Search

Utilizing big data and artificial intelligence technologies, we developed the Collaborative Structure Search Framework (CSSF) algorithm to analyze students' learning paths from real-world data to determine the optimal sequence of learning knowledge components. This study enhances sustainability and balance in education by identifying students' learning paths. This allows teachers and intelligent systems to understand students' strengths and weaknesses, thereby providing personalized teaching plans and improving educational outcomes. Identifying causal relationships within knowledge structures helps teachers pinpoint and address learning issues, forming the basis for adaptive learning systems. Using real educational datasets, the research introduces a multi-sub-population collaborative search mechanism to enhance search efficiency by maintaining individual-level superiority, population-level diversity, and solution-set simplicity across sub-populations. A bidirectional feedback mechanism is implemented to discern high-quality and low-quality edges within the knowledge graph. Oversampling high-quality edges and undersampling low-quality edges address optimization challenges in Learning Path Recognition (LPR) due to edge sparsity. The proposed Collaborative Structural Search Framework (CSSF) effectively uncovers relationships within knowledge structures. Experimental validations on real-world datasets show CSSF's effectiveness, with a 14.41% improvement in F1-score over benchmark algorithms on a dataset of 116 knowledge structures. The algorithm helps teachers identify the root causes of students' errors, enabling more effective educational strategies, thus enhancing educational quality and learning outcomes. Intelligent education systems can better adapt to individual student needs, providing personalized learning resources, facilitating a positive learning cycle, and promoting sustainable education development. [ABSTRACT FROM AUTHOR]

Malus baccata, a valuable germplasm resource in the genus Malus, is indigenous to China and widely distributed. However, little is known about the lineage composition and genetic basis of 'ZA', a mutant type of M. baccata. In this study, we compared the differences between 'ZA' and wild type from the perspective of morphology and ultrastructure and analyzed their chloroplast pigment content based on biochemical methods. Further, the complete mitogenome of M. baccata 'ZA' was assembled and obtained by next-generation sequencing. Subsequently, its molecular characteristics were analyzed using Geneious, MISA-web, and CodonW toolkits. Furthermore, by examining 106 Malus germplasms and 42 Rosaceae species, we deduced and elucidated the evolutionary position of M. baccata 'ZA', as well as interspecific variations among different individuals. In comparison, the total length of the 'ZA' mitogenome (GC content: 45.4%) is 374,023 bp, which is approximately 2.33 times larger than the size (160,202 bp) of the plastome (GC: 36.5%). The collinear analysis results revealed abundant repeats and genome rearrangements occurring between different Malus species. Additionally, we identified 14 plastid-driven fragment transfer events. A total of 54 genes have been annotated in the 'ZA' mitogenome, including 35 protein-coding genes, 16 tRNAs, and three rRNAs. By calculating nucleotide polymorphisms and selection pressure for 24 shared core mitochondrial CDSs from 42 Rosaceae species (including 'ZA'), we observed that the nad3 gene exhibited minimal variation, while nad4L appeared to be evolving rapidly. Population genetics analysis detected a total of 1578 high-quality variants (1424 SNPs, 60 insertions, and 94 deletions; variation rate: 1/237) among samples from 106 Malus individuals. Furthermore, by constructing phylogenetic trees based on both Malus and Rosaceae taxa datasets, it was preliminarily demonstrated that 'ZA' is closely related to M. baccata, M. sieversii, and other proximate species in terms of evolution. The sequencing data obtained in this study, along with our findings, contribute to expanding the mitogenomic resources available for Rosaceae research. They also hold reference significance for molecular identification studies as well as conservation and breeding efforts focused on excellent germplasms. [ABSTRACT FROM AUTHOR]

An excess of selenium (Se) can cause toxicity to plants. Treatment with melatonin (MT) can alleviate the stress conditions in plants. Limited research exists on the impact of MT on Se stress and uptake in fruit trees. To address Se stress and enhance Se accumulation in Cyphomandra betacea Sendt. (Solanum betaceum Cav.), the effects of exogenous MT (50–200 µmol L−1) on C. betacea growth and Se accumulation under Se stress were studied. MT increased the biomass, photosynthetic pigments levels, and peroxidase activity of C. betacea under Se stress. Only at concentrations of 150 and 200 µmol L−1 did MT increase superoxide dismutase and catalase activities, as well as soluble protein content in C. betacea leaves. MT treatment also boosted the Se content and bioconcentration factor of C. betacea under Se stress while reducing the translocation factor. The dose of MT was directly related to the Se content, and the highest levels of Se in roots and shoots were observed at 150 µmol L−1, which was an increase of 114% and 60%, respectively, compared with the control. In addition, correlation, principal component, cluster, grey, and path analyses revealed a strong correlation between root Se content and shoot biomass with the shoot Se content. Therefore, MT treatment can mitigate Se stress and enhance Se uptake in C. betacea, and the most effective dose of MT is 150 µmol L−1. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images. [ABSTRACT FROM AUTHOR]

The rapid mutation of SARS-CoV-2 has led to multiple rounds of large-scale breakthrough infection and reinfection worldwide. However, the dynamic changes of humoral and cellular immunity responses to several subvariants after infection remain unclear. In our study, a 6-month longitudinal immune response evaluation was conducted on 118 sera and 50 PBMC samples from 49 healthy individuals who experienced BA.5/BF.7/XBB breakthrough infection or BA.5/BF.7-XBB reinfection. By studying antibody response, memory B cell, and IFN-γ secreting CD4+/CD8+ T cell response to several SARS-CoV-2 variants, we observed that each component of immune response exhibited distinct kinetics. Either BA.5/BF.7/XBB breakthrough infection or BA.5/BF.7-XBB reinfection induces relatively high level of binding and neutralizing antibody titers against Omicron subvariants at an early time point, which rapidly decreases over time. Most of the individuals at 6 months post-breakthrough infection completely lost their neutralizing activities against BQ.1.1, CH.1.1, BA.2.86, JN.1 and XBB subvariants. Individuals with BA.5/BF.7-XBB reinfection exhibit immune imprinting shifting and recall pre-existing BA.5/BF.7 neutralization antibodies. In the BA.5 breakthrough infection group, the frequency of BA.5 and XBB.1.16-RBD specific memory B cells, resting memory B cells, and intermediate memory B cells gradually increased over time. On the other hand, the frequency of IFN-γ secreting CD4+/CD8+ T cells induced by WT/BA.5/XBB.1.16 spike trimer remains stable over time. Overall, our research indicates that individuals with breakthrough infection have rapidly declining antibody levels but have a relatively stable cellular immunity that can provide some degree of protection from future exposure to new antigens. [ABSTRACT FROM AUTHOR]

Multiple natural frequencies may be encountered when analyzing the essential natural vibration of a symmetric mechanical system or sub-structure system or a system with special parameters. The transfer matrix method (TMM) is a useful tool for analyzing the natural vibration characteristics of mechanical or structural systems. It derives a nonlinear eigen-problem (NEP) in general, even a transcendental eigen-problem. This investigation addresses the NEP in TMM and proposes a novel method, called the determinant-differentiation-based method, for calculating multiple natural frequencies and determining their multiplicities. Firstly, the characteristic determinant is differentiated with respect to frequency, transforming the even multiple natural frequencies into the odd multiple zeros of the differentiation of the characteristic determinant. The odd multiple zeros of the first derivative of the characteristic determinant and the odd multiple natural frequencies can be obtained using the bisection method. Among the odd multiple zeros, the even multiple natural frequencies are picked out by the proposed judgment criteria. Then, the natural frequency multiplicities are determined by the higher-order derivatives of the characteristic determinant. Finally, several numerical simulations including the multiple natural frequencies show that the proposed method can effectively calculate the multiple natural frequencies and determine their multiplicities. [ABSTRACT FROM AUTHOR]

