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This study aims to investigate the dynamic changes in daily step counts under different housing systems and further explore the effects of housing system on the body conformation, carcass traits, meat quality, and serum biochemical parameters of a Chinese indigenous chicken breed. At 60 d of age, 300 Jiuyuan Black male chickens with similar body weights in each housing system were further raised until the age of 150 d. At 90, 120, and 150 d of age, in both cage-reared and free-range systems, the top 20 chickens with the highest step counts measured using pedometers and the bottom 20 chickens with the lowest step counts were designated as the cage high-steps group (CHS), the cage low-steps group (CLS), the free-range high-steps group (FHS), and the free-range low-steps group (FLS), respectively. The results show that, at any age stage, the average daily steps (ADS) and total steps (TS) of the FHS group are significantly higher than the other three groups (p < 0.05). The TS of almost all groups showed an overall downward trend as the age increased. Increased exercise volume results in reduced shank length (90 d), breast width (90 d), and keel length (150 d) (p < 0.05). Only birds at 90 d of age from the FHS and FLS groups exhibited lower live body weight, carcass weight, half-eviscerated weight, eviscerated weight, breast muscle weight, leg muscle weight, and percentage of eviscerated weight than the CLS group (p < 0.05). Birds from the FHS group showed the highest heart weight values but the lowest abdominal fat weight values among these four groups (p < 0.05). Both the breast and leg muscle samples from the FHS group displayed higher dry matter and shear force than those from the CHS and CLS groups (p < 0.05). The FHS group displayed the lowest intramuscular fat among the four groups (p < 0.05). The creatine kinase (CK) and lactate dehydrogenase (LDH) levels in chickens of all age stages were almost observed to rise with increased physical activity. In conclusion, free-range chickens with more exercise volume exhibited an elevated heart weight and reduced abdominal fat but showed negative effects on some body measurements and carcass traits. These results can provide a theoretical basis for the selection of different housing systems for Chinese indigenous chickens.

Vibration mixing, characterized by the high-frequency vibrations of the mixing shaft, can enhance the mechanical properties of ultra-high-performance concrete (UHPC). However, the effects of vibration mixing on carbon nanotube (CNT)-reinforced UHPC have not been previously reported. To investigate the impact of vibration mixing on the properties of CNT-reinforced UHPC, a comparative study was conducted using different vibration mixing durations and twin-shaft mixing. The results indicate that for CNT-reinforced UHPC, vibration mixing achieves better flowability, higher wet apparent density, and superior mechanical properties in shorter mixing times compared to twin-shaft mixing, making it a more favorable method. Considering vibration mixing times ranging from 3 to 7 min, the optimal time was found to be 3 min, during which the axial compressive strength increased by 3.3%, the elastic limit tensile strength and tensile strength improved by 14.6% and 15.8%, respectively, and the initial cracking strength and flexural strength increased by 12.6% and 13.4%, respectively, compared to values after 10 min of twin-shaft mixing.

Cardiovascular diseases pose a significant public health challenge globally, necessitating the development of effective treatments to mitigate the risk of cardiovascular diseases. Recently, circular RNAs (circRNAs), a novel class of non-coding RNAs, have been recognized for their role in cardiovascular disease. Aberrant expression of circRNAs is closely linked with changes in various cellular and pathophysiological processes within the cardiovascular system, including metabolism, proliferation, stress response, and cell death. Functionally, circRNAs serve multiple roles, such as acting as a microRNA sponge, providing scaffolds for proteins, and participating in protein translation. Owing to their unique properties, circRNAs may represent a promising biomarker for predicting disease progression and a potential target for cardiovascular drug development. This review comprehensively examines the properties, biogenesis, and potential mechanisms of circRNAs, enhancing understanding of their role in the pathophysiological processes impacting cardiovascular disease. Furthermore, the prospective clinical applications of circRNAs in the diagnosis, prognosis, and treatment of cardiovascular disease are addressed.

Runoff simulation is essential for effective water resource management and plays a pivotal role in hydrological forecasting. Improving the quality of runoff simulation and forecasting continues to be a highly relevant research area. The complexity of the terrain and the scarcity of long-term runoff observation data have significantly limited the application of Physically Based Models (PBMs) in the Qinghai–Tibet Plateau (QTP). Recently, the Long Short-Term Memory (LSTM) network has been found to be effective in learning the dynamic hydrological characteristics of watersheds and outperforming some traditional PBMs in runoff simulation. However, the extent to which the LSTM works in data-scarce alpine regions remains unclear. This study aims to evaluate the applicability of LSTM in alpine basins in QTP, as well as the simulation performance of transfer-based LSTM (T-LSTM) in data-scarce alpine regions. The Lhasa River Basin (LRB) and Nyang River Basin (NRB) were the study areas, and the performance of the LSTM model was compared to that of PBMs by relying solely on the meteorological inputs. The results show that the average values of Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and Relative Bias (RBias) for B-LSTM were 0.80, 0.85, and 4.21%, respectively, while the corresponding values for G-LSTM were 0.81, 0.84, and 3.19%. In comparison to a PBM- the Block-Wise use of TOPMEDEL (BTOP), LSTM has an average enhancement of 0.23, 0.36, and −18.36%, respectively. In both basins, LSTM significantly outperforms the BTOP model. Furthermore, the transfer learning-based LSTM model (T-LSTM) at the multi-watershed scale demonstrates that, when the input data are somewhat representative, even if the amount of data are limited, T-LSTM can obtain more accurate results than hydrological models specifically calibrated for individual watersheds. This result indicates that LSTM can effectively improve the runoff simulation performance in alpine regions and can be applied to runoff simulation in data-scarce regions.

The stick tea thrip (Dendrothrips minowai) is one of the most serious sucking pests of tea plants (Camellia sinensis) in China, North Korea, and Japan. Plant volatile lures are widely used for both monitoring and mass trapping. Previously, we demonstrated that sticky traps baited with p-anisaldehyde, eugenol, farnesene, or 3-methyl butanal captured significantly more D. minowai in tea plantations, with p-anisaldehyde notably capturing the most. In this study, we showed that D. minowai adults exhibited significantly higher attraction to mixtures of p-anisaldehyde, eugenol, and farnesene compared to an equivalent dose of p-anisaldehyde alone in H-tube olfactometer assays under laboratory conditions. Moreover, in field experiments conducted in 2022, rubber septa impregnated with a ternary blend of p-anisaldehyde, eugenol, and farnesene (at 3–4.5 mg and a ratio of 3:1:1) captured the highest number of adults on sticky traps, outperforming traps bailed with individual components or a solvent control over two weeks. Significantly, the mass trapping strategy employing these lures achieved control efficacies ranging from 62.8% to 70.7% when compared to traps without attractant, which achieved control efficacies of only 14.2% to 35.4% across three test sites in 2023. These results indicate that the combination of p-anisaldehyde, eugenol, and farnesene exhibits an additive or synergistic effect on D. minowai. In conclusion, our findings establish a theoretical framework and provide practical technological support for integrating attractant-based strategies into comprehensive thrips management strategies.

