Search

Microplastics (MPs) have emerged as a significant pollutant, threatening agricultural ecosystem sustainability and global food security. However, relatively few studies have investigated biochar remedial effects on plant growth and biochemical properties in soils contaminated with MPs. In polyethylene microplastic (PE-MP)-contaminated soil, we investigated corn stover biochar effects on soybean growth, soil nutrient content, enzyme activity, and microbial biomass and assessed its impact on soil microbial resource limitations. The addition of MPs inhibited soybean growth in various forms across four stages. Conversely, the addition of biochar to MPs improved soybean growth to some extent, where above-ground biomass increased by 5.82% after adding biochar to soils containing microplastics. In soil treated with MPs and biochar, nitrate nitrogen (N), available phosphorus (P), and available potassium (K) increased by 20.1, 27.4, and 57.2%, respectively, while available nitrogen significantly decreased to 128.3 mg kg−1 compared to the MP-only treatment. PE-MPs alone significantly reduced soil carbon (C), N, and P enzyme activities, as well as microbial biomass, with β-glucosidase, leucine aminopeptidase, and acid phosphatase activities decreasing by 29.9, 27.8, and 25.5%, respectively. Interestingly, biochar addition to MPs significantly alleviated these detrimental effects. Microbial biomass C, N, and P increased by up to 56.0, 22.5, and 96.6%, respectively, following biochar addition to soils containing MPs. Analysis of vector lengths, vector angles, and scatter plots indicated that the presence of MPs reduced soil N and P availability. Overall, while MPs inhibited soybean development, biochar addition alleviated this effect to some extent. Furthermore, partial least-squares path modeling revealed that MPs negatively affected soil chemical properties, microbial biomass, and enzyme activities, whereas biochar positively influenced soil enzyme activities.

Studying the factors affecting machining accuracy, surface quality, and machining efficiency in the powerful honing machining process system, analyzing the basic law between various errors and machining quality, exploring the method of evaluating the quality of honing, and improving the machining quality and transmission performance of hardened gears has important engineering application value. Firstly, this paper establishes an effective abrasive grains micro-edge honing quality evaluation model, proposes a method based on the Trapezoidal Fuzzy Analytic Hierarchy Process (Tra-FAHP) and Set Pair Analysis (SPA) to comprehensively evaluate the quality of the honing process, and obtains the influence weights of each factor on the quality of honing. Secondly, the paper analyzes the influence rules of three types of abrasive grain sizes on helix error, tooth pitch error, tooth profile error, surface roughness, and honing efficiency. Finally, the correctness of the established comprehensive evaluation model of honing quality was verified with the threshold method and weights. The research results show that the model can correctly evaluate the quality of hardened gear honing and can be applied to studying the influence of abrasive grain micro-edge honing on machining characteristics.

This study introduces a new integrated framework by incorporating the Unified Theory of Acceptance and Use of Technology as the technological acceptance component, combined with satisfaction and perceived risk as the psychological acceptance components. The aim is to explore consumers’ behavioral intentions toward smart wearable devices and their expectations for the industry. The findings reveal that all research hypotheses were met as anticipated. Notably, the relatively low influence of Facilitating Conditions within the technological acceptance part and the prominent Perceived Functional Value within the psychological acceptance part highlight consumers’ concerns about the compatibility of smart wearable device ecosystems and their satisfaction with basic functionalities. This conclusion indicates that the market needs to address and improve the challenges posed by multi-brand product ecosystems and that addressing the lack of innovation reduces the emotional connection between products and consumers.

The phenomenon of prey switching is prevalent in the natural world and represents a strategic adaptation of predators to their environment. Neoseiulus bicaudus, a native predatory mite found in Xinjiang, serves as a natural enemy to numerous small pests. The utilization of Tyrophagus putrescentiae as an alternative prey for the mass breeding of N. bicaudus is employed to fulfill the demands of enhanced breeding efficiency and cost-effectiveness. Following the introduction of mass breed N. bicaudus into the agricultural ecosystem, there was a switching in the prey of N. bicaudus from Ty. putrescentiae to Tetranychus turkestani. The objective of this study was to investigate the impact of prey switching at various stages on N. bicaudus. This study employed the age-stage, two-sex life table to assess the life parameters of N. bicaudus undergoing prey switching at different stages. The results showed that the survival rate of N. bicaudus was significantly decreased, the developmental period was significantly prolonged, the fecundity was improved during prey switching, and the intrinsic rate of increase of N. bicaudus was significantly decreased. The negative effects of prey switching in larva and deutonymph stages were greater than those in protonymph and adult stages. The stage and age of N. bicaudus undergoing prey switching were positively correlated with the male ratio, and negatively correlated with the total preoviposition period and the mean generation time. The comprehension of prey switching effects on N. bicaudus can enhance our understanding of the predator adaptation to environmental changes and contribute to the artificial rearing and application of natural enemy.

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s2), a measuring range of up to 20 m/s2, and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle–track–tunnel–stratum–structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures.

