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Abstract Acylation stands as a fundamental process in both biological pathways and synthetic chemical reactions, with acylated saccharides and their derivatives holding diverse applications ranging from bioactive agents to synthetic building blocks. A longstanding objective in organic synthesis has been the site-selective acylation of saccharides without extensive pre-protection of alcohol units. In this study, we demonstrate that by simply altering the chirality of N-heterocyclic carbene (NHC) organic catalysts, the site-selectivity of saccharide acylation reactions can be effectively modulated. Our investigation reveals that this intriguing selectivity shift stems from a combination of factors, including chirality match/mismatch and inter- / intramolecular hydrogen bonding between the NHC catalyst and saccharide substrates. These findings provide valuable insights into catalyst design and reaction engineering, highlighting potential applications in glycoside analysis, such as fluorescent labelling, α/β identification, orthogonal reactions, and selective late-stage modifications.

Abstract Purpose To develop a method for enriching keratinocyte progenitor cells (KPCs) and establish a limbal niche (LN)-mediated transdifferentiation protocol of KPCs into corneal epithelial cells. Methods Limbal niche cells (LNCs) were isolated from limbal tissues through enzymatic digestion and characterized. Conditioned medium from LNCs cultures was collected. KPCs were enriched by rapid adhesion of Matrigel and subsequently cultured in either an LNCs-conditioned medium supplemented with KSFM (LN-KS) or SHEM (LN-SH) for 14 days. Corneal-specific marker expression was assessed to evaluate transdifferentiation efficiency. Key transcription factors and signaling pathways involved in the transdifferentiation process were identified through single-cell and RNA sequencing, and were validated by western blot and quantitative real-time PCR. Results Both LN-KS and LN-SH protocols successfully induced corneal epithelial cell transdifferentiation from KPCs, with LN-KS demonstrating higher efficiency in generating CK12 + and p63 + cells (p

Abstract Background Evidence indicates that cognitive function is influenced by potential environmental factors. We aimed to determine the variables influencing cognitive function. Methods Our study included 164,463 non-demented adults (89,644 [54.51%] female; mean [SD] age, 56.69 [8.14] years) from the UK Biobank who completed four cognitive assessments at baseline. 364 variables were finally extracted for analysis through a rigorous screening process. We performed univariate analyses to identify variables significantly associated with each cognitive function in two equal-sized split discovery and replication datasets. Subsequently, the identified variables in univariate analyses were further assessed in a multivariable model. Additionally, for the variables identified in multivariable model, we explored the associations with longitudinal cognitive decline. Moreover, one- and two- sample Mendelian randomization (MR) analyses were conducted to confirm the genetic associations. Finally, the quality of the pooled evidence for the associations between variables and cognitive function was evaluated. Results 252 variables (69%) exhibited significant associations with at least one cognitive function in the discovery dataset. Of these, 231 (92%) were successfully replicated. Subsequently, our multivariable analyses identified 41 variables that were significantly associated with at least one cognitive function, spanning categories such as education, socioeconomic status, lifestyle factors, body measurements, mental health, medical conditions, early life factors, and household characteristics. Among these 41 variables, 12 were associated with more than one cognitive domain, and were further identified in all subgroup analyses. And LASSO, rigde, and principal component analysis indicated the robustness of the primary results. Moreover, among these 41 variables, 12 were significantly associated with a longitudinal cognitive decline. Furthermore, 22 were supported by one-sample MR analysis, and 5 were further confirmed by two-sample MR analysis. Additionally, the quality of the pooled evidence for the associations between 10 variables and cognitive function was rated as high. Based on these 10 identified variables, adopting a more favorable lifestyle was significantly associated with 38% and 34% decreased risks of dementia and Alzheimer’s disease (AD). Conclusion Overall, our study constructed an evidence database of variables associated with cognitive function, which could contribute to the prevention of cognitive impairment and dementia.