Background and purpose: Clinically, the ability to identify individuals at risk of ischemic stroke remains limited. This study aimed to develop a nomogram model for predicting the risk of acute ischemic stroke. Methods: In this study, we conducted a retrospective analysis on patients who visited the Department of Neurology, collecting important information including clinical records, demographic characteristics, and complete hematological tests. Participants were randomly divided into training and internal validation sets in a 7:3 ratio. Based on their diagnosis, patients were categorized as having or not having ischemic stroke (ischemic and non-ischemic stroke groups). Subsequently, in the training set, key predictive variables were identified through multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods, and a nomogram model was constructed accordingly. The model was then evaluated on the internal validation set and an independent external validation set through area under the receiver operating characteristic curve (AUC-ROC) analysis, a Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) to verify its predictive efficacy and clinical applicability. Results: Eight predictors were identified: age, smoking status, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12 levels. Based on these factors, a nomogram with high predictive accuracy was constructed. The model demonstrated good predictive performance, with an AUC-ROC of 0.760 (95% confidence interval [CI]: 0.736–0.784). The AUC-ROC values for internal and external validation were 0.768 (95% CI: 0.732–0.804) and 0.732 (95% CI: 0.688–0.777), respectively, proving the model’s capability to predict the risk of ischemic stroke effectively. Calibration and DCA confirmed its clinical value. Conclusions: We constructed a nomogram based on eight variables, effectively quantifying the risk of ischemic stroke.

In this study, the whey protein isolate–high-methoxyl pectin (WPI-HMP) complex prepared by electrostatic interaction was utilized as an emulsifier in the preparation of docosahexaenoic acid (DHA) algal oils in order to improve their physicochemical properties and oxidation stability. The results showed that the emulsions stabilized using the WPI-HMP complex across varying oil-phase volume fractions (30–70%) exhibited consistent particle size and enhanced stability compared to emulsions stabilized solely using WPI or HMP at different ionic concentrations and heating temperatures. Furthermore, DHA algal oil emulsions stabilized using the WPI-HMP complex also showed superior storage stability, as they exhibited no discernible emulsification or oil droplet overflow and the particle size variation remained relatively minor throughout the storage at 25 °C for 30 days. The accelerated oxidation of the emulsions was assessed by measuring the rate of DHA loss, lipid hydroperoxide levels, and malondialdehyde levels. Emulsions stabilized using the WPI-HMP complex exhibited a lower rate of DHA loss and reduced levels of lipid hydroperoxides and malondialdehyde. This indicated that WPI-HMP-stabilized Pickering emulsions exhibit a greater rate of DHA retention. The excellent stability of these emulsions could prove valuable in food processing for DHA nutritional enhancement.

Background: Though children infected by SARS-CoV-2 generally experience milder symptoms compared to adults, severe cases can occur. Additionally, children can transmit the virus to others. Therefore, the availability of safe and effective COVID-19 vaccines for children and adolescents is crucial. Method: A single-center, randomized, double-blind clinical trial was conducted in Funing County, Yancheng City, Jiangsu Province, China. Healthy children and adolescents were divided into two subgroups (6–12 years old or 13–17 years old) and randomly assigned to one of three groups to receive one dose of Ad5-nCoV (3 × 1010 vp/dose). Another group, aged 18–59, received one dose of Ad5-nCoV (5 × 1010 vp/dose) as the control group. At 28, 90, 180, and 360 days post-vaccination, we measured the geometric mean titer (GMT)/concentration (GMC) of neutralizing and binding antibodies against the prototype SARS-CoV-2 strain, as well as serum antibody levels against the BA.4/5 variant. We also evaluated the incidence of adverse events within 28 days post-vaccination. Results: A total of 2413 individuals were screened from 3 June 2021 to 25 July 2021, of whom 2021 eligible participants were enrolled, including 1009 aged 6~17 years in the children and adolescent group and 1012 aged 18–59 years in the adults group. The GMT of anti-wild SARS-CoV-2 neutralizing antibodies was 18.6 (95% CI, 16.6–20.9) in children and adolescents and 13.2 (95% CI, 11.6–15.0) in adults on day 28. The incidence of solicited adverse reactions between the adult group (49.4% [124/251]) and the children and adolescent group (46.3% [156/337]) was not statistically significant. The neutralizing antibody levels decreased by a factor of 3.29 from day 28 to day 360 post-vaccination. Conclusions: A single dose of Ad5-nCoV at 3 × 1010 virus particles/dose is safe in children and adolescents, and it elicited significant immune response, which was not only non-inferior but also superior to that in adults aged 18–59 years.

Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO2 sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland > temperate meadow steppe > temperate typical steppe > temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios.

The development of an advanced dye adsorbent that possesses a range of beneficial characteristics, such as high adsorption capacity, swift adsorption kinetics, selective adsorption capability, and robust reusability, remains a challenge. This study introduces a facile method for fabricating an amine-rich porous adsorbent (ARPA), which is specifically engineered for the adsorptive removal of anionic dyes from aqueous solutions. Through a comprehensive assessment, we have evaluated the adsorption performance of ARPA using two benchmark dyes: amaranth (ART) and tartrazine (TTZ). Our findings indicate that the adsorption process reaches equilibrium in a remarkably short timeframe of just 20 min, and it exhibits an excellent correlation with both the Langmuir isotherm model and the pseudo-second-order kinetic model. Furthermore, ARPA has demonstrated an exceptional maximum adsorption capacity, with values of 675.68 mg g−1 for ART and 534.76 mg g−1 for TTZ. In addition to its high adsorption capacity, ARPA has also shown remarkable selectivity, as evidenced by its ability to selectively adsorb TTZ from a mixed dye solution, a feature that is highly desirable for practical applications. Beyond its impressive adsorption capabilities, ARPA can be efficiently regenerated and recycled. It maintains a high level of original removal efficiency for both ART (76.8%) and TTZ (78.9%) even after five consecutive cycles of adsorption and desorption. Considering the simplicity of its synthesis and its outstanding adsorption performance, ARPA emerges as a highly promising material for use in dye removal applications. Consequently, this paper presents a straightforward and feasible method for the production of an effective dye adsorbent for environmental remediation.

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.

