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Abstract Background With the rapid aging of the global population, identifying lifestyle patterns that effectively delay aging and reduce mortality risk is of paramount importance. This study utilizes the UK Biobank to analyze the associations of the Dietary Inflammatory Index, physical activity, and sleep on biological aging and all-cause mortality. Methods A prospective cohort study was conducted using data from over half a million UK Biobank participants. Two datasets were created by subjective and objective measurements of physical activity: the Subjective Physical Activity (SPA) and Objective Physical Activity (OPA) datasets. Lifestyle patterns, including diet habits, exercise levels, and sleep quality, were assessed within these datasets. Biological aging was quantified using validated methods, including Homeostatic Dysregulation, Klemera-Doubal Method Biological Age, Phenotypic Age, and Telomere Length. All-cause mortality data were obtained from the National Health Service. Statistical analyses included weighted linear regression and Cox proportional hazard models, adjusted for a range of covariates. Results The findings indicate that, in most cases, maintaining an anti-inflammatory diet, engaging in at least moderate physical activity, and ensuring healthy sleep conditions are associated with delayed physiological aging (Cohen’s d ranging from 0.274 to 0.633) and significantly reduced risk of all-cause mortality (HR-SPA: 0.690, 95% CI: 0.538, 0.884; HR-OPA: 0.493, 95% CI: 0.293, 0.828). These effects are particularly pronounced in individuals under 60 years of age and in women. However, it was observed that the level of physical activity recommended by the World Health Organization (600 MET-minutes/week) does not achieve the optimal effect in delaying biological aging. The best effect in decelerating biological aging was seen in the high-level physical activity group (≥ 3000 MET-minutes/week). The study also highlights the potential of biological age acceleration and telomere length as biomarkers for predicting the risk of mortality. Conclusions Choosing healthy lifestyle patterns, especially an anti-inflammatory diet, at least moderate physical activity, and healthy sleep patterns, is crucial for delaying aging and reducing mortality risk. These findings support the development of targeted interventions to improve public health outcomes. Future research should focus on objective assessments of lifestyle to further validate these associations.

Abstract Increasing the molecular weight while maintaining mono-dispersity has been proved crucial in innovating high-performance photovoltaic materials in giant oligomeric acceptors. However, developing efficient giant oligomeric donors to replace the batch-varied polymers remains challenging due to a lack of design principles. Here, by designing two unique isomeric rhodanine-based linkers, we successfully regulate the assembly behaviors of giant dimeric donors (G-Dimer-Ds) and fabricate the first all-giant-oligomer OSCs pairing with giant dimeric acceptor DY. Multiple characterizations demonstrate the small homo-molecular interaction with strong thermal-driven assembly capability in G-Dimer-D2 simultaneously facilitates reducing energetic disorder, improving charge transport and obtaining stable morphology, resulting in a satisfactory efficiency of 15.70% and long-term photostability with an extrapolated T 80 of ca.10,000 hours, and further enhancing thermal-driven assembly promotes efficiency of 16.05%. Our results provide construction approaches on efficient giant donors, and propose a promising type of OSC with completely definite structures, high efficiency and superior stability.

Abstract LncRNAs are highly implicated in oxidative stress (OS) during the growth of mammalian follicles. TAK1 binding protein 2 gene (TAB2) has been suggested to involve in the normal apoptosis and proliferation of granulosa cells (GCs), the main supporting cells in ovarian follicles. In this study, we found that TAB2 increased the expressions of SOD1, P50, and P65 to suppress the OS, thereby inhibiting the apoptosis and promoting the proliferation in GCs. Notably, DNMTs appeared to mediate the expression of TAB2 without the changes of DNA methylation at TAB2’s promoter. We identified an antisense lncRNA of TAB2, discovered that DNA methylation regulated the transcription of TAB2-AS in GCs, and found TAB2-AS medicated the follicular growth of ovaries in vivo. Mechanistically, the hypomethylation of the CpG site (−1759/−1760) activated the transcription of TAB2-AS, and the 1–155 nt and 156-241 nt of TAB2-AS were respectively complementary to 4368–4534 nt and 4215–4300 nt of TAB2’s mRNA to increase the expression of TAB2. Moreover, TAB2-AS inhibited the OS and apoptosis of GCs, while promoted the proliferation of GCs to expedite the follicular growth, which was in line with that of TAB2. Collectively, these findings revealed the antisense lncRNA mechanism mediated by DNA methylation, and TAB2-AS might be the target to control OS during follicular growth in mammals.