Abstract The sustainable development of the citrus industry is greatly affected by citrus canker, an important bacterial disease. To explore the transcriptional regulatory mechanism of citrus resistance to canker disease, this study used the susceptible Citrus sinensis cv. ‘Newhall’ and its citrus canker-resistant bud mutation variety ‘Longhuitian’ (LHT) as materials. Through analysing the variances in leaf phenotypes between Newhall and LHT, as well as the variations in their transcriptional expression under Xanthomonas citri subsp. citri (Xcc) inoculation, our study concluded that LHT displays markedly greater resistance to Xcc compared to Newhall. Additionally, the spongy parenchyma of LHT leaves is significantly thicker than that of Newhall, and the stomatal number is significantly higher in LHT leaves, while the length and width of individual stomata in LHT leaves are significantly smaller than those in Newhall. RNA-seq analysis indicates that the differentially expressed genes between LHT and Newhall are involved in biotic stress-related biological processes, secondary metabolite biosynthesis, as well as phytohormone signalling pathways. Furthermore, significant differences were observed in reactive oxygen metabolism and phenylalanine metabolism pathways. The findings of our study provide data support for a deeper understanding of the citrus-Xcc interactions and offer valuable clues for unravelling citrus resistance to citrus canker.

Abstract Background Microbial anaerobic metabolism is a key driver of biogeochemical cycles, influencing ecosystem function and health of both natural and engineered environments. However, the temporal dynamics of the intricate interactions between microorganisms and the organic metabolites are still poorly understood. Leveraging metagenomic and metabolomic approaches, we unveiled the principles governing microbial metabolism during a 96-day anaerobic bioreactor experiment. Results During the turnover and assembly of metabolites, homogeneous selection was predominant, peaking at 84.05% on day 12. Consistent dynamic coordination between microbes and metabolites was observed regarding their composition and assembly processes. Our findings suggested that microbes drove deterministic metabolite turnover, leading to consistent molecular conversions across parallel reactors. Moreover, due to the more favorable thermodynamics of N-containing organic biotransformations, microbes preferentially carried out sequential degradations from N-containing to S-containing compounds. Similarly, the metabolic strategy of C18 lipid-like molecules could switch from synthesis to degradation due to nutrient exhaustion and thermodynamical disadvantage. This indicated that community biotransformation thermodynamics emerged as a key regulator of both catabolic and synthetic metabolisms, shaping metabolic strategy shifts at the community level. Furthermore, the co-occurrence network of microbes-metabolites was structured around microbial metabolic functions centered on methanogenesis, with CH4 as a network hub, connecting with 62.15% of total nodes as 1st and 2nd neighbors. Microbes aggregate molecules with different molecular traits and are modularized depending on their metabolic abilities. They established increasingly positive relationships with high-molecular-weight molecules, facilitating resource acquisition and energy utilization. This metabolic complementarity and substance exchange further underscored the cooperative nature of microbial interactions. Conclusions All results revealed three key rules governing microbial anaerobic degradation. These rules indicate that microbes adapt to environmental conditions according to their community-level metabolic trade-offs and synergistic metabolic functions, further driving the deterministic dynamics of molecular composition. This research offers valuable insights for enhancing the prediction and regulation of microbial activities and carbon flow in anaerobic environments. Video Abstract

Abstract Microbial communities exhibit intricate interactions underpinned by metabolic dependencies. To elucidate these dependencies, we present a workflow utilizing random matrix theory on metagenome-assembled genomes to construct co-occurrence and metabolic complementarity networks. We apply this approach to a temperature gradient hot spring, unraveling the interplay between thermal stress and metabolic cooperation. Our analysis reveals an increase in the frequency of metabolic interactions with rising temperatures. Amino acids, coenzyme A derivatives, and carbohydrates emerge as key exchange metabolites, forming the foundation for syntrophic dependencies, in which commensalistic interactions take a greater proportion than mutualistic ones. These metabolic exchanges are most prevalent between phylogenetically distant species, especially archaea-bacteria collaborations, as a crucial adaptation to harsh environments. Furthermore, we identify a significant positive correlation between basal metabolite exchange and genome size disparity, potentially signifying a means for streamlined genomes to leverage cooperation with metabolically richer partners. This phenomenon is also confirmed by another composting system which has a similar wide range of temperature fluctuations. Our workflow provides a feasible way to decipher the metabolic complementarity mechanisms underlying microbial interactions, and our findings suggested environmental stress regulates the cooperative strategies of thermophiles, while these dependencies have been potentially hardwired into their genomes during co-evolutions.