Film cooling needs to be applied to serpentine nozzles due to an increase in thermal load. Adverse pressure gradients (APGs) near the upper wall of such nozzles hinder the forward flow of the coolant, and they may even induce a recirculation zone that complicates the cooling of the film in serpentine nozzles under different APGs. In this study, the film cooling characteristics of a serpentine nozzle under various APGs are investigated through numerical simulations. The studied pressure gradients include strong, moderate, and weak APGs. The results show that the APG weakened the adhesion of the coolant to the surface, thereby reducing the film cooling effectiveness (FCE) and the convective heat transfer coefficient (CHTC). The stronger the APG, the greater its obstructive effect. However, the recirculation zone induced by the strong APG was composed of the coolant, and it adhered tightly to the wall, thereby significantly strengthening the FCE and CHTC. The CHTC under the moderate APG significantly increased due to the convergence of two jets ejected from different holes. For the four blowing ratios, the area-averaged FCE under the strong APG was 29.8% and 24.5% higher than that under the moderate and weak APGs, while the area-averaged ratios of the CHTC under the moderate APG were 1.6% and 16.7% higher than those under the strong and weak APGs. Therefore, more holes should be arranged on the film in the zones of moderate and weak APGs.

Recent studies have demonstrated that β-catenin in dendritic cells (DCs) serves as a key mediator in promoting both CD4 and CD8 T cell tolerance, although the mechanisms underlying how β-catenin exerts its functions remain incompletely understood. Here, we report that activation of β-catenin leads to the up-regulation of inhibitory molecule T-cell immunoglobulin and mucin domain 3 (Tim-3) in type 1 conventional DCs (cDC1s). Using a cDC1-targeted vaccine model with anti-DEC-205 engineered to express the melanoma antigen human gp100 (anti-DEC-205-hgp100), we demonstrated that CD11c-β-cateninactive mice exhibited impaired cross-priming and memory responses of gp100-specific CD8 T (Pmel-1) cells upon immunization with anti-DEC-205-hgp100. Single-cell RNA sequencing (scRNA-seq) analysis revealed that β-catenin in DCs negatively regulated transcription programs for effector function and proliferation of primed Pmel-1 cells, correlating with suppressed CD8 T cell immunity in CD11c-β-cateninactive mice. Further experiments showed that treating CD11c-β-cateninactive mice with an anti-Tim-3 antibody upon anti-DEC-205-hgp100 vaccination led to restored cross-priming and memory responses of gp100-specific CD8 T cells, suggesting that anti-Tim-3 treatment likely synergizes with DC vaccines to improve their efficacy. Indeed, treating B16F10-bearing mice with DC vaccines using anti-DEC-205-hgp100 in combination with anti-Tim-3 treatment resulted in significantly reduced tumor growth compared with treatment with the DC vaccine alone. Taken together, we identified the β-catenin/Tim-3 axis as a potentially novel mechanism to inhibit anti-tumor CD8 T cell immunity and that combination immunotherapy of a DC-targeted vaccine with anti-Tim-3 treatment leads to improved anti-tumor efficacy.

Residue dissipation and risk assessment of difenoconazole and its metabolite difenoconazole-alcohol during tea growing, processing, and brewing was first investigated by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS). The limits of quantification for both difenoconazole and difenoconazole-alcohol were 0.001 mg/kg in fresh tea leaves and tea, and 0.0002 mg/L in tea infusion. In field trials, the dissipation half-lives of difenoconazole in fresh tea leaves was 1.77 days. After spraying, the residues of difenoconazole-alcohol increased and then gradually dissipated like difenoconazole. After 14 days, the dissipation rates of difenoconazole and difenoconazole-alcohol reached 99%. When fresh tea leaves were harvested on different days, the total processing factors (PFs) of difenoconazole and difenoconazole-alcohol for green tea were 0.86–1.05 and 0.78–0.85, respectively, while the total PFs for black tea were 0.83–1.13 and 0.82–1.66, respectively. Metabolism of difenoconazole was accelerated during tea processing. When brewing black tea, the leaching rates (LRs) of difenoconazole and difenoconazole-alcohol were 8.4–17.9% and 31.8–38.9%, respectively, while when brewing green tea, the LRs were 15.4–23.5% and 30.4–50.6%, respectively. The LRs of difenoconazole and difenoconazole-alcohol in black tea were higher than those in green tea. The potential threat to human health for dietary intake of difenoconazole and difenoconazole-alcohol residues from tea consumption is negligible. However, the dietary risk of difenoconazole in fruits and vegetables that are essential for daily diets is concerning, with a risk probability of 158%.

Brassinosteroids (BRs) are involved in the regulation of biotic and abiotic stresses in plants. The molecular mechanisms of BRs that alleviate the drought stress in quinoa have rarely been reported. Here, quinoa seedlings were treated with 24-epibrassinolide (EBR) and we transiently transferred CqBIN2 to the quinoa seedlings’ leaves using VIGS technology to analyze the molecular mechanism of the BR mitigation drought stress. The results showed that EBR treatment significantly increased the root growth parameters, the antioxidant enzyme activities, and the osmolyte content, resulting in a decrease in the H2O2, O2∙−, and malondialdehyde content in quinoa. A transcriptome analysis identified 8124, 2761, and 5448 differentially expressed genes (DEGs) among CK and Drought, CK and EBR + Drought, and Drought and EBR + Drought groups. WGCNA divided these DEGs into 19 modules in which these characterized genes collectively contributed significantly to drought stress. In addition, the EBR application also up-regulated the transcript levels of CqBIN2 and proline biosynthesis genes. Silenced CqBIN2 by VIGS could reduce the drought tolerance, survival rate, and proline content in quinoa seedlings. These findings not only revealed that exogenous BRs enhance drought tolerance, but also provided insight into the novel functions of CqBIN2 involved in regulating drought tolerance in plants.

Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs.

In previous studies of ship–ice interactions, most studies focused on ship–level ice interactions, overlooking potential rafted ice conditions in extreme ice conditions. The purpose of this study is to develop a numerical model for predicting ship resistance in rafted ice regions. Numerical modeling of rafted ice was carried out using preset grid cells. By comparing the model test results, the accuracy and reliability of the numerical model are verified. On this basis, we undertook the analysis of the impacts of different ice thicknesses, ship speeds, bending strengths, and crushing strengths on the ice resistance of ships under level and rafted ice conditions. The results show that the ice resistance of ships is significantly higher than that of rafted ice under the condition of level ice; however, level ice and rafted ice have different effects on ship ice resistance. Compared with level ice, the ice resistance of ships navigating in rafted ice is more concentrated. The findings of the present research can serve as a technical reference for studies focused on predicting ship resistance in rafted ice regions.

Casting defects in turbine blades can significantly reduce an aero-engine’s service life and cause secondary damage to the blades when exposed to harsh environments. Therefore, casting defect detection plays a crucial role in enhancing aircraft performance. Existing defect detection methods face challenges in effectively detecting multi-scale defects and handling imbalanced datasets, leading to unsatisfactory defect detection results. In this work, a novel blade defect detection method is proposed. This method is based on a detection transformer with a multi-scale fusion attention mechanism, considering comprehensive features. Firstly, a novel joint data augmentation (JDA) method is constructed to alleviate the imbalanced dataset issue by effectively increasing the number of sample data. Then, an attention-based channel-adaptive weighting (ACAW) feature enhancement module is established to fully apply complementary information among different feature channels, and further refine feature representations. Consequently, a multi-scale feature fusion (MFF) module is proposed to integrate high-dimensional semantic information and low-level representation features, enhancing multi-scale defect detection precision. Moreover, R-Focal loss is developed in an MFF attention-based DEtection TRansformer (DETR) to further solve the issue of imbalanced datasets and accelerate model convergence using the random hyper-parameters search strategy. An aero-engine turbine blade defect X-ray (ATBDX) image dataset is applied to validate the proposed method. The comparative results demonstrate that this proposed method can effectively integrate multi-scale image features and enhance multi-scale defect detection precision.

