can reduce the transmittance and reflectance at 275 nm and improve the absorption of incident light. In particular, the Hall measurement results indicate that the introduction of a superlattice structure can significantly increase the concentration of holes in the AlGaN photocathode material. The activation experiment demonstrated that the photocurrent of the AlGaN photocathode subassembly with a superlattice structure increased significantly after Cs/O activation and that it showed better photoemission performance. The increase in the incident light absorption to produce more photoelectrons, the enhancement of photoelectron transport capacity by the polarized electric field generated by the superlattice structure, and the reduction in the surface barrier caused by the higher hole concentration were responsible for the significant improvement in photoemission performance.

Polyethylene microplastics have been detected in farmland soil, irrigation water, and soil organisms in agroecosystems, while plastic mulching is suggested as a crucial source of microplastic pollution in the agroecosystem. Plastic mulch can be broken down from plastic mulch debris to microplastics through environmental aging and degradation process in farmlands, and the colonization of polyethylene-degrading microorganisms on polyethylene microplastics can eventually enzymatically depolymerize the polyethylene molecular chains with CO2 release through the tricarboxylic acid cycle. The selective colonization of microplastics by soil microorganisms can cause changes in soil microbial community composition, and it can consequently elicit changes in enzyme activities and nutrient element content in the soil. The biological uptake of polyethylene microplastics and the associated disturbance of energy investment are the main mechanisms impacting soil-dwelling animal development and behavior. As polyethylene microplastics are highly hydrophobic, their presence among soil particles can contribute to soil water repellency and influence soil water availability. Polyethylene microplastics have been shown to cause impacts on crop plant growth, as manifested by the effects of polyethylene microplastics on soil properties and soil biota in the agroecosystems. This review reveals the degradation process, biological impacts, and associated mechanisms of polyethylene microplastics in agroecosystems and could be a critical reference for their risk assessment and management.

To improve the understanding of the optical image resolution of transmission-mode GaAs photocathode, by establishing the surface scattering analysis model of AlGaAs window layer, we have researched the effect of the surface height distribution variance σ on scattering surface transfer function (STF) and point spread function (PSF) of transmission-mode GaAs photocathode. The simulation results show that the decrease of surface height distribution variance σ is beneficial to improve the optical image resolution. This improvement is mainly attributed to the decrease in input light energy attenuation and spatial diffusion caused by smaller surface height distribution variance σ, and thus the signal to noise ratio of scattering degraded optical image is increased.

Abstract Background Sarcopenia is defined as an age‐related progressive loss of muscle mass and/or strength. Although different factors can contribute to this disease, the underlying mechanisms remain unclear. We assessed transcriptional heterogeneity in skeletal muscles from sarcopenic and control mice at single‐cell resolution. Methods A mouse model was established to study sarcopenic skeletal muscles. Single‐cell RNA‐seq was performed on tibialis anterior (TA) muscle cells collected from sarcopenic and control mice. A series of bioinformatic analyses were carried out to identify and compare different cell types under different conditions. Immunofluorescence staining and western blotting were used to validate the findings from single‐cell experiments. Tube formation assays were conducted to further evaluate the effects of Gbp2 on endothelial cells during angiogenesis. Results A murine sarcopenia model was successfully established using a senescence‐accelerated mouse strain (SAMP6, n = 5). Sarcopenia phenotype was induced by administration of dexamethasone (20 mg/kg) and reduced physical activity. Senescence‐resistant mice strain (SAMR1) and SAMP6 strain with similar activity but injected with PBS were recruited as two control groups. As signs of sarcopenia, body weight, muscle cell counts and cross‐sectional fibre area were all significantly decreased in sarcopenic mice (P value = 0.004, 0.03 and 0.035, respectively). After quality control, 13 612 TA muscle single‐cell transcriptomes were retained for analysis. Fourteen cell clusters were identified from the profiled cells. Among them, two distinct endothelial subtypes were found to be dominant in the sarcopenia group (42.2% cells) and in the two control groups (59.1% and 47.9% cells), respectively. 191 differentially expressed genes were detected between the two endothelial subtypes. Sarcopenia‐specific endothelial cell subtype exhibited a dramatic increase in the interferon family genes and the interferon‐inducible guanylate‐binding protein (GBP) family gene expressions. For example, Igtp and Gbp2 in sarcopenic endothelial cells were 5.4 and 13.3 times higher than those in the control groups, respectively. We further validated our findings in muscle specimens of sarcopenia patients and observed that GBP2 levels were increased in endothelial cells of a subset of patients (11 of 40 patients, 27.5%), and we identified significantly higher CD31 and GBP2 co‐localization (P value = 0.001128). Finally, we overexpressed Gbp2 in human umbilical vein endothelial cells in vitro. The endothelial cells with elevated Gbp2 expression displayed compromised tube formation. Conclusions Our single‐cell‐based results suggested that endothelial cells may play critical roles in sarcopenia development through interferon‐GBP signalling pathways, highlighting new therapeutic directions to slow down or even reverse age‐related sarcopenia.