Polysaccharides from the dried tuber of Typhonium giganteum Engl. (TGEPs) were obtained by utilizing ultrasonic-assisted extraction (UAE) as the extraction method. The determination of optimal process parameters for the UAE of TGEPs (TGEP-U) was accomplished through the application of response surface methodology (RSM). The structural characteristics, antioxidant and hypoglycemic effects of TGEP-U and TGEPs obtained by hot water extraction (TGEP-H) were then compared. Consequently, the optimum extraction conditions predicted by RSM for TGEP-U were obtained as adding water at a ratio of 31 mL/g and extracted for 32 min under an ultrasound power of 440 W. In the verification experiment, the actual yield of TGEP-U was 7.32 ± 0.18 %. It was found that UAE could increase the yield and the total sugar content of TGEPs. Meanwhile, chemical composition analysis showed that both TGEP-U and TGEP-H were mainly composed of mannose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, arabinose, and fucose, but the monosaccharide molar ratios were changed by UAE. Analysis of molecular weight (Mw) revealed the presence of three primary constituents within TGEP-U, and four main components in TGEP-H, and UAE reduced the average Mw of TGEPs. No obvious difference was found in the Fourier transform infrared spectroscopy analysis of TGEP-U and TGEP-H. The Congo red and Circular dichroism tests demonstrated that TGEP-U and TGEP-H had non-three helical structure. Scanning electron microscope observation further revealed that the aggregation of functional groups within TGEPs may be influenced by ultrasound, thereby affecting their powder morphology. TGEP-U has slightly poorer thermal stability than TGEP-H, which may be affected by ultrasonic cavitation effects. The results also indicated that TGEP-U had better antioxidant and hypoglycemic activity than TGEP-H. In summary, UAE is an effective method to extract and enhance the activity of TGEPs with enormous research value and potentials.

Burns and burn sepsis, characterized by persistent and profound hypercatabolism, cause energy metabolism dysfunction that worsens organ injury and systemic disorders. Glutamine (Gln) is a key nutrient that remarkably replenishes energy metabolism in burn and sepsis patients, but its exact roles beyond substrate supply is unclear. In this study, we demonstrated that Gln alleviated liver injury by sustaining energy supply and restoring redox balance. Meanwhile, Gln also rescued the dysfunctional mitochondrial electron transport chain (ETC) complexes, improved ATP production, reduced oxidative stress, and protected hepatocytes from burn sepsis injury. Mechanistically, we revealed that Gln could activate SIRT4 by upregulating its protein synthesis and increasing the level of Nicotinamide adenine dinucleotide (NAD+), a co-enzyme that sustains the activity of SIRT4. This, in turn, reduced the acetylation of shock protein (HSP) 60 to facilitate the assembly of the HSP60-HSP10 complex, which maintains the activity of ETC complex II and III and thus sustain ATP generation and reduce reactive oxygen species release. Overall, our study uncovers a previously unknown pharmacological mechanism involving the regulation of HSP60-HSP10 assembly by which Gln recovers mitochondrial complex activity, sustains cellular energy metabolism and exerts a hepato-protective role in burn sepsis.