In this study, two previously undescribed nitrogen-containing compounds, penisimplicins A (1) and B (2), were isolated from Penicillium simplicissimum JXCC5. The structures of 1 and 2 were elucidated on the basis of comprehensive spectroscopic data analysis, including 1D and 2D NMR and HRESIMS data. The absolute configuration of 2 was determined by Marfey’s method, ECD calculation, and DP4+ analysis. Both structures of 1 and 2 feature an unprecedented manner of amino acid-derivatives attaching to a polyketide moiety by C-C bond. The postulated biosynthetic pathways for 1 and 2 were discussed. Additionally, compound 1 exhibited significant acetylcholinesterase inhibitory activity, with IC50 values of 6.35 μM.

The Port Radium U-Cu-Ni-Co-Ag deposit in northwestern Canada is hosted within a mineral system that has generated a variety of mineralization styles from iron oxide-copper-gold to iron oxide-apatite, porphyry, skarn, and epithermal. Their genesis is linked to an extensive subduction-related magmatism that formed widespread dacite-rhyodacite-andesite volcanic and volcanoclastic sequences (~1.87 Ga), which have been intruded by their equivalent intrusive plutons. Pervasive and intensive hydrothermal alterations, including albitic, magnetite-actinolite-apatite, potassic ± albitic, phyllic, and propylitic occurred before the main sulfide, sulfarsenide, and uraninite vein-type mineralization. Although scarce sulfide minerals formed at the beginning of the hydrothermal activity, the main polymetallic arsenide-sulfarsenide-sulfide ± uraninite vein-type mineralization occurred during the epithermal stage. In addition to the common arsenides and sulfarsenides including nickeline, cobaltite, rammelsbergite, safflorite, skutterudite, gersdorffite, and arsenopyrite, three unique sulfarsenides were also found: Co0.67Ni0.32Fe0.02S0.19As2.80, which could be a sulfur-rich skutterudite, Ni0.85Co0.15S0.39As1.60, and Ni0.69Co0.31S0.47As1.52, which are chemically comparable to the Port Radium rammelsbergite with substantial addition of S and Co; they could be the solid solution product of gersdorffite-cobaltite or safflorite-rammelsbergite.

An eight-week feeding trial was conducted to investigate the effects of a dietary β-glucan application strategy on the growth performance, physiological response, and gut microbiota of Pacific white shrimp (Litopenaeus vannamei) (0.49 ± 0.17 g) under low salinity. Six feeding strategies were established, including a continuous β-glucan-free diet group (control), a continuously fed group with a 0.1% β-glucan diet (T1), and groups with the following intermittent feeding patterns: 1 day of β-glucan diet and 6 days of β-glucan-free diet (T2), 2 days of β-glucan diet and 5 days of β-glucan-free diet (T3), 3 days of β-glucan diet and 4 days of β-glucan-free diet (T4), and 4 days of β-glucan diet and 3 days of β-glucan-free diet (T5) each week. No significant differences in growth performance among all the groups were found, although the condition factor was significantly higher in the T3 group than in the T1 and T5 groups (p < 0.05). The T-AOC and GPX activities were significantly lower in the T3 group than in the control group (p < 0.05). The MDA content was also significantly lower in the T2 group than in the T3 and T4 groups (p < 0.05). Additionally, the mRNA expression of the Pen3a gene was significantly upregulated in the hepatopancreas of the T4 group compared to the control and T5 groups (p < 0.05), and the Toll gene was also significantly upregulated in the T3 group compared to the T1 and T2 groups (p < 0.05). Dietary β-glucan induced changes in the alpha diversity and composition of the gut microbiota in different feeding strategies. The beta diversity of the gut microbiota in the T2 group was significantly different from that in the control group. The results of a KEGG analysis showed that gut function in the carbohydrate metabolism, immune system, and environmental adaptation pathways was significantly enhanced in the T3 group. These findings provide evidence that the intermittent feeding strategy of β-glucan could alleviate immune fatigue, impact antioxidant ability, and change gut microbiota composition of L. vannamei under low salinity.

In this study, 2219 aluminum alloy thick plate was joined by electron beam welding. Defect-free joints with excellent surface formation were obtained. There were significant differences in the microstructure along the thickness direction of the weld zone (WZ). The upper region of the WZ was mainly striated grains, while the lower region was fine equiaxed grains. The WZ of 2219 joint is composed of α-Al and Al-Cu eutectic. Fine equiaxed grains were formed in the partially melted zone (PMZ) due to the existence of high-melting nucleation particles including Ti-Al and Ti-Zr compounds. The eutectic microstructure in the PMZ and the heat-affected zone (HAZ) presented net-like and block-shape distribution. Due to the formation of fine grains and high content of Al-Cu eutectic, the WZ showed the highest microhardness (80 HV). Therefore, the 2219 joint obtained excellent mechanical properties. The tensile strength of the 2219 joint was equal to that of the base metal (BM), but the elongation of the 2219 joint significantly decreased to 15.1%, about 67.7% of that of BM. The fracture mode of the 2219 joint presented typical ductile fracture.

Liquidambar formosana is a multipurpose tree species native to China. There has been increasing interest in L. formosana due to its leaves being rich in shikimic acid, which plays a key role in the synthesis of the antiviral drug oseltamivir phosphate. Here, shikimic acid content (SAC) and other breeding traits, including tree height (HT), diameter at breast height (DBH), height to crown base (HCB), individual tree volume (VOL), leaf color (LC) and stem straightness degree (SSD), for 387 families of 19 provenances were evaluated in a provenance–family trial of L. formosana to estimate genetic parameters and reveal geographical variation patterns and, ultimately, screen out superior provenances and families. Differences among provenances and families were significant for all tested traits, indicating a high potential for selective breeding. Broad-sense heritabilities of provenance (hp2 = 0.19–0.57) and family (hf2 = 0.16–0.31) were moderate for most traits. Moderate to strong genetic correlations were found among HT, DBH, VOL, HCB and LC (rA = 0.339–0.982), while adverse correlations (rA = −0.494 to −0.816) were observed between SAC and growth traits. All target traits, excluding SSD, exhibited clinal variation in response to latitudinal gradients, and a clustering heatmap divided the 19 provenances into three groups. For single-trait selection, SAC persistently had the highest genetic gains (85.14%–163.57%). A weighted index based on breeding values was used to concomitantly improve SAC, HT and DBH. At a selection rate of 25%, the genetic gains at the provenance and family levels for SAC were 36.42% and 73.52%, and those for core growth traits ranged from −2.29% to 3.49% and 4.05% to 4.47%, respectively. As far as we know, this is the first study in L. formosana to explore the inheritance of SAC and its correlations with other traditional breeding traits. The genetic parameter estimations contribute to a better understanding of the genetic basis of SAC, and the superior provenances and families obtained lay a material foundation for the development of new varieties rich in shikimic acid, thereby promoting the in-depth exploitation and utilization of germplasm resources of L. formosana.

