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Surface runoff is a key component of the hydrological cycle and is essential for water resource management and water ecological balance in river basins. It is important to accurately reveal the spatial and temporal dynamics of regional surface runoff over long time scales and to quantify the impacts of climate change and human activities on surface runoff changes for sustainable water resources management and utilization. In this study, the Minjiang River Basin (Chengdu section) was selected, which has significant natural and anthropogenic variations, and a comprehensive analysis of runoff and its drivers will help to formulate an effective regional water resource management strategy. We mainly used SWAT to simulate the monthly-scale runoff in the Chengdu section of the Minjiang River Basin from 1990 to 2019 and combined SWAT-CUP to perform sensitivity analysis on the model parameters and Partial Least Squares Structural Equation Modeling (PLS-SEM) to quantitatively analyze the main drivers of the changes in surface runoff. The results show that the average multi-year runoff in the Minjiang River Basin (Chengdu section) ranges from 628.96 to 1088.46 mm, with an average value of 834.13 mm, and that the overall annual runoff in the past 30 years shows a fluctuating tendency. The goodness-of-fit of the PLS-SEM model is 0.507; the validity and reliability assessment indicated that the model was reasonable, and its results showed that economic and landscape factors had significant negative impacts on runoff changes, while natural factors had positive impacts on runoff changes, with path coefficients of −0.210, −0.131, and 0.367, respectively. Meanwhile, this study also identified two potential indirect impact pathways, i.e., the economic factors had an indirect negative impact on runoff by changing the distribution of landscapes, and the natural factors had indirect negative impacts on runoff by influencing economic activities, reflecting the complex interactions among economic activities, landscape distribution, and natural factors in influencing surface runoff. This study provides a research framework and methodology for quantitatively modeling surface runoff and the analysis of influencing factors in watersheds, contributing to a deeper scientific understanding of long-term runoff changes and the contribution of their drivers. [ABSTRACT FROM AUTHOR]

Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers' personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM). [ABSTRACT FROM AUTHOR]

Er3+-doped fluorite crystals, including CaF2 and SrF2, are considered as attractive laser gain materials in the mid-infrared (MIR) region with merits of high laser efficiency as well as low doping concentration. In this work, a series of Er, La:CaF2 crystals were grown and the modulation effect of co-doping La3+ ions on the spectral properties and mid-infrared laser performance was investigated. It was found that introducing La3+ ions can effectively manipulate the coordination environment of Er3+ ions embedded in CaF2 crystal, thus modulating the shape and intensity of absorption and emission bands. On the other hand, La3+ ions can partially substitute Er3+ sites in the clusters to form mixed clusters, which affects the energy transfer processes between Er3+ ions as well as ~3 μm laser performance, which is dominated by energy transfer up-conversion (ETU) processes between Er3+ ions. By co-doping La3+ ions into Er:CaF2 crystal at an appropriate concentration, the spectral parameter modulation can be achieved while maintaining a high MIR laser efficiency. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction. [ABSTRACT FROM AUTHOR]

Probiotic biofilms have been beneficial in the fight against infections, restoring the equilibrium of the host's gut microbiota, and enhancing host health. They are considered a novel strategy for probiotic gut colonization. In this case, we evaluated the effects of various active substances from traditional Chinese medicine on Escherichia coli Nissle 1917 (EcN) to determine if they promote biofilm formation. It was shown that 8–64 μg/mL of oleanolic acid increased the development of EcN biofilm. Additionally, we observed that oleanolic acid can effectively suppress biofilm formation in pathogenic bacteria such as Salmonella and Staphylococcus aureus. Next, we assessed the amount of EcN extracellular polysaccharides, the number of live bacteria, their metabolic activity, the hydrophobicity of their surface, and the shape of their biofilms using laser confocal microscopy. Through transcriptome analysis, a total of 349 differentially expressed genes were identified, comprising 134 upregulated and 215 downregulated genes. GO functional enrichment analysis and KEGG pathway enrichment analysis revealed that oleanolic acid functions are through the regulation of bacterial motility, the iron absorption system, the two-component system, and adhesion pathways. These findings suggest that the main effects of oleanolic acid are to prevent bacterial motility, increase initial adhesion, and encourage the development of EcN biofilms. In addition, oleanolic acid interacts with iron absorption to cooperatively control the production of EcN biofilms within an optimal concentration range. Taking these results together, this study suggests that oleanolic acid may enhance probiotic biofilm formation in the intestines, presenting new avenues for probiotic product development. [ABSTRACT FROM AUTHOR]

Standard beams are mainly used for the calibration of strain sensors using their load reconstruction models. However, as an ill-posed inverse problem, the solution to these models often fails to converge, especially when dealing with dynamic loads of different frequencies. To overcome this problem, a piecewise Tikhonov regularization method (PTR) is proposed to reconstruct dynamic loads. The transfer function matrix is built both using the denoised excitations and the corresponding responses. After singular value decomposition (SVD), the singular values are divided into submatrices of different sizes by utilizing a piecewise function. The regularization parameters are solved by optimizing the piecewise submatrices. The experimental result shows that the MREs of the PTR method are 6.20% at 70 Hz and 5.86% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits MREs of 28.44% and 29.61% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates MREs of 29.98% and 18.42% at the same frequencies. Furthermore, the PREs of the PTR method are 3.54% at 70 Hz and 3.73% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits PREs of 27.01% and 26.88% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates PREs of 29.50% and 15.56% at the same frequencies. All in all, the method proposed in this paper can be extensively applied to load reconstruction across different frequencies. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm for AGB downscaling that integrated a ML for AGB mapping with another for residual mapping. The accuracies of six bias-corrected ensemble ML algorithms were evaluated at resolutions of 0.05°, 0.025°, and 0.01°. Moreover, a step-by-step downscaling (SBSD) method was introduced, utilizing bias-corrected ensemble ML algorithms to downscale AGB from 0.1° to 0.05°, 0.025°, and 0.01° resolutions and was compared with the direct downscaling (DD) at three scales. A comparative analysis was conducted in the Daxing'anling Mountains and Xiaoxing'anling Mountains. AGB and corresponding uncertainty maps at three scales were generated using SBSD. The results showed that the efficacy of the XGBoost-based AGB model combined with the random forest-based residual correction model was superior. Spatial patterns in AGB maps generated by SBSD and DD were found to be similar. Notably, SBSD yielded enhanced accuracy in the Daxing'anling Mountains with complex topography, while both performed comparably in the Xiaoxing'anling Mountains with milder topography, highlighting SBSD's advantages in high heterogeneity areas. [ABSTRACT FROM AUTHOR]

This study investigates the dispersion of natural gas leakages in urban environments under varying wind conditions (Beaufort levels 1, 2, and 6) and street layouts, with a focus on the implications for mobile leak detection at a height of 0.3 m above ground. Through numerical simulations, we analyze how urban canyons influence wind field and methane (CH4) concentration distributions, highlighting the impact of wind speed and urban geometry on gas dispersion. The key findings indicate that urban structures significantly affect gas dispersion patterns, with higher wind speeds facilitating better dispersion and reducing the risk of high-concentration gas buildups. The study underscores the need to consider both meteorological conditions and urban design in enhancing gas leak detection and safety measures in cities. The results contribute to improving emergency response strategies and urban planning for mitigating the risks associated with gas leaks. [ABSTRACT FROM AUTHOR]