This paper presents an experimental and numerical investigation into the bending behaviour of reinforced concrete (RC) T-beams damaged by overheight vehicle impact strengthened with ultra-high performance concrete (UHPC). Four-point bending tests were conducted on one RC T-beam and six UHPC-strengthened RC T-beams, and the primary parameters included the length and thickness of the UHPC strengthening layer, as well as the filling area of UHPC for the damaged region. The test results demonstrated that the UHPC strengthening layer effectively delayed the development of concrete cracks, improved the bending stiffness, cracking load, and resistance of the T-beams. The UHPC-strengthened T-beams exhibited an increase in cracking and ultimate loads by 39–339 % and 26–113 %, respectively, compared to the RC T-beam. Subsequently, a finite element (FE) model of the UHPC-strengthened T-beams was developed and validated using the experimental results. A parametric study using the validated FE model was conducted, revealing that insufficient strengthened length or excessive strengthened thickness of the UHPC layer increased the risk of debonding failure. Furthermore, for T-beams experiencing bending failure, increasing the thickness and height of the UHPC layer, as well as the UHPC filling area for the damaged region, significantly improved the bending resistance, while the UHPC layer length had little effect on the bending resistance. Finally, based on the calculation theory of RC beams under bending loads and considering the strain-hardening behaviour of UHPC in tension, a design method for the bending resistance of damaged RC T-beams strengthened with UHPC was proposed and verified using the experimental and numerical results.

Abstract Objectives Patients undergoing a prior failed attempt of chronic total occlusion‐percutaneous coronary intervention (CTO‐PCI) represent a challenging subgroup across all patients undergoing CTO‐PCI. There are limited data on the effects of a prior failed attempt on the outcomes of subsequent CTO‐PCI. We aimed to compare the procedural results and 24‐month outcomes of prior‐failed‐attempt CTO‐PCI with those of initial‐attempt CTO‐PCI. Methods Patients who underwent attempted CTO‐PCI between January 2017 and December 2019 were prospectively enrolled. We analyzed the procedural results and 24‐month major adverse cardiac events (MACE) between patients who underwent prior‐failed‐attempt and initial‐attempt CTO‐PCI. MACE was defined as a composite of cardiac death, target vessel‐related myocardial infarction, and ischemia‐driven target vessel revascularization (TVR) during follow‐up. Results In total, 484 patients who underwent CTO‐PCI (prior‐failed‐attempt, n = 49; initial‐attempt, n = 435) were enrolled during the study period. After propensity score matching (1:3), 147 patients were included in the initial‐attempt group. The proportion of the Japanese‐CTO (J‐CTO) score ≥2 was higher in the patients who underwent prior failed attempt than in those who underwent initial attempt (77.5% vs. 38.8%, p

Abstract Introduction Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI. Methods Six ML algorithms were included to perform model fitting. Feature selection was based on the feature importance evaluated by the SHapley Additive exPlanations (SHAP) values. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminatory ability of the prediction model. Calibration curve and Brier score were employed to assess the calibrated ability. Our ML-based prediction models were validated both internally and externally. Results A total of 12,750 patients with SA-AKI and 55 features were included to build the prediction models. We identified the top 10 predictors including age, ICU stay and GCS score based on the feature importance. Among the six ML algorithms, the CatBoost showed the best prediction performance with an AUROC of 0.813 and Brier score of 0.119. In the external validation set, the predictive value remained favorable (AUROC = 0.784). Conclusion In this study, we developed and validated a ML-based prediction model based on 10 commonly used clinical features which could accurately and early identify the individuals at high-risk of long-term mortality in patients with SA-AKI.

W–Cu composite powders prepared via the wet chemical method in the form of W-coated Cu contributed greatly to the materials' density and the homogeneity. The powders were modified to prepare large quantities of W–Cu composites with excellent properties via metal injection molding (MIM). In this study, the precursors’ reaction process, the formation mechanism of W-coated Cu, and MIM process were systematically investigated. Results showed that W–Cu composite powders in this coating form could ensure the homogeneity of W and Cu and reduce Cu loss during sintering. In addition, W–Cu composites that fulfill the performance specifications for electronic packaging have been effectively synthesized through MIM employing the novel polyoxymethylene (POM)-based binders. Regarding thermal properties, the W–15Cu composites exhibit an impressive thermal conductivity of 212 W/(m·K) and a minimal coefficient of thermal expansion at 7.01 ppm/°C. These characteristics outperform those of other materials documented in related literature. This study provided a useful referential value to the combination of W–Cu composites in scientific research and engineering applications.