Abstract Quantitatively probing the ultimate limit of near-field enhancement around plasmonic nanostructures remains elusive, despite more than five decades since the discovery of surface-enhanced Raman scattering. Theoretical calculations have predicted an ultimate near-field enhancement exceeding 1000 using the best plasmonic material silver, but experimental estimations disperse by orders of magnitude. Here, we design a high-quality silver plasmonic nanocavity with atomic precision and precisely quantify the upper limit of near-field enhancement in ~1 nm junctions. A hot-spot averaged Raman enhancement of 4.27 × 1010 is recorded with a small fluctuation, corresponding to an averaged electric field enhancement larger than 1000 times. This result quantitatively delineates the ultimate limit of plasmonic field enhancement around plasmonic nanostructures, establishing a foundation for diverse plasmon-enhanced processes and strong light-matter interactions at the atomic scale.

Highlights The introduction of MoS2 generates a “kill three birds with one stone” effect to the original binary MXene/ANF system: lubrication toughening mechanical performance; reduction in secondary reflection pollution of electromagnetic wave; and improvement in the performance of photothermal conversion. After the introduction of MoS2 into MXene/ANF (60:40), the strain and toughness were increased by 53.5% (from 18.3% to 28.1%) and 61.7% (from 8.9 to 14.5 MJ m−3), respectively. Fortunately, the SER decreases by 22.4%, and the photothermal conversion performance was increased by 22.2% from ~ 45 to ~ 55 °C.

Abstract To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level.

Abstract Background In recent years, radiomics has been shown to be an effective tool for the diagnosis and prediction of diseases. Existing evidence suggests that imaging features play a key role in predicting the recurrence of lumbar disk herniation (rLDH). Thus, this study aimed to evaluate the risk of rLDH in patients undergoing percutaneous endoscopic lumbar discectomy (PELD) using radiomics to facilitate the development of more rational surgical and perioperative management strategies. Method This was a retrospective case-control study involving 487 patients who underwent PELD at the L4/5 level. The rLDH and negative groups were matched using propensity score matching (PSM). A total of 1409 radiomic features were extracted from preoperative lumbar MRI images using intraclass correlation coefficient (ICC) analysis, t-test, and LASSO analysis. Afterward, 6 predictive models were constructed and evaluated using ROC curve analysis, AUC, specificity, sensitivity, confusion matrix, and 2 repeated 3-fold cross-validations. Lastly, the Shapley Additive Explanation (SHAP) analysis provided visual explanations for the models. Results Following screening and matching, 128 patients were included in both the recurrence and control groups. Moreover, 18 of the extracted radiomic features were selected for generating six models, which achieved an AUC of 0.551–0.859 for predicting rLDH. Among these models, SVM, RF, and XG Boost exhibited superior performances. Finally, cross-validation revealed that their accuracy was 0.674–0.791, 0.647–0.729, and 0.674–0.718. Conclusion Radiomics based on MRI can be used to predict the risk of rLDH, offering more comprehensive guidance for perioperative treatment by extracting imaging information that cannot be visualized with the naked eye. Meanwhile, the accuracy and generalizability of the model can be improved in the future by incorporating more data and conducting multicenter studies.

Abstract The intratumoral microbiome (TM) refers to the microorganisms in the tumor tissues, including bacteria, fungi, viruses, and so on, and is distinct from the gut microbiome and circulating microbiota. TM is strongly associated with tumorigenesis, progression, metastasis, and response to therapy. This paper highlights the current status of TM. Tract sources, adjacent normal tissue, circulatory system, and concomitant tumor co‐metastasis are the main origin of TM. The advanced techniques in TM analysis are comprehensively summarized. Besides, TM is involved in tumor progression through several mechanisms, including DNA damage, activation of oncogenic signaling pathways (phosphoinositide 3‐kinase [PI3K], signal transducer and activator of transcription [STAT], WNT/β‐catenin, and extracellular regulated protein kinases [ERK]), influence of cytokines and induce inflammatory responses, and interaction with the tumor microenvironment (anti‐tumor immunity, pro‐tumor immunity, and microbial‐derived metabolites). Moreover, promising directions of TM in tumor therapy include immunotherapy, chemotherapy, radiotherapy, the application of probiotics/prebiotics/synbiotics, fecal microbiome transplantation, engineered microbiota, phage therapy, and oncolytic virus therapy. The inherent challenges of clinical application are also summarized. This review provides a comprehensive landscape for analyzing TM, especially the TM‐related mechanisms and TM‐based treatment in cancer.