To address susceptibility to noise interference in Micro-LED displays, a deep convolutional dictionary learning denoising method based on distributed image patches is proposed in this paper. In the preprocessing stage, the entire image is partitioned into locally consistent image patches, and a dictionary is learned based on the non-local self-similar sparse representation of distributed image patches. Subsequently, a convolutional dictionary learning method is employed for global self-similarity matching. Local constraints and global constraints are combined for effective denoising, and the final denoising optimization algorithm is obtained based on the confidence-weighted fusion technique. The experimental results demonstrate that compared with traditional denoising methods, the proposed denoising method effectively restores fine-edge details and contour information in images. Moreover, it exhibits superior performance in terms of PSNR and SSIM. Particularly noteworthy is its performance on the grayscale dataset Set12. When evaluated with Gaussian noise σ = 50 , it outperforms DCDicL by 3.87 dB in the PSNR and 0.0012 in SSIM. [ABSTRACT FROM AUTHOR]

PM2.5 pollution remains a significant concern in China due to its adverse environmental and health implications. This study aims to explore in depth the differences in the causes of PM2.5 pollution between some regions in China based on high temporal resolution PM2.5 component information. We used a particulate matter chemical composition vehicle (PMCCV) as a mobile monitoring platform which travelled among 23 cities in China from March 2018 to December 2019 to collect PM2.5 concentrations and chemical composition data. Observations revealed that PM2.5 concentrations were notably higher in northern cities compared than their southern counterparts. Seasonal variation was evident, with peak concentrations during winter and troughs during summer. In regions experiencing severe winter pollution, such as Hebei and Shanxi (HB/SX), organic matter (OM) emerged as the dominant contributor (47.3%), escalating with increasing PM2.5 concentrations. OM significantly impacted PM2.5 levels during autumn in Jiangxi and Anhui (AH/JX) and across the monitoring period in Liuzhou, Guangxi (GX), with the former related to vehicle emissions and the latter related to bagasse reuse and biomass burning emissions. Conversely, nitrate (NO3−) made the highest contribution to PM2.5 during winter in the AH/JX region (34.4%), which was attributed to reduced SO2 levels and favorable low-temperature conditions conducive to nitrate condensation. Notably, nitrate contribution to HB/SX rose notably in heavily polluted winter conditions and during light–moderate pollution episodes in the autumn. Sulfate (SO42−) was dominant among PM2.5 components during summer in the study regions (29.9% in HB/SX, 36.1% in HN/SD, and 49.7% in AH/JX). Additionally, pollution incidents in Chuzhou, Anhui Province, and Baoding, Hebei Province, underscored nitrates and organic matter, respectively, as the primary causes of sharp PM2.5 increases. These incidents highlighted the influence of large emissions of primary aerosols, gaseous precursors, and stagnant meteorological conditions as pivotal factors driving haze pollution in the HB/SX region. [ABSTRACT FROM AUTHOR]

Transformers have performed better than traditional convolutional neural networks (CNNs) for image super-resolution (SR) reconstruction in recent years. Currently, shifted window multi-head self-attention based on the swin transformer is a typical method. Specifically, the multi-head self-attention is used to extract local features in each window, and then a shifted window strategy is used to discover information interaction between different windows. However, this information interaction method needs to be more efficient and include some global feature information, which limits the model's performance to a certain extent. Furthermore, optimizing the utilization of shallow features, which exhibit significant energy reserves and invaluable low-frequency information, is critical for advancing the efficacy of super-resolution techniques. In order to solve the above issues, we propose the feature-enhanced fused attention (FE-FAIR) method for image super-resolution. Specifically, we design the multi-scale feature extraction module (MSFE) as a shallow feature extraction layer to extract rich low-frequency information from different scales. In addition, we propose the fused attention block (FAB), which introduces channel attention in the form of residual connection based on shifted window self-attention, effectively achieving the fusion of global and local features. Simultaneously, we also discuss other methods to enhance the performance of the FE-FAIR method, such as optimizing the loss function, increasing the window size, and using pre-training strategies. Compared with state-of-the-art SR methods, our proposed method demonstrates better performance. For instance, FE-FAIR outperforms SwinIR by over 0.9 dB when evaluated on the Urban100 (×4) dataset. [ABSTRACT FROM AUTHOR]

MiR399 plays an important role in plant growth and development. The objective of the present study was to elucidate the evolutionary characteristics of the MIR399 gene family in grapevine and investigate its role in stress response. To comprehensively investigate the functions of miR399 in grapevine, nine members of the Vvi-MIR399 family were identified based on the genome, using a miRBase database search, located on four chromosomes (Chr 2, Chr 10, Chr 15, and Chr 16). The lengths of the Vvi-miR399 precursor sequences ranged from 82 to 122 nt and they formed stable stem–loop structures, indicating that they could produce microRNAs (miRNAs). Furthermore, our results suggested that the 2 to 20 nt region of miR399 mature sequences were relatively conserved among family members. Phylogenetic analysis revealed that the Vvi-MIR399 members of dicots (Arabidopsis, tomato, and sweet orange) and monocots (rice and grapevine) could be divided into three clades, and most of the Vvi-MIR399s were closely related to sweet orange in dicots. Promoter analysis of Vvi-MIR399s showed that the majority of the predicted cis-elements were related to stress response. A total of 66.7% (6/9) of the Vvi-MIR399 promoters harbored drought, GA, and SA response elements, and 44.4% (4/9) of the Vvi-MIRR399 promoters also presented elements involved in ABA and MeJA response. The expression trend of Vvi-MIR399s was consistent in different tissues, with the lowest expression level in mature and young fruits and the highest expression level in stems and young leaves. However, nine Vvi-MIR399s and four target genes showed different expression patterns when exposed to low light, high light, heat, cold, drought, and salt stress. Interestingly, a putative target of Vvi-MIR399 targeted multiple genes; for example, seven Vvi-MIR399s simultaneously targeted VIT_213s0067g03280.1. Furthermore, overexpression of Vvi_MIR399e and Vvi_MIR399f in Arabidopsis enhanced tolerance to drought compared with wild-type (WT). In contrast, the survival rate of Vvi_MIR399d-overexpressed plants were zero after drought stress. In conclusion, Vvi-MIR399e and Vvi-MIR399f, which are related to drought tolerance in grapevine, provide candidate genes for future drought resistance breeding. [ABSTRACT FROM AUTHOR]

Dredging hoses are flexible and are particularly suitable for slurry transportations for mud or sand in dredging projects. To achieve sufficient bending stiffness and to prevent the pipe body from collapsing, this type of hose segment is a composite structure that is embedded with several cord reinforcement layers and steel wires in its rubber layer. To quickly evaluate the nonlinear bending mechanical properties of rubber hoses, this study proposes the equivalent stiffness method of linear superposition, which is verified by test data and numerical results. The results show that the equivalent bending stiffness method proposed in this study is in good agreement with numerical and experimental results. Then, by comparing the calculation results of the hose string, it was demonstrated that the linear stiffness superposition method proposed in this study can also accurately predict the bending mechanical behavior characteristics of string hose, and provide reliable guidance for hose design in practice. [ABSTRACT FROM AUTHOR]