Abstract Background Lactate dehydrogenase (LDH) has been reported in multiple heart diseases. Herein, we explored the prognostic effects of preoperative LDH on adverse outcomes in cardiac surgery patients. Methods Retrospective data analysis was conducted from two large medical databases: Medical Information Mart for Intensive Care (MIMIC) III and MIMIC IV databases. The primary outcome was in-hospital mortality, whereas the secondary outcomes were 1-year mortality, continuous renal replacement therapy, prolonged ventilation, and prolonged length of intensive care unit and hospital stay. Results Patients with a primary endpoint had significantly higher levels of LDH (p

C-doped GaAs is considered a potential material for negative electron affinity photocathodes, where the p-type doped property is beneficial to photoemission. To clarify the stability and efficiency during Cs/O activation, the gradient concentration of Cs adsorption and Cs/O co-adsorption models of C-doped GaAs are established. The work function, adsorption energy, and surface dipole moment are intensified by first principles calculation based on density functional theory. Experimental results demonstrate that Cs/O activation effectively enhances the performance of C-doped GaAs photocathodes, resulting in high levels of quantum efficiency. Therefore, we conclude that C-doped GaAs photocathodes have the potential to significantly improve the photoelectric emission performance and stability of GaAs photocathodes, making them a viable candidate for future applications.

An infrared perfect absorber structure is designed based on GaAs/Au/SiO2 metamaterial with numerical simulation, in which gold split ring resonators (SRR) embedded in the GaAs layer. The absorption exceeds 99% at 1360 nm under the plane wave excitation with its polarization perpendicular to the opening direction of the SRR. When the polarization of the plane wave is parallel to the opening direction, the absorption exceeds 97% and 56% at 970 and 2070 nm, respectively, which realize dual-band absorption. The absorption peaks are effectively modulated by controlling surface current density distribution and resonant electromagnetic response. In addition, the resonant wavelengths are further manipulated by optimizing the resonant ring structural parameters, which achieve ultrawide-band absorption ranging from near-infrared to mid-infrared region. The absorber remains absorption peaks above 96% under wide-angle plane wave incidence, and the resonant peak positions are independent of the incident angle. This work exhibits the promise of GaAs-based metamaterial in practical applications in energy harvesting and night vision imaging.

Photodynamic therapy (PDT) has become an important therapeutic strategy because it is highly controllable, effective, and does not cause drug resistance. Moreover, precise delivery of photosensitizers to tumor lesions can greatly reduce the amount of drug administered and optimize therapeutic outcomes. As alternatives to protein antibodies, peptides have been applied as useful targeting ligands for targeted biomedical imaging, drug delivery and PDT. In addition, other functionalities of peptides such as stimuli responsiveness, self-assembly, and therapeutic activity can be integrated with photosensitizers to yield versatile peptide-based nanosystems for PDT. In this article, we start with a brief introduction to PDT and peptide-based nanosystems, followed by more detailed descriptions about the structure, property, and architecture of peptides as background information. Finally, the most recent advances in peptide-based nanosystems for PDT are emphasized and summarized according to the functionalities of peptide in the system to reveal the design and development principle in different therapeutic circumstances. We hope this review could provide useful insights and valuable reference for the development of peptide-based nanosystems for PDT.