Reservoir characterization is a vital task within the oil and gas industry, with the identification of lithofacies in subsurface formations being a fundamental aspect of this process. However, lithofacies identification in complex geological environments with high dimensions, such as the Lower Indus Basin in Pakistan, poses a notable challenge, especially when dealing with limited data. To address this issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), and self-organizing map (SOM). We utilized these proposed approaches to assess their performance in scenarios with varying core sample availability, specifically evaluating their effectiveness in identifying lithofacies within the Lower Goru formation of the middle Indus Basin. The study reveals that in scenarios with a limited number of core samples, MRGC is the preferred choice, while KNN or MRGC is more suitable for larger datasets. The results demonstrate the superior performance of MRGC and KNN in lithofacies identification within the specified geological environment, with SOM following closely behind, and ANN exhibiting comparatively lower efficacy. The accurate identification of lithofacies from the selected model is complemented by the application of the truncated Gaussian simulation method for facies modeling. Comparative results confirm the excellent agreement between the model identification of lithofacies from well logs and electro-facies obtained from the truncated Gaussian simulation electro-facies volume. This study highlights the crucial role of selecting the right machine learning approach for precise lithofacies identification and modeling in complex geological environments. The comparative analysis provides practitioners in the petroleum industry with insights into the strengths and limitations of each method, enhancing existing knowledge. In conclusion, this research emphasizes the significance of comprehensive research and method selection for advancing lithofacies identification in diverse formations or study areas, ultimately benefiting the broader field of subsurface characterization in the petroleum industry.

Abstract Alternative splicing is an important process that contributes to highly diverse transcripts and protein products, which can affect the development of disease in various organisms. Cardiovascular disease (CVD) represents one of the greatest global threats to humans, particularly acute myocardial infarction (MI) and subsequent ischemic reperfusion (IR) injury, which involve complex transcriptomic changes in heart tissues associated with metabolic reshaping and immunological response. In this study, we used a newly developed ONT full-length transcriptomic approach and performed transcript-resolved differential expression profiling in murine models of MI and IR. We built an analytical pipeline to reliably identify and quantify alternative splicing products (isoforms), expanding on the currently available catalog of isoforms described in mice. The updated alternative splicing landscape included transcripts, genes, and pathways that were differentially regulated during IR and MI. Our study establishes a pipeline to profile highly diverse isoforms using state-of-the-art long-read sequencing, builds a landscape of alternative splicing in the mouse heart during MI and IR.

Abstract The industrial internet acts as a crucial accelerator for driving enterprise digital transformation and plays a vital role in enhancing performance and competitiveness. However, our understanding of how small and medium-sized enterprises (SMEs) leverage the industrial internet to improve their performance and competitiveness remains limited. This study developed a “Digital transformation paths chain” framework to shed light on the key factors that drive SMEs to adopt the industrial internet and its influence on performance and competitiveness. Data from 314 Chinese SMEs were collected and analyzed using fsQCA and PLS-SEM to examine this model. Our findings highlight the positive impact of industrial internet adoption on enterprise performance and competitive advantage. The results highlight that technology, organization, and environment (TOE) drive industrial internet adoption. Industrial internet adoption positively impacts enterprise performance and competitive advantage through integration, application, and drive. This study offers valuable insights for businesses, policymakers, and other stakeholders engaged in digital transformation initiatives through the industrial internet.