Ultra-high-performance geopolymer concrete (UHPGC) emerges as a sustainable and cost-effective alternative to Portland cement-based UHPC, offering similar mechanical properties while significantly reducing carbon footprint and energy consumption. Research on UHPGC components is extremely scarce. This study focuses on the flexural and crack behavior of UHPGC beams with different steel fiber contents and longitudinal reinforcement ratios. Five UHPGC beams were tested under four-point bending. The test results were evaluated in terms of the failure mode, load–deflection relationship, flexural capacity, ductility, average crack spacing, and short-term flexural stiffness. The results show that all the UHPGC beams failed due to crack localization. Increases in the reinforcement ratio and steel fiber content had favorable effects on the flexural capacity and flexural stiffness. When the reinforcement ratio increased from 1.18% to 2.32%, the flexural capacity and flexural stiffness increased by 60.5% and 12.3%, respectively. As the steel fiber content increased from 1.5% to 2.5%, the flexural capacity and flexural stiffness increased by 4.7% and 4.4%, respectively. Furthermore, the flexural capacity, flexural stiffness, and crack spacing of the UHPGC beams were evaluated using existing methods. The results indicate that the existing methods can effectively predict flexural capacity and flexural stiffness in UHPGC beams but overestimate crack spacing. This study will provide a reference for the structural design of UHPGC.

Neoseiulus bicaudus is a predatory mite species that could potentially be used for the biological control of spider mites and thrips. Floral resources can provide excellent habitats and abundant nutrients for natural enemies. The objective of this experiment was to evaluate the effects of eight floral resources on the longevity, fecundity, and predation ability of N. bicaudus. Among the considered plants, Cnidium monnieri led to the highest longevity (24 days) and fecundity (13.8 eggs) of N. bicaudus, while Tagetes erecta resulted in the lowest longevity (7 days) and fecundity (0.1 eggs) observed in the predatory mites. By comparing the effects of three nectar and pollen plants on the predation of predatory mites, it was observed that N. bicaudus still exhibited a type II functional response to Tetranychus turkestani. In the presence of pollen, the predation efficacy (a/Th) of N. bicaudus exhibited a lower value, compared to that in the absence of pollen (Control: a/Th = 24.00). When pollen was supplied, the maximum consumption (1/Th) of predatory mites was higher than in its absence (Control: 1/Th = 9.90 d−1), with the highest value obtained in the presence of B. officinalis pollen (B. officinalis: 1/Th = 17.86 d−1). The influence coefficient of predation of N. bicaudus on T. turkestani in the presence of pollen was compared in the presence of three nectar and pollen plants: Cnidium monnieri, Centaurea cyanus, and Borago officinalis. At low prey densities, the influence coefficient of C. cyanus exceeded that of B. officinalis, and the overall influence coefficient values were negative (i.e., the presence of pollen reduced predatory mite feeding on T. turkestani). They exhibited similar values at high prey densities, and all of the influence coefficient values were close to 0 (i.e., the presence of pollen had no effect on predatory mite feeding on T. turkestani). The findings revealed that diverse plant species exert differential impacts on N. bicaudus, with some influencing its lifespan and others affecting its reproductive capabilities. Furthermore, the presence of nectar and pollen plants had a significant impact on predatory mite feeding on T. turkestani at low prey densities; however, this effect diminished as the prey density increased. Therefore, we recommend planting C. monnieri, C. cyanus, and B. officinalis in the field to ensure an ample population of predatory mites. The obtained results hold significant implications for the utilization of nectar and pollen plants in eco-friendly pest management strategies within agricultural contexts.

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned—the ECG R peak and PPG systolic peak are aligned—before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.

Lycorine is a kind of natural active ingredient with a strong antitumor effect. In this study, folate ligand-conjugated polyethylene glycol-block-poly(l-lactide) (PEG-PLLA) nanoparticles (FA-PEG-PLLA NPs) were designed to deliver lycorine to enhance its anti-glioma activity. The successful preparation of the FA-PEG-PLLA polymer was confirmed by 1H-NMR, FT-IR, XRD, TGA, and DSC. The optimal formulation for LYC@FA-PEG-PLLA NPs was determined by response surface analysis as follows: sodium dodecyl sulfate (SDS) of 1%, carrier material of 0.03 g, organic phase volume of 3 mL, and ultrasonic power of 20%. The LYC@FA-PEG-PLLA NPs exhibited an encapsulation efficiency of 83.58% and a particle size of 49.71 nm, demonstrating good stability. Hemolysis experiments, MTT assays, and cell scratch assays revealed excellent biocompatibility of FA-PEG-PLLA and superior anti-glioma activity of LYC@FA-PEG-PLLA NPs compared to the raw drug. Additionally, cell apoptosis assays, ROS experiments, and western blot analysis demonstrated that LYC@FA-PEG-PLLA NPs contributed to cell apoptosis by inducing ROS generation and increasing the expression of NF-κB inhibitory protein IκBα. These results suggested that LYC@FA-PEG-PLLA NPs hold promise for glioma treatment.