Atmospheric water harvesting (AWH) is considered a promising strategy for sustainable freshwater production in landlocked and arid regions. Hygroscopic salt-based composite sorbents have attracted widespread attention for their water harvesting performance, but suffer from aggregation and leakage issues due to the salting-out effect. In this study, we synthesized a PML hydrogel composite by incorporating zwitterionic hydrogel (PDMAPS) and MIL-101(Cr) as a host for LiCl. The PML hydrogel was characterized using various techniques including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FTIR), and thermogravimetric analysis (TGA). The swelling properties and water vapor adsorption-desorption properties of the PML hydrogel were also assessed. The results demonstrate that the MIL-101(Cr) was uniformly embedded into PDMAP hydrogel, and the PML hydrogel exhibits a swelling ratio of 2.29 due to the salting-in behavior. The PML hydrogel exhibited exceptional water vapor sorption capacity of 0.614 g/g at 298 K, RH = 40% and 1.827 g/g at 298 K, RH = 90%. It reached 80% of its saturated adsorption capacity within 117 and 149 min at 298 K, RH = 30% and 90%, respectively. Additionally, the PML hydrogel showed excellent reversibility in terms of water vapor adsorption after ten consecutive cycles of adsorption-desorption. The remarkable adsorption capacity, favorable adsorption-desorption rate, and regeneration stability make the PML hydrogel a potential candidate for AWH. This polymer-MOF synergistic strategy for immobilization of LiCl in this work offers new insights into designing advanced materials for AWH. [ABSTRACT FROM AUTHOR]

The GLK gene family plays a crucial role in the regulation of chloroplast development and participates in chlorophyll synthesis. However, the precise mechanism by which GLK contributes to citrus's chlorophyll synthesis remains elusive. The GLK gene family causes variations in the photosynthetic capacity and chlorophyll synthesis of different citrus varieties. In this study, we identified tissue-specific members and the key CcGLKs involved in chlorophyll synthesis. A total of thirty CcGLK transcription factors (TFs) were discovered in the citrus genome, distributed across all nine chromosomes. The low occurrence of gene tandem duplication events and intronic variability suggests that intronic variation may be the primary mode of evolution for CcGLK TFs. Tissue-specific expression patterns were observed for various GLK family members; for instance, CcGLK12 and CcGLK15 were specifically expressed in the skin, while CcGLK30 was specific to the ovary, and CcGLK10, CcGLK6, CcGLK21, CcGLK2, CcGLK18, CcGLK9, CcGLK28, and CcGLK8 were specifically expressed in the leaves. CcGLK4, CcGLK5, CcGLK11, CcGLK23, CcGLKl7, CcGLK26, and CcGLK20 may participate in the regulation of the ALA, prochlorophylate, protoporphyrin IX, Mg-protoporphyrin IX, Chl b, T-Chl, MG-ProtoIX ME, and POR contents in citrus. [ABSTRACT FROM AUTHOR]

Excessive settlement of the subgrade seriously reduces the service quality of slab tracks and threatens trains' running safety. While the utilization of foamed polyurethane is recognized as an effective solution, previous research on its expansion mechanism and its impact on track lifting requires further refinement. Accordingly, a series of full-scale tests, including expansion force tests on foamed polyurethane with diverse qualities and lifting tests of polyurethane grouting with varied qualities on the track structure, have been conducted. The expansion development process of foamed polyurethane is meticulously elucidated, and key expansion parameters are analyzed. Simultaneously, this research explores the lifting behavior of foamed polyurethane grouting under the slab tracks, yielding new insights into essential lifting parameters for track formation repair and maintenance. Based on the experimental data, this study proposes new empirical formulas to comprehensively describe both the expansion mechanism of foam polyurethane and its lifting behavior under the slab tracks. The outcomes of this research offer a new breakthrough for the design of lifting mechanism for maintaining slab track structures through the utilization of foam polyurethane slurry grouting, such as determining the optimal grouting quantity. In addition, these results are instrumental to the evaluation of lifting effects and service life, enhancing the circular economy of railway track systems. [ABSTRACT FROM AUTHOR]

Phosphorylation site prediction has important application value in the field of bioinformatics. It can act as an important reference and help with protein function research, protein structure research, and drug discovery. So, it is of great significance to propose scientific and effective calculation methods to accurately predict phosphorylation sites. In this study, we propose a new method, Attenphos, based on the self-attention mechanism for predicting general phosphorylation sites in proteins. The method not only captures the long-range dependence information of proteins but also better represents the correlation between amino acids through feature vector encoding transformation. Attenphos takes advantage of the one-dimensional convolutional layer to reduce the number of model parameters, improve model efficiency and prediction accuracy, and enhance model generalization. Comparisons between our method and existing state-of-the-art prediction tools were made using balanced datasets from human proteins and unbalanced datasets from mouse proteins. We performed prediction comparisons using independent test sets. The results showed that Attenphos demonstrated the best overall performance in the prediction of Serine (S), Threonine (T), and Tyrosine (Y) sites on both balanced and unbalanced datasets. Compared to current state-of-the-art methods, Attenphos has significantly higher prediction accuracy. This proves the potential of Attenphos in accelerating the identification and functional analysis of protein phosphorylation sites and provides new tools and ideas for biological research and drug discovery. [ABSTRACT FROM AUTHOR]

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam's substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage. [ABSTRACT FROM AUTHOR]

Simple Summary: Bovine IgG1 Fc receptor (boFcγRIII) is a homologue to human FcγRIII (CD16) that has two extracellular Ig-like domains and can bind bovine IgG1 with medium–low affinity. We identified the Fc-binding site as well as its key residues for IgG1 binding using synthetic peptides, which is located in the second extracellular domain of boFcγRIII. It provides new insights for the IgG–Fcγ interaction and FcγR-targeting drugs development. The bovine IgG1 Fc receptor (boFcγRIII) is a homologue to human FcγRIII (CD16) that binds bovine IgGI with medium–low affinity. In order to identify the Fc-binding site on the bovine IgG1 Fc receptor (boFcγRIII), peptides derived from the second extracellular domain (EC2) of boFcγRIII were synthesized and conjugated with the carrier protein. With a Dot-blot assay, the ability of the peptides to bind bovine IgG1 was determined, and the IgG1-binding peptide was also identified via truncation and mutation. The minimal peptide AQRVVN corresponding to the sequence 98–103 of boFcγRIII bound bovine IgG1 in Dot-blot, suggesting that it represents a linear ligand-binding site located in the putative A–B loop of the boFcγRIII EC2 domain. Mutation analysis of the peptide showed that the residues of Ala98, Gln99, Val101, Val102 and Asn103 within the Fc-binding site are critical for IgG1 binding on boFcγRIII. The functional peptide identified in this paper is of great value to the IgG–Fc interaction study and FcR-targeting drug development. [ABSTRACT FROM AUTHOR]