Branch angles are an important plant morphological trait affecting light interception within forest canopies. However, studies on branch angles have been limited due to the time-consuming nature of manual measurements using a protractor. Terrestrial laser scanning (TLS), however, provides new opportunities to measure branch angles more efficiently. Despite this potential, studies validating branch angle measurements from TLS have been limited. Here, our aim is to evaluate both manual and automatic branch angle measurements of European beech from TLS data using traditional field-measurements with a protractor as a reference. We evaluated the accuracy of branch angle measurements based on four automated algorithms (aRchiQSM, TreeQSM, Laplacian, Semantic-Laplacian) from TLS data. Additionally, we assessed different ways of manual branch angle measurements in the field. Our study was based on a dataset comprising 124 branch angles measured from six European beech in a European deciduous forest. Our results show that manual branch angle measurements from TLS data are in high agreement with the reference (root-mean-squared error, RMSE: [3.57°–4.18°], concordance correlation coefficient, CCC: [0.95−0.97]) across different branch length positions. Automated algorithms also are in high agreement with the reference although RMSE is approximately twice as large compared to manual branch angle measurements from TLS (RMSE: [9.29°–10.55°], CCC: [0.83−0.86]) with manual leaf points removal. When applying the automatic wood-leaf separation algorithm, the performance of the four methods declined significantly, with only approximately 20 branch angles successfully identified. Moreover, it is important to note that there is no influence of the measurement position (branch surface versus center) for branch angle measurements. However, for curved branches, the selection of branch measurement length significantly impacts the branch angle measurement. This study provides a comprehensive understanding of branch angle measurements in forests. We show that automated measurement methods based on TLS data of branch angles are a valuable tool to quantify branch angles at larger scales.

Dendrobium Officinale is a traditional Chinese medicinal and edible herb, and polysaccharides being its principal active components. We employed ultrasonic-assisted enzymatic extraction to obtain polysaccharides from Dendrobium Officinale (DOP) and optimized the extraction process using the GA-BP method. Subsequently, DOP was characterized using HPLC, SEM, and HPGPC. Finally, an immunocompromised mouse model induced by cyclophosphamide was adopted to evaluate the immunomodulatory effects. The results showed that the optimal extraction process of ultrasound-assisted enzyme method was 102 min ultrasonic extraction time, 48 °C extraction temperature, 1:80 material-liquid ratio, and 1600 U/g enzyme additions. The predicted polysaccharide yield was 29.64 %, and the actual polysaccharide yield was 29.71 ± 0.31 %. DOP is a white powder with pore-like structure, whose polysaccharide content is 90.66 ± 2.47 %, and with a composition of D-mannose, glucose, galactose, arabinose in a molar ratio of 74.17: 47.80: 9.03: 1. DOP has demonstrated the ability to improve the general condition of mice with cyclophosphamide-induced immunosuppression. It mitigates damage to the thymus and spleen tissues, increases white blood cell counts, regulates the ratio of T lymphocytes and the levels of related factors, enhances macrophage phagocytic function to improve nonspecific immune responses, and boosts the levels of immunoglobulins IgA and IgG to enhance specific immune function. Therefore, DOP can alleviate immunosuppression induced by cyclophosphamide.