Cotton processing is the process of converting harvested seed cotton into lint by cleaning, ginning, and cleaning the lint. The real-time acquisition of lint parameters during processing is critical in improving cotton processing quality and efficiency. The existing online inspection system cannot realize quantitative sampling detection, resulting in large fluctuations in the detection of moisture rate, and the impurity content of lint can only be measured according to the number of impurity grains and the percentage of impurity areas. This research developed a quantitative sampling device for cotton lint processing that can collect the right number of cotton samples and obtain the weight of the samples, laying the foundation for the accurate detection of cotton lint dampness and impurity rates. This research aimed to develop an online quantitative sampling device with a sampling plate as its core. The quantitative sampling procedure, consisting of a gas–solid two-phase flow in a cotton pipeline, was numerically simulated and experimentally analyzed using computational fluid dynamics (CFD) and the discrete element method (DEM). According to the coupling results, the maximum pressure differential between the top and bottom regions of the sampling plate when conveying was 1024.45 Pa. This pressure is adequate to allow for cotton samples to accumulate on the sampling plate. Simultaneously, the steady conveying speed of lint is 59.31% of the unloaded conveying wind speed, providing a theoretical foundation for the sampling time of the quantitative sample device in the processing chain. The results from testing the prototype indicate that the quantitative sampling device in the cotton flow can effectively perform the quantitative sampling of cotton lint under uniform conditions, with a sampling pass rate of 84%.

BackgroundT cell lymphomas (TCL) are a group of heterogeneous diseases with over 40 subtypes. In this study, we identified a novel TCL subtype which was featured by a unique T cell receptor (TCR) presentation, α, β and γ chains were co-existing in a single malignant T cell.Case presentationA 45-year-old male patient was diagnosed T cell lymphoma after 2-month of abdominal distension and liver enlargement. Combining histology review, PET-CT scanning and immunophenotyes, the patient was not classified to any existing TCL subtypes. To better understand this unclassified TCL case, we performed single cell RNA sequencing paired with TCR sequencing on the patient’s PBMC and bone marrow samples. To our surprise, we identified that the malignant T cells had a very rare TCR combination, by expressing two α chains, one β chain and one γ chain simultaneously. We further studied the molecular pathogenesis and tumor cell heterogeneity of this rare TCL subtype. A set of potential therapeutic targets were identified from the transcriptome data, such as CCL5, KLRG1 and CD38.ConclusionsWe identified the first TCL case co-expressing α, β and γ chains and dissected its molecular pathogenesis, providing valuable information for precision medicine options for this novel TCL subtype.

Spermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis. We showed that single-cell sequencing assay for transposase-accessible chromatin (scATAC-Seq) allowed us to deconvolve distinct cell populations and identify cis-regulatory elements (CREs) underlying cell-type specification. We identified sets of transcription factors associated with cell type-specific accessibility, revealing novel regulators of cell fate specification and maintenance. Pseudotime reconstruction revealed detailed regulatory dynamics coordinating the sequential developmental progressions of germ cells and somatic cells. This high-resolution dataset also unveiled previously unreported subpopulations within both the Sertoli and Leydig cell groups. Further, we defined candidate target cell types and genes of several genome-wide association study (GWAS) signals, including those associated with testosterone levels and coronary artery disease. Collectively, our data provide a blueprint of the ‘regulon’ of the mouse male germline and supporting somatic cells.

Due to its noninvasiveness, high resolution, and high sensitivity, photoacoustic imaging has developed rapidly in the field of biomedicine. However, research on dermatosis detection by photoacoustic imaging is still lacking. In this paper, the skin is modeled as a multilayer planar medium based on the non-homogeneous, complex layered structure of the skin tissue. Then, the analytical expression for the photoacoustic signal of multilayer skin tissue was derived under the assumption that the thermal and optical parameters of the skin tissue do not vary with temperature. The expression not only considers the influence of optical, thermal, and mechanical parameters of the tissue on the photoacoustic signal but also, for the first time, the influence of the number of skin layers on the photoacoustic signal. The analytical expression of the photoacoustic signal containing the number of skin layers is also given. The numerical simulation results show that the difference between the photoacoustic signal of the seven-layer skin model and the single-layer skin model is 15.206 × 10−6 MPa when ω = 3.5 MHz and μa = 2.70 cm−1. Therefore, the increase in the number of model layers enhances the amplitude of its photoacoustic signal. This work provides a comprehensive study of photoacoustic mechanisms in dermatosis tissues and establishes a theoretical foundation for the application of photoacoustic imaging detection technology in the diagnosis and treatment of dermatosis, which may improve treatment plans.

