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Prostaglandin E2 (PGE2) is a potent lipid mediator that plays a key role in inflammation and carcinogenesis. NAD+-dependent 15-hydroxyprostaglandin dehydrogenase (15-PGDH) catalyzes the oxidation of the 15(S)-hydroxyl group of PGE2, which leads to PGE2 biotransformation. In this study, we showed that the 15-PGDH-derived 15-keto-PGE2 is an endogenous peroxisome proliferator-activated receptor-γ (PPAR-γ) ligand that causes PPAR-γ dissociation from Smad2/3, allowing Smad2/3 association with the TGF-γ receptor I and Smad anchor for receptor activation and subsequent Smad2/3 phosphorylation and transcription activation in human cholangiocarcinoma cells. The 15-PGDH/15-keto-PGE2-induced Smad2/3 phosphorylation resulted in the formation of the pSmad2/3-TAP63-p53 ternary complex and their binding to the TAP63 promoter, inducing TAP63 autotranscription. The role of TAP63 in 15-PGDH/15- keto-PGE2-induced inhibition of tumor growth was further supported by the observation that knockdown of TAP63 prevented 15-PGDH-induced inhibition of tumor cell proliferation, colony formation, and migration. These findings disclose a novel 15-PGDH-mediated 15-keto-PGE2 signaling cascade that interacts with PPAR-γ, Smad2/3, and TAP63. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection. [ABSTRACT FROM AUTHOR]

Copyright of Diamond & Abrasives Engineering is the property of Zhengzhou Research Institute for Abrasives & Grinding and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coding transcript-derived siRNAs (ct-siRNAs) produced from specific endogenous loci can suppress the translation of their source genes to balance plant growth and stress response. In this study, we generated Arabidopsis mutants with deficiencies in RNA decay and/or post-transcriptional gene silencing (PTGS) pathways and performed comparative sRNA-seq analysis, revealing that multiple RNA decay and PTGS factors impede the ct-siRNA selective production. Genes that produce ct-siRNAs often show increased or unchanged expression and typically have higher GC content in sequence composition. The growth and development of plants can perturb the dynamic accumulation of ct-siRNAs from different gene loci. Two nitrate reductase genes, NIA1 and NIA2, produce massive amounts of 22-nt ct-siRNAs and are highly expressed in a subtype of mesophyll cells where DCL2 exhibits higher expression relative to DCL4, suggesting a potential role of cell-specific expression of ct-siRNAs. Overall, our findings unveil the multifaceted factors and features involved in the selective production and regulation of ct-siRNAs and enrich our understanding of gene silencing process in plants. Research on Arabidopsis indicates that RNA decay, PTGS pathway, GC content, and spatial-temporal gene expression impact ct-siRNA production, vital for gene silencing during plant growth and stress responses. [ABSTRACT FROM AUTHOR]

Backgrounds: The intestinal development in early life is profoundly influenced by multiple biological components of breast milk, in which milk-derived extracellular vesicles (mEVs) contain a large amount of vertically transmitted signal from the mother. However, little is known about how maternal fiber-rich diet regulates offspring intestinal development by influencing the mEVs. Results: In this study, we found that maternal resistant starch (RS) consumption during late gestation and lactation improved the growth and intestinal health of offspring. The mEVs in breast milk are the primary factor driving these beneficial effects, especially enhancing intestinal cell proliferation and migration. To be specific, administration of mEVs after maternal RS intake enhanced intestinal cell proliferation and migration in vivo (performed in mice model and indicated by intestinal histological observation, EdU assay, and the quantification of cyclin proteins) and in vitro (indicated by CCK8, MTT, EdU, and wound healing experiments). Noteworthily, miR-146a-5p was found to be highly expressed in the mEVs from maternal RS group, which also promotes intestinal cell proliferation in cells and mice models. Mechanically, miR-146a-5p target to silence the expression of ubiquitin ligase 3 gene NEDD4L, thereby inhibiting DVL2 ubiquitination, activating the Wnt pathway, and promoting intestinal development. Conclusion: These findings demonstrated the beneficial role of mEVs in the connection between maternal fiber rich diet and offspring intestinal growth. In addition, we identified a novel miRNA-146a-5p-NEDD4L-β-catenin/Wnt signaling axis in regulating early intestinal development. This work provided a new perspective for studying the influence of maternal diet on offspring development. [ABSTRACT FROM AUTHOR]

Fluorescent hydrogels have emerged as one of the most promising candidates for developing biomimetic materials and artificial intelligence owing to their unique fluorescence and responsive properties. However, it is still challenging to fabricate hydrogel that exhibits synergistic changes in fluorescence color and shape in response to multistimulus via a simple method. Herein, blue‐ and orange‐emitting fluorescent microgels (MGs) both are designed and synthesized with pH‐, thermal‐, and cationic‐sensitivity via one‐step polymerization, respectively. The two fluorescent MGs are incorporated into transparent doubly crosslinked microgel (DX MG) hydrogels with a preset ratio. The DX MG hydrogels can tune the fluorescent color accompanied by size variation via subjecting to external multistimulus. Thus, DX MG hydrogels can be exploited for multiresponsive fluorescent bilayer actuators. The actuators can undergo complex shape deformation and color changes. Inspired by natural organisms, an artificial morning glory with color and size changes are showcased in response to buffer solutions of different pH values. Besides, an intelligent skin hydrogel, imitating natural calotes versicolor, by assembling four layers of DX MG with different ratios of MGs, is tailored. This work serves as an inspiration for the design and fabrication of novel biomimetic smart materials with synergistic functions. [ABSTRACT FROM AUTHOR]

Background: Dressing change is the most important part of postoperative wound care. The aim of this study was to evaluate whether a more effective, simple and less painful method of dressing change for anal fistulas could be found without the need for debridement and packing. Data related to postoperative recovery were recorded at postoperative days 3, 7, 14, 21 and 180. Methods: In this experiment, 76 subjects diagnosed with high anal fistula were randomly divided into a simplified dressing change (SDC) group and a traditional debridement dressing change(TDDC) group according to a ratio of 1:1. Results: The SDC group had significantly fewer pain scores, bleeding rates, dressing change times, inpatient days and lower average inpatient costs than the TDDC group. There were no significant differences in wound healing time, area and depth and Wexner score between the two groups. Conclusions: Studies have shown that the use of simplified dressing changes does not affect cure or recurrence rates, but significantly reduces dressing change times and pain during changes, reducing patient inpatient length of stay and costs. [ABSTRACT FROM AUTHOR]

Achieving green economic recovery is crucial to improving environmental quality and sustainable development. This study examines the influence of new digital infrastructure on green total factor productivity (GTFP) using panel data from 30 regions in China from 2008 to 2019. The results are as follows: (1) New digital infrastructure has a significant improvement effect on GTFP. After a series of robustness tests, the conclusion is still valid. (2) The improvement effect of new digital infrastructure on GTFP shows significant heterogeneity. In regions with high industrial agglomeration, high environmental regulation and strong government environmental preference, the improvement effect of new digital infrastructure on GTFP is more obvious. (3) New digital infrastructure improves GTFP through green technology innovation and factor allocation optimization. The government should strengthen the fiscal incentives for green technology development while increasing R&D investment in fiscal expenditure, thus promoting green economic recovery. [ABSTRACT FROM AUTHOR]

Electronic skins (e-skins) have shown great potential in "bionic-machine" technologies. The development of bio-compatible e-skins that mimic the functions of the human skin, which is sensitive to multiple external stimuli, remains a challenge and a highly pursued goal. In this work, we report the synthesis, characterization, and performance-optimization of a thermo-sensitive and conductive hydrogel that displays multimodal sensing capacity. The optimal composition of a hydrogel with a volume phase transition temperature (VPTT) of 38 °C and a transition width of 15 °C was developed. This gel has an elastic modulus similar to that of the human skin, a large stretchability of 1900%, and a high gauge factor of 9.8. The gel is also puncture-resistant, self-healable, and self-adhesive. The as-fabricated hydrogel can sense multimodal stimuli, including force and temperature, with high sensitivity. We demonstrate that the gel possesses important attributes as a candidate for e-skin to monitor physiological signals including temperature, bending, and pressure in real time. [ABSTRACT FROM AUTHOR]

Reinforcement of fibers was carried out by adding carbon black (CB), and hydroxylated and carboxylated carbon nanotubes (CNTs) into electrospinning solution containing doped polyaniline (CSA-PANI) and polyacrylonitrile (PAN). CB/CSA-PANI/PAN and CNT/CSA-PANI/PAN electrospun nanofiber composite membrane was formed in high-voltage electric field. The CSA-PANI/CB/PAN fiber membrane was found to be more brittle than the MWCNTs/CSA-PANI/PAN fiber membrane. The average diameter of the CSA-PANI/CB/PAN nanofibers increased with CB addition, while the average diameter of CNT-added MWCNTs/CSA-PANI/PAN nanofibers decreased with increasing CNT concentrations. Upon greater CB and CNT addition, agglomeration occurred, and the surface of the fibers was raised slightly. The fracture strength of the nanofiber membrane was greatly improved with 1% added CB but then decreased upon further CB addition. Upon addition of CNTs, the fracture strength of the nanofiber membrane first increased and then decreased, and the addition of carboxylated CNTs was more advantageous for improving the fracture strength of the fiber membrane. The electromagnetic shielding performance of the fiber membranes was essentially the same for different radiation frequencies. Upon addition of CB and CNTs, the electromagnetic shielding performance of the fiber first increased and then decreased, with a more pronounced decrease obtained by the addition of CB. [ABSTRACT FROM AUTHOR]

Ship target recognition is a vital task in synthetic aperture radar (SAR) imaging applications. Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information. Furthermore, several complex-valued neural networks utilize average pooling to achieve full complex values, resulting in suboptimal performance. To address these concerns, this letter introduces a complex-valued convolutional neural network (CVGG-Net) specifically designed for SAR image ship recognition. CVGG-Net effectively leverages both the amplitude and phase information in complex-valued SAR data. Additionally, this study examines the impact of various widely used complex activation functions on network performance and presents a novel complex max-pooling (CMaxPool) method, called Complex Area Max-Pooling. Experimental results from two measured SAR datasets demonstrate that the proposed algorithm outperforms conventional real-valued convolutional neural networks. The proposed framework is validated on several SAR datasets. [ABSTRACT FROM AUTHOR]

Helicobacter pylori (H. pylori), as a harmful bacteria associated with gastric cancer, can have adverse effects on human normal flora and metabolism. However, the effects of H. pylori on human metabolism have not been fully elucidated. The 13C breathing test was used as the basis for distinguishing negative and positive groups. Serum samples were collected from the two groups for targeted quantitative metabolomics detection; multidimensional statistics were used, including partial least squares discriminant analysis (PLS‐DA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS‐DA), and differential metabolites were screened. Unidimensional statistics combined with multidimensional statistics were used to further screen potential biomarkers, and finally pathway analysis was performed. SPSS 21.0 software package was used for statistical analysis of experimental data. Multivariate statistical analysis such as PLS‐DA, PCA, and OPLS‐DA was performed using Simca‐P 13.0 to search for differential metabolites. This study confirmed that H. pylori caused significant changes in human metabolism. In this experiment, 211 metabolites were detected in the serum of the two groups. Multivariate statistical analysis showed that PCA of metabolites was not significantly different between the two groups. PLS−DA indicated that the serum of the two groups was well clustered. There were significant differences in metabolites between OPLS−DA groups. By setting the variable importance in projection (VIP) threshold as one and the corresponding P−value <0.05, a total of 40 metabolites were screened in this study. P <0.05 and ∣log2FC∣>0 (where FC is the fold change) were used together as a unidimensional statistical filter condition. The analysis found that the expression of 15 metabolites such as propionic acid, acetic acid, adipic acid increased, and the metabolism of six products such as deoxycholic acid (DCA), 4−hydroxyphenylpyruvic acid, pyruvic acid decreased. P <0.05, false discovery rate <0.5, ∣log2FC∣>1, and OPLSDA_VIP>1 were used together as a condition for filter screening potential biomarkers. Four potential biomarkers were screened, which were sebacic acid, isovaleric acid, DCA, and indole−3−carboxylic acid. Finally, the different metabolites were added to the pathway‐associated metabolite sets (SMPDB) library for the corresponding pathway enrichment analysis. The significant abnormal metabolic pathways were taurine and subtaurine metabolism, tyrosine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, etc. This study shows that H. pylori has an impact on human metabolism. Not only a variety of metabolites have significant changes, but also metabolic pathways are abnormal, which may be the reason for the high risk of H. pylori causing gastric cancer. [ABSTRACT FROM AUTHOR]