Precision modeling of radar signal behavior in maritime environments holds paramount importance in ensuring the robust functionality of maritime radar systems. This work delves into the intricate dynamics of radar signal propagation in maritime environments, with a particular focus on the effects of falling rain. A theoretical model encompassing raindrop scattering, gaseous absorption, and ocean surface backscattering was developed and validated. Key findings reveal that rain significantly alters radar backscattering, with a noticeable decrease in signal strength under higher rainrates. Additionally, gaseous absorption, particularly at elevated frequencies and humidity levels, emerged as a critical factor. The study also highlights the complex interplay between wind-induced ocean surface roughness and radar signal behavior. We think these insights are pivotal for enhancing maritime radar system accuracy, particularly in adverse weather conditions, and paving the way for future research in refining environmental impact models on radar signals. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the preventive effects of polysaccharides extracted from the roots of Arctium lappa (ALP) against acute lung injury (ALI) models induced by lipopolysaccharide (LPS). The polysaccharides were extracted and characterized, and their anti-inflammatory and antioxidant capacities were assessed. The findings demonstrated that ALP could mitigate the infiltration of inflammatory cells and reduce alveolar collapse in LPS-induced ALI in mice. The expression levels of the pro-inflammatory factor TNF-α decreased, while the anti-inflammatory factor IL-10 increased. Furthermore, the administration of ALP improved the activities of lung antioxidant enzymes, including SOD, GSH, and CAT, and lowered MDA levels. These results suggest that ALP exhibits a preventive effect on ALI and has potential as an alternative treatment for lung injury. [ABSTRACT FROM AUTHOR]

In bacteria, the Rho protein mediates Rho-dependent termination (RDT) by identifying a non-specific cytosine-rich Rho utilization site on the newly synthesized RNA. As a result of RDT, downstream RNA transcription is reduced. Due to the bias in reverse transcription and PCR amplification, we could not identify the RDT site by directly measuring the amount of mRNA upstream and downstream of RDT sites. To overcome this difficulty, we employed a 77 bp reporter gene argX, (coding tRNAarg) from Brevibacterium albidum, and we transcriptionally fused it to the sequences to be assayed. We constructed a series of plasmids by combining a segment of the galactose (gal) operon sequences, both with and without the RDT regions at the ends of cistrons (galE, galT, and galM) upstream of argX. The RNA polymerase will transcribe the gal operon sequence and argX unless it encounters the RDT encoded by the inserted sequence. Since the quantitative real-time PCR (qRT-PCR) method detects the steady state following mRNA synthesis and degradation, we observed that tRNAarg is degraded at the same rate in these transcriptional fusion plasmids. Therefore, the amount of tRNAarg can directly reflect the mRNA synthesis. Using this approach, we were able to effectively assay the RDTs and Rho-independent termination (RIT) in the gal operon by quantifying the relative amount of tRNAarg using qRT-PCR analyses. The resultant RDT% for galET, galTK, and at the end of galM were 36, 26, and 63, individually. The resultant RIT% at the end of the gal operon is 33%. Our findings demonstrate that combining tRNAarg with qRT-PCR can directly measure RIT, RDT, or any other signal that attenuates transcription efficiencies in vivo, making it a useful tool for gene expression research. [ABSTRACT FROM AUTHOR]

Protein dephosphorylation is the process of removing phosphate groups from protein molecules, which plays a vital role in regulating various cellular processes and intricate protein signaling networks. The identification and prediction of dephosphorylation sites are crucial for this process. Previously, there was a lack of effective deep learning models for predicting these sites, often resulting in suboptimal outcomes. In this study, we introduce a deep learning framework known as "DephosNet", which leverages transfer learning to enhance dephosphorylation site prediction. DephosNet employs dual-window sequential inputs that are embedded and subsequently processed through a series of network architectures, including ResBlock, Multi-Head Attention, and BiGRU layers. It generates predictions for both dephosphorylation and phosphorylation site probabilities. DephosNet is pre-trained on a phosphorylation dataset and then fine-tuned on the parameters with a dephosphorylation dataset. Notably, transfer learning significantly enhances DephosNet's performance on the same dataset. Experimental results demonstrate that, when compared with other state-of-the-art models, DephosNet outperforms them on both the independent test sets for phosphorylation and dephosphorylation. [ABSTRACT FROM AUTHOR]

Formation overpressure is an important controlling factor for the formation of high-yield oil reservoirs in the shale series of the Permian Fengcheng Formation in the west and south of Mahu Sag, the Junggar Basin. Hydrocarbon-generating pressurization (HGP) is an important cause of overpressure in the shale series of Fengcheng Formation, but the evidence for this viewpoint is insufficient. There is still no systematic study on the quantitative calculation and distribution characteristics of HGP in the Fengcheng Formation shale series. The control effect of HGP on the formation of high-pressure and high-yield reservoirs is still unclear. Therefore, by using the data of mudstone logging and measured formation pressure, the causes of overpressure in the Fengcheng Formation shale series are clarified. The predicted organic matter abundance, the predicted maturity and thickness of source rocks, and the statistical ratios of mudstone thickness to formation thickness in each section of Fengcheng Formation are used for HGP of shale series. Combined with the physical characteristics of reservoir rocks and the geochemical characteristics of oil, the control effect of HGP on the formation of high-pressure and high-yield reservoirs is analyzed. The results indicate the following: (1) The organic matter abundance and the thickness of source rocks predicted by the logging data gradually decrease from the eastern lake basin area to the western slope area. (2) The HGP of shale series in Fengcheng Formation is related to the hydrocarbon generation capacity and the overpressure preservation conditions of the source rocks. The HGP can be quantitatively predicted by comprehensively using the organic matter abundance, the maturity, the thickness of source rocks, and the ratios of mudstone thickness to formation thickness. (3) The HGP in the Fengcheng Formation shale series also gradually decreases in distribution characteristic from the eastern lake basin area to the western slope area. (4) The oil accumulation mode of the Fengcheng Formation shale series is that, with the drive of overpressure, the oil migrates slightly within the layer, and finally accumulates to form the oil reservoir. The research results are helpful to understand the distribution characteristics of HGP and the formation mechanism of high-pressure and high-yield reservoirs in the shale series of Fengcheng Formation in the west and south of Mahu Sag, and are of great significance to guide the exploration and development of shale oil and tight oil. [ABSTRACT FROM AUTHOR]

Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective. [ABSTRACT FROM AUTHOR]

Loss of quality in citrus fruit is a common occurrence during postharvest storage due to oxidative stress and energy consumption. In recent years, glycine betaine (GB) has been widely applied to postharvest horticulture fruit. This study aimed to investigate the effect of GB treatment (10 mM and 20 mM) on the quality and antioxidant activity of 'Huangguogan' fruit during postharvest storage at room temperature. Our results indicated that both 10 mM and 20 mM treatments effectively reduced weight and firmness losses and maintained total soluble solid (TSS), titratable acidity (TA), and ascorbic acid contents. Additionally, GB treatment significantly increased the activity of antioxidant enzymes, maintained higher levels of total phenols and total flavonoids, and led to slower accumulation of H2O2. A transcriptome analysis conducted at 28 days after treatment (DAT)identified 391 differentially expressed genes (DEGs) between 20 mM GB (GB-2) and the control (CK) group. These DEGs were enriched in various pathways, particularly related to oxygen oxidoreductase, peroxidase activity, and flavonoid biosynthesis. Overall, the application of GB proved beneficial in enhancing the storability and extending the shelf life of 'Huangguogan' fruit. [ABSTRACT FROM AUTHOR]

Evidence of antibody-dependent enhancement (ADE) of other viruses has raised concerns about the safety of SARS-CoV-2 vaccines and antibody therapeutics. In vitro studies have shown ADE of SARS-CoV-2 infection. In this study, we also found that vaccination/convalescent sera and some approved monoclonal antibodies can enhance SARS-CoV-2 infection of FcR-expressing B cells in vitro. However, the enhancement of SARS-CoV-2 infection can be prevented by blocking Fc–FcR interaction through the addition of human serum/IgG or the introduction of mutations in the Fc portion of the antibody. It should be noted that ADE activity observed on FcR-expressing cells in vitro may not necessarily reflect the situation in vivo; therefore, animal and clinical data should be included for ADE evaluation. [ABSTRACT FROM AUTHOR]

Separating light hydrocarbons (C2H6, C3H8, and C4H10) from CH4 is challenging but important for natural gas upgrading. A microporous metal-organic framework, Zn(bdc)(ted)0.5, based on terephthalic acid (bdc) and 1,4-diazabicyclo[2.2.2]octane (ted) ligands, is synthesized and characterized through various techniques, including powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and porosity analysis. The adsorption isotherms of light hydrocarbons on the material are measured and the isosteric adsorption heats of CH4, C2H6, C3H8, and C4H10 are calculated. The prediction of C2–4/C1 adsorption selectivities is accomplished using ideal adsorbed solution theory (IAST). The results indicate that the material exhibits exceptional characteristics, including a Brunauer-Emmett-Teller (BET) surface area of 1904 m2/g and a pore volume of 0.73 cm3/g. Notably, the material demonstrates remarkable C2H6 adsorption capacities (4.9 mmol/g), while CH4 uptake remains minimal at 0.4 mmol/g at 298 K and 100 kPa. These findings surpass those of most reported MOFs, highlighting the material's outstanding performance. The isosteric adsorption heats of C2H6, C3H8, and C4H10 on the Zn(bdc)(ted)0.5 are higher than CH4, suggesting a stronger interaction between C2H6, C3H8, and C4H10 molecules and Zn(bdc)(ted)0.5. The molecular simulation reveals that Zn(bdc)(ted)0.5 prefers to adsorb hydrocarbon molecules with richer C-H bonds and larger polarizability, which results in a stronger dispersion force generated by an adsorbent-adsorbate induced polarization effect. Therefore, the selectivity of C4H10/CH4 is up to 180 at 100 kPa, C3H8/CH4 selectivity is 67, and the selectivity of C2H6/CH4 is 13, showing a great potential for separating C2–4 over methane. [ABSTRACT FROM AUTHOR]

Simple Summary: The yak is a unique species found on the Tibetan plateau, adapted to the high-altitude, low-oxygen environment, providing meat, milk, and other resources to local herders, and is an indispensable means of production and livelihood for herders on the Tibetan plateau and a major source of economic income for Tibetan herders. A change in diet leads to indigestion and anorexia during the acclimatization phase, which can lead to changes in dry matter intake and daily weight gain in the early part of the housing period. The rumen microbiota will be reshaped. Ruminal fluid transplantation can reshape the rumen microbiota of the recipient animals, which can rapidly restore the microbiota to a new homeostasis. Considering the special characteristics of yaks on the Tibetan plateau, the dynamic seasonal changes in pasture and the trend of changing from natural grazing to confinement farming, this study focused on the changes in rumen flora due to dietary changes and the improvement of rumen fluid transplantation on the remodeling of yak flora and production performance when yak breeding patterns were changed. A relatively stable microbial ecological balance system in the rumen plays an important role in rumen environment stability and ruminant health maintenance. No studies have reported how rumen fluid transplantation (RFT) affects the composition of rumen microorganisms and yak growth performance. In this experiment, we transplanted fresh rumen fluid adapted to house-feeding yaks to yaks transitioned from natural pastures to house-feeding periods to investigate the effects of rumen fluid transplantation on rumen microbial community regulation and production performance. Twenty yaks were randomly divided into the control group (CON; n = 10) and the rumen fluid transplantation group (RT; n = 10). Ten yaks that had been adapted to stall fattening feed in one month were selected as the rumen fluid donor group to provide fresh rumen fluid. Ruminal fluid transplantation trials were conducted on the 1st, 3rd, and 5th. Overall, 1 L of ruminal fluid was transplanted to each yak in the RT and CON group. The formal trial then began with both groups fed the same diet. After this, growth performance was measured, rumen fluid was collected, and rumen microbial composition was compared using 16s rRNA sequencing data. The results showed that rumen fluid transplantation had no significant effect on yak total weight gain or daily weight gain (p > 0.05), and feed efficiency was higher in the RT group than in the CON group at 3 months (treatment × month: p < 0.01). Ruminal fluid transplantation significantly affected rumen alpha diversity (p < 0.05). Up to day 60, the RT group had significantly higher OTU numbers, Shannon diversity, and Simpson homogeneity than the CON group. Principal coordinate analysis showed that the rumen microbiota differed significantly on days 4 and 7 (p < 0.05). Bacteroidota, Firmicutes, Proteobacteria, and Spirochaetes were the most abundant phyla in the rumen. The relative abundances of Bacteroidota, Proteobacteria, and Spirochaetes were lower in the RT group than in the CON group, with a decrease observed in Bacteroidota in the RT group on days 7 and 28 after rumen fluid transplantation (p = 0.013), while Proteobacteria showed a decreasing trend in the CON group and an increasing trend in RT; however, this was only at day 4 (p = 0.019). The relative abundance of Firmicutes was significantly higher in the RT group than in the CON group on days 4, 7, and 28 (p = 0.001). Prevotella and Rikenellaceae_RC9_gut_group were the predominant genera. In conclusion, our findings suggest that rumen fluid transplantation improves yak growth performance and rumen microbial reshaping. The findings of this study provide new insights into yak microbial community transplantation and a reference for improving feed efficiency in the yak industry. [ABSTRACT FROM AUTHOR]

Flavonoids are one of the most essential compounds in various plants. This study used the cellulase-assisted method to extract flavonoid-enriched antioxidants from Equisetum arvense L. (EAL). According to a three-factor and three-level central composite design, the response surface methodology was used to obtain the maximum total flavonoid yield, thereby providing the most potent antioxidant activity of EAL extracts. The results indicated that the optimal condition for the total flavonoid extraction was 0.52% (base substrate) enzyme concentration and enzymolysis for 50.58 min at 49.03 °C. The extraction rate of flavonoids from horsetail reached 4.88 mg/g under these optimal conditions. The yield was 51.23% higher than that from the traditional solid–liquid extraction method. The highest DPPH-scavenging ability of the extracts obtained under 0.50% enzyme concentration and enzymolysis for 49.41 min at 46.59 °C was 77.36%. HPLC results revealed that the antioxidant substances had diverse flavonoids. Thus, the extraction condition was consistent for antioxidant activity and total flavonoids. Therefore, the current study provided an efficient method to extract flavonoid compounds from EAL, facilitating technical support for utilizing the plant. [ABSTRACT FROM AUTHOR]

Malus baccata (L.) Borkh. is an important wild species of Malus. Its rich variation types and population history are not well understood. Chloroplast genome mining plays an active role in germplasm identification and genetic evolution. In this study, by assembly and annotation, six complete cp genome sequences, ranging in size from 160,083 to 160,295 bp, were obtained. The GC content of stable IR regions (42.7%) was significantly higher than that of full length (36.5%) and SC regions (LSC-34.2%, SSC-30.4%). Compared with other Malus species, it was found that there were more sites of polymorphisms and hotspots of variation in LSC and SSC regions, with high variation sites including trnR/UCU-atpA, trnT/UGU-trnL/UAA, ndhF-rpl32 and ccsA-ndhD. The intraspecific and interspecific collinearity was good, and no structural rearrangement was observed. A large number of repeating elements and different boundary expansions may be involved in shaping the cp genome size. Up to 77 or 78 coding genes were annotated in the cp genomes of M. baccata, and high frequency codons such as UUA (Leu), GCU (Ala) and AGA (Arg) were identified by relative synonymous codon usage analysis. Phylogeographic analysis showed that 12 individuals of M. baccata clustered into three different groups with complex structure, whereas variant xiaojinensis (M.H. Cheng & N.G. Jiang) was not closely related to M. baccata evolutionarily. The phylogenetic analysis suggested that two main clades of different M. baccata in the genus Malus were formed and that I and II diverged about 9.7 MYA. In conclusion, through cp genome assembly and comparison, the interspecific relationships and molecular variations of M. baccata were further elucidated, and the results of this study provide valuable information for the phylogenetic evolution and germplasm conservation of M. baccata and Malus. [ABSTRACT FROM AUTHOR]

Delayed judgment of learning (JOL) is a widely used metacognitive monitoring strategy that can also enhance learning outcomes. However, the potential benefits of delayed JOL on subsequent learning of new material, known as the forward effect of delayed JOL, and its stability and underlying mechanisms have yet to be fully explored. In this study, we investigated the forward effect of delayed JOL using previously unexamined word pair materials and explored the boundary conditions of this effect by manipulating the difficulty of the materials. We also examined this effect within the context of category learning. Our findings demonstrate that delayed JOL significantly enhanced the retention of new information (Experiment 1A), while the forward effect of the delayed JOL occurred only for material with a certain degree of difficulty rather than for easy material (Experiment 1B). These findings were extended and replicated using category learning (Experiment 2). These results suggest that delayed JOL can be used as a preparation strategy for subsequent learning, particularly when faced with challenging materials. Our study provides novel insights into the potential benefits and limitations of delayed JOL and contributes to our understanding of the underlying mechanisms that govern metacognitive monitoring and learning strategies. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is generally used to measure muscles' activity. The sEMG signal can be affected using several factors and vary among individuals and even measurement trials. Thus, to consistently evaluate data among individuals and trials, the maximum voluntary contraction (MVC) value is usually calculated and used to normalize sEMG signals. However, the sEMG amplitude collected from low back muscles can be frequently larger than that found when conventional MVC measurement procedures are used. To address this limitation, in this study, we proposed a new dynamic MVC measurement procedure for low back muscles. Inspired by weightlifting, we designed a detailed dynamic MVC procedure, and then collected data from 10 able-bodied participants and compared their performances using several conventional MVC procedures by normalizing the sEMG amplitude for the same test. The sEMG amplitude normalized by our dynamic MVC procedure showed a much lower value than those obtained using other procedures (Wilcoxon signed-rank test, with p < 0.05), indicating that the sEMG collected during dynamic MVC procedure had a larger amplitude than those of conventional MVC procedures. Therefore, our proposed dynamic MVC obtained sEMG amplitudes closer to its physiological maximum value and is thus more capable of normalizing the sEMG amplitude for low back muscles. [ABSTRACT FROM AUTHOR]

In autumn and spring, citrus leaves with a Ponkan (Citrus reticulata Blanco cv. Ponkan) genetic background (Harumi, Daya, etc.) are prone to abnormal physiological chlorosis. The effects of different degrees of chlorosis (normal, mild, moderate and severe) on photosynthesis and the chlorophyll metabolism of leaves of Citrus cultivar (Harumi) were studied via field experiment. Compared with severe chlorotic leaves, the results showed that chlorosis could break leaf metabolism balance, including reduced chlorophyll content, photosynthetic parameters, antioxidant enzyme activity and enzyme activity related to chlorophyll synthesis, increased catalase and decreased enzyme activity. In addition, the content of chlorophyll synthesis precursors showed an overall downward trend expected for uroporphyrinogen III. Furthermore, the relative expression of genes for chlorophyll synthesis (HEMA1, HEME2, HEMG1 and CHLH) was down-regulated to some extent and chlorophyll degradation (CAO, CLH, PPH, PAO and SGR) showed the opposite trend with increased chlorosis. Changes in degradation were more significant. In general, the chlorosis of Harumi leaves might be related to the blocked transformation of uroporphyrinogen III (Urogen III) to coproporphyrinogen III (Coprogen III), the weakening of antioxidant enzyme system activity, the weakening of chlorophyll synthesis and the enhancement in degradation. [ABSTRACT FROM AUTHOR]

A reliable inspection of the tree trunk internal defects is often considered vital in the health condition assessment for the living tree. There has been a desire to reconstruct the internal structure quantitatively using a non-destructive testing technology. This paper intends to apply wavefield reconstruction inversion (WRI) to obtain high-precision information from tree trunk detection using ground penetrating radar data. The variational projection method and the grouped multi-frequency strategy are adopted to strengthen the algorithm stability and adaptability by inverting frequency components sequentially. Through an irregular trunk model test, the influence of the penalty parameter, initial model, frequency strategy, and grid generation methods are investigated on WRI. Additionally, the comparison between full waveform inversion and WRI is discussed in detail. This synthetic case indicates that WRI is efficient and for a reasonable result, a proper multi-frequency strategy and an accurate mesh closer to reality are important. Furthermore, a field case of a historical tree is used to prove the validity and reliability of the algorithm. The success in this case indicates that our algorithm can characterize the distribution of media parameters of tree trunks accurately, which could provide data support for the rejuvenation and maintenance of living trees. [ABSTRACT FROM AUTHOR]

The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model. However, it faces problems of crosstalk between multiple parameters and strong nonlinearity. This research proposes a multi-constrained, multi-parameter FWI framework based on the projected quasi-Newton algorithm. This framework can introduce multiple types of prior geological information, which can effectively improve the problem of multi-parameter inversion. Additionally, the quasi-Newton method can eliminate the crosstalk phenomenon to further improve the inversion convergence speed. Taking the 1994BP model as an example, the results show that the projected quasi-Newton method has a faster convergence speed than the spectral projected gradient method, and reduces the crosstalk between parameters; multiple constraint sets are uniquely projected onto the intersection to ensure that the estimated values of model parameters meet multiple constraints. We also experiment with the overthrust model, which shows that the framework we proposed can improve the inversion accuracy and has good adaptability. The proposed multi-parameter inversion framework can be compatible with more prior information to obtain an inversion model that conforms to geological understanding and shows great potential in seismic exploration. [ABSTRACT FROM AUTHOR]

With the thriving development of sensor technology and pervasive computing, sensor-based human activity recognition (HAR) has become more and more widely used in healthcare, sports, health monitoring, and human interaction with smart devices. Inertial sensors were one of the most commonly used sensors in HAR. In recent years, the demand for comfort and flexibility in wearable devices has gradually increased, and with the continuous development and advancement of flexible electronics technology, attempts to incorporate stretch sensors into HAR have begun. In this paper, we propose a two-channel network model based on residual blocks, an efficient channel attention module (ECA), and a gated recurrent unit (GRU) that is capable of the long-term sequence modeling of data, efficiently extracting spatial–temporal features, and performing activity classification. A dataset named IS-Data was designed and collected from six subjects wearing stretch sensors and inertial sensors while performing six daily activities. We conducted experiments using IS-Data and a public dataset called w-HAR to validate the feasibility of using stretch sensors in human action recognition and to investigate the effectiveness of combining flexible and inertial data in human activity recognition, and our proposed method showed superior performance and good generalization performance when compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the recent significant rise in atmospheric CO2, seawater's continuous acidification is altering the marine environment's chemical structure at an unprecedented rate. Due to its potential socioeconomic impact, this subject attracted significant research interest. This study used traditional linear regression, nonlinear regression random forest, and the BP neural network algorithm to establish a prediction model for surface seawater pH based on data of North Pacific sea surface temperature (SST), salinity (SSS), chlorophyll-a concentration (Chl-a), and pressure of carbon dioxide on the sea surface (pCO2) from 1993 to 2018. According to existing research, three approaches were found to be highly accurate in reconstructing the surface seawater pH of the North Pacific. The highest-performing models were the linear regression model using SSS, Chl-a, and pCO2, the random forest model using SST and pCO2, and the BP neural network model using SST, SSS, Chl-a, and pCO2. The BP neural network model outperformed the linear regression and random forest model when comparing the root mean square error and fitting coefficient of the three best models. In addition, the best BP neural network model had substantially higher seasonal applicability than the best linear regression and the best random forest model, with good fitting effects in all four seasons—spring, summer, autumn, and winter. The process of CO2 exchange at the sea–air interface was the key factor affecting the pH of the surface seawater, which was found to be negatively correlated with pCO2 and SST, and positively correlated with SSS and Chl-a. Using the best BP neural network model to reconstruct the surface seawater pH over the North Pacific, it was found that the pH exhibited significant temporal and spatiotemporal variation characteristics. The surface seawater pH value was greater in the winter than the summer, and the pH decline rate over the past 26 years averaged 0.0013 yr−1, with a general decreasing tendency from the northwest to the southeast. The highest value was observed in the tropical western Pacific, while the lowest value was observed in the eastern equatorial region with upwelling, which is consistent with the findings of previous studies. [ABSTRACT FROM AUTHOR]

Xyloglucan endotransglycosylase (XET) genes are widely distributed in most plants, but the codon usage bias of XET genes has remained uncharacterized. Thus, we analyzed the codon usage bias using 4500 codons of 20 XET genes to elucidate the genetic and evolutionary patterns. Phylogenetic and hierarchical cluster analyses revealed that the 20 XET genes belonged to two groups. The closer the genetic distance, the more similar the codon usage preference. The codon usage bias of most XET genes was weak, but there was also some codon usage bias. AGA, AGG, AUC, and GUG were the top four codons (RSCU > 1.5) in the 20 XET genes. CitXET had a stronger codon usage bias, and there were eight optimal codons of CitXET (i.e., AGA, AUU, UCU, CUU, CCA, GCU, GUU, and AAA). The RSCU values underwent a correspondence analysis. The two main factors affecting codon usage bias (i.e., Axes 1 and 2) accounted for 54.8% and 17.6% of the total variation, respectively. Multiple correspondence analysis revealed that XET genes were widely distributed, with Group 1 genes being closer to Axis 1 than Group 2 genes, which were closer to Axis 2. Codons with A/U at the third codon position were distributed closer to Axis 1 than codons with G/C at the third codon position. PgXET, ZmXET, VlXET, VrXET, and PcXET were biased toward codons ending with G/C. In contrast, CitXET, DpXET, and BrpXET were strongly biased toward codons ending with A/U, indicating that these XET genes have a strong codon usage bias. Translational selection and base composition (especially A and U at the third codon position), followed by mutation pressure and natural selection, may be the most important factors affecting codon usage of 20 XET genes. These results may be useful in clarifying the codon usage bias of XET genes and the relevant evolutionary characteristics. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (RNA-seq) has been demonstrated to be a proven method for quantifying gene-expression heterogeneity and providing insight into the transcriptome at the single-cell level. When combining multiple single-cell transcriptome datasets for analysis, it is common to first correct the batch effect. Most of the state-of-the-art processing methods are unsupervised, i.e., they do not utilize single-cell cluster labeling information, which could improve the performance of batch correction methods, especially in the case of multiple cell types. To better utilize known labels for complex dataset scenarios, we propose a novel deep learning model named IMAAE (i.e., integrating multiple single-cell datasets via an adversarial autoencoder) to correct the batch effects. After conducting experiments with various dataset scenarios, the results show that IMAAE outperforms existing methods for both qualitative measures and quantitative evaluation. In addition, IMAAE is able to retain both corrected dimension reduction data and corrected gene expression data. These features make it a potential new option for large-scale single-cell gene expression data analysis. [ABSTRACT FROM AUTHOR]

Zeolites have ordered pore structures, good spatial constraints, and superior hydrothermal stability. In addition, the active metal elements inside and outside the zeolite framework provide the porous material with adjustable acid–base property and good redox performance. Thus, zeolites-based catalysts are more and more widely used in chemical industries. Combining the advantages of zeolites and active metal components, the zeolites-based materials are used to catalyze the oxidation of methane to produce various products, such as carbon dioxide, methanol, formaldehyde, formic acid, acetic acid, and etc. This multifunction, high selectivity, and good activity are the key factors that enable the zeolites-based catalysts to be used for methane activation and conversion. In this review article, we briefly introduce and discuss the effect of zeolite materials on the activation of C–H bonds in methane and the reaction mechanisms of complete methane oxidation and selective methane oxidation. Pd/zeolite is used for the complete oxidation of methane to carbon dioxide and water, and Fe- and Cu-zeolite catalysts are used for the partial oxidation of methane to methanol, formaldehyde, formic acid, and etc. The prospects and challenges of zeolite-based catalysts in the future research work and practical applications are also envisioned. We hope that the outcome of this review can stimulate more researchers to develop more effective zeolite-based catalysts for the complete or selective oxidation of methane. [ABSTRACT FROM AUTHOR]

China is part of the western Pacific region, which is the source of the most frequent tropical cyclones in the world. These cause severe disasters each year, including huge economic losses and casualties. To better understand their frequency and intensity, remote sensing tropical cyclone data were obtained for the entire Northwest Pacific region for the period 1977–2018. MATLAB and ArcGIS were used to analyse the frequency and intensity of tropical cyclones and their characteristics in various regions of China. At the same time, the influence factors of tropical cyclone characteristics such as El Niño and SST were analyzed by correlation analysis and Geographical detector. The annual frequency of tropical cyclones in the Northwest Pacific showed a fluctuating state, but the overall trend was decreasing. In particular, since 1994, the overall frequency decreased significantly but rebounded in recent years, while the intensity did not change significantly. It was found that cyclone intensity is lower when the frequency is higher, and vice versa. 85% of tropical cyclones occurred in summer and autumn, with the highest intensities in autumn, when the maximum average wind speed peaks at 37 m/s. The area with the most frequent tropical cyclones was 5–20° N, 125–155° E. A total of 314 tropical cyclones arrived in China during the study period, an average of about 7.5 per year. Their frequency and intensity gradually decreased as they moved from coastal to inland areas. Both SST and El Niño are significantly related to the formation and development of tropical cyclones, and the contribution of multiple factors interaction to the variation characteristics of tropical cyclones is significantly higher than that of single factors. Understanding the characteristics of the Pacific tropical cyclones is an important step in planning disaster prevention framework. [ABSTRACT FROM AUTHOR]

Currently, information available on the comprehensive changes in the rumen bacteria and metabolites of yaks fed high-grain diets is limited. This study aimed to investigate the effects of high-grain diet feeding on the blood metabolites, rumen microbiome, and metabolomics of yaks by using 16S rDNA gene sequencing and liquid chromatography–mass spectrometry (LC/MS). Here, fourteen healthy male yaks (body weight, 249.61 ± 8.13 kg) were randomly assigned to two different diets: a hay diet (0% grain, CON, n = 7), or a high-grain diet (70% grain, HG, n = 7). At the 74th day of treatment, blood and ruminal fluid samples were collected for the blood metabolites, rumen microbiome, and metabolomics analyses. The HG diet increased lipopolysaccharides (LPS), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), haptoglobin (HPT), serum amyloid-A (SAA), interleukin-1β (IL1-β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) serum concentrations (p < 0.05). Compared with the CON diet, the HG diet decreased rumen pH (p < 0.05), and increased total volatile fatty acids concentration, and proportion of butyrate (p < 0.05). The relative abundance of Firmicutes and Saccharibacteria were higher (p < 0.05), while Bacteroidetes was lower (p < 0.05) in the HG group than those in the CON group. At the genus level, the relative abundance of Christensenelaceae_R-7_group, Ruminococcaceae_NK4A214_group, Lachnospiraceae_NK3A20_group, and Acetitomaculum were higher than in those in the HG diet (p < 0.05). Compared with the CON group, the HG diet increased the concentrations of biogenic amines (histamine, tyramine, and putrescine), common amino acids (phenylalanine, threonine, serine, etc.), and arachidonic acid (prostaglandin H2, prostaglandin E2, 12(S)-HPETE, etc.). Collectively, these findings demonstrate that the HG diet altered the microbiota and metabolites, as well as potentially damaged their rumen health and induced inflammation in yaks. [ABSTRACT FROM AUTHOR]

In this study, we test the therapeutic effects of rapamycin in a murine model of SLE-like experimental lupus nephritis induced by chronic graft-versus-host disease (cGVHD). Our results suggest that rapamycin treatment reduced autoantibody production, inhibited T lymphocyte and subsequent B cell activation, and reduced inflammatory cytokine and chemokine production, thereby protecting renal function and alleviating histological lupus nephritis by reducing the occurrence of albuminuria. To explore the potential mechanism of rapamycin's reduction of kidney damage in mice with lupus nephritis, a series of functional assays were conducted. As expected, rapamycin remarkably inhibited the lymphocytes' proliferation within the morbid mice. Interestingly, significantly increased proportions of peripheral CD4+FOXP3+ and CD4+CD25high T cells were observed in rapamycin-treated group animals, suggesting an up-regulation of regulatory T cells (Tregs) in the periphery by rapamycin treatment. Furthermore, consistent with the results regarding changes in mRNA abundance in kidney by real-time PCR analysis, intracellular cytokine staining demonstrated that rapamycin treatment remarkably diminished the secretion of Th1 and Th2 cytokines, including IFN-γ, IL-4 and IL-10, in splenocytes of the morbid mice. However, the production of IL-2 from splenocytes in rapamycin-treated mice was significantly higher than in the cells from control group animals. These findings suggest that rapamycin treatment might alleviate systemic lupus erythematosus (SLE)-like experimental lupus nephritis through the recovery of IL-2 production, which promotes the expansion of regulatory T cells while inhibiting effector T cell activation. Our studies demonstrated that, unlike other commonly used immunosuppressants, rapamycin does not appear to interfere with tolerance induction but permits the expansion and suppressive function of Tregs in vivo. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios. [ABSTRACT FROM AUTHOR]

Selenium (Se) deficiency causes various diseases in humans. Se can be obtained from fruits and vegetables. In this study, the fruit tree Cyphomandra betacea was intercropped with three Solanum sect. Solanum (Solanaceae) wild vegetables [diploid (S. photeinocarpum), tetraploid (colchicine-induced S. photeinocarpum), and hexaploid (S. nigrum)], respectively, and Se uptakes of these plants were determined by a pot experiment. Intercropping decreased the biomass, photosynthetic pigment content, and superoxide dismutase activity of C. betacea, but increased the peroxidase (POD) activity, catalase (CAT) activity, and soluble protein content of C. betacea. These indicators' values of sect. Solanum increased after intercropping. The contents of Se increased in C. betacea and sect. Solanum after intercropping. Intercropped with diploid, tetraploid, and hexaploid increased the shoot Se contents in C. betacea by 13.73%, 17.49%, and 26.50%, respectively, relative to that of C. betacea monoculture. Intercropped with C. betacea increased the shoot Se contents in diploid, tetraploid, and hexaploid by 35.22%, 68.86%, and 74.46%, respectively, compared with their respective monoculture. The biomass and Se content of intercropped sect. Solanum showed linear relationships with the biomass and Se content of their monocultures. The biomass and Se content of intercropped C. betacea also exhibited linear relationships with that of sect. Solanum monocultures. Correlation and grey relational analyses revealed that the CAT activity, POD activity, and soluble protein content were the top three indicators closely associated with the C. betacea shoot Se content. The POD activity, soluble protein content, and translocation factor were the top three indicators closely associated with sect. Solanum shoot Se content. Therefore, intercropping can promote the Se uptake in C. betacea and sect. Solanum wild vegetables. [ABSTRACT FROM AUTHOR]

Simple Summary: Newcastle disease (ND) is an Office International Des Epizooties (OIE) notifiable terrestrial and aquatic animal disease that can infect chickens of all ages with a high mortality rate, causing ongoing damage to the poultry industry in China currently. In this study, we have developed a blocking lateral flow assay (bLFA) strip for the rapid detection of Newcastle disease virus (NDV) antibodies, using neutralizing monoclonal antibodies against hemagglutinin-neuraminidase. The results showed that the chicken NDV hyperimmunized serum had a complete blocking titer of 11 log 2, and half-blocking titer of 13 log 2, which are 4 times less than and the same as that of the Hemagglutination inhibition (HI) test (13 log 2), and 8 and 2 times less than that of the virus neutralization test (14 log 2), respectively. Additionally, the bLFA strip has no cross-reactivity with positive serum of other avian pathogens. A total of 510 clinical samples were tested for NDV antibodies. The coincidence rate between the results of the bLFA strip and HI test was 97.65%. Therefore, it is an ideal alternative method for assessing the clinical immunity of ND vaccines in the field in real-time. Newcastle disease (ND) is an acute septicemic infectious disease caused by Newcastle disease virus (NDV). Considering that vaccination is currently the main modality for the prevention of ND, it is essential to assess the effectiveness of clinical immunization. In this study, we have developed a blocking lateral flow assay (bLFA) strip for the rapid detection of NDV antibodies using the monoclonal antibody 9C1 against haemagglutinin-neuraminidase (HN), which allows for the determination of an NDV-specific antibody titer within 10 min at room temperature. In addition, the bLFA strip has no cross-reactivity with the positive serum of other avian pathogens including avian influenza subtypes H5, H7, and H9, MD, IBD, IB, EDS, and avian adenovirus. The ability of the bLFA strip for detecting a neutralizing antibody was also estimated. The results showed that the chicken NDV hyperimmunized serum had a complete blocking (100%) titer of 11 log 2, and half-blocking titer of 13 log 2, which are 4 times less than and the same as that of the HI test (13 log 2), and 8 and 2 times less than that of the VN test (14 log 2), respectively. A total of 510 clinical samples were tested for NDV antibodies. The coincidence rate between the results of the bLFA strip and HI test was 97.65%. Therefore, it is an ideal alternative method for assessing the clinical immunity of ND vaccines in the field in real-time. [ABSTRACT FROM AUTHOR]

Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used to extract the contextual features of the molecule. Three different protein-encoding methods were used to extract the physicochemical properties and amino acid information of the target protein. Protein-encoding and molecular sequence information are combined to guide molecule generation. Transfer learning was used to fine-tune the pretrained model to generate molecules with better binding ability to the target protein. The model was validated using three different targets. The docking results show that our model is capable of generating new molecules with higher docking scores for the target proteins. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR), geophysics exploring technology, could non-destructively acquire high-precision information about the shallow subsurface. This Special Issue aims to introduce and report on recent advancements in GPR, encompassing aspects such as numerical simulation, inversion imaging, data processing methodologies, and various domains of GPR application. In the modern epoch, equipment for GPR data acquisition is well established, but the explication of GPR data still relies on artificial judgment and experience, lacking automation. [Extracted from the article]