Abstract This retrospective study used cone-beam computed tomography to investigated the crown-root morphology and alveolar bone structure in incisors region in subjects with closed deep overbite and comparison the difference between gender and age. The CBCT images for 40 subjects (group C) with Angle II division 2 were selected from patients of the HeFei Stomatological Hospital from November 2023 to March 2024.20 individual normal occlusion subjects (group A),20 patients with Angle II division 1(group B) were included. The crown-root ratio of the maxillary and mandibular anterior teeth, crown-root angle and alveolar bone structure were measured on the CBCT images, the data were statistically analyzed. In addition to mandibular lateral incisor, the crown-root ratio of maxillary and mandibular anterior teeth in the Class II division 2 group was higher than that in the individual normal occlusion group and the Class II division 1 (P

Abstract Thyroid cancer (TC) represents one of the most prevalent endocrine malignancies, with a rising incidence worldwide. Epigenetic alterations, which modify gene expression without altering the underlying DNA sequence, have garnered significant attention in recent years. Increasing evidence underscores the pivotal role of epigenetic modifications, including DNA methylation, RNA methylation, and histone methylation, in the pathogenesis of TC. This review provides a comprehensive overview of these reversible and environmentally influenced epigenetic modifications, highlighting their molecular mechanisms and functional roles in TC. Additionally, the clinical implications, challenges associated with studying these epigenetic modifications, and potential future research directions are explored. Graphical Abstract

Abstract Carboxy-terminus of Hsc70-interacting protein (CHIP), an E3 ligase, modulates the stability of its targeted proteins to alleviate various pathological perturbations in various organ systems. Cisplatin is a widely used chemotherapeutic agent, but it is also known for its alarming renal toxicity. The role of CHIP in the pathogenesis of cisplatin-induced acute kidney injury (AKI) has not been adequately investigated. Herein, we demonstrated that CHIP was abundantly expressed in the renal proximal tubular epithelia, and its expression was downregulated in cisplatin-induced AKI. Further investigation revealed that CHIP overexpression or activation alleviated, while its gene disruption promoted, oxidative stress and apoptosis in renal proximal tubular epithelia induced by cisplatin. In terms of mechanism, CHIP interacted with and ubiquitinated NUR77 to promote its degradation, which consequently shielded BCL2 to maintain mitochondrial permeability of renal proximal tubular cells in the presence of cisplatin. Also, we demonstrated that CHIP interacted with NUR77 via its central coiled-coil (CC) domain, a non-canonical interactive pattern. In conclusion, these findings indicated that CHIP ubiquitinated and degraded its substrate NUR77 to attenuate intrinsic apoptosis in cisplatin-treated renal proximal tubular epithelia, thus providing a novel insight for the pathogenesis of cisplatin-induced AKI.

Abstract Computer holography is a prominent technique for reconstructing customized three-dimensional (3D) diffraction fields. However, the quality of optical reconstruction remains a fundamental challenge in 3D computer holography, especially for the 3D diffraction fields with physically continuous and extensive depth range. Here, we propose a 3D computer-generated hologram (CGH) optimization framework with phase space tailoring. Based on phase space analysis of the space and frequency properties in both lateral and axial directions, the intensity of the 3D diffraction field is adequately sampled across a large depth range. This sampling ensures the reconstructed intensity distribution to be comprehensively constrained with physical consistency. A physics-informed loss function is constructed based on the phase space tailoring to optimize the CGH with suppression of vortex stagnation. Numerical and optical experiments demonstrate the proposed method significantly enhances the 3D optical reconstructions with suppressed speckle noise across a continuous and extensive depth range.

Abstract Clinical evidence supports the notion that T cell exhaustion and terminal differentiation pose challenges to the persistence and effectiveness of chimeric antigen receptor-T (CAR-T) cells. MEK1/2 inhibitors (MEKIs), widely used in cancer treatment due to their ability to inhibit aberrant MAPK signaling, have shown potential synergistic effects when combined with immunotherapy. However, the impact and mechanisms of MEKIs on CAR-T cells remain uncertain and controversial. To address this, we conducted a comprehensive investigation to determine whether MEKIs enhance or impair the efficacy of CAR-T cells. Our findings revealed that MEKIs attenuated CAR-T cell exhaustion and terminal differentiation induced by tonic signaling and antigen stimulation, thereby improving CAR-T cell efficacy against hematological and solid tumors. Remarkably, these effects were independent of the specific scFvs and costimulatory domains utilized in CARs. Mechanistically, analysis of bulk and single-cell transcriptional profiles demonstrates that the effect of MEK inhibition was related to diminish anabolic metabolism and downregulation of c-Fos and JunB. Additionally, the overexpression of c-Fos or JunB in CAR-T cells counteracted the effects of MEK inhibition. Furthermore, our Cut-and-Tag assay revealed that MEK inhibition downregulated the JunB-driven gene profiles associated with exhaustion, differentiation, anergy, glycolysis, and apoptosis. In summary, our research unveil the critical role of the MAPK-c-Fos-JunB axis in driving CAR-T cell exhaustion and terminal differentiation. These mechanistic insights significantly broaden the potential application of MEKIs to enhance the effectiveness of CAR-T therapy.

Abstract In recent years, deep learning models have dramatically influenced image inpainting. However, many existing studies still suffer from over‐smoothed or blurred textures when missing regions are large or contain rich visual details. To restore textures at a fine‐grained level, a multi‐stage inpainting approach is proposed, which applies a series of partial inpainting modules as well as a progressive inpainting module to inpaint missing areas from their boundaries to the centre successively. Some improvements are made on the partial convolutions to reduce artifacts like blurriness, which require a convolution kernel to contain known pixels more than a certain proportion. Towards photorealistic inpainting results, the intermediate outputs from each stage are used to compute the loss. Finally, to facilitate the training process, a multi‐step training is designed that progressively adds inpainting modules to optimize the model. Experiments show that this method outperforms the current excellent techniques on the publicly available datasets: CelebA, Places2 and Paris StreetView.

Abstract Background Alcohol dehydrogenase (ADH) is an enzyme that binds to zinc, facilitating the interconversion of ethanol and acetaldehyde or other corresponding alcohols/aldehydes in the pathway of ethanol fermentation. It plays a pivotal role in responding to environmental stress. However, the response of the ADH family to abiotic stress remains unknown in rapeseed. Result In this study, we conducted a comprehensive genome-wide investigation of the ADH family in rapeseed, encompassing analysis of their gene structure, replication patterns, conserved motifs, cis-acting elements, and response to stress. A total of 47 ADH genes were identified within the rapeseed genome. Through phylogenetic analysis, BnADHs were classified into four distinct clades (I, II, IV, V). Prediction of protein domains revealed that all BnADH members possessed a GroES-like (ADH_N) domain and a zinc-bound (ADH_zinc_N) domain. Analysis of promoter sequences demonstrated that BnADHs contained numerous cis-acting elements associated with hormone and stress responses, indicating their widespread involvement in various biological regulatory processes. Expression profiling under different concentrations of salt stress treatments (0%, 0.4%, 0.8%, 1.0% NaCl) further highlighted the significant role played by the BnADH family in abiotic stress response mechanisms. Overexpression of BnADH36 in rapeseed significantly improved the salt tolerance of rapeseed. Conclusion The features of the BnADH family in rapeseed was comprehensively characterized in this study, which could provide reference to the research of BnADHs in abiotic stress response.

Abstract Background In clinical oncology practice, patient-reported outcomes (PROs) are essential for assessing the symptom burden, quality of life, and psychological status of patients. However, there remains a gap between the use of PROs in an oncologic setting and its implementation. Furthermore, numerous reviews in PRO implementation are often based on one particular technology, setting, or health condition, making it difficult to obtain a comprehensive and coherent summary of available evidence to help plan and undertake implementation. This systematic review aims to identify and integrate enablers and barriers to PRO implementation through the comprehensive framework for implementation research (CFIR) to provide a reference for implementing patient-reported outcomes management in oncology settings. Methods This review strictly observed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. PubMed, Web of Science, CINAHL, Embase, and PsycINFO databases were systematically searched using a three-step search strategy. The search was limited from the inception of each database to April 2024. Articles describing facilitators and barriers to implementing PROs in clinical oncology practice were included. Two researchers screened the literature independently, and the quality assessment of cross-sectional, qualitative, and mixed studies was completed using the critical appraisal tools recommended by Joanna Briggs Institute (JBI) and the mixed methods assessment tool, respectively. Basic information about the included studies and determinants affecting PRO implementation was extracted, and coding categorization of facilitators and barriers was completed based on the 48 constructs provided by the CFIR framework. Results We included 30 studies from 5,649 search results, including 25 original and 5 review studies. The quality of the literature for qualitative studies was generally good, and the quality for quantitative and mixed studies was assessed as fair. We identified 52 facilitators and 50 barriers in the included literature, covering the domains used in the CFIR framework and 39 constructs, mainly including "Innovation Evidence-Base", "Innovation Complexity", "Innovation Design", "Structural Characteristics", "Compatibility", "Incentive Systems", "Access to Knowledge & Information", "Innovation Deliverers", "Innovation Recipients", and "Planning". Conclusions This systematic review integrated facilitators and barriers affecting PRO implementation in routine oncology clinical practice settings and categorized them through the CFIR framework. These influencing factors should be fully considered in future clinical practice to ensure the successful implementation of PROs. Trial registration It has been registered prospectively in PROSPERO under the registration number 42024532983.