The differences in the response of the leaves of different bamboo types to sudden snowfalls in winter were analyzed in order to provide scientific references for the introduction and cultivation of cold-resistant bamboo species and to provide new theoretical information on bamboo afforestation and disaster reduction. A total of nine bamboo species were selected to analyze the physiological and chemical changes in the leaves caused by a sudden snowfall. The results showed that sudden snowfall in winter led to a decrease in the moisture, soluble sugar, and starch contents in the leaves of all of the bamboo species analyzed, but there were increases in the contents of proline, MDA, and H2O2 and in the ratios of AsA/DHA and GSH/GSSG. Both the enzymatic activities (SOD, POD, CAT, APX, DHAR, GPX, and GR) and non-enzymatic antioxidant contents (AsA and GSH) were increased after the snowfall, which indicated that the sudden snowfall caused an increase in the antioxidant abilities in the leaves of all bamboo species analyzed. Different bamboo species adopted different strategies for resisting the freezing damage caused by the sudden snowfall. The prevention and mitigation of snow disasters in winter can be scientifically carried out in bamboo forests according to their tolerance abilities.

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost.

This study investigated the dissolved oxygen (DO) variation pattern in a Litopenaeus vannamei recirculating aquaculture system (RAS) and established an oxygen-utilization rate (UROxygen) model, pure oxygen addition (QOxygen) model, and control model that linked a microscreen drum filter (MDF) with a U-tube oxygenator. The main objective was to promote the application of the U-tube oxygenator and achieve the efficient, accurate, and automated management of DO in an RAS. To avoid wasting oxygen and ensure production safety, it was recommended to maintain the effluent of the aquaculture pond at 6.9 ± 0.4 mg/L. The modeled relationship between the RAS flow (QRAS), QOxygen, and UROxygen was UROxygen = 0.9626 × (−105.3406 + 0.9911QRAS + 10.6202QOxygen − 0.05964QRASQOxygen − 1.2628 × 10−3QRAS2 − 0.1821QOxygen2 + 6.8888 × 10−5QRAS2QOxygen + 6.3993 × 10−4QRASQOxygen2). The modeled relationship between QRAS, daily feeding rate (MFeeding), and QOxygen was QOxygen = 1.09 × (−12.8633 − 0.02793QRAS + 0.9369 MFeeding − 8.9286 × 10−4MFeedingQRAS + 5.6122 × 10−5QRAS2 − 2.3281 × 10−3MFeeding2). The modeled relationship between the MDF backwashing period (TMDF) and QOxygen was QOxygen = −11.57ln(TMDF) + 78.319. This study provided a theoretical basis and novel methods for the management of DO in an RAS, thus promoting the healthy and stable development of an L. vannamei RAS.

Runoff simulation is an ongoing challenge in the field of hydrology. Process-based (PB) hydrological models often gain unsatisfactory simulation accuracy due to incomplete physical process representations. While the deep learning (DL) models demonstrate their capacity to grasp intricate hydrological response processes, they still face constraints pertaining to the representative training data and comprehensive hydrological observations. In order to provide unobservable hydrological variables from the PB model to the DL model, this study constructed hybrid models by feeding the output variables of the PB model (BTOP) into the DL model (LSTM) as additional input features. These variables underwent feature dimensionality reduction using the feature selection method (Pearson Correlation Coefficient, PCC) and the feature extraction method (Principal Component Analysis, PCA) before input into LSTM. The results showed that the standalone LSTM performed well across the basin, with NSE values all exceeding 0.70. The hybrid models enhanced the simulation performance of the standalone LSTM. The NSE values increased from 0.75 to nearly 0.80 in a sub-basin. Lastly, if the BTOP output is directly fed into LSTM without feature dimensionality reduction, the model’s accuracy significantly decreases due to noise interference. The NSE value decreased by 0.09 compared to the standalone LSTM in a sub-basin. The results demonstrated the effectiveness of PCC and PCA in removing redundant information within hydrological variables. These findings provide new insights into incorporating physical information into LSTM and constructing hybrid models.

In 2020, the world experienced the threat of the COVID-19 epidemic, and seniors and chronic disease patients generally reduced their exercise and social activities to avoid increasing the risk of infection, which could lead to increased loneliness and even many diseases. Combining golf croquet games with a mobile application (App) and AIoT companion robots, this research constructs a home-based intelligent exercise system, uses the technology acceptance model (TAM), deduces users’ intention to use this system based on perceived usefulness and perceived ease of use, and adds the needs of love and belonging, esteem, cognitive, aesthetic, and self-actualization in Maslow’s hierarchy of needs theory (HNT) to conduct an analysis of system needs. This study collected empirical data, totaling 320 participants including seniors and their caregivers, from elderly care centers in northern Taiwan using a cross-sectional survey and purposive sampling. Based on regression and variance analysis, the results show that participants have a high level of acceptance of this system, believing that it is easy to learn and operate and can increase interaction with others, improve self-confirmation, satisfy the thirst for knowledge, increase the feeling of happiness, and fulfill self-actualization needs. In the future, by collecting and recording the process of seniors using the App, so as to find out their health problems as soon as possible, expand their daily life through this exercise, and achieve the goal of happy living and better healthcare.

Satellite precipitation products (SPPs) have advanced remarkably in recent decades. However, the bias correction of SPPs still performs unsatisfactorily in the case of a limited rain-gauge network. This study proposes a new real-time bias correction approach that includes three steps to improve the precipitation quality with limited gauges and facilitate the hydrological simulation in the Min River Basin, China. This paper employed 66 gauges as available ground observation precipitation, Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) as the historical precipitation to correct Global Satellite Mapping of Precipitation NOW (GNOW) and Global Satellite Mapping of Precipitation NRT (GNRT) in 2020. A total of 1020 auto-rainfall stations were used as the benchmark to evaluate the original and corrected SPPs with six criteria. The results show that the statistic and dynamic bias correction method (SDBC) improved the SPPs significantly and the cumulative distribution function matching method (CDF) could reduce the overcorrection error from SDBC. The inverse error variance weighting method (IEVW) integrations of GNOW and GNRT did not have noticeable improvement as they use similar hardware and software processes. The corrected SPPs show better performance in hydrological simulations. It is recommended to employ different SPPs for integration. The proposed bias correction approach is significant for precipitation estimation and flood prediction in data-sparse basins worldwide.