Simple Summary: Lung adenocarcinoma (LADC) is the pathological type with the highest morbidity and mortality among lung cancers. Although achievements in new therapeutic approaches have been developed, chemotherapy is still the most widely choice for control of LADC. However, the increasing drug resistance becomes the major challenge, so the development of the novel and efficient chemotherapeutic drug is still urgent. Elaiophylin, a new type of autophagy inhibitor, has been shown to possess unique anti-cancer activity. In this study, we have deeply investigated the therapeutic effect of elaiophylin on LADC and found elaiophylin exerts its anti-cancer effect though inhibiting mitophagy and oxidative stress and targeting SIRT1/Nrf2 signaling. This innovative and comprehensive research may provide the possibility for the development of novel chemotherapy drug for LADC. Lung adenocarcinoma (LADC), the most common type of lung cancer, is still one of the most aggressive and rapidly fatal tumor types, even though achievements in new therapeutic approaches have been developed. Elaiophylin as a C2 symmetrically glycosylated 16 macrolides has been reported to be a late-stage autophagy inhibitor with a potent anti-tumor effect on various cancers. This study investigated the anti-tumor effect of elaiophylin on human LADC for the first time in in vitro and in vivo models. The in vitro study in LADC A549 cells showed that elaiophylin significantly inhibited cell viability and induced cell apoptosis through the suppression of mitophagy and induction of cellular and mitochondrial oxidative stress. Proteomic analysis and molecular docking assay implicated that SIRT1 was likely the direct target of elaiophylin in A549 cells. Further mechanistic study verified that elaiophylin reduced Nrf2 deacetylation, expression, and transcriptional activity as well as cytoplasm translocation by downregulating SIRT1 expression and deacetylase activity. Additionally, SIRT1/Nrf2 activation could attenuate elaiophylin-induced mitophagy inhibition and oxidative stress. The in vivo study in the A549-xenograft mice model showed that the anti-tumor effect of elaiophylin was accompanied by the decreased expressions of SIRT1, Nrf2, Parkin, and PINK1. Thus, the present study reports that elaiophylin has potent anti-tumor properties in LADC, which effect is likely mediated through suppressing the SIRT1/Nrf2 signaling. In conclusion, elaiophylin may be a novel drug candidate for LADC and SIRT1 may be a new therapeutic target for such devastating malignancy. [ABSTRACT FROM AUTHOR]

Warburg Micro syndrome (WARBM) is an autosomal recessive neuro-ophthalmologic syndrome characterized by microcephaly, microphthalmia, congenital cataracts, cortical dysplasia, corpus callosum hypoplasia, spasticity, and hypogonadism. WARBM is divided into four subtypes according to the causative genes, of which RAB3GAP1 (OMIM# 602536) accounts for the highest proportion. We collected detailed medical records and performed whole-exome sequencing (WES) for a congenital cataract patient. A novel heterozygous frameshift RAB3GAP1 variant was detected in a boy with a rare ocular phenotype of bilateral membranous cataracts accompanied by a persistent papillary membrane. Further copy number variation (CNV) analysis identified a novel deletion on chromosome 2q21.3 that removed 4 of the 24 exons of RAB3GAP1. The patient was diagnosed with WARBM following genetic testing. The present study expands the genotypic and phenotypic spectrum of WARBM. It suggests applying whole exome sequencing (WES) and CNV analysis for the early diagnosis of syndromic diseases in children with congenital cataracts. [ABSTRACT FROM AUTHOR]

The breast muscle is essential for flight and determines the meat yield and quality of the meat type in pigeons. At present, studies about long non-coding RNA (lncRNA) expression profiles in skeletal muscles across the postnatal development of pigeons have not been reported. Here, we used transcriptome sequencing to examine the White-King pigeon breast muscle at four different ages (1 day, 14 days, 28 days, and 2 years old). We identified 12,918 mRNAs and 9158 lncRNAs (5492 known lncRNAs and 3666 novel lncRNAs) in the breast muscle, and 7352 mRNAs and 4494 lncRNAs were differentially expressed in the process of development. We found that highly expressed mRNAs were mainly related to cell-basic and muscle-specific functions. Differential expression and time-series analysis showed that differentially expressed genes were primarily associated with muscle development and functions, blood vessel development, cell cycle, and energy metabolism. To further predict the possible role of lncRNAs, we also conducted the WGCNA and trans/cis analyses. We found that differentially expressed lncRNAs such as lncRNA-LOC102093252, lncRNA-G12653, lncRNA-LOC110357465, lncRNA-G14790, and lncRNA-LOC110360188 might respectively target UBE2B, Pax7, AGTR2, HDAC1, Sox8 and participate in the development of the muscle. Our study provides a valuable resource for studying the lncRNAs and mRNAs of pigeon muscles and for improving the understanding of molecular mechanisms in muscle development. [ABSTRACT FROM AUTHOR]

Congenital cataracts (CCs) have significant genotypic and phenotypic heterogeneity. The major intrinsic protein (MIP) gene, one of the causative genes of CCs, plays a vital role in maintaining the homeostasis and transparency of the lens. In this study, we identified a unique phenotype of anterior umbilication of the lens in a four-generation pedigree with CCs. All patients in the observed family had nystagmus, nuclear cataracts, and elongated axial lengths compared with their healthy counterparts except for patient I:2, whose axial length was unavailable, and patientII:4, who had total cataracts. We confirmed, using Sanger sequencing based on whole-exon sequencing (WES) data, that all patients carried a heterozygous variant NM_012064.4:c.97C > T (NP_036196.1:p.R33C) in their MIP gene. To our knowledge, 29 variants of the human MIP gene and the relative phenotypes associated with CCs have been identified. Nevertheless, this is the first report on the anterior umbilication of the lens with nuclear or total opacity caused by the c.97C > T (p.R33C) variant in the MIP gene. These results also provide evidence that the elongated axial length might be associated with this variant. This study further confirms the phenotypic heterogeneity of CCs. [ABSTRACT FROM AUTHOR]

The realization of microsatellite intelligent mission planning is the current research focus in the field of satellite planning, and mission schedulability prediction is the basis of this research. Aiming at the influence of the sequence tasks before and after the task sequence to be predicted, we propose an online schedulability prediction model of satellite tasks based on bidirectional long short-term memory (Bi-LSTM) on the basis of describing and establishing the satellite task planning and solving model. The model is trained using satellite offline mission planning data as learning samples. In the experiment, the prediction effect of the model is excellent, with a recall rate of 93.17% and a precision rate of 92.59%, which proves that the model can be effectively applied to predict the schedulability of satellite tasks. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LADC) is the most prevalent lung cancer sub-type, and targeted therapy developed in recent years has made progress in its treatment. Erdafitinib, a potent and selective pan-FGFR tyrosine kinase inhibitor, has been confirmed to be effective for the treatment of LADC; however, the molecular mechanism responsible for this effect is unclear. The in vitro study showed that erdafitinib exhibited an outstanding anti-cancer activity in human LADC cell line A549 by inducing S-phase cell-cycle arrest and cell apoptosis. The mechanistic study based on the transcriptomic data revealed that erdafitinib exerted its anti-cancer effect by affecting the cell cycle-related pathway, and CDK2 was the regulatory target of this drug. In addition, CDK2 overexpression significantly attenuated the anti-cancer effect of erdafitinib by affecting the transcriptional activity and expression of E2F1, as well as the expression of CDK1. The in vivo study showed that erdafitinib presented an obvious anti-cancer effect in the A549 xenograft mice model, which was accompanied by the reduced expression of CDK2. Thus, this study demonstrates the anti-cancer effect of erdafitinib against LADC for the first time based on in vitro and in vivo models, whose activity is achieved by targeting CDK2 and regulating downstream E2F1-CDK1 signaling. This study may be helpful for expanding the clinical application of erdafitinib in treating LADC. [ABSTRACT FROM AUTHOR]