The development of the tree ring is a process occurring under limitations caused by a complex of environmental factors and intrinsic regulatory mechanisms. Its understanding is of interest in many scientific fields, but most quantitative models trying to describe its details meet several issues stemming from the difficulty of its verification. This study attempted to combine several observational and modeling approaches to verify intermediate details of the description of xylogenesis, aiming to restore the tree-ring seasonal growth kinetics on the basis of dendrochronological and wood anatomical data. It was carried out for Scots pine in two semiarid habitats in South Siberia. The Vaganov-Shashkin model was used jointly with tree-ring width chronology and climatic data to model the tree radial growth rate with daily precision. The Band-model was then used to calculate the kinetics of tracheid production from the growth rate and actual final number of cells per radial file in the ring. Seasonal observations of cell population and final measurements of cell sizes were used to fit model parameters and verify the numbers of developing tracheids produced by the Band-model. The patterns of modeled seasonal kinetics for six seasons and two sites were found to repeat the actual drought-derived deviations in tree growth and observations (R2 = 0.70–0.84). Further research is required to test other climatic limitations and species-specific ecophysiological mechanisms of growth regulation. [ABSTRACT FROM AUTHOR]

Europium (Eu) in coal and sedimentary rocks has important mineral resource potential as well as being a crucial parameter in geochemical studies that can represent changes in the depositional environment during coal deposition and identify the depositional source region. Therefore, it is essential to realize the precise measurement of Eu in coal as this could be a useful parameter for paleoenvironmental reconstruction studies and the exploration of mineral resources. During inductively coupled plasma mass spectrometry (ICP-MS) analysis, polyatomic ions of Ba may interfere with Eu, causing the observed value to be higher than the actual value. This paper develops a new approach for Eu determination by using a high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS). The mass spectral interference and correction of Eu in the coal and sedimentary rock samples at low, medium, and high resolutions were investigated. The results showed that in the high-resolution mode (resolution = 10,000 amu), the interference of polyatomic ions of Ba could be distinguished from Eu; hence, Eu was determined under this circumstance. Under the optimal experimental circumstances, the detection limit was 0.006 μg/mL, the relative standard deviation was 0.80%–1.22%, and the linear correlation coefficient of the standard curve was over 0.9999. The recoveries of the 103Rh internal standard solution ranged from 94.41% to 100.10%. This method was verified using standard reference materials and selected samples, which demonstrated its high sensitivity, accuracy, and reliability, and a low detection limit, making it appropriate for detecting Eu in samples of coal and sedimentary rocks. [ABSTRACT FROM AUTHOR]

With the development of ultra-long-range visual sensors, the application of unsupervised person re-identification algorithms to them has become increasingly important. However, these algorithms inevitably generate noisy pseudo-labels, which seriously hinder the performance of tasks over a large range. Mixup, a data enhancement technique, has been validated in supervised learning for its generalization to noisy labels. Based on this observation, to our knowledge, this study is the first to explore the impact of the mixup technique on unsupervised person re-identification, which is a downstream task of contrastive learning, in detail. Specifically, mixup was applied in different locations (at the pixel level and feature level) in an unsupervised person re-identification framework to explore its influences on task performance. In addition, based on the richness of the information contained in the person samples to be mixed, we propose an uncertainty-aware mixup (UnA-Mix) method, which reduces the over-learning of simple person samples and avoids the information damage that occurs when information-rich person samples are mixed. The experimental results on three benchmark person re-identification datasets demonstrated the applicability of the proposed method, especially on the MSMT17, where it outperformed state-of-the-art methods by 5.2% and 4.8% in terms of the mAP and rank-1, respectively. [ABSTRACT FROM AUTHOR]

There are considerable reserves of low-grade solid potash resources in the shallow part of Mahai Salt Lake in the Qaidam Basin, and the lithium brine resources resulting from solid–liquid conversion and mining are quite abundant. The comprehensive utilization of these resources is an important and urgent problem. In this study, to fully utilize these resources, the shallow low-grade solid potash ore in Mahai Salt Lake was used for systematic simulated ore dissolution experiments, combined with geochemical and X-ray diffraction analyses. The following key results were obtained: (1) Most Li+ in the Mahai mining area was deposited on the soluble salt minerals in silt or clay, and the appropriate concentration of solvent can help to dissolve more Li+ and K+; (2) the saturation time of Li+ was longer than that of K+. Therefore, the dissolution time for the mine can be appropriately extended during the production process to dissolve more Li+; (3) the solid–liquid conversion aqueous solution mining method can separate the lithium part of clay deposits and is associated with salt rock in the brine, which is a potential lithium resource. These experimental results provide a theoretical basis for salt pan production. [ABSTRACT FROM AUTHOR]

Dysregulated B cell receptor-associated protein 31 (BAP31) plays a crucial role in tumor progression. This study aimed to investigate the functions and molecular mechanism of BAP31 on the miR-206/133b cluster in colorectal cancer (CRC). qPCR was conducted to detect miRNA and mRNA levels in tissues and cells. Western blot assays were used to assess the levels of biomarkers and targets, as well as the levels of BAP31 and HOXD10. Wound healing, coculture and transwell assays were conducted to assess the transendothelial migration abilities of CRC cells. A luciferase assay was employed to assess miRNA binding effects on targets, as well as the initiating transcription effect of genomic fragments. Tumor growth and lung metastatic models were established through an in vivo animal study. BAP31 overexpression in CRC cells led to a reduction in the expression of the miR-206/133b cluster. The expression of the miR-206/133b cluster was correlated with the transendothelial migration capability of CRC cells. The miR-206/133b cluster was found to directly regulate cell division cycle 42 (CDC42) and actin-related protein 2/3 complex subunit 5 (ARPC5) in the tight junction pathway (hsa04530). Moreover, a potential transcription regulator of the miR-206/133b cluster was also found to be Homeobox D10 (HOXD10). We further elucidated the molecular mechanisms and functional mechanisms of BAP31's regulatory role in the expression levels of the miR-206/133b cluster by inhibiting HOXD10 translocation from the cytoplasm to the nucleus. In conclusion, this study provides valuable insights into how BAP31 regulates the transcription of the miR-206/133b cluster and how BAP31-related lung metastases arise in CRC. [ABSTRACT FROM AUTHOR]

Fipronil (FIP), a broad-spectrum phenylpyrazole insecticide, is highly toxic and threatens human health and ecological balance. Developing convenient, rapid, portable analytical technology for on-site and high-frequency testing of FIP is essential to reduce its damage. Herein, a monoclonal antibody (Clone F-3F6) against FIP, with high affinity and specificity, was produced using a novel immunogen, FIP-BSA, which was simply and directly synthesized by conjugating FIP with bovine serum albumin (BSA). Among the previously reported antibodies, F-3F6 acts more specifically against FIP. The FIP metabolites fipronil desulfinyl, fipronil sulfide, and fipronil sulfone showed lower cross-reactivity, and other pesticides were not recognized. To achieve high-frequency and on-site measurements of FIP, an evanescent wave fluorescence biosensor was built by integrating evanescent wave fluorescence technology, a functionalized fiber bioprobe, and a fluorescence-labeled F-3F6 antibody. The detection limit of FIP was 0.032 μg/L. The detection results of real milk and water samples showed that all the coefficients of variation were less than 10%, and the recovery ranged from 90 to 120%. The high reusability and stability of functionalized fiber bioprobe enables the accurate, cost-effective, high-frequency, and facile quantitative detection of FIP. This highly specific and reliable evanescent wave fluorescence biosensor will be well suited to the sensitive and high-frequency on-site analysis of only FIP in food. [ABSTRACT FROM AUTHOR]