Hemerocallis is a characteristic vegetable with outstanding edible and medicinal value. Flavonoids are important bioactive components of Hemerocallis. To improve the extraction efficiency and detection accuracy of flavonoids from Hemerocallis, we established a high-performance liquid chromatography (HPLC) detection system, which can simultaneously detect multiple flavonoids. In addition to the previously developed organic solvent extraction method, an ultrasonic-assisted extraction technique that uses fewer samples was established to extract flavonoids from Hemerocallis. The extraction conditions of the ultrasonic-assisted extraction were optimized via a single-factor experiment and a response surface experiment. The HPLC system detected and determined the contents of rutin, isoquercetin, myricetin, quercetin, apigenin, and diosmetin from 70 Hemerocallis germplasm resources. In addition, we evaluated the antioxidant activity of flavonoids in Hemerocallis using DPPH free radical scavenging capacity with ascorbic acid (Vc) as a positive control. The results showed that the optimum conditions for the ultrasonic extraction process were as follows: sample weight of 0.25 g, ethanol volume fraction of 72%, ethanol volume of 2.5 mL, and ultrasonic extraction time of 17 min. Under these conditions, flavonoid extraction had a strong scavenging effect on DPPH. With the increase in the sample solutions’ concentrations, its antioxidant capacity was gradually enhanced, and the DPPH scavenging rate reached 70.2%. The optimized ultrasonic-assisted extraction technology can increase the total content of six flavonoids in day lily bud by 59.01%, especially the content of rutin (increased by 64.41%) in Hemerocallis flower buds. Among 70 Hemerocallis plant resources, we selected materials H0087 and H0059 with high and stable flavonoid content, with the total content of six substances being 4390.54 ug/g and 3777.13 ug/g. Thus, this study provides a reference for extracting and determining flavonoid contents in Hemerocallis materials. It also provides a theoretical basis for high-quality individual plant breeding.

Squid ink melanin can be efficiently extracted from the byproduct ink sac generated during squid processing. As a natural food colorant, it possesses inherent antioxidant properties and the capability to adsorb heavy metals. This study aims to investigate the solubility of water-soluble squid ink melanin (WSSM) obtained from the ink sac, as well as its stability under various conditions including temperature, pH, salt, sugar, potassium sorbate, metal ions, sodium benzoate, sodium sulfite (reducing agent), and hydrogen peroxide (oxidizing agent). Moreover, it explores the scavenging effects of WSSM on free radicals and cadmium ions. The findings suggest that WSSM’s stability is insignificantly affected by high temperature, sucrose, and salt. However, acidity, sodium benzoate, potassium sorbate, sodium sulfite (Na2SO3), and hydrogen peroxide (H2O2) significantly influence its stability. Most metal ions do not impact the stability of WSSM, except for Fe2+, Fe3+, Al3+, and Cu2+, which result in the precipitation of WSSM. Additionally, WSSM exhibits remarkable antioxidant activity with IC50 values of 0.91, 0.56, and 0.52 mg/mL for scavenging superoxide anion radicals (O2−·), hydroxyl radicals (·OH), and DPPH radicals, respectively. It also demonstrates the ability to adsorb the heavy metal Cd2+, with the adsorption rate gradually increasing with a higher temperature and larger amounts of WSSM added. Infrared spectroscopy analysis reveals the weakening of characteristic peaks (-COOH and -OH) during the process of Cd2+ adsorption by WSSM, while SEM confirms surface roughening and structural damage after Cd2+ adsorption. This study provides valuable insights for the utilization of squid melanin products as natural antioxidants and heavy metal adsorbents in the food industry.

Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection. Such a mapping may cause a significant loss of instance-level information. While an ideal projection to instance-level attributes would be desirable, it can also be prohibitively expensive and thus impractical in many scenarios. In this work, we propose a variational disentangle zero-shot learning (VDZSL) framework that addresses this problem by constructing variational instance-specific attributes from a class-specific semantic latent distribution. Specifically, our approach disentangles each instance into class-specific attributes and the corresponding variant features. Unlike transductive ZSL, which assumes that unseen classes’ attributions are known beforehand, our VDZSL method does not rely on this strong assumption, making it more applicable in real-world scenarios. Extensive experiments conducted on three popular ZSL benchmark datasets (i.e., AwA2, CUB, and FLO) validate the effectiveness of our approach. In the conventional ZSL setting, our method demonstrates an improvement of 12∼15% relative to the advanced approaches and achieves a classification accuracy of 70% on the AwA2 dataset. Furthermore, under the more challenging generalized ZSL setting, our approach can gain an improvement of 5∼15% compared with the advanced methods.