Liquid–liquid phase separation (LLPS) is a biophysical process that mediates the precise and complex spatiotemporal coordination of cellular processes. Proteins and nucleic acids are compartmentalized into micron-scale membrane-less droplets via LLPS. These droplets, termed biomolecular condensates, are highly dynamic, have concentrated components, and perform specific functions. Biomolecular condensates have been observed to organize diverse key biological processes, including gene transcription, signal transduction, DNA damage repair, chromatin organization, and autophagy. The dysregulation of these biological activities owing to aberrant LLPS is important in cardiovascular diseases. This review provides a detailed overview of the regulation and functions of biomolecular condensates, provides a comprehensive depiction of LLPS in several common cardiovascular diseases, and discusses the revolutionary therapeutic perspective of modulating LLPS in cardiovascular diseases and new treatment strategies relevant to LLPS.

Because of the ease with which oxide films form on its surfaces, stainless steel has strong corrosion resistance and excellent processing performance. Electrochemical machining (ECM) is a flexible process that can create microstructures on stainless steel (SS304); however, with traditional masked ECM, the efficiency and accuracy of microstructure machining are low. Proposed here is the use of a non-Newtonian fluid [polyacrylamide (PAM)] as the electrolyte. To date, there have been few papers on the electrochemical dissolution behavior of stainless-steel micromachining with a non-Newtonian fluid as the electrolyte. The aims of the study reported here were to investigate the electrochemical properties of SS304 with PAM and PAM–NaOH as electrolytes, and to explain their electrochemical corrosion mechanisms. The effects of different electrolytes were compared, and the polarization curves of SS304 in PAM and PAM–NaOH electrolyte solutions with different components were analyzed and compared with that in NaNO3 electrolyte. Then, the effects of the main processing parameters (pulse voltage, frequency, and duty ratio) on the machining performance were investigated in detail. A microhole array was obtained with a good quality comprising an average diameter of 330.11 µm, an average depth of 16.13 µm, and a depth-to-diameter ratio of 0.048. Using PAM to process microstructures on stainless-steel surfaces was shown to be feasible, and experiments indicated that the mixed electrolyte (PAM–NaOH) had not only the physical characteristics of a non-Newtonian fluid but also the advantages of a traditional electrolyte to dissolve processing products, and it effectively improved the processing accuracy of masked ECM for SS304.

When ships navigate in polar regions, they may collide with ice masses, which may cause structural damage and endanger the safety of their occupants. Therefore, it is essential to promptly detect sea ice, icebergs, and passing ships. However, individual data sources have limits and should be combined and integrated to obtain more thorough information. A polar multi-target local-scale dataset with five categories was constructed. Sea ice, icebergs, ice melt ponds, icebreakers, and inter-ice channels were identified by a single-shot detector (SSD), with a final mAP value of 70.19%. A remote sensing sea ice dataset with 15,948 labels was constructed. The You Only Look Once (YOLOv5) model was improved with Squeeze-and-Excitation Networks (SE), Funnel Activation (FReLU), Fast Spatial Pyramid Pooling, and Cross Stage Partial Network (SPPCSPC-F). In the detection stage, a slicing operation was performed on remote sensing images to detect small targets. Simulated sea ice data were included to verify the model’s generalization ability. Then, the improved model was trained and evaluated in an ablation experiment. The mAP, recall (R), and precision (P) values of the improved YOLOv5 were 75.3%, 70.3, and 75.4%, with value increases of 3.5%, 3.4%, and 1.9%, respectively, compared to the original model. The improved YOLOv5 was also compared with other models such as YOLOv3, Faster-RCNN, and YOLOv4-tiny. The results indicated that the performance of the proposed model surpassed those of the other conventional models. This study achieved the detection of multiple targets on different scales in a polar region and realized data fusion, avoiding the limitations of using a single data source, and provides a method to support polar ship path planning.

This study investigated the effects of three extraction methods, including cold pressing (CP), microwave pretreatment pressing (MP), and supercritical fluid extraction (SFE), on the yield, physicochemical properties, bioactive compounds content, and antioxidant properties of pumpkin seed oil (PSO). Furthermore, the correlation between bioactive compounds and the antioxidant properties of PSO was determined. The results revealed that the yield of PSO extracted using the three methods was in the order of SFE > MP > CP. Additionally, the PSO generated by SFE showed the highest unsaturated fatty acid content, followed by MP and CP. Additionally, MP-PSO exhibited the highest acid value and saponification value, while SFE-PSO displayed the highest moisture content, peroxide value, and iodine value. Moreover, the PSO generated by MP demonstrated superior antioxidant properties compared to that of PSOs from CP and SFE in the oxidation induction, DPPH, FRAP, and ABTS tests. Finally, the correlation analysis revealed that specific types of bioactive compounds, such as β-sitosterol and γ-tocopherol, were highly correlated with the antioxidant properties of PSOs. Consequently, this study provides comprehensive knowledge regarding PSO extraction, physicochemical properties, bioactive compound extraction, and the correlated antioxidant properties.

Understanding the water status of specific organs can be helpful in evaluating the life activities and growth conditions of maize. To accurately judge organ growth conditions and thus design appropriate interventions, it is necessary to clarify the true water dynamics of each maize organ. Using multiple maize cultivars with different growth periods, spatio-temporal water dynamics were analyzed here in the leaves, stalks, and ear components. Leaf water content was found to gradually decrease from both the bottom and top of the plant to the middle, whereas stalk water content decreased sequentially from the top to the bottom. Each successively higher node from the bottom of the plant was associated with decreases of 0.99% and 1.27% water content in the leaves and stalks, respectively. The water dynamics in leaves and internodes showed three clear stages: the slow loss, rapid loss, and balance stage. A water content of 60% appeared to be an irreversible turning point for initiation of senescence. Using normalized growth period as a measure, each of the tested cultivars could be assigned into one of two types based on their water dynamics: stay-water or general type. General-type cultivars had a shorter duration with a high water content and a water loss rate approximately twice as high as that of the stay-water type. This may have been related to the leaf senescence characteristics. However, the stay-water trait did not interfere with water dynamics of the ear components. Therefore, it may not be robust to evaluate the kernel dehydration of maize according to leaf senescence conditions due to the weak correlation between kernel water content and leaf senescence characteristics.

The recognition of ice channels plays a crucial role in developing intelligent ship navigation systems in ice-covered waters. Navigating through ice channels with the assistance of icebreakers is a common operation for merchant ships. Maneuvering within such narrow channels presents a significant challenge for the captain’s skills and ship performance. Therefore, it becomes essential to explore methods for enabling ships to navigate through these channels automatically. A key step in achieving this is the accurate recognition and extraction of boundary lines on both sides of the ice channel. An ice channel line recognition method based on the lane line detection algorithm UFAST is implemented. The method is trained and tested on the constructed ice channel dataset, with the test results showing that the average recognition accuracy reaches 84.1% and the recognition speed reaches 138.3 frames per second, meeting the real-time requirement. In order to solve the current lack of authentic ice channel images, ice channel navigation scenes are built based on UE4, and synthetic ice channel images are rendered. The method in this paper is also compared with the traditional non-intelligent Otsu threshold segmentation method and the intelligent instance segmentation method YOLACT for performance analysis. The method in this paper has 9.5% higher ice channel recognition accuracy and 103.7 frames per second higher recognition speed compared with YOLACT. Furthermore, ablation studies are conducted to analyze the relationship between the number of gridding cells in the proposed method and ice channel recognition accuracy.