Simple indigestion in cows leads to substantial economic losses in the dairy industry. Despite ongoing efforts, an effective treatment for this issue remains elusive. Previous studies have emphasized the vital role of rumen microbes in maintaining ruminant health. To deepen our comprehension of the intricate interplay between rumen microbiota and simple indigestion, we undertook a study involving the analysis of rumen fluid from eight cows with simple indigestion and ten healthy cows. Additionally, we collected data pertaining to milk production, rumination behavior, and rumen characteristics. The results showed that cows with simple indigestion displayed significantly lower milk yield, reduced rumination duration, and weakened rumen contraction when contrasted with the healthy cows (p < 0.05). However, no significant difference in microbiota α-diversity emerged (p > 0.05). Principal coordinate analysis (PCoA) illuminated substantial variations in rumen microbial structure among the two groups (p < 0.05). Further analysis spotlighted distinctive bacteria in the rumen of the cows with indigestion, including Allisonella, Synergistes, Megasphaera, Clostridium_XIVb, Campylobacter, and Acidaminococcus. In contrast, Coraliomargarita, Syntrophococcus, and Coprococcus are the dominant bacterial genera in the rumen of healthy dairy cows. Importantly, these key bacterial genera also dominated the overarching microbial interaction network. The observation suggests that changes in the abundance of these dominant bacterial genera potentially underlie the principal etiology of cows with simple indigestion. The present findings can provide insights into simple indigestion prevention and treatment in dairy cows. [ABSTRACT FROM AUTHOR]

Because the associations between different dietary protein sources and the risks of gestational diabetes mellitus (GDM) are inconsistent, and those of eating habits with GDM have rarely been explored, we aimed to investigate the independent and joint association of major dietary protein sources and eating habits with GDM in a case-control study including 353 GDM cases and 718 controls in China. Dietary protein intake and eating habits prior to GDM diagnosis were collected through questionnaires at 24~28 gestational weeks. Multivariate logistic regression was used to evaluate the independent and joint associations of dietary protein intake and eating habits with GDM. The Anderson model was used assess if there is an additive interaction between them. Animal protein, red meat protein and dairy products protein intake were significantly and positively associated with GDM. Among the eating habits, preferences for hot food, firm food and soft food were significantly associated with higher odds of GDM. Individuals with unhealthy eating habits and high dietary protein simultaneously had the highest odds of GDM, and the ORs were 2.06 (1.25, 3.41) for the total protein, 2.97 (1.78, 4.96) for animal meat, 3.98 (2.41, 6.57) for the red meat protein and 2.82 (1.81, 4.41) for the dairy protein; the p values for the trend were all significant (p < 0.001). However, no additive interaction was detected. In conclusion, our study found that dietary protein intake and eating habits prior to GDM diagnosis were both independently and jointly associated with the odds of GDM. [ABSTRACT FROM AUTHOR]

H-bonding has achieved massive advancements by utilizing an H-bond donor (HBD) to interact with the electron-rich site of the substrate, and an H-bond acceptor (HBA) to coordinate with the electron-deficient site. Rapid transformation is often correlated with the acidity of HBD, namely the degree of charge deficiency of the hydrogen proton. In addition, the positive cations were employed to enhance the HBD; the electron-withdrawing groups were also a dissimilar approach for increasing the capability of the H-bond donor. We first introduced the H-bonding organic ion pair tris(phenylamino)cyclopropenium (TPAC·Cl) into the Friedel–Crafts alkylation of indoles with nitroalkenes, which was implemented via vicinal positive charges on the cyclopropenium core. The counter ion chloride anion became a potential HBA to activate the electron-deficient part of the substrate. X-ray analyses of a single crystal of TPAC·Cl described the 3D architecture and the delocalized cationic charge in the solid state. The aromatic cyclopropenium endowed the N–H moieties with the ability of the H-bond donor to activate the nitroalkene; meanwhile, the chloride anion acted as the H-bond acceptor to activate the indole. The amino-cyclopropenium-offered HBD and HBA displayed cooperative organocatalysis in the Friedel–Crafts alkylation of indole with nitroalkene. A new class of hydrogen bonding catalysis and a working mechanism were proposed. [ABSTRACT FROM AUTHOR]

Increased stemness is causally linked to the development of chemoresistance in cancers. B-cell receptor-associated protein 31 (BAP31) has been identified to play an oncogenic role in many types of cancer. However, the role of BAP31 in 5-fluorouracil (5-FU) chemosensitivity and stemness of colorectal cancer (CRC) is still unknown. The aim of this study was to investigate the biological function and molecular mechanism of BAP31 in regulating 5-FU chemosensitivity and stemness. The correlation between BAP31 expression and 5-FU chemosensitivity was examined using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide and colony formation assays. Cancer stemness was analyzed using tumor sphere formation and Western blot assays. Western blot and immunofluorescence analyses of the knockdown cell lines were performed to explore the possible mechanisms. Finally, we investigated the function of BAP31 by constructing xenograft nude mouse models in vivo. In this study, we demonstrated that BAP31 was increased in CRC cells, and knockdown of BAP31 reduced the half maximal inhibitory concentration (IC50) of 5-FU, while this effect was reversed by overexpression of BAP31. In addition, knockdown of BAP31 substantially reduced the stemness of CRC cells in vitro. Consistently, knockdown of BAP31 significantly suppressed the tumorigenicity and stemness of CRC in vivo. The functional study further suggested that knockdown of BAP31 downregulated galectin-3 to inhibit the accumulation of β-catenin, which in turn repressed the transcription of downstream target genes (c-MYC, SOX2) of the Wnt/β-catenin signaling pathway. Knockdown of BAP31 reduced stemness by inhibiting the Wnt/β-catenin signaling pathway to increase 5-FU chemosensitivity. Importantly, intrabodies against BAP31 suppressed tumor growth and enhanced the antitumor effects of 5-FU in vivo. Therefore, using intrabodies against BAP31 may be a strategy for improving the antitumor effect of 5-FU in CRC. [ABSTRACT FROM AUTHOR]

Despite the numerous treatments for triple-negative breast cancer (TNBC), chemotherapy is still one of the most effective methods. However, the impact of chemotherapy on immune cells is not yet clear. Therefore, this study aims to explore the different roles of immune cells and their relationship with treatment outcomes in the tumor and blood before and after paclitaxel therapy. We analyzed the single-cell sequencing data of immune cells in tumors and blood before and after paclitaxel treatment. We confirmed a high correlation between T cells, innate lymphoid cells (ILCs), and therapeutic efficacy. The differences in T cells were analyzed related to therapeutic outcomes before and after paclitaxel treatment. In the effective treatment group, post-treatment tumor-infiltrating CD8+ T cells were associated with elevated inflammation, cytokines, and Toll-like-receptor-related gene expression, which were expected to enhance anti-tumor capabilities in tumor immune cells. Moreover, we found that the expression of immune-checkpoint-related genes is also correlated with treatment outcomes. In addition, an ILC subgroup, b_ILC1-XCL1, in which the corresponding marker gene XCL1 was highly expressed, was mainly present in the effective treatment group and was also associated with higher patient survival rates. Overall, we found differences in gene expression in T cells across different groups and a correlation between the expression of immune checkpoint genes in T cells, the b_ILC1-XCL1 subgroup, and patient prognosis. [ABSTRACT FROM AUTHOR]