Cell types have been established during organogenesis based on early mouse embryos. However, our understanding of cell types and molecular mechanisms in the early embryo development of Mongolian sheep has been hampered. This study presents the first comprehensive single-cell transcriptomic characterization at E16 in Ujumqin sheep and Hulunbuir short-tailed sheep. Thirteen major cell types were identified at E16 in Ujumqin sheep, and eight major cell types were identified at E16 in Hulunbuir short-tailed sheep. Function enrichment analysis showed that several pathways were significantly enriched in the TGF-beta signaling pathway, the Hippo signaling pathway, the platelet activation pathway, the riboflavin metabolism pathway, the Wnt signaling pathway, regulation of the actin cytoskeleton, and the insulin signaling pathway in the notochord cluster. Glutathione metabolism, glyoxylate, and dicarboxylate metabolism, the citrate cycle, thyroid hormone synthesis, pyruvate metabolism, cysteine and methionine metabolism, thermogenesis, and the VEGF signaling pathway were significantly enriched in the spinal cord cluster. Steroid biosynthesis, riboflavin metabolism, the cell cycle, the Hippo signaling pathway, the Hedgehog signaling pathway, the FoxO signaling pathway, the JAK-STAT signaling pathway, and the Wnt signaling pathway were significantly enriched in the paraxial mesoderm cluster. The notochord cluster, spinal cord cluster, and paraxial mesoderm cluster were found to be highly associated with tail development. Pseudo-time analysis demonstrated that the mesenchyme can translate to the notochord in Ujumqin sheep. Molecular assays revealed that the Hippo signaling pathway was enriched in Ujumqin sheep. This comprehensive single-cell map revealed previously unrecognized signaling pathways that will further our understanding of the mechanism of short-tailed sheep formation.

Protein lysine lactylation (Kla) is a novel protein acylation reported in recent years, which plays an important role in the development of several diseases with pathologically elevated lactate levels, such as tumors. The concentration of lactate as a donor is directly related to the Kla level. High-intensity interval training (HIIT) is a workout pattern that has positive effects in many metabolic diseases, but the mechanisms by which HIIT promotes health are not yet clear. Lactate is the main metabolite of HIIT, and it is unknown as to whether high lactate during HIIT can induce changes in Kla levels, as well as whether Kla levels differ in different tissues and how time-dependent Kla levels are. In this study, we observed the specificity and time-dependent effects of a single HIIT on the regulation of Kla in mouse tissues. In addition, we aimed to select tissues with high Kla specificity and obvious time dependence for lactylation quantitative omics and analyze the possible biological targets of HIIT-induced Kla regulation. A single HIIT induces Kla in tissues with high lactate uptake and metabolism, such as iWAT, BAT, soleus muscle and liver proteins, and Kla levels peak at 24 h after HIIT and return to steady state at 72 h. Kla proteins in iWAT may affect pathways related to glycolipid metabolism and are highly associated with de novo synthesis. It is speculated that the changes in energy expenditure, lipolytic effects and metabolic characteristics during the recovery period after HIIT may be related to the regulation of Kla in iWAT.

The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we find meaningful information can be explored using the correlation of dermatological segmentation and classification tasks, especially when the sample data are insufficient. In this paper, we propose a collaborative learning deep convolutional neural networks (CL-DCNN) model based on the teacher–student learning method for dermatological segmentation and classification. To generate high-quality pseudo-labels, we provide a self-training method. The segmentation network is selectively retrained through classification network screening pseudo-labels. Specially, we obtain high-quality pseudo-labels for the segmentation network by providing a reliability measure method. We also employ class activation maps to improve the location ability of the segmentation network. Furthermore, we provide the lesion contour information by using the lesion segmentation masks to improve the recognition ability of the classification network. Experiments are carried on the ISIC 2017 and ISIC Archive datasets. The CL-DCNN model achieved a Jaccard of 79.1% on the skin lesion segmentation task and an average AUC of 93.7% on the skin disease classification task, which is superior to the advanced skin lesion segmentation methods and classification methods.

Cannabidiol (CBD), derived from the plant cannabis, can be used in the cosmetics industry for its antioxidant, anti-inflammatory, anti-wrinkle and whitening effects. However, CBD is purified from the hemp plant extract, its source is very limited and under strict control. So in this study, computational and experimental methods were combined to search for novel CBD substitutes with high biology potencies. The action mode between CBD and target protein cannabidiol receptor 1 was studied to find the key skeleton, which was used to virtually screen a natural products database to search for compounds with 70% similarity. The hit compounds with high docking scores were selected for the ABTS and DPPH free radical scavenging experiments for antioxidant evaluation. The effects on the expressions of nitric oxide (NO), interleukin-6 (IL-6), COX-2 and iNOS in RAW264.7 cell line were detected to demonstrate their anti-inflammatory abilities. The effect of anti-wrinkle ability were evaluated by detecting the extracellular matrix, such as collagen, elastin, fibronectin and reactive oxygen species (ROS) in HFF-1. The effects on melanin production and tyrosinase activity in Bb16F10 were also detected. As a result, two compounds were found to be superior to cannabidiol, in terms of antioxidant, anti-wrinkle and whitening efficacy with a lower cytotoxicity.