Background and objectivesAlzheimer’s disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.Materials and methodsThirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student’s t-test and Mann–Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.ResultsKinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p

Abstract Background China has a unique cotton planting pattern. Cotton is densely planted in alternating wide and narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate estimation of cotton yield using remote sensing in such field with branches occluded and overlapped. Results In this study, unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate densely planted cotton yield. Images of cotton fields were acquired by the UAV at an altitude of 5 m. Cotton bolls were manually harvested and weighed afterwards. Then, a modified DCNN model (CD-SegNet) was constructed for pixel-level segmentation of cotton boll images by reorganizing the encoder-decoder and adding dilated convolutions. Besides, linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Finally, the estimated yield for four cotton fields were verified by weighing harvested cotton. The results showed that CD-SegNet outperformed the other tested models, including SegNet, support vector machine (SVM), and random forest (RF). The average error in yield estimates of the cotton fields was as low as 6.2%. Conclusions Overall, the estimation of densely planted cotton yields based on low-altitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.

The linkage mechanism of a cotton bundle fiber strength tester will have an unstable clamping force when clamping fiber bundle samples with uneven thickness, resulting in slippage or damage to the fibers increasing the pectin residue, leading to inaccurate test results and increased maintenance costs. To address this problem, according to the structural principle of the connecting rod-clamping mechanism, through the geometric relationship between the connecting rods to establish a parametric model of the mechanism and the use of the principle of virtual work on the mechanism to solve the force, the proposed new Dynamic Alternative Static Approximate Analysis Method (DASAAM) was based on Adams 2020. The Isight integrated Adams automatic optimization design framework was built. The variance of the change curve of the end force of the mechanism when clamping samples of different thicknesses was used as the evaluation function and the assembly conditions were used as the constraints. The dimensional parameters and angles of the mechanism were optimized using the multi-island genetic algorithm. The simulation results showed that when the thickness of the clamped sample varied in the range of 0–4 mm, the clamping force of the mechanism varied in the range of 8920–8630 N. Finally, the variance of the clamping force measured by the clamping force measurement component was 0.0367. The above results show that the DASAAM provided a new method for solving the static problem of mechanism morphological and position change, and the optimized linkage mechanism had better clamping force stability, which made the strength detection of cotton fiber more accurate, thus improving the quality of textile products.

Despite the considerable research attention paid to block copolymer (BCP)-toughened epoxy resins, the effects of their terminal groups on their phase structure are not thoroughly understood. This study fills this gap by closely examining the effects of amino and carboxyl groups on the fracture toughness of epoxy resins at different temperatures. Through the combination of scanning electron microscopy and digital image correlation (DIC), it was found that the amino-terminated BCP was capable of forming a stress-distributing network in pure epoxy resin, resulting in better toughening effects at room temperature. In a 60 wt.% silica-filled epoxy composite system, the addition of a carboxyl-terminated BCP showed little toughening effect due to the weaker filler/matrix interface caused by the random dispersion of the microphase of BCPs and distributed silica. The fracture toughness of the epoxy system at high temperatures was not affected by the terminal groups, regardless of the addition of silica. Their dynamic mechanical properties and thermal expansion coefficients are also reported in this article.

The measurement of seed cotton moisture regain (MR) during harvesting operations is an open and challenging problem. In this study, a new method for resistive sensing of seed cotton MR measurement based on pressure compensation is proposed. First, an experimental platform was designed. After that, the change of cotton bale parameters during the cotton picker packaging process was simulated through the experimental platform, and the correlations among the compression volume, compression density, contact pressure, and conductivity of seed cotton were analyzed. Then, support vector regression (SVR), random forest (RF), and a backpropagation neural network (BPNN) were employed to build seed cotton MR prediction models. Finally, the performance of the method was evaluated through the experimental platform test. The results showed that there was a weak correlation between contact pressure and compression volume, while there was a significant correlation (p < 0.01) between contact pressure and compression density. Moreover, the nonlinear mathematical models exhibited better fitting performance than the linear mathematical models in describing the relationships among compression density, contact pressure, and conductivity. The comparative analysis results of the three MR prediction models showed that the BPNN algorithm had the highest prediction accuracy, with a coefficient of determination (R2) of 0.986 and a root mean square error (RMSE) of 0.204%. The mean RMSE and mean coefficient of variation (CV) of the performance evaluation test results were 0.20% and 2.22%, respectively. Therefore, the method proposed in this study is reliable. In addition, the study will provide a technical reference for the accurate and rapid measurement of seed cotton MR during harvesting operations.