Abstract With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co‐occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four‐module process for metabolic interaction analysis, namely: (I) Prepare genome‐scale metabolic models; (II) Infer pairwise interactions of genome‐scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome‐assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance‐adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross‐feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome‐assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Abstract The static frequency converter (SFC) in a pumped storage power plant often causes harmonic problems in the dragging processes, which may lead to the false operation of automatic devices in the power station, and even damage to the power equipment. These harmonics caused by SFC contain both characteristic and non‐characteristic degrees, and the components are more complex. Hence, the existing harmonic suppression methods using active power filter or passive power filter compensation all have some problems. The SFC starting control strategy based on linear active disturbance rejection control (LADRC) is proposed here to reduce the non‐characteristic harmonics. First, the factors affecting the non‐characteristic harmonic content are analysed, and a mathematical model of the conventional SFC starting transfer function is established. On this basis, the LADRC controller is used as the speed loop of SFC starting to replace the proportional integral (PI) controller. The stability is judged by drawing the Bode plot and the zero‐pole plot. Finally, a Matlab/Simulink simulation model of LADRC‐based SFC starting is established in a pumped storage power plant with actual parameters, and simulation accurate measurements verify the effectiveness of the proposed control strategy for non‐characteristic harmonic current suppression.

Background: The substrates for arrhythmias in myocarditis and ischemic heart disease (IHD) are different, but it is yet to be determined whether there is a difference in outcomes following catheter ablation (CA) for ventricular tachycardia (VT) associated with these two conditions. This study aimed to compare outcomes after CA of VT in patients with myocarditis versus those with IHD. Methods: Patients undergoing CA for sustained VT confirmed by endomyocardial biopsy as myocarditis, and patients with IHD experiencing sustained VT undergoing CA were retrospectively enrolled from February 2017 to March 2023. Initially, an endocardial approach was employed, reserving epicardial ablation procedures for non-responders. The primary endpoint was VT recurrence during follow up. All-cause mortality, repeat CA for VT and implantable cardioverter-defibrillator (ICD) implantation served as secondary endpoints. Kaplan-Meier curves compared outcomes between patient groups. Results: This study included 109 patients with IHD and 20 patients with myocarditis who underwent CA for sustained VT, from February 2017 to March 2023. Compared with IHD patients, myocarditis patients had a statistically significant lower complete short-term success rate of CA (60.0% vs. 85.3%, p = 0.013). During a follow-up period of 37 ± 21 months, 8 (40.0%) myocarditis patients experienced VT recurrence compared to 57 (52.3%) IHD patients, with no statistically significant difference between the two groups. During follow-up, 2 (10.0%) myocarditis patients died and 2 (10.0%) underwent repeat CA for VT recurrence, while 9 (8.3%) IHD patients died, 14 (12.8%) underwent a second CA for VT recurrence, and 8 (7.3%) received an ICD implantation. Additionally, there were no notable variations between the two groups regarding all-cause mortality, repeat CA for VT and ICD implantation. Conclusions: It was demonstrated that the efficacy of CA in sustained VT in myocarditis patients was similar to that in IHD. For myocarditis patients with VT, CA might be equally effective.

Chloride ion concentration significantly impacts the durability of reinforced concrete, particularly regarding corrosion. Accurately assessing how this concentration varies with the age of structures is crucial for ensuring their safety and longevity. Recently, several predictive models have emerged to analyze chloride ion concentration over time, classified into empirical models and machine learning models based on their data processing techniques. Empirical models directly relate chloride ion concentration to the age of concrete through specific functions. Their primary advantage lies in their low data requirements, making them convenient for engineering use. However, these models often fail to account for multiple influencing factors, which can limit their accuracy. Conversely, machine learning models can handle various factors simultaneously, providing a more detailed understanding of how chloride concentration evolves. When adequately trained with sufficient experimental data, these models generally offer superior prediction accuracy compared to mathematical models. The downside is that they necessitate a larger dataset for training, which can complicate their practical application. Future research could focus on combining machine learning and empirical models, leveraging their respective strengths to achieve a more precise evaluation of chloride ion concentration in relation to structural age.