Alveolar macrophage (AM) proliferation and self-renewal play an important role in the lung tissue microenvironment. However, the impact of immune cells, especially the neutrophils, on AM homeostasis or function is not well characterized. In this study, we induced in vivo migration of neutrophils into bronchoalveolar lavage (BAL) fluid and lung using CXCL1, and then co-cultured these with AMs in vitro. Neutrophils in the BAL (BAL−neutrophils), rather than neutrophils of bone marrow (BM-neutrophils), were found to inhibit AM proliferation. Analysis of publicly available data showed high heterogeneity of lung neutrophils with distinct molecular signatures of BM− and blood−neutrophils. Unexpectedly, BAL−neutrophils from influenza virus PR8-infected mice (PR8−neutrophils) did not inhibit the proliferation of AMs. Bulk RNA sequencing further revealed that co-culture of AMs with PR8−neutrophils induced IFN-α and -γ responses and inflammatory response, and AMs co-cultured with BAL−neutrophils showed higher expression of metabolism- and ROS-associated genes; in addition, BAL−neutrophils from PR8-infected mice modulated AM polarization and phagocytosis. BAL−neutrophil-mediated suppression of AM proliferation was abrogated by a combination of inhibitors of different neutrophil death pathways. Collectively, our findings suggest that multiple cell death pathways of neutrophils regulate the proliferation of AMs. Targeting neutrophil death may represent a potential therapeutic strategy for improving AM homeostasis during respiratory diseases.

Dosage compensation is a mechanism first proposed by Susumu Ohno, whereby X inactivation balances X gene output between males (XY) and females (XX), while X upregulation balances X genes with autosomal gene output. These mechanisms have been actively studied in Drosophila and mice, but research regarding them lags behind in domestic species. It is unclear how the X chromosome is regulated in the sheep male germline. To address this, using single-cell RNA sequencing, we analyzed testes in three important developmental stages of sheep. We observed that the total RNA per cell from X and autosomes peaked in SSCs and spermatogonia and was then reduced in early spermatocytes. Furthermore, we counted the detected reads per gene in each cell type for X and autosomes. In cells experiencing dose compensation, close proximity to MSL (male-specific lethal), which is regulated the active X chromosome and was observed. Our results suggest that there is no dose compensation in the pre-meiotic germ cells of sheep testes and, in addition, MSL1 and MSL2 are expressed in early germ cells and involved in regulating mammalian X-chromosome inactivation and activation.

Wire arc additive manufacturing technology with cold metal transfer and pulse welding (CMT+P) is a promising technology for fabricating complex metal structures. In this paper, a lot of basic research was conducted on the corner-constrained and unconstrained zones of 4043 aluminum alloy made using CMT+P. In particular, the microstructure morphology and mechanical properties of the corner-constrained and unconstrained zones of 4043 aluminum alloy made by CMT+P were studied by using a thermal field emission scanning electron microscope, a microhardness tester, etc. The results showed that there were cellular crystals at the top, columnar dendritic crystals in the middle and bottom, and smaller equiaxed crystals in the bottom center. The grain size in the corner-constrained zone was larger than that in the unconstrained zone, and the grain size increased by about 88.34%. Moreover, the microhardness of the unconstrained zone was 50 HV, while the microhardness of the corner-constrained zone was 45 HV. Furthermore, the tensile strength of this material was 148 MPa, the elongation was 31%, the fracture behavior in the middle and top areas was typical of a ductile fracture, and the fracture in the bottom area was a mixed ductile–brittle fracture dominated by the ductile fracture.

Accurate short-term wind speed forecasting plays an important role in the development of wind energy. However, the inertia of airflow means that wind speed has the properties of time variance and inertia, which pose a challenge in the task of wind speed forecasting. We employ the variable support segment method to describe these two properties. We then propose a variable support segment-based short-term wind speed forecasting model to improve wind speed forecasting accuracy. The core idea is to adaptively determine the variable support segment of the future wind speed by a self-attention mechanism. Historical wind speed series are first decomposed into several components by variational mode decomposition (VMD). Then, the future values of each component are forecast using a modified Transformer model. Finally, the forecasting values of these components are summed to obtain the future wind speed forecasting values. Wind speed data collected from a wind farm were employed to validate the performance of the proposed model. The mean absolute error of the proposed model in spring, summer, autumn, and winter is 0.25, 0.33, 0.31, and 0.29, respectively. Experimental results show that the proposed model achieves significant accuracy and that the modified Transformer model has good performance.

Mercury ion is one of the most widespread heavy metal contaminants which can accumulate in the body through multiple channels, posing a detrimental impact on human health. We demonstrate a simple and low-cost method for the detection of Hg2+ assisted by a cholesteric liquid crystal photonic hydrogel (polyacrylic acid (PAA)) film with immobilized urease (CLC-PAAurease film). In the absence of Hg2+, a significant change in color and an obvious red shift in the reflected light wavelength of the prepared film were observed, since urease can hydrolyze urea to produce NH3, resulting in an increasing pH value of the microenvironment of CLC-PAAurease film. Hg2+ can inhibit the activity of urease so that the color change of the film is not obvious, corresponding to a relatively small variation of the reflected light wavelength. Therefore, Hg2+ can be quantitatively detected by measuring the displacement of the reflected light wavelength of the film. The detection limit of Hg2+ is about 10 nM. This approach has a good application prospect in the monitoring of heavy metal ions in environmental water resources.