Named entity recognition (NER) is a fundamental and important task in natural language processing. Existing methods attempt to utilize convolutional neural network (CNN) to solve NER task. However, a disadvantage of CNN is that it fails to obtain the global information of texts, leading to an unsatisfied performance on medical NER task. In view of the disadvantages of CNN in medical NER task, this paper proposes to utilize the dilated convolutional neural network (DCNN) and bidirectional long short-term memory (BiLSTM) for hierarchical encoding, and make use of the advantages of DCNN to capture global information with fast computing speed. At the same time, multiple feature words are inserted into the medical text datasets for improving the performance of medical NER. Extensive experiments are done on three real-world datasets, which demonstrate that our method is superior to the compared models.

Integrated sensing and communication (ISAC) has been widely recognized as a key technology in future sixth-generation (6G) wireless networks, especially for emerging applications and scenarios demanding both high-performance sensing and communication functionalities. The ISAC technique has the potential of enabling sensing and communication functionalities in a single hardware platform, even sharing the same waveform, thus, achieving the reduced cost of hardware implementation and the efficient use of spectrum resources. In this paper, we commence with the discussion on sensing and communication at the current stage from the view of motivations, applications, and challenges. Then, we provide a comprehensive survey of the state-of-the-art approaches to the ISAC technique from the waveform design perspective. To be specific, we classify the waveform design methods into three categories, namely, communication-centric waveform design, sensing-centric waveform design, and joint waveform optimization and design. In addition, potential research directions and challenges for future ISAC waveform design are outlined.

In this paper, a method for predicting residual film content in the cotton field plough layer based on UAV imaging and deep learning was proposed to solve the issues of high labour intensity, low efficiency, and high cost of traditional methods for residual film content monitoring. Images of residual film on soil surface in the cotton field were collected by UAV, and residual film content in the plough layer was obtained by manual sampling. Based on the three deep learning frameworks of LinkNet, FCN, and DeepLabv3, a model for segmenting residual film from the cotton field image was built. After comparing the segmentation results, DeepLabv3 was determined to be the best model for segmenting residual film, and then the area of residual film was obtained. In addition, a linear regression prediction model between the residual film coverage area on the cotton field surface and the residual film content in the plough layer was built. The results showed that the correlation coefficient (R2), root mean square error, and average relative error of the prediction of residual film content in the plough layer were 0.83, 0.48, and 11.06%, respectively. It indicates that a quick and accurate prediction of residual film content in the cotton field plough layer can be realized based on UAV imaging and deep learning. This study provides certain technical support for monitoring and evaluating residual film pollution in the cotton field plough layer.

To accurately evaluate residual plastic film pollution in pre-sowing cotton fields, a method based on modified U-Net model was proposed in this research. Images of pre-sowing cotton fields were collected using UAV imaging from different heights under different weather conditions. Residual films were manually labelled, and the degree of residual film pollution was defined based on the residual film coverage rate. The modified U-Net model for evaluating residual film pollution was built by simplifying the U-Net model framework and introducing the inception module, and the evaluation results were compared to those of the U-Net, SegNet, and FCN models. The segmentation results showed that the modified U-Net model had the best performance, with a mean intersection over union (MIOU) of 87.53%. The segmentation results on images of cloudy days were better than those on images of sunny days, with accuracy gradually decreasing with increasing image-acquiring height. The evaluation results of residual film pollution showed that the modified U-Net model outperformed the other models. The coefficient of determination(R2), root mean square error (RMSE), mean relative error (MRE) and average evaluation time per image of the modified U-Net model on the CPU were 0.9849, 0.0563, 5.33% and 4.85 s, respectively. The results indicate that UAV imaging combined with the modified U-Net model can accurately evaluate residual film pollution. This study provides technical support for the rapid and accurate evaluation of residual plastic film pollution in pre-sowing cotton fields.

Copper-based nanoinks are emerging as promising low-cost alternatives to widely used silver nanoinks in electronic printing. However, the spontaneous oxidation of copper under ambient conditions poses significant challenges to its broader application. To address this issue, this paper presents an economical, large-scale, and environmentally friendly method for fabricating Cu@Ag nanoparticles (Cu@Ag NPs). The as-prepared nanoparticles exhibit a narrow size distribution of approximately 100 nm and can withstand ambient exposure for at least 60 days without significant oxidation. The Cu@Ag-based ink, with a 60 wt% loading, was screen-printed onto a flexible polyimide substrate and subsequently heat-treated at 290 °C for 15 minutes under a nitrogen atmosphere. The sintered pattern displayed a low electrical resistivity of 25.5 μΩ·cm (approximately 15 times the resistivity of bulk copper) along with excellent reliability and mechanical fatigue strength. The innovative Cu@Ag NPs fabrication method holds considerable potential for advancing large-scale applications of copper-based inks in flexible electronics.

