To understand the implications of protein glycosylation for clinical diagnostics and biopharmaceuticals, innovative glycoproteomic technologies are required. Recently, significant advances have been made in such technologies, particularly in the area of structure-focused N-glyco-proteomic analyses. Mass spectrometric analysis of intact N-glycopeptides using stepped collision fragmentation along with glycan oxonium ion profiling now makes it possible to reliably discriminate between different N-glycan structures. Still, current N-glycoproteomic approaches have weaknesses that must be overcome, namely ① the handling of incorrect identifications, ② the identification of rare and modified N-glycans, and ③ insufficient glycoproteomic coverage, especially in complex samples. To address these shortcomings, we have established an innovative N-glycoproteomic workflow that aims to provide comprehensive site-specific and structural N-glycoproteomic data on human blood plasma (HBP) glycoproteins. The workflow features protein depletion plus a fractionation strategy and the use of high-resolution mass spectrometry with stepped collision fragmentation. Furthermore, by including a new decision tree procedure developed for data validation, we can significantly improve the description of N-glycan micro-heterogeneity. Our advanced data analysis workflow allows the reliable differentiation of ambiguous N-glycan structures such as antenna versus core fucosylation, as well as modified and rare N-glycans such as sulfated and glucuronidated ones. With this workflow, we were able to achieve the detection of HBP glycoproteins with reported concentrations within the ng·mL−1 level. A total of 1929 N-glycopeptides and 942 N-glycosites derived from 805 human middle- to low-abundant glycoproteins were identified. Overall, the presented workflow holds great potential to improve our understanding of protein glycosylation and to foster the discovery of blood plasma biomarkers.
Glycosylation-omics has emerged as a prominent field for early detection and diagnosis by identifying alterations in glycosylation patterns linked to cancer. In the realm of clinical multi-glycosylation-omics applications, there is a critical need for robust, efficient, and cost-effective preprocessing methodologies capable of handling large sample cohorts. To bridge this gap, we introduce the GlycoPro platform, an innovative solution designed to overcome the limitations of existing analysis methods. Tailored for multi-glycosylation-omics sample preprocessing, GlycoPro refines existing workflows by seamlessly integrating steps including protein extraction, desalting, digestion, derivatization, and enrichment. The GlycoPro platform employs a 96-well plate format, enabling the efficient enrichment or desalting of up to 384 samples in a single day. This capability represents a significant increase in throughput, meeting the demands of large-scale clinical sample preprocessing for mass spectrometry analysis. The GlycoPro platform was used to successfully enrich serum N-glycans from breast cancer patients, revealing unique glycomic signatures that distinguish malignant from benign conditions. We have developed a robust N-glycan biomarker panel, demonstrating a sensitivity of 88.24% and a specificity of 78.95% in diagnostics.
Recent studies indicate the involvement of glycosylation in the pathogenesis of Alzheimer’s disease (AD). α2,6-Sialylation, catalyzed by α2,6-sialyltransferase-I (ST6Gal-I), corresponds to the development of the infant brain and nervous system, however the mechanism of aberrant α2,6-sialylation affects multiple physiological and pathological conditions remains unclear. The present study, in vitro and in vivo, showed that expression of ST6Gal-I and α2,6-sialylation levels were up-regulated in cerebrospinal fluid and sera of AD patients. In addition, levels of α2,6-sialylation were also increased in brain and sera of AD model mice. Furthermore, deletion of ST6Gal-I reduced β-site amyloid precursor protein cleaving enzyme 1 (BACE1) levels and alleviated the impairment of learning and memory induced by scopolamine in rats. BACE1, a hyper-sialylated protein, plays a critical role in amyloid-β42 (Aβ42) production. ST6Gal-I knockdown in Neuro-2a neuroblastoma cells (ST6Gal-I-KD-N2a) reduced the expression of BACE1 via promoting its ubiquitination. Deletion of ST6Gal-I suppressed amyloid precursor protein (APP) cleaved by BACE1, followed by a decrease in Aβ42 production, while alleviated Aβ42-induced apoptosis. This study first reveals a significant role of α2,6-sialylation in development and progression of AD, suggesting that ST6Gal-I is a novel glycan therapeutic target for AD diagnosis and treatment.
Protein glycosylation is one of the most vital modifications. Understanding the role of protein glycosylation in coronavirus disease 2019 (COVID-19) is the key in elucidating its pathogenesis and developing therapeutic strategies. We conducted a case-control study to examine the total fucosylation levels and the levels of individual immunoglobulin G (IgG) subtypes in the serum of COVID-19 patients. Notably, we identified 13 glycosyltransferase-related and glycosidase-related genes displaying differential expression among COVID-19 patients. Our findings from the detection of serum fucosylation levels in COVID-19 patients revealed a diminished degree of glycosylation. Furthermore, the analysis of the levels of different IgG subtypes revealed an increase in IgG1 fucosylation and a decrease in IgG2 fucosylation, with the latter being linked to patients’ body temperature and disease progression. The change in COVID-19 disease severity from mild to severe may be related to fucosylation. The single-cell sequencing analysis revealed the expression of members of the fucosyltransferase family in the plasma cells and plasmablasts of COVID-19 patients. We leveraged the recommended medication for severe COVID-19, Fuzheng Jiedu Decoction (FZJDD), to confirm the importance of fucosylation in severe COVID-19. The network pharmacology analysis of FZJDD revealed that fucosylation inhibition might contribute to its antiviral effects against COVID-19. We assessed the efficacy of this compound in septic mice, by monitoring serum fucosylation levels, and found that FZJDD significantly alleviated inflammation in lipopolysaccharide (LPS)-induced septic mice. Concurrently, the analysis of plasma fucosylation levels in septic mice indicated a marked decrease in total fucosylation. The glycan analysis revealed the involvement of α1,6-fucosyltransferase (FUT8) and α-L-fucosidase 1 (FUCA1), a pair of interacting fucosidases, in COVID-19 pathogenesis. This study revealed substantial alterations in fucosylation among patients with severe COVID-19, with the primary variations observed in the IgG2 subtype. These changes are intricately coordinated by the mutual regulation of the FUT8 and FUCA1 enzymes. Furthermore, the endorsement of FZJDD as a recommended therapeutic option for severe COVID-19 underscores the promising potential of defucosylation as a viable treatment strategy for this disease.
Building on coding mutations and splicing variants, post-translational modifications add a final layer to protein diversity that operates at developmental and physiological timescales. Although protein glycosylation is one of the most common post-translational modifications, its evolutionary origin remains largely unexplored. Here, we performed a phylostratigraphic tracking of glycosylation machinery (GM) genes and their targets—glycoproteins (GPs)—in a broad phylogenetic context. Our results show that the vast majority of human GM genes trace back to two evolutionary periods: the origin of all cellular organisms and the origin of all eukaryotes. This indicates that protein glycosylation is an ancient process likely common to all life, further elaborated in early eukaryotes. In contrast, human glycoproteins exhibited prominent enrichment signals in more recent evolutionary periods, suggesting an important role in the transition from metazoans to vertebrates. Focusing specifically on the N-glycosylation (NG) pathway, we noted that the majority of NG genes acting on the cytoplasmic side of the endoplasmic reticulum (ER) trace back to the origin of cellular organisms. This sharply contrasts with the rest of the NG pathway, which is oriented toward the ER lumen, where genes of eukaryotic origin predominate. In the Golgi, we also identified an analogous binary evolutionary origin of GM genes. We discuss these findings in the context of the evolutionary emergence of the eukaryotic endomembrane system and propose that the ER evolved through the invagination of a prokaryotic cell membrane containing an NG pathway.
Previous studies have demonstrated that the immunoglobulin G (IgG) N-glycome and transcriptome are potential biochemical signatures of chronological and biological ages, and several aging clocks have been developed. By integrating the IgG N-glycome and transcriptome, we propose a novel aging clock, gtAge. We developed a deep reinforcement learning-based multiomics integration method called AlphaSnake. The results showed that AlphaSnake achieved a predicted coefficient of determination (R2) value of 0.853, outperforming the concatenation-based integration method (R2 = 0.820). The gtAge estimated by AlphaSnake explained up to 85.3% of the variance in chronological age, which was higher than that in age predicted from IgG N-glycome solely (gAge; R2 = 0.290) and age predicted from transcriptome solely (tAge; R2 = 0.812). We also found that the delta age—the difference between the predicted age and chronological age—was associated with several age-related phenotypes. Both delta gtAge and tAge were negatively associated with high-density lipoprotein (p = 0.02 and p = 0.022, respectively), whereas delta gAge was positively correlated with cholesterol (p = 0.006), triglyceride (p = 0.002), fasting plasma glucose (p = 0.014), low-density lipoprotein (p = 0.006), and glycated hemoglobin (p = 0.039). These findings suggest that gtAge, tAge, and gAge are potential biomarkers for biological age.
Immunoglobulin G (IgG) N-glycans are associated with aging. In this study, we introduce a novel strategy for discovering aging-associated IgG glycans and establish a prediction model on the basis of their absolute concentration alterations. We employed glycomic quantification technology to identify alterations in the amount of IgG glycan in natural aging and antiaging (caloric restriction (CR)) models and discovered aging-related glycans. The glycomic analysis revealed key features: downregulation of the bisected glycan GP3 (F(6)A2B) and upregulation of the digalactosylated glycan GP8 (F(6)A2G2). These glycan changes showed significant fold changes from an early stage. Using external standards of these two glycans, we subsequently measured their absolute concentrations, allowing for us to establish a predictive model, abGlycoAge, for biological aging. The abGlycoAge index suggested a younger state under CR, with an average age reduction of 3.9-14.0 weeks. Additionally, RNA sequencing of splenic B cells revealed that Derl3, Smarcb1, Ankrd55, Tbkbp1, and Slc38a10 may contribute to alterations in GP3 and GP8 during the aging process. In a preliminary therapeutic study, we tested IgG modified with young signature N-glycans (IgG-Ny). High-dose IgG-Ny showed promising results, alleviating aging-related physiological declines, including reductions in inflammatory markers and improvements in organ senescence, particularly in the brain, kidney, and lungs. This research provides new insights into glycan changes during aging and lays the groundwork for potential antiaging therapies. GP3 and GP8 may serve as biomarkers for aging, offering new perspectives on aging mechanisms and therapeutic approaches.
Childhood epilepsy poses a serious threat to patients’ growth, development, and life safety, creating an urgent need for precise, non-invasive, and longitudinally monitorable biomarkers. Previous studies have confirmed that extracellular vesicles (EVs) with abnormal N-glycosylation modifications regulate various neurological disorders, where characteristic N-glycans serve as potential diagnostic markers. In this study, we systematically compared the properties of EVs isolated via three different methods. The results demonstrated that an exosome purification filter column (EPF) combined with ultrafiltration emerged as the optimal approach for isolating EVs from large-scale clinical samples. Subsequent matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS)-based glycomic profiling of EVs and serum revealed distinct N-glycan signatures. Utilizing a novel two-step machine learning model, we identified 47 characteristic N-glycans in EVs as biomarkers for epilepsy diagnosis and classification. These biomarkers effectively distinguished between normal, focal, and generalized epilepsy subtypes while also exhibiting superior diagnostic performance compared to serum N-glycan profiles. Furthermore, we constructed a correlation network map of glycans, which highlighted dynamic alterations in the expression patterns of EV glycans during epileptogenesis. Taken together, the N-glycans of EVs exhibit promising potential as biomarkers for epilepsy detection, offering new insights into non-invasive diagnosis and disease monitoring.
Adoptive cell therapies (ACTs) have achieved remarkable clinical success in treating cancers; however, their broader application is greatly impeded by high cost and restricted antigen specificity. Recently, engineering the glycocalyx has provided a convenient transgene-free means to design ACTs with high-avidity glycan ligands to target CD22, offering a new avenue for B lymphoma immunotherapy. In this work, we perform a comparative analysis of the molecular profiles involved in metabolic or chemoenzymatic glycocalyx engineering and explore their multiplexing capability. The glycoproteomic results revealed content-dependent customization of the natural killer (NK)-92MI glycocalyx. Compared with metabolic engineering, exogenous chemoenzymatic engineering has comparable or even superior ligand-loading efficiency, with some immune synapse components modified to facilitate their spatial recognition against target cells. Next, we tested the orthogonal creation of ligands on NK-92MI cells by further engineering α2,3-sialylated N-acetyllactosamine moieties to produce selectin ligands that are essential for better in vivo eradication of mouse xenograft B lymphoma. Finally, we demonstrate that analogous engineering of CD19-targeted chimeric antigen receptor T (CAR-T) cells to produce CD19/CD22 bitargeted therapy can enhance antigen targeting and tumor cell killing, offering an alternative cost-efficient agent for treating cancer relapse with decreased levels of CD19 antigens. These findings establish a mechanistic foundation for glycocalyx engineering and support the rational design of next-generation ACTs against B lymphoma.
Immunoglobulin G (IgG) is recognized as a key regulator of metabolic dysfunction and fibrosis in adipose tissue, and its functional properties are tightly regulated by its glycosylation profile. However, the role of IgG glycosylation in adipose aging remains unclear. Here, we performed transcriptomic and glycoproteomic analyses of epididymal white adipose tissue (eWAT) from young and aged mice. RNA sequencing (RNA-seq) analysis revealed a significant downregulation of adipogenic genes in aged eWAT, accompanied by elevated expression levels of inflammatory and fibrotic markers, which were further validated by quantitative polymerase chain reaction (qPCR). N- and O-glycoproteomic analyses revealed widespread changes in glycosylation. Differentially glycosylated proteins are primarily localized to the extracellular space and participate in innate immune responses, transport and signal transduction, extracellular matrix (ECM)-receptor interaction pathways, and so on. Notably, IgG glycosylation levels were significantly increased in aged mice. Specifically, the N-fucosylation of IgG1, IgG2a, and IgG3 was elevated by 3.1-, 10.4-, and 3.2-fold, respectively, while only IgG2a showed increased O-fucosylation. These findings suggest that N-fucosylation is a common age-related modification across IgG subtypes. Using in vivo models, we further demonstrated that B-cell depletion-induced IgG reduction increased adipogenic and inflammatory gene expression, while the expression of fibrotic markers was suppressed. These effects were reversed upon repletion with either fucosylated or nonfucosylated IgG. Importantly, compared with nonfucosylated IgG, fucosylated IgG exacerbated inflammation and fibrosis but inhibited adipogenesis more strongly. Taken together, our results identify fucosylated IgG as a key mediator of adipose dysfunction during aging and suggest that modulating IgG fucosylation may offer therapeutic potential for age-related metabolic disorders.
Action errors—unintentional deviations from goals, rules, or standards—are an inevitable part of work in construction. Understanding how individuals and organizations can embrace and “learn through errors” (i.e., how to handle them effectively) is crucial for contributing to project success. However, within construction, a prevailing belief persists that errors can and should be eliminated, fostering a zero-tolerance mindset. Organizations that adopt this mindset risk stifling their capacity to learn, innovate, and improve profitability. While errors can indeed have negative consequences, they also play a vital role in enabling learning and innovation. Given the limited empirical research on action errors in construction, this paper aims to stimulate inquiry into this promising area of study. It briefly outlines different forms of error orientation and proposes directions for future research relevant to construction organizations. The contributions of this paper are twofold, as it: ① advocates for construction organizations to broaden their understanding of errors to enhance their learning capability and ② identifies ways in which organizations can improve their capacity to learn and innovate through error management.
The global spread of antibiotic resistance genes (ARGs) continues to worsen, with plasmid-mediated conjugation serving as a major transmission route. Although developing conjugation inhibitors to block this process is a promising strategy, current options are limited by toxicity and poor in vivo efficacy. This study evaluated the effect of cinnamic acid (CA; 3-phenyl-2-acrylic acid), a widely abundant food additive found in cinnamon, on plasmid conjugation. CA effectively inhibited the conjugation of various resistance plasmids in vitro, ex vivo, and in vivo. Transcriptomic analysis indicated that CA disrupts the electron transport chain (ETC) and proton motive force (PMF) by inhibiting the tricarboxylic acid (TCA) cycle, leading to reduced intracellular adenosine triphosphate (ATP)—a critical factor for plasmid conjugation. Biocompatibility assays showed that CA maintains high biosafety while preserving gut microbiota homeostasis. Therefore, these findings provide new insights into ARG inhibition and highlight the potential of CA as a novel strategy to combat the global rise in antibiotic-resistant infections.
Halotolerant plant growth-promoting bacteria (PGPB) have great potential for alleviating salinity stress in crops. However, the current methods used with these bacteria are typically based on one-time inoculations, including soil basal application, seed dressing and plant infestation, all of which make it difficult to guarantee the desired plant effects. Here, we investigated the effects of seven halotolerant PGPB individually applied through a drip irrigation system in small quantities and at high frequency during the plant’s growth period on the soil physicochemical properties, plant agronomic performance and bacterial community in saline soil. Our findings revealed that drip irrigation with halotolerant PGPB notably decreased the soil pH and electrical conductivity while increasing the yield and fruit quality of jujube plants. Specifically, the Bacillus licheniformis (BL) and Bacillus mucilaginous (BM) treatments outperformed the control (no PGPB irrigation) by increasing the yield and vitamin C (VC) content by 23% and 22%, respectively. Additionally, the presence of halotolerant PGPB enriched the diversity of the bacterial community in the jujube rhizosphere and increased the relative abundance of beneficial bacterial groups at both the phylum (e.g., Cyanobacteria and Nitrospirota) and genus (e.g., Psychrobacter, Flavobacterium, and Steroidobacter) levels. Bacterial interactions, represented by co-occurrence networks, were more complex in the treatments involving PGPB irrigation, contributing to the transformation of the network keystones involved in soil nutrient cycling. Applications of BL, Bacillus cereus (BC), and BM reduced the soil salinity and increased the soil available nutrient contents and plant antioxidant enzyme activities, alleviating salinity stress and resulting in increases in crop yield and quality. This study highlights the feasibility and efficiency of applying halotolerant PGPB via drip irrigation in saline soil environments, thereby enhancing crop performance under salt stress.
Magnetic resonance imaging (MRI) systems, outfitted with internal gradient coils capable of manipulating magnetic gradients in three-dimensional (3D) space, offer an intriguing platform for the navigation of medical magnetic robots. These robots offer considerable promise for applications in minimally invasive therapy, targeted drug delivery, and theranostic interventions. However, an MRI-driven robot presents a challenging contradiction between real-time control and image resolution, resulting in suboptimal tracking accuracy—attributed to the inefficiency of conventional signal acquisition and the presence of metal artifacts. In this paper, we report a multi-frequency excitation sequence with dual-echo (MFDE) that reduces the repetition time (TR) to 30 ms, allowing the precise tracking of magnetic particles (relative error < 1%) without artifacts. The duty cycle of the driving gradient is as high as 77%, and perturbations from the imaging gradients are eliminated. Expanding on these foundations, we adapted our technique to 3D operations. We established an integrated platform for imaging and motion control by creating a three-view window and developing a control joystick to be used in conjunction with the platform. Demonstrations of navigation in a maze, in a phantom vessel, and in vivo animal trials validate its feasibility and effectiveness, providing a significant advancement in the field of MRI-guided magnetic robot control.
Laser nanofabrication with tightly focused ultrafast laser pulses enable versatile fabrication of arbitrary two-dimensional (2D)/three-dimensional (3D) micro/nanostructures. Accurate positioning of the laser focal spot is crucial, especially for high-resolution integrated devices in 2D materials, requiring precise placement on atomically thin surfaces. However, uneven surfaces and surface tilt poses significant challenges. Existing methods to detect focal positions often involve complex setups with additional optical components or sensors, achieving limited accuracy. This study introduces a machine learning-based method to accurately detect the focal position during laser nanofabrication by analyzing the shape and intensity of the laser focal spot. We compare four machine learning methods: rational quadratic Gaussian process regression, kernel approximation least square, quadratic support vector machine, and trilayered neural network. Our experiments show that trilayered neural network (TNN) method achieves a detection accuracy of 257 nm (half the fabrication laser wavelength), surpassing the required accuracy for tightly focused laser beam (typically larger than one wavelength). This method can map focal positions along the fabrication trajectory to compensate for surface roughness or tilt. Moreover, it can be directly implemented in any laser nanofabrication system with a camera for in situ monitoring, without requiring additional optical components, indicating broad applicability.
Metallic Ni is widely used for oxygen evolution reaction (OER) catalysis. It acts as a precatalyst and undergoes surface reconstruction into NiOOH, which is the active species used in OER. Consequently, OER performance is highly related to the NiOOH structure, which is determined by the precatalyst. Thus, the modulation of metallic Ni to obtain superior NiOOH is critical. Herein, an interfacial redox modulation strategy is proposed to oxidize a Ni foam (NF) surface into the desired Ni2+ species using electrochemically exfoliated graphene (EG). OER-favorable γ-NiOOH on EG-oxidized NF was investigated under anodic potentials by in situ characterization techniques, whereby the formation of inferior β-NiOOH was found to be inhibited. Single Ni atoms and clusters were anchored onto the EG layers after reduction. The altered γ-NiOOH and Ni single atoms and clusters improved the OER performance of the EG-oxidized NF with low overpotential and enhanced stability. Subsequently, controllable EG and Ni-based metals were used to verify the versatility of the proposed interfacial redox modulation strategy. The optimized EG-oxidized NiFe system achieved an overpotential of 243 mV at 10 mA⋅cm−2 and long-term stability at 500 mA⋅cm−2 for 100 h.
Understanding the intrinsic features of dehydrogenation reactions of liquid organic hydrogen carriers (LOHCs) is a formidable challenge due to the combined impact of electronic and geometric effects. Herein, we constructed a series of Pt/MOx catalysts (CeO2, MgO, ZrO2, TiO2, Al2O3, or SiO2) with similar sizes of Pt (∼1.7 nm) to investigate the effects of Pt electron structures (tuned by electronic metal-support interactions) on the catalytic dehydrogenation of LOHCs. The results revealed a volcano-shaped correlation between Pt d electrons on different supports and the turnover frequency of catalytic dehydrogenation. Importantly, the Pt/MgO catalyst exhibited the highest dehydrogenation activity. With decreasing d electron content, Pt/MgO increases the bonding orbital dominance of Pt-C bonds and leads to stable adsorption of H6-monobenzyltoluene (MBT), which facilitates subsequent C-H bond scission. This study offers insight for the strategic development of high-efficiency dehydrogenation catalysts via d electron density modulation of Pt sites.
This study establishes a high-speed nano-positioning stage composed of a symmetrically driven structure with multiple parallel-bonded thin piezoelectric ceramic layers capable of performing micro- or nano-scale manipulations. Accordingly, a neural-network-based switching output regulation controller (NN-SORC) was developed to compensate for the associated hysteresis nonlinearity. To address the challenges of slow floating-point computation speeds and low compilation efficiency, a closed-loop control system with a field-programmable gate array-central processing unit (FPGA-CPU) dual-layer data-processing framework was developed. A feedback linearization method was designed to linearize the hysteresis nonlinearity of the framework, resulting in a switching-tracking error system. With the assistance of Lyapunov theory and an average dwell time technique, sufficient conditions were derived to ensure the asymptotic stability of the NN-SORC governing closed-loop system using the switching reference signals often encountered in realistic micro-/nano-scale detection and manufacturing processes. Finally, extensive comparative experiments were conducted to verify the effectiveness and superiority of the proposed NN-SORC scheme.
Microbial functions and metabolism are intrinsic drivers of pollutant removal in mixotrophic denitrification systems. Four pyrite-based mixotrophic denitrifying biofilters were constructed and monitored for 304 days. Variations in pollutant characteristics indicated that the hot zones of heterotrophic denitrification, autotrophic denitrification, and sulfate reduction were located in the bottom, middle-lower, and upper parts of biofilters, respectively. These hot zones corresponded to preferential enrichment of heterotrophic denitrifying, S-based mixotrophic denitrifying, and sulfate-reducing bacteria, respectively, highlighting microbial spatial stratification. Differential functional gene analysis for S reduction revealed that only a dissimilated sulfate reduction process could consistently provide biogenic S0 as a new electron donor via the flavocytochrome c sulfide dehydrogenase (Fcc) enzyme and extracellular polymeric substance protection systems, enhancing the denitrification process. X-ray photoelectron spectroscopy confirmed the accumulation of biogenic S0. Untargeted metabolomic analysis suggested that vitamin B12 and tryptophan might be the key metabolites for realizing synergistic promotion of autotrophic and heterotrophic denitrification. The microbe-metabolite network indicated that dominant bacteria (e.g., Thiothrix and unclassified_f_Rhodocyclaceae) were specialists with less cross-feeding metabolism, while rare species (e.g., Thiobacillus and Desulfobacter) were generalists with complex cross-feeding metabolism in the constructed mixotrophic denitrification systems. The electron transfer pattern indicated that most of the electrons released from S, C, and Fe oxidation were utilized in denitrification processes as the dominant nitrogen removal pathway, including S2−/S0-based autotrophic, fermentation acetic acid production-heterotrophic, and Fe(II)-based autotrophic denitrification. Some electrons were utilized for coupling dissimilatory nitrate reduction to ammonia (DNRA) and anammox processes as an auxiliary pathway for systemic nitrogen removal. The findings of this study advance our understanding of the deeper intrinsic drivers of nitrogen removal by pyrite-based mixotrophic denitrifying biofilters, facilitating their optimization.
Current first-line medications for sleep disorders often overlap with antidepressants, yet their effectiveness remains suboptimal, highlighting the urgent need for novel therapeutic strategies based on new mechanisms. Our study initially identified a significant reduction in serum S-adenosylmethionine (SAM) levels in insomnia patients through a cross-sectional analysis, suggesting SAM as a potential biomarker and therapeutic target for sleep disorders. We then screened 60 gut strains across seven species and identified a high-SAM-producing probiotic, Lactobacillus helveticus CCFM1320, which improved cognitive and memory impairments caused by sleep deprivation in mice. Mechanistically, CCFM1320 enhances the methylation of N-acetylserotonin, a precursor of melatonin synthesis, normalizing the expression of downstream circadian rhythm genes. A subsequent four-week placebo-controlled clinical trial demonstrated that CCFM1320 significantly improved sleep quality in patients with sleep disorders, as evidenced by reduced Pittsburgh sleep quality index (PSQI) scores, lower serum cortisol levels, and decreased prevalence of pathogenic species in the gut. Probiotic treatment also elevated the abundance of SAM synthesis and metabolism-related enzyme genes in the gut microbiome, likely due to the introduction of high-SAM-producing probiotics and subsequent SAM accumulation. This study presents a promising probiotic-based strategy for managing sleep disorders, offering a potential non-pharmacological treatment alternative.
This study evaluates four groups of non-traditional aluminosilicate industrial coal byproducts and natural pozzolans—ground bottom ashes (GBAs), low-purity calcined clays (CCs), volcanic ashes (VAs), and fluidized bed combustion ashes (FBCAs)—as potential precursors for alkali-activated binders. Each group was activated using a mixture of sodium silicate and sodium hydroxide solution under optimized solution parameters. Their reaction behavior, pore solution changes, phase assemblage, and composition were analyzed using various material characterization tools. Results indicate that the reaction rate and total heat are similar to those of conventional fly ash precursors, with total heat release 38%-82% lower than that of Portland cement. Pore solution analyses reveal the formation of typical alkali-activated gel phases, and nuclear magnetic resonance (NMR) revealed calcium sodium aluminosilicate hydrates with aluminum/silicon (Al/Si) ranging 0.07-0.36. A novel reactivity index was proposed using Al-NMR. CCs and GBAs exhibited superior performance compared to other two materials used.
This study presents a novel self-sensing steel fiber-reinforced polymer composite bar (SFCB). The SFCB combines damage control, self-sensing, and structural reinforcement functions using distributed fiber optic sensing (DFOS) technology. By combining DFOS strains with theoretical and numerical models, a multilevel performance method for damage assessment is proposed from the perspectives of safety, suitability, and durability. Stiffness is a metric used to assess the complete service history of the reinforced concrete (RC) structure, which was used to define the damage variables. Initially, a basic correlation is created between the SFCB strain and several performance characteristics, such as moment, curvature, load, deflection, stiffness, and crack breadth, at characteristic points. The threshold values of damage variables for safety, serviceability, and durability were determined based on loading peak, mid-span deflection limits, and crack width limits corresponding to the damage variables. Then, a modified fiber damage model based on DFOS strain data is proposed to improve identification, quantification, and tracking for fiber damage. Finally, the reliability of the proposed theoretical and numerical models was verified by three-point flexural tests of SFCB-RC beams, and the test beams were analyzed using the proposed method. The results show that increasing the reinforcement ratio can lower the threshold at all levels and improve the ability of the flexural beams to control damage. This study contributes to advancing the intelligence of RC structures and offers valuable insights for the design of intelligent RC structures.