The short-tailed phenotype has long been considered one of the best traits for population genetic improvement in sheep breeding. In short-tailed sheep, not only is tail fat eliminated but also the pubic area is exposed due to the lack of a tail covering, giving them an advantage in reproduction. Recent studies have shown that two linked mutations in sheep TBXT at nucleotides 333 and 334 are associated with the short-tailed phenotype. In the population of short-tailed sheep, several heterozygous mutants of this gene are found. In our research, we used high-resolution melting (HRM) to identify homozygous and heterozygous genotypes in a flock of short-tailed sheep and compared the results with those of Sanger sequencing, which were identical. This demonstrates that our established HRM method, a rapid and inexpensive genotyping method, can be used to identify homozygous and heterozygous individuals in short-tailed sheep flocks.

Southwest China faces harsh environmental pollution challenges and rapid development. Against this backdrop, exploring the impact mechanism of the urban network on carbon emissions in rapidly developing regions is of great significance to the balance between regional development and carbon emissions reduction, as well as regional sustainable development. The objective of this study is to quantify the relationship between carbon emissions and the urban network, using panel data analysis for 47 cities in southwest China from 2010 to 2019. Therefore, several urban network indices were selected and quantitatively studied by using the spatial Durbin model to reveal the impact mechanism of the urban network on carbon emissions in rapidly developing regions. The results show that: (1) the growth of carbon emissions in a city has a significant positive spatial spillover effect on the surrounding areas; (2) the temporal and spatial distribution of carbon emissions is highly coincident with the urban network; (3) the urban network has a two-sided impact mechanism of promoting and inhibiting carbon emissions; and (4) the effect of the impact mechanism is affected by regional development conditions, and the promotion effect plays the main role in rapidly developing regions.

Accurate identification and intelligent counting of pig herds can effectively improve the level of fine management of pig farms. A semantic segmentation and counting network was proposed in this study to improve the segmentation accuracy and counting efficiency of pigs in complex image segmentation. In this study, we built our own datasets of pigs under different scenarios, and set three levels of number detection difficulty—namely, lightweight, middleweight, and heavyweight. First, an image segmentation model of a small sample of pigs was established based on the DeepLab V3+ deep learning method to reduce the training cost and obtain initial features. Second, a lightweight attention mechanism was introduced, and attention modules based on rows and columns can accelerate the efficiency of feature calculation and reduce the problem of excessive parameters and feature redundancy caused by network depth. Third, a recursive cascade method was used to optimize the fusion of high- and low-frequency features for mining potential semantic information. Finally, the improved model was integrated to build a graphical platform for the accurate counting of pigs. Compared with FCNNs, U-Net, SegNet, and DenseNet methods, the DeepLab V3+ experimental results show that the values of the comprehensive evaluation indices P, R, AP, F1-score, and MIoU of LA-DeepLab V3+ (single tag) are higher than those of other semantic segmentation models, at 86.04%, 75.06%, 78.67%, 0.8, and 76.31%, respectively. The P, AP, and MIoU values of LA-DeepLab V3+ (multiple tags) are also higher than those of other models, at 88.36%, 76.75%, and 74.62%, respectively. The segmentation accuracy of pig images with simple backgrounds reaches 99%. The pressure test of the counting network can calculate the number of pigs with a maximum of 50, which meets the requirements of free-range breeding in standard piggeries. The model has strong generalization ability in pig herd detection under different scenarios, which can serve as a reference for intelligent pig farm management and animal life research.

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

Alcalase, neutral protease, and pepsin were used to hydrolyze the skin of Takifugu flavidus. The T. flavidus hydrolysates (TFHs) with the maximum degree of hydrolysis (DH) and angiotensin-I-converting enzyme (ACE)-inhibitory activity were selected and then ultra-filtered to obtain fractions with components of different molecular weights (MWs) (50 kDa). The components with MWs < 1 kDa showed the strongest ACE-inhibitory activity with a half-maximal inhibitory concentration (IC50) of 0.58 mg/mL. Purification and identification using semi-preparative liquid chromatography, Sephadex G-15 gel chromatography, RP-HPLC, and LC–MS/MS yielded one new potential ACE-inhibitory peptide, PPLLFAAL (non-competitive suppression mode; IC50 of 28 μmmol·L−1). Molecular docking and molecular dynamics simulations indicated that the peptides should bind well to ACE and interact with amino acid residues and the zinc ion at the ACE active site. Furthermore, a short-term assay of antihypertensive activity in spontaneously hypertensive rats (SHRs) revealed that PPLLFAAL could significantly decrease the systolic blood pressure (SBP) and diastolic blood pressure (DBP) of SHRs after intravenous administration. These results suggested that PPLLFAAL may have potential applications in functional foods or pharmaceuticals as an antihypertensive agent.

Tracking the behavior trajectories in pigs in group is becoming increasingly important for welfare feeding. A novel method was proposed in this study to accurately track individual trajectories of pigs in group and analyze their behavior characteristics. First, a multi-pig trajectory tracking model was established based on DeepLabCut (DLC) to realize the daily trajectory tracking of piglets. Second, a high-dimensional spatiotemporal feature model was established based on kernel principal component analysis (KPCA) to achieve nonlinear trajectory optimal clustering. At the same time, the abnormal trajectory correction model was established from five dimensions (semantic, space, angle, time, and velocity) to avoid trajectory loss and drift. Finally, the thermal map of the track distribution was established to analyze the four activity areas of the piggery (resting, drinking, excretion, and feeding areas). Experimental results show that the trajectory tracking accuracy of our method reaches 96.88%, the tracking speed is 350 fps, and the loss value is 0.002. Thus, the method based on DLC–KPCA can meet the requirements of identification of piggery area and tracking of piglets’ behavior. This study is helpful for automatic monitoring of animal behavior and provides data support for breeding.