Abstract Background Lignin is one of the main components of the cell wall and is directly associated with plant development and defence mechanisms in plants, especially in response to Fusarium graminearum (Fg) infection. Caffeoyl-coenzyme A O-methyltransferase (CCoAOMT) is the main regulator determining the efficiency of lignin synthesis and composition. Although it has been characterized in many plants, to date, the importance of the CCoAOMT family in wheat is not well understood. Results Here, a total of 21 wheat CCoAOMT genes (TaCCoAOMT) were identified through an in silico genome search method and they were classified into four groups based on phylogenetic analysis, with the members of the same group sharing similar gene structures and conserved motif compositions. Furthermore, the expression patterns and co-expression network in which TaCCoAOMT is involved were comprehensively investigated using 48 RNA-seq samples from Fg infected and mock samples of 4 wheat genotypes. Combined with qRT-PCR validation of 11 Fg-responsive TaCCoAOMT genes, potential candidates involved in the FHB response and their regulation modules were preliminarily suggested. Additionally, we investigated the genetic diversity and main haplotypes of these CCoAOMT genes in bread wheat and its relative populations based on resequencing data. Conclusions This study identified and characterized the CCoAOMT family in wheat, which not only provided potential targets for further functional analysis, but also contributed to uncovering the mechanism of lignin biosynthesis and its role in FHB tolerance in wheat and beyond.

Panea et al showed that epithelia-specific butyrophilinlike 2 (Btnl2) suppressed homeostatic proliferation of γδ intraepithelial lymphocytes (IELs) preferentially in the ileum and used high throughput transcriptomic characterization of Btnl2-deficient γδ IELs to demonstrate that Btnl2 impacts γδ TCR specificities and repertoire diversity of ileal γδ IELs. In addition, they showed that Btnl2-deficient mice exhibited increased inflammation and delayed mucosal repair in the colon, suggesting that it plays a key immunological function in intestinal diseases.

Due to the advantages of hole forming and transplanting at the same time, even on a film-covered field ridge, the cup-hanging transplanter has become one of the most widely used transplanters in agricultural operations. The cup-hanging planter is the main operating part of the transplanter. Different shapes of the hanging cup can affect the soil action mechanism, cavitation performance, and planting quality. In this study, the discrete element simulation software EDEM and the multi-body system dynamics analysis software RecurDyn are used to carry out the coupling simulation analysis. The results showed that the planting holes formed by hanging cups with two different shapes and under four different planting frequencies all met the planting requirements. After soil reflow, the transverse dimensions of the two holes are similar, and the longitudinal dimensions of the hole of the conical hanging cup are smaller than those of the multilateral hanging cup. Under the same planting frequency, the tear film size and hole-forming performance of the conical cup were better than those of the multilateral hanging cup. The result of the simulation test is consistent with that of the bench test, which proved the feasibility of using EDEM-RecurDyn coupling simulation to predict the hole-forming characteristics of the cup-hanging planter. The filmless hole-forming performances of two different shapes of hanging cups are close to each other. In terms of the film damage of film hole-forming, the conical cup is better than that of the multilateral hanging cup.

COVID-19 caused by SARS-CoV-2 can cause various systemic diseases such as acute pneumonia with cytokine storm. Constituted of necroptosis, pyroptosis, and ferroptosis, regulated necrosis constitutes the cell death patterns under the low apoptosis condition commonly observed in COVID-19. Regulated necrosis is involved in the release of cytokines like TNF-α, IL-1 β, and IL-6 and cell contents such as alarmins, PAMPs, and DAMPs, leading to more severe inflammation. Uncontrolled regulated necrosis may explain the poor prognosis and cytokine storm observed in COVID-19. In this review, the pathophysiology and mechanism of regulated necrosis with the double-edged sword effect in COVID-19 are thoroughly discussed in detail. Furthermore, this review also focuses on the biomarkers and potential therapeutic targets of the regulated necrosis pathway in COVID-19, providing practical guidance to judge the severity, prognosis, and clinical treatment of COVID-19 and guiding the development of clinical anti-SARS-CoV-2 drugs.