Cooking methods affect the compositions of Lentinus edodes metabolites. Nevertheless, little information is available on the specific impact of different cooking methods on Lentinus edodes via metabolomic analysis. This study determined the influence of boiling, steaming, air-frying, and roasting on the metabolomic profiles of Lentinus edodes based on UHPLC-Q-Exactive Orbitrap MS/MS in combination with chemometrics. A total of 990 metabolites were detected and classified into 11 super-classes. Subsequently, the metabolites of the four cooking methods were distinguished using multivariate statistical analysis. The results showed that boiling caused a massive loss of metabolites while roasting and air-frying led to an evident upregulation. The upregulation of metabolites in the steaming groups was not as significant as in roasting and air-frying. This study provided reference data for a comprehensive understanding of the metabolites associated with domestic cooking methods and valuable guidance for the development of Lentinus edodes and its products in the future.

Formation navigation in brash ice channels is increasingly utilized by merchant vessels in the Arctic and Baltic Sea, offering benefits such as improved efficiency and reduced carbon emissions. However, ship–ship interference poses a significant challenge to this method, impacting resistance performance. This paper presents full-scale simulations using the CFD–DEM coupling method in brash ice channels, which is validated by comparing simulation results with ice tank measurements. By varying the distance between two ships from 0.05 to 5 ship lengths, ship–ship interference in full-scale brash ice channels is analyzed using the CFD–DEM coupling strategy. The study examines hydrodynamic and ship–ice interactions, ice resistance effects, and simulation results. It is found that ship-to-ship distance significantly influences the velocity field, dynamic pressure distribution on the hull, and hydrodynamic interaction forces. Distances less than one ship length result in increased water resistance for the forward ship and decreased resistance for the rear ship. The forward ship demonstrates favorable interference with the ice accumulation of the rear ship. When distances are less than two ship lengths, the ice resistance of the forward ship remains mostly unaffected, while the ice resistance of the rear ship decreases as the distance decreases. These insights enhance our understanding of ship–ship interference in formation navigation, aiding in the optimization of brash ice channel navigation strategies.

In recent years, safety issues respecting polar ship navigation in the presence of ice have become a research hotspot. The accurate prediction of propulsion power plays an important role in ensuring safe ship navigation and evaluating ship navigation ability, and deep learning has been widely applied in the field of shipping, of which the artificial neural network (ANN) is a common method. This study combines the scientific problems of ice resistance and propulsion power for polar ship design, focusing on the design of an ANN model for predicting the propulsion power of polar ships. Reference is made to the traditional propulsion power requirements of various classification societies, as well as ship model test and full-scale test data, to select appropriate input features and a training dataset. Three prediction methods are considered: building a radial basis function–particle swarm optimization algorithm (RBF-PSO) model to directly predict the propulsion power; based on the full-scale test and model test data, calculating the propulsion power using the Finnish–Swedish Ice Class Rules (FSICR) formula; using an ice resistance artificial neural network model (ANN-IR) to predict the ice resistance and calculate the propulsion power using the FSICR formula. Prediction errors are determined, and a sensitivity analysis is carried out with respect to the relevant parameters of propulsion power based on the above methods. This study shows that the RBF-PSO model based on nine feature inputs has a reasonable generalization effect. Compared with the data of the ship model test and full-scale test, the average error is about 14%, which shows that the method has high accuracy and can be used as a propulsion power prediction tool.

The current study aimed to examine the association between serum selenium levels and lipids and explore whether the association was modified by diabetic status. A total of 4132 adults from the National Health and Nutrition Examination Survey (2011–2016) were included in this study. Multiple linear regression models were used to estimate the association between serum selenium and lipids. Higher serum selenium levels were significantly associated with increased total cholesterol (TC) (p < 0.001), triglyceride (TG) (p = 0.003), and low-density lipoprotein cholesterol (LDL-C) (p = 0.003) in the overall population. Diabetic status interacted with serum selenium for TC and LDL-C (p for interaction = 0.007 and

In response to the new concept of the impact of total climate production factors on plant phenology, this study verifies the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plants from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors have a significant impact on plant phenological changes and can be effectively utilized for simulating phenology and determining triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plants, and their triggering thresholds depend on the parameters of climate resource changes and the characteristics of plant biology, which are (0.085, −5.363) and (0.086, −27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plants, and its triggering thresholds also depend on the parameters of climate resource changes and the characteristics of plant biology, which is (394.632, −38,026.268). Furthermore, the results support the viewpoint that abrupt changes to the climate determine the beginning and ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

Langerhans cells (LCs) are skin-resident macrophage that act similarly to dendritic cells for controlling adaptive immunity and immune tolerance in the skin, and they are key players in the development of numerous skin diseases. While TGF-β and related downstream signaling pathways are known to control numerous aspects of LC biology, little is known about the epigenetic signals that coordinate cell signaling during LC ontogeny, maintenance, and function. Our previous studies in a total miRNA deletion mouse model showed that miRNAs are critically involved in embryonic LC development and postnatal LC homeostasis; however, the specific miRNA(s) that regulate LCs remain unknown. miR-23a is the first member of the miR-23a-27a-24-2 cluster, a direct downstream target of PU.1 and TGF-b, which regulate the determination of myeloid versus lymphoid fates. Therefore, we used a myeloid-specific miR-23a deletion mouse model to explore whether and how miR-23a affects LC ontogeny and function in the skin. We observed the indispensable role of miR-23a in LC antigen uptake and inflammation-induced LC epidermal repopulation; however, embryonic LC development and postnatal homeostasis were not affected by cells lacking miR23a. Our results suggest that miR-23a controls LC phagocytosis by targeting molecules that regulate efferocytosis and endocytosis, whereas miR-23a promotes homeostasis in bone marrow-derived LCs that repopulate the skin after inflammatory insult by targeting Fas and Bcl-2 family proapoptotic molecules. Collectively, the context-dependent regulatory role of miR-23a in LCs represents an extra-epigenetic layer that incorporates TGF-b- and PU.1-mediated regulation during steady-state and inflammation-induced repopulation.