In power gear honing, the random distribution of abrasive grains on the tooth surface of the honing wheel is the main factor that influences the machining performance of high-quality hardened gears. In order to investigate the micro-edge cutting performance of the active abrasive grains on the workpiece gear, the real honing process is simplified into a micro-edge cutting model with random distribution of active abrasive grains in the cells of the meshing area by obtaining the random distribution states such as the position, orientation and quantity of the honing wheel teeth. The results show that although the active abrasive grains are distributed at different locations, they all experience three types of material removal—slip rubbing, plowing and cutting—allowing the gear honing process to have the combined machining characteristics of grinding, lapping and polishing. The active abrasive grains in first contact produce high honing force, high material removal efficiency and poor surface roughness on the machined workpiece, while the latter ones have the opposite effects. The dislocation angle affects the chip shape and chip discharging direction, and the highest honing force and material removal efficiency is achieved with a dislocation angle of 135°. The higher the number of active abrasive grains in a given contact area, the higher the material removal efficiency.

Regularly dressing of CBN honing wheel is an effective way to keep its sharpness and correct geometry during honing process. This study aims to understand the fracture mechanism of single CBN grain in the dressing process of honing wheel. The honing wheel dressing process was simplified into the dressing process of grinding wheel, and the bond-based Peridynamic method considering bond rotation effect was developed to investigate the progressive fracture evolution, stress characteristics, and fracture modes of CBN grains in this process. It was found that fracture evolution of CBN grains mainly underwent four stages: elastic deformation, damage initiation, crack formation, and macro fracture. In addition, the fracture initiation and propagation were mainly determined by the tensile and shear stress, where the former led to mode I fractures and the latter led to mode II fractures. The propagation of mode I fractures was stable while the propagation of mode II fracture was unstable. The results show that the Peridynamic approach has great potential to predict the fracture mechanism of CBN grain in the dressing process of honing and grinding wheels.

The von Willebrand factor type D (VWD) domain in vitellogenin has recently been found to bind tetrodotoxin. The way in which this protein domain associates with tetrodotoxin and participates in transporting tetrodotoxin in vivo remains unclear. A cDNA fragment of the vitellogenin gene containing the VWD domain from pufferfish (Takifugu flavidus) (TfVWD) was cloned. Using in silico structural and docking analyses of the predicted protein, we determined that key amino acids (namely, Val115, ASP116, Val117, and Lys122) in TfVWD mediate its binding to tetrodotoxin, which was supported by in vitro surface plasmon resonance analysis. Moreover, incubating recombinant rTfVWD together with tetrodotoxin attenuated its toxicity in vivo, further supporting protein–toxin binding and indicating associated toxicity-neutralizing effects. Finally, the expression profiling of TfVWD across different tissues and developmental stages indicated that its distribution patterns mirrored those of tetrodotoxin, suggesting that TfVWD may be involved in tetrodotoxin transport in pufferfish. For the first time, this study reveals the amino acids that mediate the binding of TfVWD to tetrodotoxin and provides a basis for further exploration of the molecular mechanisms underlying the enrichment and transfer of tetrodotoxin in pufferfish.

The haze-fog particle dispersion in urban communities will cause serious health and environmental problems, which has aroused society attention. The aim of the present investigation is to reveal the underlying mechanisms of haze-fog particle dispersion via Computational Fluid Dynamics (CFD) method, and then to provide a groundwork for the optimal spatial arrangement of urban architecture. The Delayed Detached-eddy Simulation turbulence model (DDES) and Discrete Phase Model (DPM) are utilized to investigate the wind flow distribution and the particle dispersion around the building group. The numerical results show that the particle dispersion is dominated by the incoming wind flow, the layout of architectural space and the type and distribution of vortex. The ‘single body’ wake pattern and the vortex impingement wake pattern are identified in the wind flow field, which have different effects on the distribution of haze-fog particle. The cavity formed by the layout of the building group induces primary vortex and secondary vortex, which will make it more difficult for the particles entering the square cavity to flow out. Moreover, the concentration of the particle in the rear of the buildings is relatively low due the effect of attached vortices.

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January−June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976−2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 104 km2/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 104 km2/yr (5.2% per decade). The annual maximum extent reached 2.3 × 106 km2 in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 106 km2, which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone.