Benzo(a)pyrene, as the main polycyclic aromatic hydrocarbon pollutant in marine oil spill pollution, has negative effects on marine ecology and human health. A facile and sensitive method of rapid benzo(a)pyrene detection in seawater is essential for marine conservation. In this paper, a novel immunosensor is fabricated using a multi-walled carbon nanotubes-chitosan composite loaded with benzo(a)pyrene antibody. This immunosensor is based on a biosensing assay mechanism that uses multi-walled carbon nanotubes-chitosan composites as conductive mediators to enhance electron transfer kinetics. Then, potassium ferricyanide was used as an electrochemical probe to produce an electrochemical signal for the voltammetric behavior investigation of the immune response by differential pulse voltammetry. Under optimal experimental conditions, the peak current change was inversely proportional to the benzo(a)pyrene concentration in the range of 0.5 ng⋅ml−1 and 80 ng⋅ml−1 with a detection limit of 0.27 ng⋅ml−1. The immunosensor was successfully applied to assay BaP in seawater, and the recovery was between 96.6 and 100%, which exhibited a novel, sensitive and interference-resistant analytical method for real-time water environment monitoring. The results demonstrate that the proposed immunosensor has a great potential for application in the monitoring of seawater.

Insect pest is an essential factor affecting crop yield, and the effect of pest control depends on the timeliness and accuracy of pest forecasting. The traditional method forecasts pest outbreaks by manually observing (capturing), identifying, and counting insects, which is very time-consuming and laborious. Therefore, developing a method that can more timely and accurately identify insects and obtain insect information. This study designed an image acquisition device that can quickly collect real-time photos of phototactic insects. A pest identification model was established based on a deep learning algorithm. In addition, a model update strategy and a pest outbreak warning method based on the identification results were proposed. Insect images were processed to establish the identification model by removing the background; a laboratory image collection test verified the feasibility. The results showed that the proportion of images with the background completely removed was 90.2%. Dataset 1 was obtained using reared target insects, and the identification accuracy of the ResNet V2 model on the test set was 96%. Furthermore, Dataset 2 was obtained in the cotton field using a designed field device. In exploring the model update strategy, firstly, the T_ResNet V2 model was trained with Dataset 2 using transfer learning based on the ResNet V2 model; its identification accuracy on the test set was 84.6%. Secondly, after reasonably mixing the indoor and field datasets, the SM_ResNet V2 model had an identification accuracy of 85.7%. The cotton pest image acquisition, transmission, and automatic identification system provide a good tool for accurately forecasting pest outbreaks in cotton fields.

Non-alcoholic fatty liver disease (NAFLD), ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), hepatic fibrosis and even hepatocellular carcinoma, is a liver disease worldwide without approved therapeutic drugs. Baicalein (BAL), a flavonoid compound extracted from the Traditional Chinese Medicine (TCM) Scutellariae Radix (Scutellaria baicalensis Georgi.), has been used in TCM clinical practice for thousands of years to treat liver diseases due to its “hepatoprotective effect”. However, the underlying liver-protecting mechanisms remain largely unknown. Here, we found that oral administration of BAL significantly decreased excess serum levels of triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST) as well as hepatic TG in fructose-fed rats. Attenuation of the increased vacuolization and Oil Red O staining area was evident on hepatic histological examination in BAL-treated rats. Mechanistically, results of RNA-sequencing, western-blot, real-time quantitative PCR (RT-qPCR) and hepatic metabolomics analyses indicated that BAL decreased fructose-induced excessive nuclear expressions of mature sterol regulatory element-binding protein 1c (mSREBP1c) and carbohydrate response element-binding protein (ChREBP), which led to the decline of lipogenic molecules [including fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), elongation of very long chain fatty acids 6 (ELOVL6), acetyl-CoA carboxylase (ACC)], accompanying with the alternation of hepatic fatty acids composition. Meanwhile, BAL enhanced fatty acid oxidation by activating AMPK/PGC1α signaling axis and PPARα signal pathway, which elicited high expression of carnitine palmitoyl transferase 1α (CPT1α) and Acyl-CoA oxidase 1 (ACO1) in livers of fructose-fed rats, respectively. BAL ameliorated fructose-induced hepatic steatosis, which is associated with regulating fatty acid synthesis, elongation and oxidation.