The sky-radiometer/sun-photometer is the most widely used instrument for obtaining aerosol optical depth (AOD) or aerosol optical properties worldwide. Due to the existence of field of view (FOV, 1°), the radiation received by the sky-radiometer includes the forward scattering in addition to direct solar irradiance. This leads to more diffuse light errors of retrieved AODs, especially for shorter wavelength and heavily polluted weather conditions. Using simulation data of three typical aerosol particles (dust, soot, water-soluble), we first verified the accuracy of the Monte Carlo method for calculating the forward scattering effect. Based on the sky-radiometer data collected in Xi'an (2015–2020) where heavy pollution weather is common, the relative errors and correction factors of the AOD were obtained under different conditions, including various short wavelengths (≤400 nm), solar zenith angles (SZAs) and AODs. Our analysis indicates the close dependence of AOD correction factors on wavelength, SZA, AOD and the optical properties of aerosol particles. The mean relative error in Xi'an increases with the decrease of wavelength (~16.1% at 315 nm) and decreases first and then increases with the increase of the SZA. The relative errors caused by forward scattering can exceed 10% when the AOD is greater than 1 and 25% when the AOD is larger than 2 in the ultraviolet (UV) band. The errors with a wavelength greater than 400 nm and an AOD below 1.0 can be within 5%, which can be ignored. The correlation coefficients of AODs before and after a correction from 315 nm to 400 nm are greater than 0.96, which basically increase with the increase of the wavelength. This indicates that the significance of the forward scattering effect in the Xi'an area with heavy pollution cannot be ignored for short wavelengths. However, such effect is negligible at the longer wavelengths and lower AODs (<1.0) of a sky-radiometer. [ABSTRACT FROM AUTHOR]

The measurement of cloud microphysical parameters plays an important role in describing characteristics of liquid phase clouds and investigating mutual relationships between clouds and precipitation. In this paper, cloud microphysical parameters at Liupan Mountain Weather Station in Ningxia are measured with a high-resolution coaxial digital holographic imager and a fog monitor 120. There are differences in the measurement results between the two instruments. The number concentration measured by the digital holographic imager is about 1.5 times that of the fog monitor 120. However, their Pearson correlation coefficient is above 0.9. Through analysis, we found that the measurement results of the digital holographic imager and fog monitor 120 are differences in 2–4 µm and 7–50µm. For the droplets with the diameters of 4–7 µm, their measurement results have good consistency. By analyzing the influence of wind field and detection sensitivity on the measurement principle, the reasons which caused the difference are proposed. Advice is given to observe topographic clouds by using the above two instruments. In addition, the differences in liquid water content and visibility are analyzed due to the absence of small and large droplets. The study provides data support for improving the accuracy of instruments in measuring cloud droplets and is useful for research in the field of cloud microphysical processes. [ABSTRACT FROM AUTHOR]

In today's information society, with the growing integration of the Internet into individuals' lives, problematic Internet use (PIU) among adolescents has become more prevalent. Therefore, we conducted this study to investigate the correlation between active parental mediation and PIU in adolescents, as well as the potential mediating roles of parent–child relationship and adolescents' hiding online behavior. A total of 539 middle school students (mean age = 13.384) were recruited for this study and participated by completing a series of paper-and-pencil questionnaires. The findings indicated a significant negative relationship between active parental mediation and PIU. Furthermore, both the mediating role of the parent–child relationship and the role of hiding online behavior were found to be significant. Specifically, the mediating role is comprised of two paths: the independent mediating role of the parent–child relationship, and the sequential mediating role involving both the parent–child relationship and hiding online behavior. The study contributes an innovative theoretical perspective to deepen the understanding of the formation mechanism of PIU. Moreover, it offers practical empirical insights for the prevention and intervention of PIU among adolescents. [ABSTRACT FROM AUTHOR]

The adverse effects of high temperatures on fruit quality and yield have been reported in many studies of grapevines. MiRNAs play crucial roles in plant growth and development and also fulfill functions in regulating the high-temperature response. In this research, miRNA-based SSR (simple sequence repeat) polymorphisms were analyzed according to the grape genome sequence and then combined with polymerase chain reaction (PCR) amplification and Sanger sequencing methods to analyze the miRNA-SSR diversity of different heat-resistant grape varieties. A statistical analysis of SSR sequences showed that 391 and 12 SSRs were specific to pri-miRNA and pre-miRNA, respectively. In pri-miRNA containing SSR, hexa-nucleotide repeats were the most abundant (52.69%), followed by tetra-nucleotide (13.04%) and minimum penta-nucleotide (4.09%), which were also observed in pre-miRNA sequences. On the other hand, differences in heat resistance among grape varieties were observed. Based on the results of leaf Fv/Fm images, values and phenotypic changes under high-temperature stress, 20 heat-resistant (e.g., Niagara Rosada and Grand Noir) and 20 heat-sensitive (e.g., Shine Muscat and Jumeigui) grape varieties were identified. Further, PCR-Sanger sequencing was used to screen SSR differences in four thermos-tolerant and four thermos-sensitive grape varieties, and finally, eight SSR differential primers were found to be able to distinguish these varieties. Of these, four pairs of primers were selected for validation in 40 grape germplasm resources (20 thermo-tolerance and 20 thermo-sensitivity). The VMIRSSR167c3 highlights that the ratio of SSR (CT) repeat values greater than 17 in heat-resistant varieties was 90%. In conclusion, the VMIRSSR167c3 marker can accurately distinguish between heat-resistant and heat-sensitive grape varieties. The results provide a novel molecular marker for the genetic improvement of grape germplasm resources and will be beneficial to the breeding of heat-resistant varieties in the future. [ABSTRACT FROM AUTHOR]

Callus induction is a key step in establishing plant regeneration and genetic transformation. In this study, we present a comprehensive large-scale investigation of the callus induction rate (CIR) in peach trees, which revealed significant variability within the peach germplasm. Notably, the late-maturing cultivars exhibited significantly higher levels of CIR. Moreover, cultivars characterized as having high CIR exhibited potential for the development of adventitious roots (ARs) during callus induction, and a positive correlation was observed between CIR and the ability to regenerate ARs. However, long-term subculture callus lost root regeneration capacity due to changes in cellular morphology and starch and flavonoid content. Additionally, PpLBD1 was identified as a good candidate gene involved in the regulation of callus adventitious rooting in peach trees. Our results provide an insight into the mechanisms underlying callus induction and adventitious root development and will be helpful for developing regeneration systems in peach trees. [ABSTRACT FROM AUTHOR]