Lodging resistance is one of the most important traits of machine-picked cotton. Lodging directly affects the cotton yield, quality and mechanical harvesting effect. However, there are only a few reports on the lodging resistance of cotton. In this study, the morphological and physiological characteristics and transcriptome of two upland cotton varieties with different lodging resistance were compared. The results showed that the stem strength; the contents of lignin, soluble sugar and cellulose; and the activities of several lignin biosynthesis-related enzymes of the lodging-resistant variety M153 were significantly higher than those of the lodging-susceptible variety M5330. Transcriptomic analysis showed that the expression level of several genes related to lignin, cellulose, starch and sucrose synthesis, and photosynthesis were significantly up-regulated in the lodging-resistant variety M153, which was consistent with the content determination results of lignin, cellulose and soluble sugar. Silencing two lignin biosynthesis-related genes (GhPAL and Gh4CL) in cotton via VIGS (Virus-Induced Gene Silencing) resulted in reduced lignin content and decreased lodging resistance in cotton. These results suggested that lignin, cellulose and soluble sugar contents were positively correlated with the lodging resistance of cotton, and lignin, cellulose and soluble sugar biosynthesis-related genes can be used as potential targets for improving the lodging resistance of cotton. These findings provide a theoretical basis for the cultivation of cotton varieties with strong lodging resistance in the future.

The rapid expansion of BeiDou satellite navigation applications has led to a growing demand for real-time high-precision positioning services. Currently, high-precision positioning services face challenges such as a long initialization time and heavy reliance on reference station networks, thereby failing to fulfill the requirements for real-time, wide-area, and centimeter-level positioning. In this study, we consider the multi-frequency signals that are broadcast by a satellite to share a common reference clock and possess identical RF channels and propagation paths with strict temporal, spectral, and spatial coupling between signal components, resulting in strongly coherent propagation delays. Firstly, we accurately establish a multi-frequency signal model that fully exploits those coherent characteristics among the multi-frequency BDS signals. Subsequently, we propose a rapid high-precision ranging technique using the code and carrier phases of multi-frequency signals. The proposed method unitizes multi-frequency signals via a coherent joint processing unit consisting of a joint tracking state estimator and a coherent signal generator. The joint tracking state estimator simultaneously estimates the biased pseudorange and its change rate, ionospheric delay and its change rate, and ambiguities. The coherent signal generator updates the numerically controlled oscillator (NCO) to adjust the local reference signal’s code and carrier replicas of different frequencies, changing them according to the state estimated by the joint tracking state estimator. Finally, the simulation results indicate that the proposed method efficiently diminishes the estimated biased pseudorange and ionospheric delay errors to below 0.1 m. Furthermore, this method reduces the carrier phase errors by more than 60% compared with conventional single-frequency-independent tracking methods. Consequently, the proposed method can achieve rapid centimeter-level results ranging for up to 1 min without using precise atmosphere corrections and provide enhanced tracking sensitivity and robustness.

Cotton is often threatened by Verticillium wilt caused by V. dahliae. Understanding the molecular mechanism of V. dahlia–cotton interaction is important for the prevention of this disease. To analyze the transcriptome profiles in V. dahliae and cotton simultaneously, the strongly pathogenic strain Vd592 was inoculated into cotton, and the infected cotton roots at 36 h and 3 d post infection were subjected to dual RNA-seq analysis. For the V. dahliae, transcriptomic analysis identified 317 differentially expressed genes (DEGs) encoding classical secreted proteins, which were up-regulated at least at one time point during infection. The 317 DEGs included 126 carbohydrate-active enzyme (CAZyme) and 108 small cysteine-rich protein genes. A pectinesterase gene (VDAG_01782) belonging to CAZyme, designated as VdPE1, was selected for functional validation. VdPE1 silencing by HIGS (host-induced gene silencing) resulted in reduced disease symptoms and the increased resistance of cotton to V. dahliae. For the cotton, transcriptomic analysis found that many DEGs involved in well-known disease resistance pathways (flavonoid biosynthesis, plant hormone signaling, and plant–pathogen interaction) as well as PTI (pattern-triggered immunity) and ETI (effector-triggered immunity) processes were significantly down-regulated in infected cotton roots. The dual RNA-seq data thus potentially connected the genes encoding secreted proteins to the pathogenicity of V. dahliae, and the genes were involved in some disease resistance pathways and PTI and ETI processes for the susceptibility of cotton to V. dahliae. These findings are helpful in the further characterization of candidate genes and breeding resistant cotton varieties via genetic engineering.

Non-contact heart rate monitoring from facial videos utilizing remote photoplethysmography (rPPG) has gained significant traction in remote health monitoring. Given that rPPG captures the dynamic blood flow within the human body and constitutes a time-series signal characterized by periodic properties, this study introduced a three-dimensional convolutional neural network (3D CNN) designed to simultaneously address long-term periodic and short-term temporal characteristics for effective rPPG signal extraction. Firstly, differential operations are employed to preprocess video data, enhancing the face’s dynamic features. Secondly, building upon the 3D CNN framework, multi-scale dilated convolutions and self-attention mechanisms were integrated to enhance the model’s temporal modeling capabilities further. Finally, interpolation techniques are applied to refine the heart rate calculation methodology. The experiments conducted on the UBFC-rPPG dataset indicate that, compared with the existing optimal algorithm, the average absolute error (MAE) and the root mean square error (RMSE) realized significant enhancements of approximately 28% and 35%. Additionally, through comprehensive analyses such as cross-dataset experiments and complexity analyses, the validity and stability of the proposed algorithm in the task of heart rate estimation were manifested.

Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning.