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Abstract Exploiting the hierarchical dependence behind user behaviour is critical for click‐through rate (CRT) prediction in recommender systems. Existing methods apply attention mechanisms to obtain the weights of items; however, the authors argue that deterministic attention mechanisms cannot capture the hierarchical dependence between user behaviours because they treat each user behaviour as an independent individual and cannot accurately express users' flexible and changeable interests. To tackle this issue, the authors introduce the Bayesian attention to the CTR prediction model, which treats attention weights as data‐dependent local random variables and learns their distribution by approximating their posterior distribution. Specifically, the prior knowledge is constructed into the attention weight distribution, and then the posterior inference is utilised to capture the implicit and flexible user intentions. Extensive experiments on public datasets demonstrate that our algorithm outperforms state‐of‐the‐art algorithms. Empirical evidence shows that random attention weights can predict user intentions better than deterministic ones.

Abstract Excessive glucocorticoid (GC) action is linked to various metabolic disorders. Recent findings suggest that disrupting skeletal GC signaling prevents bone loss and alleviates metabolic disorders in high-fat diet (HFD)-fed obese mice, underpinning the neglected contribution of skeletal GC action to obesity and related bone loss. Here, we show that the elevated expression of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), the enzyme driving local GC activation, and GC signaling in osteoblasts, are associated with bone loss and obesity in HFD-fed male mice. Osteoblast-specific 11β-HSD1 knockout male mice exhibit resistance to HFD-induced bone loss and metabolic disorders. Mechanistically, elevated 11β-HSD1 restrains glucose uptake and osteogenic activity in osteoblast. Pharmacologically inhibiting osteoblastic 11β-HSD1 by using bone-targeted 11β-HSD1 inhibitor markedly promotes bone formation, ameliorates glucose handling and mitigated obesity in HFD-fed male mice. Taken together, our study demonstrates that osteoblastic 11β-HSD1 directly contributes to HFD-induced bone loss, glucose handling impairment and obesity.

Abstract To solve the problem of a large amount of fly ash accumulation and study the axial compression and bearing capacity prediction of the self-compacting fly ash concrete filled circle steel tube (SCCFST) columns, eight specimens are designed to explore the impact of concrete strength grade, internal structural measures, and additional parameters. The stress, progression of deformation, and failure mode of each specimen are observed during the loading process. The load–displacement curves, load-strain curves, characteristic load and displacement, ductility, and stiffness degradation are analyzed. The findings revealed that shear deformation occurred predominantly in the middle and upper portions of the steel tubes. Enhancing the strength of the concrete or adopting internal structural measures could increase the bearing capacity and ductility of the specimens. The peak load and ductility could be increased by up to 17.6 and 53.6%, respectively. The proposed unified calculation equation for the axial compression bearing capacity of SCCFST columns demonstrates notable reliability and precision. Furthermore, these tests offer valuable references for the engineering application of various forms of SCCFST columns, which are of significant importance in practical engineering.

Sclerostin emerges as a novel target for bone anabolic therapy in bone diseases. Osteogenesis imperfecta (OI) and X-linked hypophosphatemia (XLH) are rare bone diseases in which therapeutic potential of sclerostin inhibition cannot be ignored. In OI, genetic/pharmacologic sclerostin inhibition promoted bone formation of mice, but responses varied by genotype and age. Serum sclerostin levels were higher in young OI-I patients, while lower in adult OI-I/III/IV. It's worth investigating whether therapeutic response of OI to sclerostin inhibition could be clinically predicted by genotype and age. In XLH, preclinical/clinical data suggested factors other than identified FGF23 contributing to XLH. Higher levels of circulating sclerostin were detected in XLH. Sclerostin inhibition promoted bone formation in Hyp mice, while restored phosphate homeostasis in age-/gender-dependent manner. The role of sclerostin in regulating phosphate metabolism deserves investigation. Sclerostin/FGF23 levels of XLH patients with/without response to FGF23-antibody warrants study to develop precise sclerostin/FGF23 inhibition strategy or synergistic/additive strategy. Notably, OI patients were associated with cardiovascular abnormalities, so were XLH patients receiving conventional therapy. Targeting sclerostin loop3 promoted bone formation without cardiovascular risks. Further, blockade of sclerostin loop3-LRP4 interaction while preserving sclerostin loop2-ApoER2 interaction could be a potential precise sclerostin inhibition strategy for OI and XLH with cardiovascular safety.The Translational Potential of this Article.Preclinical data on the molecular understanding of sclerostin inhibition in OI and therapeutic efficacy in mouse models of different genotypes, as well as clinical data on serum sclerostin levels in patients with different phenotypes of OI, were reviewed and discussed. Translationally, it would facilitate to develop clinical prediction strategies (e.g. based on genotype and age, not just phenotype) for OI patients responsive to sclerostin inhibition. Both preclinical and clinical data suggested sclerostin as another factor contributing to XLH, in addition to the identified FGF23. The molecular understanding and therapeutic effects of sclerostin inhibition on both promoting bone anabolism and improving phosphate homostasis in Hyp mice were reviewed and discussed. Translationaly, it would facilitate the development of precise sclerostin/FGF23 inhibition strategy or synergistic/additive strategy for the treatment of XLH. Cardiovascular risk could not be ruled out during sclerostin inhibition treatment, especially for OI and XLH patients with cardiovascular diseases history and cardiovascular abnormalities. Studies on the role of sclerostin in inhiting bone formation and protecting cardiovascular system were reviewed and discussed. Translationaly, blockade of sclerostin loop3-LRP4 interaction while preserving sclerostin loop2-ApoER2 interaction could be a potential precise sclerostin inhibition strategy for OI and XLH with cardiovascular safety.

Abstract Background Kiwifruit (Actinidiaceae family) is an economically important fruit tree in China and New Zealand. It is a typical dioecious plant that has undergone frequent natural hybridization, along with chromosomal ploidy diversity within the genus Actinidia, resulting in higher genetic differences and horticultural diversity between interspecific and intraspecific traits. This diversity provides a rich genetic base for breeding. China is not only the original center of speciation for the Actinidia genus but also its distribution center, housing the most domesticated species: A. chinensis var. chinensis, A. chinensis var. deliciosa, A. arguta, and A. polygama. However, there have been relatively few studies on the application of DNA markers and the genetic basis of kiwifruit plants. By combining information from chloroplast-specific SNPs and nuclear SCoT (nSCoT) markers, we can uncover complementary aspects of genetic variation, population structure, and evolutionary relationships. In this study, one chloroplast DNA (cpDNA) marker pair was selected out of nine cpDNA candidate pairs. Twenty nSCoT markers were selected and used to assess the population structure and chloroplast-specific DNA haplotype diversity in 55 kiwifruit plants (Actinidia), including 20 samples of A. chinensis var. chinensis, 22 samples of A. chinensis var. deliciosa, 11 samples of A. arguta, and two samples of A. polygama, based on morphological observations collected from China. Results The average genetic distance among the 55 samples was 0.26 with chloroplast-specific SNP markers and 0.57 with nSCoT markers. The Mantel test revealed a very small correlation (r = 0.21). The 55 samples were categorized into different sub-populations using Bayesian analysis, the Unweighted Pair Group Method with the Arithmetic Mean (UPGMA), and the Principal Component Analysis (PCA) method, respectively. Based on the analysis of 205 variable sites, a total of 15 chloroplast-specific DNA haplotypes were observed, contributing to a higher level of polymorphism with an Hd of 0.78. Most of the chloroplast-specific DNA haplotype diversity was distributed among populations, but significant diversity was also observed within populations. H1 was shared by 24 samples, including 12 of A. chinensis var. chinensis and 12 of A. chinensis var. deliciosa, indicating that H1 is an ancient and dominant haplotype among the 55 chloroplast-specific sequences. H2 may not have evolved further.The remaining haplotypes were rare and unique, with some appearing to be exclusive to a particular variety and often detected in single individuals. For example, the H15 haplotype was found exclusively in A. polygama. Conclusion The population genetic variation explained by chloroplast-specific SNP markers has greater power than that explained by nSCoTs, with chloroplast-specific DNA haplotypes being the most efficient. Gene flow appears to be more evident between A. chinensis var. chinensis and A. chinensis var. deliciosa, as they share chloroplast-specific DNA haplotypes, In contrast, A.arguta and A. polygama possess their own characteristic haplotypes, derived from the haplotype of A. chinensis var. chinensis. Compared with A. chinensis, the A.arguta and A. polygama showed better grouping. It also seems crucial to screen out, for each type of molecular marker, especially haplotypes, the core markers of the Actinidia genus.

Cu–3Zr alloy was prepared by powder metallurgy and two-stage thermomechanical process, and the microstructure evolution and properties were investigated. The results show that the microstructure of as-sintered Cu–3Zr alloy is consisted of the coarse Cu–Zr intermetallic compounds and equiaxed Cu grains. After extrusion, the second phase is smaller in cross section, while deformation texture of the Cu matrix can be seen from longitudinal section. After further drawing, the Cu grains are elongated and the Cu–Zr intermetallic phase is broken into fine particles. The yield strength (YS), ultimate tensile strength (UTS) and electrical conductivity are 593 MPa, 684 MPa, and 74%IACS, which are respectively increased by 38%, 34% and 21% as compared to the as-sintered Cu–3Zr alloy. Phase analysis and microstructural characterization demonstrate that the Cu5Zr, Cu8Zr3, and Cu10Zr7 precipitates are uniformly distributed in the Cu matrix. Cu5Zr/Cu and Cu8Zr3/Cu are combined in the form of coherent relation. Cu10Zr7/Cu are combined in the form of semi-coherent relation. The enhanced mechanical properties can be attributed to the multiple synergistical strengthening effect. The elongated Cu grains and fine Cu–Zr precipitation phases block the movement of dislocation, resulting in the remarkable enhancement of strength. Moreover, the elongated Cu grains can provide fast channel for the electron transport, giving rise to simultaneous increase of the electrical conductivity.

Evaluation of landslide susceptibility is essential to planning of land and space utilization. For this purpose, the paper presents a case study from Fugu County, Shaanxi Province, China. Firstly, the geological environment and current state of landslides in Fugu County were investigated. Then, slope, aspect, terrain relief, curvature, lithology, land type, and normalized difference vegetation index (NDVI) were considered as the landslide susceptibility condition factors, and the correlation between these carried out by using Multicollinearity Analysis method. Next, landslide and non-landslide samples were divided into training samples and testing samples according to the sample ratios of 8/2, 7/3, 6/4, and 5/5, respectively. The landslide susceptibility mapping was carried out by using Random Forest (RF) model and Frequency Ratio coupled with Random Forest (FR-RF) model, respectively. Lastly, the landslide density (LD), landslide frequency ratio (LFR), the area under the curve (AUC) of the receiver operator, and other indicators were used to validate the rationality, accuracy, and performance of the landslide susceptibility maps produced from different models and ratios. The results indicated that all maps are reasonable, except the map when ratio is 5/5. For each map, regardless of ratios, the LD and LFR are the greatest in the zones classed as having a very high susceptibility, followed by those with a high, moderate, low, and very low classes.In the Random Forest (RF) model, when the training test set is not at the same time its in the area of extremely high sensitivity of LD and the size of the FR value respectively 7/3 (201.026) ​> ​8/2 (154.440) ​> ​6/4 (93.696) >5/5 (136.364) and 7/3 (4.806) ​> ​8/2 (3.692) ​> ​6/4 (3.260) ​> ​5/5 (2.240); in the Frequency Ratio coupled with Random Forest (FR-RF) model, Inall the training test sets the size of the proportion of LD and FR value respectively 7/3 (145.693) ​> ​6/4 (127.151) ​> ​5/5 (122.857) ​> ​8/2 (113.263) and 7/3 (3.334) ​> ​6/4 (3.073) ​> ​5/5 (2.811) ​> ​8/2 (2.592). What else, from the comparison of ROC curves, when ratio is 7/3, the accuracy of the two models is higher than that of other ratios. Similarly, the results of the ensemble model (A combination of two models with different learning abilities.) are not more reasonable than the results of the single model, which reflects that the combination of a weaker learner model (Frequency Ratio model here) with a stronger learner model (Random Forest model here) can diminish the performance of the stronger model.

Abstract Regulated cell death (RCD) plays a pivotal role in various biological processes, including development, tissue homeostasis, and immune response. However, a comprehensive assessment of RCD status and its associated features at the pan-cancer level remains unexplored. Furthermore, despite significant advancements in immune checkpoint inhibitors (ICI), only a fraction of cancer patients currently benefit from treatments. Given the emerging evidence linking RCD and ICI efficacy, we hypothesize that the RCD status could serve as a promising biomarker for predicting the ICI response and overall survival (OS) in patients with malignant tumors. We defined the RCD levels as the RCD score, allowing us to delineate the RCD landscape across 30 cancer types, 29 normal tissues in bulk, and 2,573,921 cells from 82 scRNA-Seq datasets. By leveraging large-scale datasets, we aimed to establish the positive association of RCD with immunity and identify the RCD signature. Utilizing 7 machine-learning algorithms and 18 ICI cohorts, we developed an RCD signature (RCD.Sig) for predicting ICI response. Additionally, we employed 101 combinations of 10 machine-learning algorithms to construct a novel RCD survival-related signature (RCD.Sur.Sig) for predicting OS. Furthermore, we obtained CRISPR data to identify potential therapeutic targets. Our study presents an integrative framework for assessing RCD status and reveals a strong connection between RCD status and ICI effectiveness. Moreover, we establish two clinically applicable signatures and identify promising potential therapeutic targets for patients with tumors.

Abstract Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

In the past two decades, machine learning (ML) has been extensively adopted in protein-targeted small molecule (SM) discovery. Once trained, ML models could exert their predicting abilities on large volumes of molecules within a short time. However, applying ML approaches to discover RNA-targeted SMs is still in its early stages. This is primarily because of the intrinsic structural instability of RNA molecules that impede the structure-based screening or designing of RNA-targeted SMs. Recently, with more studies revealing RNA structures and a growing number of RNA-targeted ligands being identified, it resulted in an increased interest in the field of drugging RNA. Undeniably, intracellular RNA is much more abundant than protein and, if successfully targeted, will be a major alternative target for therapeutics. Therefore, in this context, as well as under the premise of having RNA-related research data, ML-based methods can get involved in improving the speed of traditional experimental processes.

To reduce the amount of ordinary concrete and then reduce carbon dioxide emission, improving the engineering application range of self-compacting fly ash concrete (FASCC), this study explored the bond–slip traits between FASCC and a steel tube. Six samples were created, and bond–slip push-out tests were performed with varying concrete strength grades and steel tube internal setups. Digital image correlation (DIC) technology was applied to track the surface strain of four samples throughout the experiment. The results show that the outer surface of the steel tube stays mostly undistorted after the concrete is pushed out. Prior to reaching peak load, the load–slip curves of each specimen exhibit a primarily linear load–displacement relationship. Post-peak, the curves diverge into two distinct patterns, namely a sudden drop and a gradual decline. As the strength grade of the inner concrete increases, the interfacial bond between the steel tube and FASCC improves. Additionally, under the same conditions, the internal structure of the steel tube significantly enhances bonding strength. The FA40-Z specimen shows a maximum load that is 25.6% and 53.7% higher than the FA40-G and FA40-C specimens, respectively. The strain evolution patterns of steel tubes within FASCC and regular self-compacting concrete demonstrate similar characteristics. These observations provide valuable insights for the application of FASCC in engineering projects.

Osteogenesis imperfecta (OI) is a rare genetic disease characterized by bone fragility and bone formation. Sclerostin could negatively regulate bone formation by antagonizing the Wnt signal pathway, whereas it imposes severe cardiac ischemic events in clinic. Our team has screened an aptamer that could promote bone anabolic potential without cardiovascular risk. However, the affinity of the aptamer is lower and needs to be improved. In the study, hydrophobic quinoline molecule with unique orientations (seven subtypes) were incorporated into key sites of a bone anabolic aptamer against sclerostin to form a modified aptamer library. Among all the quinoline modifications, 5-quinoline modification could shape the molecular recognition of modified aptamers to sclerostin to facilitate enhancing its binding to sclerostin toward the highest affinity by interacting with newly participated binding sites in sclerostin. Further, 5-quinoline modification could facilitate the modified aptamer attenuating the suppressed effect of the transfected sclerostin on both Wnt signaling and bone formation marker expression levels in vitro, promoting bone anabolism in OI mice (Col1a2+/G610C). The proposed quinoline-oriented modification strategy could shape the molecular recognition of modified aptamers to proteins to facilitate enhancing its binding affinity and therapeutic potency.

Abstract Background Cottonseed oil is a promising edible plant oil with abundant unsaturated fatty acids. However, few studies have been conducted to explore the characteristics of cottonseed oil. The molecular mechanism of cottonseed oil accumulation remains unclear. Results In the present study, we conducted comparative transcriptome and weighted gene co-expression network (WGCNA) analysis for two G. hirsutum materials with significant difference in cottonseed oil content. Results showed that, between the high oil genotype 6053 (H6053) and the low oil genotype 2052 (L2052), a total of 412, 507, 1,121, 1,953, and 2,019 differentially expressed genes (DEGs) were detected at 10, 15, 20, 25, and 30 DPA, respectively. Remarkably, a large number of the down-regulated DEGs were enriched in the phenylalanine metabolic processes. Investigation into the dynamic changes of expression profiling of genes associated with both phenylalanine metabolism and oil biosynthesis has shed light on a significant competitive relationship in substrate allocation during cottonseed development. Additionally, the WGCNA analysis of all DEGs identified eight distinct modules, one of which includes GhPXN1, a gene closely associated with oil accumulation. Through phylogenetic analysis, we hypothesized that GhPXN1 in G. hirsutum might have been introgressed from G. arboreum. Overexpression of the GhPXN1 gene in tobacco leaf suggested a significant reduction in oil content compared to the empty-vector transformants. Furthermore, ten other crucial oil candidate genes identified in this study were also validated using quantitative real-time PCR (qRT-PCR). Conclusions Overall, this study enhances our comprehension of the molecular mechanisms underlying cottonseed oil accumulation.

Nitroreductase (NTR) is an enzyme expressed at an abnormally high level in solid tumors, which is associated with the hypoxia level in tumors. The establishment of a high-performance and convenient fluorescent platform for the fast monitoring of NTR is of pivotal importance. Herein, a novel host–guest complex was created by encapsulating a fluorescent substrate GP-NTR within a metal–organic capsule Zn-MPB that included a NADH mimic for the detection of hypoxia via responding to nitroreductase (NTR) with fast responsiveness and good fluorescence imaging. Notably, the double-substrate process was streamlined to a single–substrate process by the host–guest supramolecular method in the catalytic process of NTR, which enabled the reaction to be independent of the cofactor NADH supply and shortened the distance between the substrate and the active site of NTR. The increasing fluorescence intensity of Zn-MPB⊃GP-NTR exhibits a linear relationship with NTR concentration and shows a fast response toward NTR in solution in tens of seconds. Zn-MPB⊃GP-NTR also displays high sensitivity to NTR with a low detection limit of 6.4 ng/mL. Cells and in vivo studies have confirmed that Zn-MPB⊃GP-NTR could be successfully applied for the fast imaging of NTR in NTR-overexpressed tumor cells and tumor-bearing animals. The host–guest platform not only provides a new avenue for the design and optimization of a fluorescence detection platform for the rapid and quantitative detection of NTR activity, but also offers an imaging tool for the early diagnosis of hypoxia-related tumors.

Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed.

Recovering valuable active substances from the by-products of agricultural processing is a crucial concern for scientific researchers. This paper focuses on the enrichment of soybean trypsin inhibitor (STI) from soybean whey wastewater using either ammonium sulfate salting or ethanol precipitation, and discusses their physicochemical properties. The results show that at a 60% ethanol content, the yield of STI was 3.983 mg/mL, whereas the yield was 3.833 mg/mL at 60% ammonium sulfate saturation. The inhibitory activity of STI obtained by ammonium sulfate salting out (A-STI) was higher than that obtained by ethanol precipitation (E-STI). A-STI exhibited better solubility than E-STI at specific temperatures and pH levels, as confirmed by turbidity and surface hydrophobicity measurements. Thermal characterization revealed that both A-STI and E-STI showed thermal transition temperatures above 90 °C. Scanning electron microscopy demonstrated that A-STI had a smooth surface with fewer pores, while E-STI had a rough surface with more pores. In conclusion, there was no significant difference in the yield of A-STI and E-STI (p < 0.05); however, the physicochemical properties of A-STI were superior to those of E-STI, making it more suitable for further processing and utilization. This study provides a theoretical reference for the enrichment of STI from soybean whey wastewater.

Abstract Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.

IntroductionColorectal cancer (CRC) is the third most common malignant tumor, and neoadjuvant chemo-radiotherapy is usually recommended for advanced stage colorectal cancer. Radiotherapy can cause damage to intestinal mucosal barrier, which may be related to perioperative complications. Intestinal microbiota is one of the constituents of the intestinal mucosal biological barrier, and literature reports that patients with CRC have changes in corresponding oral microbiota. This study aims to analyze the levels of immunoglobulin SIgA, inflammatory factors, lymphocyte subsets quantity, and proportion in surgical specimens of intestinal mucosa at different time intervals after radiotherapy, in order to seek investigation for the optimal surgical time after radiotherapy and to provide evidence for finding probiotics or immunomodulators through high-throughput sequencing of bacterial 16s rRNA in patients' saliva microbiota. Ultimately, this may provide new ideas for reducing perioperative complications caused by radiotherapy-induced intestinal damage.MethodsWe selected intestinal mucosal tissue and saliva samples from over 40 patients in our center who did not undergo radiotherapy and underwent surgery at different time intervals after radiotherapy. Detection of SIgA was performed using ELISA assay. Western Blotting was used to detect IL-1β, IL-6, and IL-17 in the intestinal mucosal tissue. Flow cytometry was used to detect CD4 and CD8. And the microbial community changes in saliva samples were detected through 16s rRNA sequencing.ResultsAfter radiotherapy, changes in SIgA, various cytokines, CD4CD8 lymphocyte subsets, and oral microbiota in the intestinal mucosal tissue of rectal cancer patients may occur. Over time, this change may gradually recover.DiscussionIn colorectal cancer, oncological aspects often receive more attention, while studies focusing on the intestinal mucosal barrier are less common. This study aims to understand the repair mechanisms of the intestinal mucosal barrier and reduce complications arising from radiotherapy-induced damage. The relationship between oral microbiota and systemic diseases has gained interest in recent years. However, the literature on the oral microbiota after radiotherapy for rectal cancer remains scarce. This study addresses this gap by analysing changes in the salivary microbiota of rectal cancer patients before and after radiotherapy, shedding light on microbiota changes. It aims to lay the groundwork for identifying suitable probiotics or immunomodulators to alleviate perioperative complications and improve the prognosis of CRC.

Plants lack behavioral responses to avoid dramatic environmental changes associated with the annual seasons. For survival, they have evolved complex sensory systems to sense fluctuations in light and optimize their architecture in response to changes in these cues. Phytochrome A (phyA) was initially identified as a photoreceptor that senses far-red light signals. It was then identified as playing a central role in promoting hypocotyl growth, fiber development, and flowering time in a variety of plants including Arabidopsis, rice, soybean and cotton. Under dark conditions, phyA is present in the cytoplasm in the physiologically inactive (Pr) form. Far-red light signals induce the transformation of Pr into the physiologically active (Pfr) form, after which Pfr-phyA is recognized by FAR-RED ELONGATED HYPOCOTYL 1 (FHY1) and FHY1-LIKE (FHL) and translocated to the nucleus, initiating a series of signaling cascades. The current review comprehensively summarizes recent advances in understanding the function of phyA in plants, including phyA-mediated shade avoidance and flowering time. Remaining issues and possible directions for future research on phyA are also discussed.

Background Immunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers.Methods To address these gaps, we constructed The Cancer CircRNA Immunome Atlas (TCCIA), the first database that combines circRNA profiles, immunotherapy response data, and clinical outcomes across multicancer types. The construction of TCCIA involved applying standardized preprocessing to the raw sequencing FASTQ files, characterizing circRNA profiles using an ensemble approach based on four established circRNA detection tools, analyzing tumor immunophenotypes, and compiling immunotherapy response data from diverse cohorts treated with immune checkpoint blockades (ICBs).Results TCCIA encompasses over 4,000 clinical samples obtained from 25 cohorts treated with ICBs along with other treatment modalities. The database provides researchers and clinicians with a cloud-based platform that enables interactive exploration of circRNA data in the context of ICB. The platform offers a range of analytical tools, including browse of identified circRNAs, visualization of circRNA abundance and correlation, association analysis between circRNAs and clinical variables, assessment of the tumor immune microenvironment, exploration of tumor molecular signatures, evaluation of treatment response or prognosis, and identification of altered circRNAs in immunotherapy-sensitive and resistant tumors. To illustrate the utility of TCCIA, we showcase two examples, including circTMTC3 and circMGA, by employing analysis of large-scale melanoma and bladder cancer cohorts, which unveil distinct impacts and clinical implications of different circRNA expression in cancer immunotherapy.Conclusions TCCIA represents a significant advancement over existing resources, providing a comprehensive platform to investigate the role of circRNAs in immuno-oncology.

The molecular weight of nucleic acid aptamers (20 kDa) is lower than the cutoff threshold of the renal filtration (30–50 kDa), resulting in a very short half-life, which dramatically limits their druggability. To address this, we utilized 3-(2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-(4-hydroxy-2-oxo-2H-chromen-6-yl)propenamide (HC) and 12-((2,5-dioxopyrrolidin-1-yl)oxy)-12-oxododecanoic acid (DA), two newly designed coupling agents, for synergistic binding to human serum albumin (HSA). Both HC and DA are conjugated to a bone anabolic aptamer (Apc001) against sclerostin to form an Apc001OC conjugate with high binding affinity to HSA. Notably, HC and DA could synergistically facilitate prolonging the half-life of the conjugated Apc001 and promoting its bone anabolic potential. Using the designed blocking peptides, the mechanism studies indicate that the synergistic effect of HC-DA on pharmacokinetics and bone anabolic potential of the conjugated Apc001 is achieved via their synergistic binding to HSA. Moreover, biweekly Apc001OC at 50 mg/kg shows comparable bone anabolic potential to the marketed sclerostin antibody given weekly at 25 mg/kg. This proposed bimolecular modification strategy could help address the druggability challenge for aptamers with a short half-life.

Abstract Background Verticillium wilt is one of the most devasting diseases for many plants, leading to global economic loss. Cotton is known to be vulnerable to its fungal pathogen, Verticillium dahliae, yet the related genetic mechanism remains unknown. Results By genome-wide association studies of 419 accessions of the upland cotton, Gossypium hirsutum, we identify ten loci that are associated with resistance against Verticillium wilt. Among these loci, SHZDI1/SHZDP2/AYDP1 from chromosome A10 is located on a fragment introgressed from Gossypium arboreum. We characterize a large cluster of Toll/interleukin 1 (TIR) nucleotide-binding leucine-rich repeat receptors in this fragment. We then identify a dual-TIR domain gene from this cluster, GhRVD1, which triggers an effector-independent cell death and is induced by Verticillium dahliae. We confirm that GhRVD1 is one of the causal gene for SHZDI1. Allelic variation in the TIR domain attenuates GhRVD1-mediated resistance against Verticillium dahliae. Homodimerization between TIR1-TIR2 mediates rapid immune response, while disruption of its αD- and αE-helices interface eliminates the autoactivity and self-association of TIR1-TIR2. We further demonstrate that GhTIRP1 inhibits the autoactivity and self-association of TIR1-TIR2 by competing for binding to them, thereby preventing the resistance to Verticillium dahliae. Conclusions We propose the first working model for TIRP1 involved self-association and autoactivity of dual-TIR domain proteins that confer compromised pathogen resistance of dual-TIR domain proteins in plants. The findings reveal a novel mechanism on Verticillium dahliae resistance and provide genetic basis for breeding in future.

The single-event effects (SEEs) of frequency divider circuits and the radiation tolerance of the hardened circuit are studied in this paper. Based on the experimental results of SEEs in InP HBTs, a transient current model for sensitive transistors is established, taking into account the influence of factors such as laser energy, base-collector junction voltage, and radiation position. Moreover, the SEEs of the (2:1) static frequency divider circuit with the InP DHBT process are simulated under different laser energies by adding the transient current model at sensitive nodes. The effect of the time relationship between the pulsed laser and clock signal are discussed. Changes in differential output voltage and the degradation mechanism of unhardened circuits are analyzed, which are mainly attributed to the cross-coupling effect between the transistors in the differential pair. Furthermore, the inverted output is directly connected to the input, leading to a feedback loop and causing significant logic upsets. Finally, an effective hardened method is proposed to provide redundancy and mitigate the impacts of SEEs on the divider. The simulation results demonstrate a notable improvement in the radiation tolerance of the divider.

With the rapid progress in a power conversion efficiency reaching up to 26.1%, which is among the highest efficiency for single-junction solar cells, organic–inorganic hybrid perovskite solar cells have become a research focus in photovoltaic technology all over the world, while the instability of these perovskite solar cells, due to the decomposition of its unstable organic components, has restricted the development of all-inorganic perovskite solar cells. In recent years, Br-mixed halogen all-inorganic perovskites (CsPbI3−xBrx) have aroused great interests due to their ability to balance the band gap and phase stability of pure CsPbX3. However, the photoinduced phase segregation in lead mixed halide perovskites is still a big burden on their practical industrial production and commercialization. Here, we demonstrate inhibited photoinduced phase segregation all-inorganic CsPbI1.2Br1.8 films and their corresponding perovskite solar cells by incorporating a 1-butyl-1-methylpiperidinium tetrafluoroborate ([BMP]+[BF4]−) compound into the CsPbI1.2Br1.8 films. Then, its effect on the perovskite films and the corresponding hole transport layer-free CsPbI1.2Br1.8 solar cells with carbon electrodes under light is investigated. With a prolonged time added to the reduced phase segregation terminal, this additive shows an inhibitory effect on the photoinduced phase segregation phenomenon for perovskite films and devices with enhanced cell efficiency. Our study reveals an efficient and simple route that suppresses photoinduced phase segregation in cesium lead mixed halide perovskite solar cells with enhanced efficiency.

Forest height is of great significance for forest resource management and forest carbon sink estimation. Tomographic synthetic aperture radar (TomoSAR) technology provides an effective means for the accurate inversion of this parameter. Several multi-polarization synthetic aperture radar (SAR) images are generally required to obtain forest height. However, it is common that only a small number of single-polarization images can be acquired, due to the complexity of the systems and the limitations of the observation cycles, and there may be only one fully polarimetric image available. This means that it is impossible to use TomoSAR to estimate forest height over a wide area. Based on this, in this study, we combined polarimetric SAR (PolSAR) variables and single-polarization TomoSAR (SP-TomoSAR) features to estimate forest height for the first time. The image fusion was achieved through the use of six machine learning methods: light gradient-boosting machine (lightGBM), random forest (RF), extreme gradient boosting (XGBoost), gradient-boosted decision tree (GBDT), k-nearest neighbor (KNN), and support vector machine regression (SVR). To investigate the advantages of the proposed method, a small amount of SP-TomoSAR data with non-uniformly distributed baselines and one PolSAR image were acquired over the tropical rainforest of French Guiana. We then used H/A/alpha and Freeman-Durden decomposition methods to obtain the polarization features and applied the Capon algorithm to obtain the tomographic features. Four sets of comparative experiments were carried out, and the results confirmed that the combination of SP-TomoSAR and PolSAR can achieve an accurate estimation of forest height, and the estimation result of the HV tomographic features is better than that of the HH tomographic features. Moreover, after adding the polarization features, the estimation accuracy was clearly improved, compared to using only tomographic features, which suggests that PolSAR can provide important supplementary information for SP-TomoSAR. In addition, among the six machine learning algorithms, the RF algorithm has the highest estimation accuracy with a root mean square error (RMSE) of 5.14 m and an R of 0.83, while the lightGBM algorithm is significantly ahead of the others in terms of computational efficiency.

According to the World Health Organization, more and more people in the world are suffering from somnipathy. Automatic sleep staging is critical for assessing sleep quality and assisting in the diagnosis of psychiatric and neurological disorders caused by somnipathy. Many researchers employ deep learning methods for sleep stage classification and have achieved high performance. However, there are still no effective methods to modeling intrinsic characteristics of salient wave in different sleep stages from physiological signals. And transition rules hidden in signals from one to another sleep stage cannot be identified and captured. In addition, class imbalance problem in dataset is not conducive to building a robust classification model. To solve these problems, we construct a deep neural network combining MSE(Multi-Scale Extraction) based U-structure and CBAM (Convolutional Block Attention Module) to extract the multi-scale salient waves from single-channel EEG signals. The U-structured convolutional network with MSE is utilized to extract multi-scale features from raw EEG signals. After that, the CBAM is used to focus more on salient variation and then learn transition rules between successive sleep stages. Further, a class adaptive weight cross entropy loss function is proposed to solve the class imbalance problem. Experiments in three public datasets show that our model greatly outperform the state-of-the-art results compared with existing methods. The overall accuracy and macro F1-score (Sleep-EDF-39: 90.3%-86.2, Sleep-EDF-153: 89.7%-85.2, SHHS: 86.8%-83.5) on three public datasets suggest that the proposed model is very promising to completely take place of human experts for sleep staging.

Abstract Background Destruction of articular cartilage and bone is the main cause of joint dysfunction in rheumatoid arthritis (RA). Acid-sensing ion channel 1a (ASIC1a) is a key molecule that mediates the destruction of RA articular cartilage. Estrogen has been proven to have a protective effect against articular cartilage damage, however, the underlying mechanisms remain unclear. Methods We treated rat articular chondrocytes with an acidic environment, analyzed the expression levels of mitochondrial stress protein HSP10, ClpP, LONP1 by q-PCR and immunofluorescence staining. Transmission electron microscopy was used to analyze the mitochondrial morphological changes. Laser confocal microscopy was used to analyze the Ca2+, mitochondrial membrane potential (Δψm) and reactive oxygen species (ROS) level. Moreover, ASIC1a specific inhibitor Psalmotoxin 1 (Pctx-1) and Ethylene Glycol Tetraacetic Acid (EGTA) were used to observe whether acid stimulation damage mitochondrial function through Ca2+ influx mediated by ASIC1a and whether pretreatment with estrogen could counteract these phenomena. Furthermore, the ovariectomized (OVX) adjuvant arthritis (AA) rat model was treated with estrogen to explore the effect of estrogen on disease progression. Results Our results indicated that HSP10, ClpP, LONP1 protein and mRNA expression and mitochondrial ROS level were elevated in acid-stimulated chondrocytes. Moreover, acid stimulation decreased mitochondrial membrane potential and damaged mitochondrial structure of chondrocytes. Furthermore, ASIC1a specific inhibitor PcTx-1 and EGTA inhibited acid-induced mitochondrial abnormalities. In addition, estrogen could protect acid-stimulated induced mitochondrial stress by regulating the activity of ASIC1a in rat chondrocytes and protects cartilage damage in OVX AA rat. Conclusions Extracellular acidification induces mitochondrial stress by activating ASIC1a, leading to the damage of rat articular chondrocytes. Estrogen antagonizes acidosis-induced joint damage by inhibiting ASIC1a activity. Our study provides new insights into the protective effect and mechanism of action of estrogen in RA.

The cooling-induced thermal shrinkage behavior and residual stress in fabrication of 2.5D woven fiber reinforced Al composites were investigated by numerical and experimental approach. A microscale finite element (FE) model was established to determine the transversely isotropic thermal shrinkage of the Al-impregnated yarns. The validated properties were subsequently input into a mesoscale FE model for the composites, in which the temperature-dependent properties of the constituent materials were incorporated. Parallel experiments were carried out to verify the thermal shrinkage behaviors and the residual stresses that were predicted by the FE simulations. The residual stress distribution of the matrix pocket, warp and weft yarns were studied, and their contributions to the local damage of matrix pocket and interface were analyzed. Based on the verified model, the effect of fabric structure on the residual stresses and macroscopic shrinkage properties were parametrically evaluated. The results make a contribution to evaluating the cooling-induced residual stress, local damage, and shrinkage behavior of metal matrix composites reinforced with complex fabric.

Abstract Anti‐angiogenic therapy has long been considered a promising strategy for solid cancers. Intrinsic resistance to hypoxia is a major cause for the failure of anti‐angiogenic therapy, but the underlying mechanism remains unclear. Here, it is revealed that N4‐acetylcytidine (ac4C), a newly identified mRNA modification, enhances hypoxia tolerance in gastric cancer (GC) cells by promoting glycolysis addiction. Specifically, acetyltransferase NAT10 transcription is regulated by HIF‐1α, a key transcription factor of the cellular response to hypoxia. Further, acRIP‐sequencing, Ribosome profiling sequencing, RNA‐sequencing, and functional studies confirm that NAT10 in turn activates the HIF‐1 pathway and subsequent glucose metabolism reprogramming by mediating SEPT9 mRNA ac4C modification. The formation of the NAT10/SEPT9/HIF‐1α positive feedback loop leads to excessive activation of the HIF‐1 pathway and induces glycolysis addiction. Combined anti‐angiogenesis and ac4C inhibition attenuate hypoxia tolerance and inhibit tumor progression in vivo. This study highlights the critical roles of ac4C in the regulation of glycolysis addiction and proposes a promising strategy to overcome resistance to anti‐angiogenic therapy by combining apatinib with ac4C inhibition.

The problem of detecting key nodes in a network (i.e. nodes with the greatest ability to spread an infection) has been studied extensively in the past. Some approaches to key node detection compute node centrality, but there is no formal proof that central nodes also have the greatest spreading capacity. Other methods use epidemiological models (e.g., the SIR model) to describe the spread of an infection and rely on numerical simulations to find out key nodes; these methods are highly accurate but computationally expensive. To efficiently but accurately detect key nodes, we propose a novel deep learning method called Rank by Graph Convolutional Network, RGCN. Our method constructs a subnetwork around each node to estimate its spreading power; then RGCN applies a graph convolutional network to each subnetwork and the adjacency matrix of the network to learn node embeddings. Finally, a neural network is applied to the node embeddings to detect key nodes. Our RGCN method outperforms state-of-the-art approaches such as RCNN and MRCNN by 11.84% and 13.99%, respectively, when we compare the Kendall’s τ coefficient between the node ranking produced by each method with the true ranking obtained by SIR simulations.

IntroductionPatients undergoing cranial ionizing radiation therapy for brain malignancies are at increased risk of long-term neurocognitive decline, which is poorly understood and currently untreatable. Although the molecular pathogenesis has been intensively researched in many organisms, whether and how ionizing radiation alters functional neurotransmission remains unknown. This is the first study addressing physiological changes in neurotransmission after ionizing radiation exposure.MethodsTo elucidate the cellular mechanisms of radiation damage, using calcium imaging, we analyzed the effects of ionizing radiation on the neurotransmitter-evoked responses of prothoracicotropic hormone (PTTH)-releasing neurons in Drosophila larvae, which play essential roles in normal larval development.ResultsThe neurotransmitters dopamine and tyramine decreased intracellular calcium levels of PTTH neurons in a dose-dependent manner. In gamma irradiated third-instar larvae, a dose of 25 Gy increased the sensitivity of PTTH neurons to dopamine and tyramine, and delayed development, possibly in response to abnormal functional neurotransmission. This irradiation level did not affect the viability and arborization of PTTH neurons and successful survival to adulthood. Exposure to a 40-Gy dose of gamma irradiation decreased the neurotransmitter sensitivity, physiological viability and axo-dendritic length of PTTH neurons. These serious damages led to substantial developmental delays and a precipitous reduction in the percentage of larvae that survived to adulthood. Our results demonstrate that gamma irradiation alters neurotransmitter-evoked responses, indicating synapses are vulnerable targets of ionizing radiation.DiscussionThe current study provides new insights into ionizing radiation-induced disruption of physiological neurotransmitter signaling, which should be considered in preventive therapeutic interventions to reduce risks of neurological deficits after photon therapy.

The pyrolysis of metal-organic frameworks (MOFs) is a popular strategy for the synthesis of nanoporous structures. Polymetallic oxides (POMs) are a class of polyhedral structural compounds with unique physicochemical properties. Little effort has been paid to evaluate MOF-POM hybrid-derived materials for peroxomonosulfate (PMS) activation. In this study, a cobalt-based MOF, ZIF-67, together with three types of POMs (phosphomolybdic acid, silicotungstic acid, and phosphotungstic acid), were used as precursors for the synthesis of PMS activation catalyst via pyrolysis. Three T-POMs@ZIF-67 nanohybrids (T-PMo@ZIF-67, T-SiW@ZIF-67, and T-PW@ZIF-67) were obtained by pyrolyzing the prepared precursors at 500 °C. Furthermore, the prepared T-POMs@ZIF-67 nanomaterials were evaluated for the catalytic activation of PMS in the degradation of levofloxacin (LEV). The results showed that the LEV degradation rate could reach 91.46% within 30 min under the optimized conditions when T-PW@ZIF-67 was used as the PMS activation catalyst. The catalytic efficiency of the catalyst decreased by only 9.63% after five cycles, indicating that the material has good stability. This work demonstrates the great potential of POMs@MOF derivatives for application in the field of wastewater treatment.

In this study, we report on how to design efficient catalysts for glucose oxidation via the transitional metal doping of nanohybrids of polyoxometalates (POMs) and metal-organic frameworks (MOFs). ZIF-67, a cobalt-based MOF, as well as phosphomolybdic acid (PMo), were used as precursors for the fabrication of pyrolyzed PMo@ZIF-67 (T-PMo@ZIF-67). A different amount of Ni2+ was doped into PMo@ZIF-67 to produce NixCoy@T-PMo@ZIF-67. Among them, Ni2Co2@T-PMo@ZIF-67 had the best performance. The power density of the fuel cell that used Ni2Co2@T-PMo@ZIF-67 as an anode catalyst was 3.76 times that of the cell that used active carbon as an anode catalyst. SEM and EDS mapping results indicate that Ni2Co2@T-PMo@ZIF-67 has a spherical structure and rough surface, and elements such as cobalt, nickel, and molybdenum are evenly distributed. XRD characterization indicates that Co3O4, CoMoO4, CoNiO4, and MoNiO4 co-exist in the composites. It is supposed that Co2+, Mo6+, and Ni2+ in the composites may have synergistic effects on the catalytic oxidation of glucose.

Abstract Due to the difficulty in sampling of metastatic tumors, patient selection is commonly based on results of primary tumor samples when metastatic samples are not available. However, due to tumor heterogeneity, metastatic tumors may be different from primary tumors in their phenotypes. The aim of this study was to investigate the expression of EGFR, HER2, and HER3 between primary and lymph node metastatic lesions of colorectal cancer. Paired primary tumors and lymph node metastases from 79 patients with colorectal cancer were retrospectively collected and analyzed for EGFR, HER2, and HER3 expression. High EGFR, HER2, and HER3 expression (2+ and 3+) was found in 64.2%, 66.0%, and 85.0% of primary tumors, and 56.8%, 46.0%, and 76.0% of lymph node metastases, respectively. Correlation rates between primary and metastatic lesions were 67.1%, 63.3%, and 74.7% for EGFR, HER2, and HER3, respectively. Stage IV tumors (with distant metastasis) had higher correlation rates of HER2 expression compared to stage III tumors (without distant metastasis) (P = 0.050). Moderate correlation rates in EGFR, HER2, and HER3 expression were observed between primary and metastatic lesions of colorectal cancer. Tumor stage or existence of distant metastasis could serve as potential predictive markers for the correlation of HER2 expression between primary tumors and lymph node metastases of colorectal cancer.

Association of tumor microenvironment and immune checkpoint (e.g., PD-L1) is important for immune escape, impacting chemotherapy and immunotherapy efficacy. We aimed to investigate biomarkers and therapeutic targets against treatment resistance in gastric cancer. Abundances of tumor-infiltrating immune cells were estimated in multiple datasets. Three patient subgroups (A, B, and C) were identified based on seven types of PD-L1- and IFN-γ-associated immune cells. Patients yielded increased prognosis from subgroup A to C (p = 0.027). Subgroup A was characterized by high activated CD4+ memory T cell infiltration, while more resting CD4+ memory T cells were in subgroup C. Further, a risk score was developed for prognostication. Lipoma preferred partner (LPP), as the hub gene in subgroup-related regulatory network, was upregulated (p < 0.01) and was associated with high risk score (p < 0.001) and poor survival (p < 0.05). Bioinformatics analyses and experiments found that LPP expressed restrictively in fibroblasts and associated with activated CD4+ memory T cell infiltration and tumor growth. High-LPP patients yielded fewer benefits from chemotherapy or immunotherapy, compared with the low-LPP group. We finally identified 28 compounds as sensitive drugs for high-LPP patients. Our findings suggested LPP might be a biomarker for treatment response and therapeutic target in gastric cancer.

In environments like coal mines and oil wells, electrical equipment carries the risk of disasters such as underground fires and methane gas explosions. However, communication equipment is essential for work. Our team has developed a long-range (approximately 25 km) audio transmission system that operates without the need for terminal power sources, thereby eliminating the risk of electrical sparks. This system leverages the reliability of optical fiber and employs a 1550 nm laser for analog audio transmission. After traveling through 25 km of optical fiber, the signal is converted back into electrical energy using a custom-designed Laser Power Converter (LPC). The optical fiber’s carrying capacity imposes limits on the light signal intensity, which, in turn, affects the signal transmission distance. To enable long-distance transmission, we have carefully chosen the optical wavelength with minimal loss. We observed that different LPC structures operating within the same wavelength band have an impact on the audio quality at the terminal. By comparing their characteristics, we have identified the key factors influencing audio output. The optimal LPC allows audio transmission over 25 km, with an output exceeding 12 mVrms.

The effectiveness of human security-based guard patrol systems often faces challenges related to the consistency of perimeter checks regarding timing and patterns. Some solutions use autonomous drones for monitoring assistance but primarily optimize their camera-based object detection capabilities for favorable lighting conditions. This research introduces an innovative approach to address these limitations—a flying watchdog designed to augment patrol operations with predetermined flight patterns, enabling checkpoint identification and position verification through vision-based methods. The system has a laser-based data transmitter to relay real-time location and timing information to a receiver. The proposed system consists of drone and ground checkpoints with distinctive shapes and colored lights, further enhanced by solar panels serving as laser data receivers. The result demonstrates the drone’s ability to detect four white dot LEDs with square configurations at distances ranging from 18 to 20 m, even under deficient light conditions based on the OpenCV detection algorithm. Notably, the study underscores the significance of achieving an even distribution of light shapes to mitigate light scattering effects on readings while also confirming that ambient light levels up to a maximum of 390 Lux have no adverse impact on the performance of the sensing device.

Collecting energy from the ambient environment through green and sustainable methods is highly expected to alleviate pollution and energy problems worldwide. Here, we report a facile and flexible hydrovoltaic generator capable of utilizing natural water evaporation for sustainable electricity production. The generator was fabricated by coating nano-Al2O3 on a twistable polyethylene terephthalate film. An open circuit voltage of 1.7 V was obtained on a piece of centimeter-sized hydrovoltaic generator under ambient conditions. The supercapacitor charged by the hydrovoltaic device can power a mini-motor efficiently. Moreover, by expanding the size or connecting it in series/parallel, the energy output of the generator can be further improved. Finally, the influence factors and the mechanism for power generation were primarily investigated. Electrical energy is produced by the migration of water through charged capillary channels. The environmental conditions, the properties of the solution and the morphology of the film have important effects on the electrical performance. This study is anticipated to offer enlightenment into designing novel hydrovoltaic devices, providing diverse energy sources for various self-powered devices and systems.

Polyoxometalates (POMs) are three-dimensional materials with unique, exceptional physical and chemical characteristics. The performance of POM-derived materials is anticipated to be enhanced by the combination of POM and metal–organic frameworks (MOFs) due to the high surface areas of MOF materials. In this study, three kinds of T-POMs@ZIF-67 (T-PMo@ZIF-67, T-SiW@ZIF-67, and T-PW@ZIF-67) were prepared by doping a cobalt-based MOF (ZIF-67) with three POMs (phosphomolybdic acid, silicotungstic acid, and phosphotungstic acid). The results show that the power density of the T-PMo@ZIF-67 catalyst anode is 3.08 times that of the blank control anode and 1.34 times that of the CoMoO4 catalyst. These findings suggest that the synthesis of MOF derivatives by doping MOFs with POM will have significant potential for use in the fuel cell industry.

In order to investigate the factors affecting the acoustic performance of the extrinsic Fabry–Perot interferometer (EFPI) fiber-optic acoustic pressure sensor and to effectively improve its detection capability, this paper enhances the sensor’s detection sensitivity by adding more sensitized rings to its acoustic pressure-sensitive film. Furthermore, a novel real-time coupled acoustic test method is proposed to simultaneously monitor the changes in the spectral and acoustic metrics of the sensor to characterize its overall performance. Finally, an EFPI-type fiber-optic acoustic pressure sensor was developed based on the Micro-Optical Electro-Mechanical System (MOEMS). The acoustic tests indicate that the optimized fiber-optic acoustic pressure sensor has a sensitivity as high as 2253.2 mV/Pa, and the acoustic overload point (AOP) and signal-to-noise ratios (SNRs) can reach 108.85 dB SPL and 79.22 dB, respectively. These results show that the sensor produced through performance characterization experiments and subsequent optimization has a very high acoustic performance index, which provides a scientific theoretical basis for improving the overall performance of the sensor and will have broad application prospects in the field of acoustic detection.

Zhang et al. create an oscillator circuit that can be orthogonally controlled by two inducers in Escherichia coli and by systematically tuning the concentration of the inducers, the amplitude and period can be modulated independently. This design allows the oscillator to be modularized and used in more complex circuit designs, which can be a valuable contribution to the Synthetic Biology field.

Nucleic acid aptamers are ssDNA or ssRNA fragments that specifically recognize targets. However, the pharmacodynamic properties of natural aptamers consisting of 4 naturally occurring nucleosides (A, G, C, T/U) are generally restricted for inferior binding affinity than the cognate antibodies. The development of high-affinity modification strategies has attracted extensive attention in aptamer applications. Chemically modified aptamers with stable three-dimensional shapes can tightly interact with the target proteins via enhanced non-covalent bonding, possibly resulting in hundreds of affinity enhancements. This review overviewed high-affinity modification strategies used in aptamers, including nucleobase modifications, fluorine modifications (2′-fluoro nucleic acid, 2′-fluoro arabino nucleic acid, 2′,2′-difluoro nucleic acid), structural alteration modifications (locked nucleic acid, unlocked nucleic acid), phosphate modifications (phosphorothioates, phosphorodithioates), and extended alphabets. The review emphasized how these high-affinity modifications function in effect as the interactions with target proteins, thereby refining the pharmacodynamic properties of aptamers.

To control COVID-19 pandemic, complete lockdown was initiated in 2020. We investigated the impact of lockdown on tertiary-level academic performance, by comparing educational outcomes amongst first-year students during second semester of their medical course prior to and during lockdown. Evidence: The demographics, including educational outcomes of the two groups were not significantly different during semester one (prior to the lockdown). The academic performance amongst women was better than men prior to lockdown. However, the scores were improved significantly for both sexes during lockdown in 2020, following the complete online teaching, compared to that in 2019, showing no significant difference between men and women in 2020, for English and Chinese History. There were significant different scores between men and women in lab-based Histology Practice in 2019 (in-person tuition) and 2020 (online digital tuition), although only a significant improvement in women was observed between 2019 and 2020. Implication: the forced change to online delivery of the second semester of the first-year medical program in 2020 due to the COVID-19 pandemic did not result in any decline in assessment outcomes in any of the subjects undertaken. We believe extensive online digital media should continue to be available to students in future.

With the growth of people's environmental awareness and the encouragement of government policies, the use of electric vehicles in logistics distribution is gradually increasing. In order to solve the dual demand of customers' simultaneous pick-up and delivery in the "last kilometer logistics", an electric vehicle routing problem with simultaneous pick-up and delivery and time window (EVRPSPDTW) is considered from the perspective of multi-objective distribution in this paper. Firstly, a decision-making model based on distribution cost and power consumption function is established. In this model, distribution cost includes transportation cost, vehicle use cost, penalty cost of not arriving on time and charging cost. Power consumption function is the energy loss caused by air resistance, tire rolling friction and transmission system. Secondly, a multi-objective genetic algorithm (NSGA-II) optimization solution with fast nondominated ranking and elite strategy is designed, and in view of the shortcomings of traditional NSGA-II, it is proposed to complete population initialization through greedy algorithm and random rules, introduce adaptive cross-mutation strategy in the chromosome crossing and mutation stage, and design three different neighborhood operators in mutation operation based on variant fitness function. Finally, the sensitivity analysis of traffic congestion coefficient further proves the effectiveness of the proposed model and the improved algorithm.

IntroductionRecent work has proposed that the nitrogen isotopes in marine sediments can be impacted by anaerobic oxidation of methane (AOM), since nitrogen uptake by anaerobic methanotrophic archaea (ANME) modifies the nitrogen isotope compositions of bulk sediment. Thus, unraveling the AOM-driven nitrogen cycle in the sulfate-methane transition zone (SMTZ) becomes significant. Additional study of the nitrogen cycle between sediment and interstitial water in SMTZ is needed.MethodsTo better understand the nitrogen cycle in the SMTZ, we analyzed NH4+ concentrations of interstitial water and nitrogen isotopes of sediment in the core GC10 from the southwestern Taiwan Basin in the South China Sea.ResultsThe defined SMTZ is located at 560–830 cmbsf, based on methane and sulfate concentrations, as well as TS/TOC ratios, δ13CTIC and δ34S values. In the SMTZ, the NH4+ concentration decreases, the δ15NTN shows a negative excursion, δ15Ndecarb displays a positive excursion.DiscussionsNH4+ concentration decrease is interpreted by sulfate-reducing ammonium oxidation (SRAO). The δ15NTN shows negative excursion, which is most likely interpreted to N2 (δ15N=0‰) released from SRAO that was fixed into marine sediment via ANME nitrogen fixation. The δ15Ndecarb shows a negative correlation with NH4+ concentrations, indicating that it was controlled by organic matter decomposition. In the SMTZ, the methane competes with organic matter for becoming the substrate of sulfate reduction bacteria, which possibly decreases the organic matter degradation rate and causes δ15Ndecarb relative positive excursion. Although δ15Ndecarb is controlled by organic matter degradation, δ15NTN still reveals a negative excursion in the SMTZ. This likely indicates that nitrogen uptake by ANME/AOM microbial consortiums mainly modifies the nitrogen isotope of soluble nitrogen in the SMTZ.ConclusionsThis study indicates unique geochemistry processes in SMTZ will modify nitrogen characteristics in sediment/interstitial water, and the latter can serve as a proxy for AOM.

ABSTRACT Verticillium dahliae is a soilborne fungus that causes destructive vascular wilt diseases in a wide range of plant hosts. In this study, we identified two M35 family metalloproteinases: VdM35-1 and VdASPF2, and investigated their function in vitro and in vivo. The results showed that VdM35-1 and VdASPF2 were located in the cell membrane, as secreted proteins depended on signal peptide, and two histidine residues (H) induced cell death and activated plant immune response. VdM35-1 depended on membrane receptor proteins NbBAK1 and NbSOBIR1 in the process of inducing cell death, while VdASPF2 did not depend on them. The deletion of VdM35-1 and VdASPF2 led to the decrease of sporulation and the slow shortening of mycelial branch growth, and the spore morphology of VdM35-1-deficient strain became malformed. In addition, ΔVdM35-1 and ΔVdASPF2 showed more sensitive to osmotic stress, SDS, Congo red (CR), and high temperature. In terms of the utilization of carbon sources, the knockout mutants exhibited decreased utilization of carbon sources, and the growth rates on the medium containing sucrose, starch, and pectin were lower than the wild type strain, with significantly limited growth, especially on galactose-containing medium. Furthermore, ΔVdM35-1 and ΔVdASPF2 showed a significant reduction in pathogenicity. Collectively, these results suggested that VdM35-1 and VdASPF2 were important multifunction factors in the pathogenicity of V. dahliae and relative to stress adaptation and activated plant immune response. IMPORTANCE Verticillium wilt, caused by the notorious fungal pathogen V. dahliae, is one of the main limiting factors for agricultural production. Metalloproteases played an important role in the pathogenic mechanism of pathogens. Our research found that M35 family metalloproteases VdM35-1 and VdASPF2 played an important role in the development, adaptability, and pathogenicity of V. dahliae, providing a new perspective for further understanding the molecular mechanism of virulence of fungal pathogens.

ABSTRACT Klebsiella pneumoniae is an opportunistic pathogen that causes serious infections in humans and animals. However, the availability of epidemiological information on clinical mastitis due to K. pneumoniae is limited. To acquire new information regarding K. pneumoniae mastitis, data were mined about K. pneumoniae strains on dairy cattle farms (farms A to H) in 7 Chinese provinces in 2021. Hypermucoviscous strains of K. pneumoniae were obtained by the string test. MICs of antimicrobial agents were determined via the broth microdilution method. Ten antimicrobial resistance genes and virulence genes were identified by PCR. The prevalence of K. pneumoniae was 35.91% (65/181), and 100% of the bacteria were sensitive to enrofloxacin. Nine antimicrobial resistance genes and virulence genes were identified and compared among farms. The hypermucoviscous phenotype was present in 94.44% of isolates from farm B, which may be a function of the rmpA virulence gene. Based on these data, the multidrug-resistant strains SD-14 and HB-21 were chosen and sequenced. Genotypes were assayed for K. pneumoniae isolates from different countries and different hosts using multilocus sequence typing (MLST). Ninety-four sequence types (STs) were found, and 6 STs present a risk for spreading in specific regions. Interestingly, ST43 was observed in bovine isolates for the first time. Our study partially reveals the current distribution characteristics of bovine K. pneumoniae in China and may provide a theoretical basis for the prevention and treatment of bovine K. pneumoniae mastitis. IMPORTANCE K. pneumonia is ubiquitous in nature and infects a wide range of hosts, including animals, and humans. It is one of the leading inducements of clinical mastitis (CM) in dairy cows, a prevalent and costly disease that is predominantly associated with bacterial infection. In general, CM caused by Gram-negative bacteria is more difficult to cure than that associated with Gram-positive pathogens, with an average cost per case of 211.03 U.S. dollars (USD) for Gram-negative bacterial infections compared with 133.73 USD for Gram-positive bacterial CM cases. After Escherichia coli, K. pneumoniae is the second most common Gram-negative cause of bovine CM, but it is the most detrimental in terms of decreased milk yield, discarded milk, treatment costs, death, and culling. In view of the economic implications of K. pneumoniae infection in dairy farming, research into population structure and antibiotic resistance is particularly important.

Aptamers are short, single-stranded DNA or RNA oligonucleotide sequences that can bind specific targets. The molecular weight of aptamers (

The self-biased photodetectors without an external power source, which have been extensively utilized in wireless communication, imaging, light-sensing and so on, are urgently in demand. In this study, we successfully constructed a self-energized photodetector on the basis of n-SnO2 microwire/p-InGaN heterojunction, which exhibits an good current rectification property in darkness and superior photovoltaic behaviors upon ultraviolet–visible (330–530 nm) light irradiation. Under 360 nm light irradiation, the designed photodetector reveals a superior photoresponsivity surpassing 152.5 mA/W, a high detectivity exceeding 3.77 × 1012 Jones, a fast response speed (the rising/decaying times ∼0.2/14.6 ms) and a high Ion/Ioff ratio over 105 when operated in a self-driven manner. Additionally, the responsivities of our self-powered photodetector were derived to about ∼85.2 and 5.5 mA/W upon illumination with visible light of 405 and 532 nm, respectively. The highly-sensitive broad-spectrum photodetection mechanism of the as-constructed n-SnO2 microwire/p-InGaN heterojunction is resulted from the combination of ultraviolet-sensitive SnO2 monocrystals and visible-sensitive capability of InGaN material. The resultant photoresponse properties of the designed n-SnO2 microwire/p-InGaN heterojunction photodetector that having merits of low cost and facile construction, can enable a competitive and potential candidate with prospect of developing high-performance self-biased photodetectors in the broadband ranging from ultraviolet to visible.