In lower-limb rehabilitation, human action recognition (HAR) technology can be introduced to analyze the surface electromyography (sEMG) signal generated by movements, which can provide an objective and accurate evaluation of the patient's action. To balance the long cycle required for rehabilitation and the inconvenient factors brought by wearing sEMG devices, a portable sEMG signal acquisition device was developed that can be used under daily scenarios. Additionally, a mobile application was developed to meet the demand for real-time monitoring and analysis of sEMG signals. This application can monitor data in real time and has functions such as plotting, filtering, storage, and action capture and recognition. To build the dataset required for the recognition model, six lower-limb motions were developed for rehabilitation (kick, toe off, heel off, toe off and heel up, step back and kick, and full gait). The sEMG segment and action label were combined for training a convolutional neural network (CNN) to achieve high-precision recognition performance for human lower-limb actions (with a maximum accuracy of 97.96% and recognition accuracy for all actions reaching over 97%). The results show that the smartphone-based sEMG analysis system proposed in this paper can provide reliable information for the clinical evaluation of lower-limb rehabilitation. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) serve as covalently closed single-stranded RNAs and have been proposed to influence plant development and stress resistance. Grapevine is one of the most economically valuable fruit crops cultivated worldwide and is threatened by various abiotic stresses. Herein, we reported that a circRNA (Vv-circPTCD1) processed from the second exon of the pentatricopeptide repeat family gene PTCD1 was preferentially expressed in leaves and responded to salt and drought but not heat stress in grapevine. Additionally, the second exon sequence of PTCD1 was highly conserved, but the biogenesis of Vv-circPTCD1 is species-dependent in plants. It was further found that the overexpressed Vv-circPTCD1 can slightly decrease the abundance of the cognate host gene, and the neighboring genes are barely affected in the grapevine callus. Furthermore, we also successfully overexpressed the Vv-circPTCD1 and found that the Vv-circPTCD1 deteriorated the growth during heat, salt, and drought stresses in Arabidopsis. However, the biological effects on grapevine callus were not always consistent with those of Arabidopsis. Interestingly, we found that the transgenic plants of linear counterpart sequence also conferred the same phenotypes as those of circRNA during the three stress conditions, no matter what species it is. Those results imply that although the sequences are conserved, the biogenesis and functions of Vv-circPTCD1 are species-dependent. Our results indicate that the plant circRNA function investigation should be conducted in homologous species, which supports a valuable reference for further plant circRNA studies. [ABSTRACT FROM AUTHOR]

Multi-modal image fusion can provide more image information, which improves the image quality for subsequent image processing tasks. Because the images acquired using photon counting devices always suffer from Poisson noise, this paper proposes a new three-step method based on the fractional-order variational method and data-driven tight frame to solve the problem of multi-modal image fusion for images corrupted by Poisson noise. Thus, this article obtains fused high-quality images while removing Poisson noise. The proposed image fusion model can be solved by the split Bregman algorithm which has significant stability and fast convergence. The numerical results on various modal images show the excellent performance of the proposed three-step method in terms of numerical evaluation metrics and visual quality. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on image fusion with Poisson noise. [ABSTRACT FROM AUTHOR]

In the past few decades, solidification/stabilization (S/S) technology has been put forward for the purpose of improving soil strength and inhibiting contaminant migration in the remediation of heavy metal-contaminated sites. Cement, lime, and fly ash are among the most common and effective binders to treat contaminated soils. During S/S processing, the main interactions that are responsible for improving the soil's behaviors can be summarized as gelification, self-hardening, and aggregation. Currently, precipitation, incorporation, and substitution have been commonly accepted as the predominant immobilization mechanisms for heavy metal ions and have been directly verified by some micro-testing techniques. While replacement of Ca2+/Si4+ in the cementitious products and physical encapsulation remain controversial, which is proposed dependent on the indirect results. Lead and zinc can retard both the initial and final setting times of cement hydration, while chromium can accelerate the initial cement hydration. Though cadmium can shorten the initial setting time, further cement hydration will be inhibited. While for mercury, the interference impact is closely associated with its adapted anion. It should be pointed out that obtaining a better understanding of the remediation mechanism involved in S/S processing will contribute to facilitating technical improvement, further extension, and application. [ABSTRACT FROM AUTHOR]

Although keratin can effectively repair hair fibres and enhance their moisture content and flexibility, it has a relatively low affinity for hair. In this study, the effects of transglutaminase (TGase)—commonly used to catalyse crosslinking of proteins or amino acids—in crosslinking serine and hydrolysed wool keratin to repair damaged hair and protect healthy hair were studied. Treatment with a repair solution containing hydrolysed wool keratin, serine, and TGase improved the physical and chemical properties of damaged hair samples. The alkali solubility of damaged hair samples decreased by 50.53%, fracture stress increased from 1.031 to 1.806 N, and fracture strain increased from 9.51 to 19.88 mm. Fourier transform infrared spectroscopy and X-ray analysis showed that amide bonds increased in damaged hair samples treated with the repair solution and hair crystallinity increased. Differential scanning calorimetry showed that the repair solution improved the thermal stability of damaged hair. After five cycles of washing, the effects of the repair solution were still apparent in damaged hair samples. The enzymatic solution had stronger repair effects than general hair care products and reduced water loss rates in damaged hair samples; repaired hair samples were also softer and brighter. The repair solution was effective in protecting healthy hair samples against chemical damage. The materials used to prepare the repair solution are all bio-based, and treatment with this product is safer and longer lasting. [ABSTRACT FROM AUTHOR]

Legume medicinal plants Astragalus membranaceus are widely used in the world and have very important economic value, ecological value, medicinal value, and ornamental value. The bioengineering technology of medicinal plants is used in the protection of endangered species, the rapid propagation of important resources, detoxification, and the improvement of degraded germplasm. Using bioengineering technology can effectively increase the content of secondary metabolites in A. membranaceus and improve the probability of solving the problem of medicinal plant resource shortage. In this review, we focused on biotechnological research into A. membranaceus, such as the latest advances in tissue culture, including callus, adventitious roots, hairy roots, suspension cells, etc., the metabolic regulation of chemical compounds in A. membranaceus, and the research progress on the synthetic biology of astragalosides, including the biosynthesis pathway of astragalosides, microbial transformation of astragalosides, and metabolic engineering of astragalosides. The review also looks forward to the new development trend of medicinal plant biotechnology, hoping to provide a broader development prospect for the in-depth study of medicinal plants. [ABSTRACT FROM AUTHOR]

Stem-cell-based therapy is very promising for Alzheimer's disease (AD), yet has not become a reality. A critical challenge is the transplantation microenvironment, which impacts the therapeutic effect of stem cells. In AD brains, amyloid-beta (Aβ) peptides and inflammatory cytokines continuously poison the tissue microenvironment, leading to low survival of grafted cells and restricted efficacy. It is necessary to create a growth-supporting microenvironment for transplanted cells. Recent advances in AD studies suggest that the asparaginyl endopeptidase (AEP) is a potential intervention target for modifying pathological changes. We here chose APP/PS1 mice as an AD model and employed pharmacological inhibition of the AEP for one month to improve the brain microenvironment. Thereafter, we transplanted neural stem cells (NSCs) into the hippocampus and maintained therapy for one more month. We found that inhibition of AEPs resulted in a significant decrease of Aβ, TNF-α, IL-6 and IL-1β in their brains. In AD mice receiving NSC transplantation alone, the survival of NSCs was at a low level, while in combination with AEP inhibition pre-treatment the survival rate of engrafted cells was doubled. Within the 2-month treatment period, implantation of NSCs plus pre-inhibition of the AEP significantly enhanced neural plasticity of the hippocampus and rescued cognitive impairment. Neither NSC transplantation alone nor AEP inhibition alone achieved significant efficacy. In conclusion, pharmacological inhibition of the AEP ameliorated brain microenvironment of AD mice, and thus improved the survival and therapeutic efficacy of transplanted stem cells. [ABSTRACT FROM AUTHOR]

The expression of B-cell receptor associated protein 31 (BAP31) is increased in many tumor types, and it is reported to participate in proliferation, migration, and apoptosis. However, the relationship between BAP31 and chemoresistance is uncertain. This study investigated the role of BAP31 in regulating the doxorubicin (Dox) resistance of hepatocellular carcinoma (HCC). The expression of proteins was assessed by Western blotting. The correlation between BAP31 expression and Dox resistance was examined by MTT and colony formation assays. Apoptosis was analyzed by flow cytometry and TdT-mediated dUTP nick end labeling assays. Western blot and immunofluorescence analyses were performed in the knockdown cell lines to explore the possible mechanisms. In this study, BAP31 was strongly expressed, and knockdown of BAP31 increased Dox chemosensitivity in cancer cells. Furthermore, the expression of BAP31 was higher in the Dox-resistant HCC cells than that in their parental cells; knockdown of BAP31 reduced the half maximal inhibitory concentration value and overcame Dox resistance in Dox-resistant HCC cells. In HCC cells, knockdown of BAP31 increased Dox-induced apoptosis and enhanced Dox chemosensitivity in vitro and in vivo. The potential mechanism by which BAP31 increased Dox-induced apoptosis is that BAP31 inhibited survivin expression by promoting FoxO1 nucleus–cytoplasm translocation. Knockdown of BAP31 and survivin had a synergistic effect on Dox chemosensitivity by enhancing the apoptosis of HCC cells. These findings reveal that BAP31 knockdown enhances Dox chemosensitivity through the downregulation of survivin, suggesting that BAP31 is a potential therapeutic target for improving the treatment response of HCC with resistance to Dox. [ABSTRACT FROM AUTHOR]

Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability in modeling dependencies. However, existing methods usually require a pre-defined graph structure, which is not optimal for the downstream task and limits the application scope of GNN. In this paper, we propose adaptive graph-learning convolutional networks (AGLCN) that can automatically infer hidden associations among multi-nodes through a graph-learning module. It simultaneously integrates the temporal and graph convolutional modules to capture temporal and spatial features in the data. Experiments are conducted on real-world multi-node wind speed data from the China Sea. The results show that our model achieves state-of-the-art results in all multi-scale wind speed predictions. Moreover, the learned graph can reveal spatial correlations from a data-driven perspective. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57 × 10 − 5 and 3.2 × 10 − 3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments. [ABSTRACT FROM AUTHOR]

Detailed study of historical drought events in North-Central China (NCC) is important to understand current hydroclimate variability in the background of global warming. Here, we combined 12 published tree-ring chronologies and 12 dryness/wetness indices (DWI) to reconstruct dry and wet climate variability across NCC. These 24 proxy records showed similarly significant responses to warm season (May–June–July–August–September, MJJAS) moisture signals. A new 530-year-long reconstruction of self-calibrating Palmer Drought Severity Index (scPDSI) values for the warm season in NCC was determined using a nested principal component regression (PCR) approach. The new reconstruction shows significant correlations with the instrumental MJJAS scPDSI data across NCC during the period AD 1901–2012. The reconstructed MJJAS scPDSI revealed seven severe dry/wet events from AD 1470 to 2012. The periods AD 1701–1727 and AD 1985–2011 represent the longest dry periods, and the drought during the 1920s is identified as the most severe one over the past 530 years. Our reconstruction shows significant interannual spectral peaks at the frequency domain of 2–7 years, together with relatively weaker decadal frequencies of 16, 24, and 78 years. The results of superposed epoch analysis (SEA) show that extreme North Atlantic Oscillation (NAO) years may modulate drought variability in NCC. [ABSTRACT FROM AUTHOR]

Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB) prediction from multiple satellite data and identify important predictors, but the role of FS in AGB estimation has not received sufficient attention. Here, we aimed to quantify the degree to which FS can benefit forest AGB prediction. To this end, we extracted a series of features from Landsat, Phased Array L-band Synthetic Aperture Radar (PALSAR), and climatic and topographical information, and evaluated the performance of four state-of-the-art FS methods in selecting predictive features and improving the estimation accuracy with selected features. We then proposed an ensemble FS method that takes inro account the stability of an individual FS algorithm with respect to different training datasets used; the heterogeneity or diversity of different FS methods; the correlations between features and forest AGB; and the multicollinearity between the selected features. We further investigated the performance of the proposed stability-heterogeneity-correlation-based ensemble (SHCE) method for AGB estimation. The results showed that selected features by SHCE provided a more accurate prediction of forest AGB than existing state-of-the-art FS methods, with R2 = 0.66 ± 0.01, RMSE = 14.35 ± 0.12 Mg ha−1, MAE = 9.34 ± 0.09 Mg ha−1, and bias = 1.67 ± 0.11 Mg ha−1 at 90 m resolution. Boruta yielded comparable prediction accuracy of forest AGB, but could not identify the importance of features, which led to a slightly greater bias than the proposed SHCE method. SHCE not only ranked selected features by importance but provided feature subsets that enabled accurate AGB prediction. Moreover, SHCE provides a flexible framework to combine FS results, which will be crucial in many scenarios, particularly the wide-area mapping of land-surface parameters from various satellite datasets. [ABSTRACT FROM AUTHOR]

Since its reform and opening up, China's economy has undergone rapid development and has experienced problems such as the overexploitation of resources and the destruction of the ecological environment. To achieve a balance between economic growth and environmental protection and to follow the sustainable development path, China must implement corresponding environmental regulation policies and vigorously encourage enterprises to pursue green technology innovation. In this paper, environmental regulation is divided into command-and-control, market incentive and voluntary participation. Command-and-control environmental regulation is measured using the entropy method and the logarithm of the pollution discharge fee income in each region is used as the measurement index of market-incentive environmental regulations. At the same time, the logarithm of the number of environmental protection proposals planned by the National People's Congress and the number of environmental protection proposals planned by the CPPCC is used as the measurement index of voluntary participation in environmental regulations. Based on a regression equation of the effects of environmental regulations on green technology innovation, this paper uses the two-step system GMM method to analyze the panel data of industrial enterprises larger than a designated size in 30 provinces, autonomous regions and municipalities in China from 2006 to 2017. Moreover, the paper compares the effects of command-and-control, market-incentive and voluntary participatory environmental regulations on green technology innovation. The empirical results show that command-and-control environmental regulations initially have an inverted U-shaped effect on green technology innovation and market incentive and voluntary participatory environmental regulations have a U-shaped effect on green technology innovation. A comparison of the three environmental regulation policies shows that the effect of command-and-control environmental regulation is more significant. [ABSTRACT FROM AUTHOR]

Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications. [ABSTRACT FROM AUTHOR]

Piggery wastewater contains high amounts of feces, carbon, nitrogen, phosphorus, and other contaminants, introducing serious pollution into water, soil, and the atmosphere. Biological treatment technology is widely used in large-scale pig farms because of its high efficiency and economical advantages. In this study, two typical biological treatment systems—a distributed-inflow biological reactor (DBR) and a two-stage anoxic/aerobic (A/O/A/O)—were adopted to treat piggery wastewater to compare the treatment performance, the dissolved organic matter (DOM) composition, and the microbial distribution characteristics. The results show that the A/O/A/O system had better removal performance in terms of chemical oxygen demand (COD) compared to the DBR system, and similarly effective at removing and ammonia nitrogen (NH 4 + -N) and total nitrogen (TN). Using parallel factor analysis of the fluorescence excitation–emission matrix, four DOM components—namely fulvic acid-like/humic-like substances (C1), tyrosine-like substances (C2), humic-like substances (C3), and tryptophan-like substances (C4)—were tracked in piggery wastewater. Protein-like substances were significantly degraded, while humic-like substances were difficult for microorganisms to utilize. The endogenous input and humus characteristics of effluents were enhanced. Bacteroidetes (43.9% and 37.5%) and Proteobacteria (43.1% and 56.7%) are the dominant bacteria in DBR and A/O/A/O systems. The microbial metabolites in DBR and A/O/A/O systems are mainly composed of amino acids, sugars, alcohols, and other small molecules, while those in the municipal sewage treatment plant system is mainly composed of ketones, amines, acids, lipids, and other small molecules. The results of microbial communities and metabolites can help to trace the process of biological systems treating piggery wastewater. [ABSTRACT FROM AUTHOR]

Resuscitation promoting factors (Rpf), a class of proteins secreted by gram-positive bacteria including actinobacteria, promote the resuscitation of dormant bacteria and spore germination. Here, we describe the reconstitution of the resuscitation promoting activity of the Rpf protein from Nocardiopsis halophila CGMCC 4.1195T in vitro and in vivo. The Rpf protein was expressed in the host Escherichia coli BL21 codon plus (DE3) and was confirmed to have a significant resuscitation effect on the viable but non-culturable (VBNC) N. halophila. Subsequently, the rpf gene of N. halophila was knocked out. We found that the growth rate of the mutant strain (Δrpf) was slower than that of the wild strain, and the former produced significantly shorter spores than the wild-type strain. Our results confirmed the activity of the Rpf protein in N. halophila to promote dormant bacteria resuscitation. This study will lay the foundation for the application of the Rpf protein from N. halophila to exploit actinomycetes resources. [ABSTRACT FROM AUTHOR]

3D printing is the most suitable method to manufacture the frame parts of powered ankle-foot prostheses but the compressive strength of the 3D-printed part needs to be ensured. According to the compression test standard ASTM D695, the effect of infill pattern and density, which is transferred to the mass of the standard specimen, on the compressive strength is investigated with a carbon fiber-reinforced nylon material. With the same infill pattern, specimens with more mass have a higher compressive strength. With the same mass, specimens with triangular fill have a higher compressive strength than those with rectangular and gyroid fills. Compared with specimens with a solid fill, specimens with a triangular fill can also provide more compressive strength in a unit mass. According to the results of standard specimens, following the requirement of strength and lightweight, 41% triangular fill is selected to manufacture the supporting part of a powered ankle-foot prosthesis. Under a compressive load of 1225 N, the strain of the assembly of the standard adaptor and the 3D-printed part is 1.32 ± 0.04%, which can meet the requirement of the design. This study can provide evidence for other 3D-printed applications with the requirement of compressive strength. [ABSTRACT FROM AUTHOR]

It is very important to rapidly test the key indicators of water in the field to fully evaluate the quality of the regional water environment. However, a high-resolution measuring device that can generate small currents for low-concentration analytes in water samples is often bulky, complex to operate, and difficult for data sharing. This work introduces a portable multi-channel electrochemical device with a small volume, good interaction, and data-sharing capabilities called PMCED. The PMCED provides an easy-to-operate graphical interactive interface to conveniently set the parameters for cyclic voltammetry or a differential pulse method performed by the four electrode channels. At the same time, the device, with a current sensitivity of 100 nA V−1, was applied to the detection of water samples with high background current and achieved a high-resolution measurement at low current levels. The PMCED uses the Narrow Band Internet of Things (NB-IoT) to meet the needs for uploading data to the cloud in remote areas. The electrochemical signal preprocessing and chemometrics models run in the cloud, and the final results are visualized on a web page, providing a remote access channel for on-site testing results. [ABSTRACT FROM AUTHOR]

Background: Existing studies indicate that some computed tomography perfusion (CTP) parameters may predict hemorrhagic transformation (HT) after acute ischemic stroke (AIS), but there is an inconsistency in the conclusions alongside a lack of comprehensive comparison. Objective: To comprehensively evaluate the predictive value of CTP parameters in HT after AIS. Data sources: A systematical literature review of existing studies was conducted up to 1st October 2022 in six mainstream databases that included original data on the CTP parameters of HT and non-HT groups or on the diagnostic performance of relative cerebral blood flow (rCBF), relative permeability-surface area product (rPS), or relative cerebral blood volume (rCBV) in patients with AIS that completed CTP within 24 h of onset. Data Synthesis: Eighteen observational studies were included. HT and non-HT groups had statistically significant differences in CBF, CBV, PS, rCBF, rCBV, and rPS (p < 0.05 for all). The hierarchical summary receiver operating characteristic (HSROC) revealed that rCBF (area under the curve (AUC) = 0.9), rPS (AUC = 0.89), and rCBV (AUC = 0.85) had moderate diagnostic performances in predicting HT. The pooled sensitivity and specificity of rCBF were 0.85 (95% CI, 0.75–0.91) and 0.83 (95% CI, 0.63–0.94), respectively. Conclusions: rCBF, rPS, and rCBV had moderate diagnostic performances in predicting HT, and rCBF had the best pooled sensitivity and specificity. [ABSTRACT FROM AUTHOR]

The rampant bayberry decline disease has been regarded as related to soil with the long-term plantation bayberry. These parameters, hydrogen, aluminum, other alkali cations, and plant-related nutrients, were measured from the soil around diseased tree roots 10, 20, and 30 years old. The pH significantly declined in topsoil with increasing tree age and rose with increasing depth of the soil layer with an age of 10, 20, and 30 years. The concentration of exchangeable aluminum has risen significantly with the increase of the tree ages in the top soil layer and also in 0 to 40 cm soils layer with ten-year-old trees. In the top soil layer with a depth of 0 to 10 cm, the cation concentrations of Ca2+, Mg2+, and K+ has fallen significantly with the increase of tree ages. A higher concentration of exchangeable aluminum was observed in the soil with trees more seriously affected by the disease and was accompanied with lower concentrations of Ca2+, Mg2+, and K+. The correlation analysis showed that the soil pH is significantly positively related to the concentration of exchangeable Ca2+, total nitrogen, and total phosphorus and negatively to exchangeable aluminum. These findings provided a new insight to mitigate the disease by regulating the soil parameters. [ABSTRACT FROM AUTHOR]

Background: Pancreatic diseases may affect nutritional status, which is one of the important associated factors of bone health. High prevalence of osteoporosis or osteopenia has been reported in patients with pancreatitis. The bone loss in pancreatic neuroendocrine tumors (PNETs) has not been reported. In this study, we showed the prevalence of bone loss and possible associated factors in PNET patients. Methods: A total of 91 PNET patients were included. Bone status was evaluated based on computed tomography (CT) attenuation (Housfield units, HU): >160 HU, normal bone mineral density; osteopenia, 135 HU ≤ CT value ≤ 160 HU; osteoporosis, <135 HU. Associated factors for bone loss were identified by logistic regression analyses. Results: The average age was 55.76 years old in PNET patients. The prevalence of osteoporosis and low bone mass was 37.4% and 60.4%, respectively. Higher prevalence of osteoporosis was observed in patients older than 50 years (64.0%). Multivariate logistic analysis showed that age was an associated factor for low bone mass (odds ratio (OR) = 1.13, 95% confidence interval (CI): 1.04–1.22) and osteoporosis (OR = 1.14, 95% CI: 1.03–1.20). Diabetes was also associated with bone loss in PNET patients after adjusting with confounders (OR = 13.56, 95% CI: 1.02–132.4). Conclusions: Our data show that bone loss is common in patients with PNETs. Age and diabetes are associated with bone loss in PNET patients. [ABSTRACT FROM AUTHOR]