Glyphosate has excellent herbicidal activity, and its extensive use may induce residue in the environment and enter into humans living through the food chain, causing negative impact. Here, water-soluble 1.55 nm size nitrogen-doped carbon quantum dots (NCDs) with strong blue fluorescence were synthesized using sodium citrate and adenine. The maximum excitation and emission wavelengths of NCDs were 380 nm and 440 nm, respectively. The above synthesized NCDs were first used for the construction of a fluorescence sensor for glyphosate detection. It was found that Cu2+ could quench the fluorescence of NCDs effectively through the photoinduced electron transfer (PET) process, which was confirmed using fluorescence lifetime measurements. Additionally, the fluorescence was restored with the addition of glyphosate. Hence, a sensitive turn-on fluorescence sensor based on NCDs/Cu2+ for glyphosate analysis was developed. The LODs of glyphosate for water and rice samples were recorded as 0.021 μg/mL and 0.049 μg/mL, respectively. The sensor was applied successfully for ultrasensitive and selective detection of glyphosate in environmental water and rice samples with satisfied recoveries from 82.1% to 113.0% using a simple sample pretreatment technique. The proposed strategy can provide a significant potential for monitoring glyphosate residue in water and agricultural product samples.

Escherichia coli (E. coli) O157:H7 is a major foodborne and waterborne pathogen that can threaten human health. Due to its high toxicity at low concentrations, it is crucial to establish a time-saving and highly sensitive in situ detection method. Herein, we developed a rapid, ultrasensitive, and visualized method for detecting E. coli O157:H7 based on a combination of Recombinase-Aided Amplification (RAA) and CRISPR/Cas12a technology. The CRISPR/Cas12a-based system was pre-amplified using the RAA method, which showed high sensitivity and enabled detecting as low as ~1 CFU/mL (fluorescence method) and 1 × 102 CFU/mL (lateral flow assay) of E. coli O157:H7, which was much lower than the detection limit of the traditional real-time PCR technology (103 CFU/mL) and ELISA (104~107 CFU/mL). In addition, we demonstrated that this method still has good applicability in practical samples by simulating the detection in real milk and drinking water samples. Importantly, our RAA-CRISPR/Cas12a detection system could complete the overall process (including extraction, amplification, and detection) within 55 min under optimized conditions, which is faster than most other reported sensors, which take several hours to several days. The signal readout could also be visualized by fluorescence generated with a handheld UV lamp or a naked-eye-detected lateral flow assay depending on the DNA reporters used. Because of the advantages of being fast, having high sensitivity, and not requiring sophisticated equipment, this method has a promising application prospect for in situ detection of trace amounts of pathogens.

The hot deformation behavior of Al-Zn-Mg-Er-Zr alloy was investigated through an isothermal compression experiment at a strain rate ranging from 0.01 to 10 s−1 and temperature ranging from 350 to 500 °C. The constitutive equation of thermal deformation characteristics based on strain was established, and the microstructure (including grain, substructure and dynamic precipitation) under different deformation conditions was analyzed. It is shown that the steady-state flow stress can be described using the hyperbolic sinusoidal constitutive equation with a deformation activation energy of 160.03 kJ/mol. Two kinds of second phases exist in the deformed alloy; one is the η phase, whose size and quantity changes according to the deformation parameters, and the other is spherical Al3(Er, Zr) particles with good thermal stability. Both kinds of particles pin the dislocation. However, with a decrease in strain rate or increase in temperature, η phases coarsen and their density decreases, and their dislocation locking ability is weakened. However, the size of Al3(Er, Zr) particles does not change with the variation in deformation conditions. So, at higher deformation temperatures, Al3(Er, Zr) particles still pin dislocations and thus refine the subgrain and enhance the strength. Compared with the η phase, Al3(Er, Zr) particles are superior for dislocation locking during hot deformation. A strain rate ranging from 0.1 to 1 s−1 and a deformation temperature ranging from 450 to 500 °C form the safest hot working domain in the processing map.

When navigating ships in cold regions, sea ice concentration plays a crucial role in determining a ship’s navigability. However, automatically extracting the sea ice concentration and floe size distribution remains challenging, due to the difficulty in detecting all the ice floes from the images captured in complex polar environments, particularly those that include both ships and sea ice. In this paper, we propose using the YOLACT network to address this issue. Cameras installed on the ship collect images during transit and an image dataset is constructed to train a model that can intelligently identify all the targets in the image and remove any noisy targets. To overcome the challenge of identifying seemingly connected ice floes, the non-maximum suppression (NMS) in YOLACT is improved. Binarization is then applied to process the detection results, with the aim of obtaining an accurate sea ice concentration. We present a color map and histogram of the associated floe size distribution based on the ice size. The speed of calculating the sea ice density of each image reaches 21 FPS and the results show that sea ice concentration and floe size distribution can be accurately measured. We provide a case study to demonstrate the effectiveness of the proposed approach.

Genome duplication supplies raw genetic materials and has been thought to be essential for evolutionary innovation and ecological adaptation. Here, we select Kelch-like (klhl) genes to study the evolution of the duplicated genes in the polyploid Carassius complex, including amphidiploid C. auratus and amphitriploid C. gibelio. Phylogenetic, chromosomal location and read coverage analyses indicate that most of Carassius klhl genes exhibit a 2:1 relationship with zebrafish orthologs and confirm two rounds of polyploidy, an allotetraploidy followed by an autotriploidy, occurred during Carassius evolution. The lineage-specific expansion and biased retention/loss of klhl genes are also found in Carassius. Transcriptome analyses across eight adult tissues and seven embryogenesis stages reveal varied expression dominance and divergence between the two species. The expression of klhls in response to Carassius herpesvirus 2 infection shows different expression changes corresponding to distinct herpesvirus resistances in three C. gibelio gynogenetic clones. Finally, we find that most C. gibelio klhl genes possess three alleles except eight genes that have lost one or two alleles due to genome rearrangement. The allele expression bias is prosperous for Cgklhl genes and varies during embryogenesis owning to the sequential expression manner of the alleles. The current study provides global insights into the genomic and transcriptional evolution of duplicated genes in a given superfamily resulting from multiple rounds of polyploidization.

In this study, ultra-high-performance liquid chromatography high-resolution accurate mass-mass spectrometry (UHPLC-HRAM/MS) was applied to characterize the lipid profiles of five crab species. A total of 203 lipid molecular species in muscle tissue and 176 in edible viscera were quantified. The results indicate that Cancer pagurus contained high levels of lipids with a docosahexaenoic acid (DHA) and eicosapntemacnioc acid (EPA) structure in the muscle tissue and edible viscera. A partial least squares discriminant analysis (PLS-DA) showed that PE 16:0/22:6, PE P-18:0/20:5, PA 16:0/22:6 and PC 16:0/16:1 could be used as potential biomarkers to discriminate the five kinds of crabs. In addition, some lipids, such as PE 18:0/20:5, PC 16:0/16:1, PE P-18:0/22:6 and SM 12:1;2O/20:0, could be used as characteristic molecules to distinguish between Cancer magister and Cancer pagurus, which are similar in appearance. This study provides a new perspective on discriminating crab species from MS-based lipidomics.