The rapid development of the textile industry has led to the demand for more advanced textile equipment because the current covering yarn machines are large and expensive and have a large physical footprint. Also, the current technology is unsuitable for most laboratory research and small factory proofing. In this paper, the principle of forming covered yarn is analyzed and simplified in three systems: the unwinding system, the covering system, and the winding system. A small sample of low volume and better flexibility is developed, the production process and primary structure of the covered yarn prototype are introduced, and the covering effect of the small prototype is debugged and analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Polylactic acid (PLA) fiber membrane needs to match the wound healing time when used as a medical adhesive membrane. In order to adjust the degradation cycle of PLA fiber, the composite microspheres of nano-SiO2/lipase/polycaprolactone (PCL) with degradation regulation function were designed and prepared. And then the composite microspheres were added into PLA fiber membrane to verify the degradation regulation function. The results showed that: the optimal preparation process of microspheres was obtained as follows: 5 mg/mL SiO2/lipase complex concentration, 10 mg/mL PCL concentration, 1000 r/min emulsification speed and 600 r/min curing speed. The amount of enzyme, loading in the microspheres, can be regulated in the range of 10.24%–11.29%. And the drug-loaded microspheres had a good sustained release effect. Finally, it was verified that the microspheres could accelerate the degradation rate of PLA fiber membrane. [ABSTRACT FROM AUTHOR]

Ship detection in synthetic aperture radar (SAR) images has attracted widespread attention due to its significance and challenges. In recent years, numerous detectors based on deep learning have achieved good performance in the field of SAR ship detection. However, ship targets of the same type always have various representations in SAR images under different imaging conditions, while different types of ships may have a high degree of similarity, which considerably complicates SAR target recognition. Meanwhile, the ship target in the SAR image is also obscured by background and noise. To address these issues, this paper proposes a novel oriented ship detection method in SAR images named SPG-OSD. First, we propose an oriented two-stage detection module based on the scattering characteristics. Second, to reduce false alarms and missing ships, we improve the performance of the network by incorporating SAR scattering characteristics in the first stage of the detector. A scattering-point-guided region proposal network (RPN) is designed to predict possible key scattering points and make the regression and classification stages of RPN increase attention to the vicinity of key scattering points and reduce attention to background and noise. Third, supervised contrastive learning is introduced to alleviate the problem of minute discrepancies among SAR object classes. Region-of-Interest (RoI) contrastive loss is proposed to enhance inter-class distinction and diminish intra-class variance. Extensive experiments are conducted on the SAR ship detection dataset from the Gaofen-3 satellite, and the experimental results demonstrate the effectiveness of SPG-OSD and show that our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

I Biomedicines i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

Background: Low birth weight (LBW) is associated with intestinal inflammation and dysbiosis after birth. However, the underlying mechanism remains largely unknown. Objective: In the present study, we aimed to investigate the metabolism, therapeutic potential, and mechanisms of action of bile acids (BAs) in LBW-induced intestinal inflammation in a piglet model. Methods: The fecal microbiome and BA profile between LBW and normal birth weight (NBW) neonatal piglets were compared. Fecal microbiota transplantation (FMT) was employed to further confirm the linkage between microbial BA metabolism and intestinal inflammation. The therapeutic potential of ursodeoxycholic acid (UDCA), a highly differentially abundant BA between LBW and NBW piglets, in alleviating colonic inflammation was evaluated in both LBW piglets, an LBW-FMT mice model, and a DSS-induced colitis mouse model. The underlying cellular and molecular mechanisms by which UDCA suppresses intestinal inflammation were also investigated in both DSS-treated mice and a macrophage cell line. Microbiomes were analyzed by using 16S ribosomal RNA sequencing. Fecal and intestinal BA profiles were measured by using targeted BA metabolomics. Levels of farnesoid X receptor (FXR) were knocked down in J774A.1 cells with small interfering RNAs. Results: We show a significant difference in both the fecal microbiome and BA profiles between LBW and normal birth weight animals in a piglet model. Transplantation of the microbiota of LBW piglets to antibiotic-treated mice leads to intestinal inflammation. Importantly, oral administration of UDCA, a major BA diminished in the intestinal tract of LBW piglets, markedly alleviates intestinal inflammation in LBW piglets, an LBW-FMT mice model, and a mouse model of colitis by inducing M2 macrophage polarization. Mechanistically, UDCA reduces inflammatory cytokine production by engaging BA receptor FXR while suppressing NF-κB activation in macrophages. Conclusions: These findings establish a causal relationship between LBW-associated intestinal abnormalities and dysbiosis, suggesting that restoring intestinal health and postnatal maldevelopment of LBW infants may be achieved by targeting intestinal microbiota and BA metabolism. Csb1K8fsfkBeFAHhY-1wro Video Abstract [ABSTRACT FROM AUTHOR]