Abstract The intricate balance among cost, output, and quality has substantially hindered the practical application of graphene within the downstream industry chain. Here we present a scalable and green supercritical CO2-assisted mechano-exfoliation (SCME) process that omits the use of organic solvents and oxidants throughout the production lifecycle, including exfoliation, separation, and purification. The SCME process achieves graphene powder space-time yields exceeding 40 kg/(m³·day) at laboratory (0.06–0.2 kg) and pilot scales ( > 4 kg), with resultant free-standing films showing conductivities up to 5.26 × 10⁵ S/m. Further kinetic investigations propose general guidelines for grinding-assisted exfoliation: (1) the macroscopic optimizing ability of mechanotechnics for mass transfer frequency and stress distribution and (2) the microscopic multiplication ability of exfoliation medium for shear-delamination. The comprehensive techno-economic analysis also underscores the economic viability of the SCME process for large-scale production.

ObjectiveTo address the problem of ultra-short baseline (USBL) relative navigation and positioning during the dynamic docking of an autonomous underwater vehicle (AUV) in shallow water, a compensated unscented particle filter (CUPF) algorithm based on a disturbance observer is proposed. MethodsThe CUPF algorithm compensates for missing values in the USBL data based on the dead reckoning model, estimates the unknown disturbances in the dynamic docking of the AUV using observer technology and filters outliers in the USBL data combined with the unscented particle filter to realize the estimation of the AUV's state. Results The lake trial data shows that the proposed CUPF algorithm can effectively remove outliers and fill in missing values for USBL positioning with a speed estimation error of less than 15% and computation time reduced by 57% compared with traditional methods. ConclusionsThe CUPF algorithm can improve the positioning accuracy of USBL while smoothing the AUV motion trajectory and state estimation by fully utilizing relative measurement information for AUV docking.

Streptococcus thermophilus is one of the most prevalent species in stool samples of westernized populations due to continuous exposure to fermented dairy products. However, few studies have explored the effect on host physiology by multiple S. thermophilus strains and considered the inter-strain differences in regulating host. In the present study, we investigated how four S. thermophilus strains influenced the gut microbiota, mucin changes, and host metabolism after 28 days of intervention in conventional mice. The results indicated that the consumption of S. thermophilus affected the host with strain specificity. Among four S. thermophilus strains, DYNDL13-4 and DQHXNQ38M61, especially DQHXNQ38M61, had more effect on host physiology by modulating gut microbiota and host metabolism than LMD9 and 4M6. Ingestion of strains DYNDL13-4 and DQHXNQ38M61 resulted in more remarkable changes in amino acid metabolism and lipid metabolism than that of strains LMD9 and 4M6, which may be related to the elevation of intestinal Bifi dobacterium by DYNDL13-4 and DQHXNQ38M61. The enriched Coriobacteriaceae UCG-002, Rikenellaceae RC9 gut group, and Lactobacillus only in the DQHXNQ38M61 group, had a close relationship with the prominent effect of DQHXNQ38M61 on regulating amino acid and lipid metabolism. In addition, DQHXNQ38M61 had a strong influence on degrading colonic mucin fucose by decreased α-fucosidase activity in feces, and improving mucin sulfation by upregulated Gal3ST2 expression. Comparative genomic analysis revealed that the four S. thermophilus strains belonged to different branches in the phylogenetic tree, and DYNDL13-4 and DQHXNQ38M61 had more genes involved in carbohydrate metabolism, amino acid metabolism, membrane transport, and signal transduction, which may confer the capacity of nutrient utilization and gastrointestinal adaptation of the strains and be associated with their strong regulation in host. Our study provides valuable information for understanding the regulation of host metabolism after consuming different S. thermophilus strains and could facilitate potential personalized applications of S. thermophilus based on strain varieties.

Abstract The authors present global‐interval and local‐continuous feature extraction networks for gait recognition. Unlike conventional gait recognition methods focussing on the full gait cycle, the authors introduce a novel global‐ continuous‐dilated temporal feature extraction (TFE) to extract continuous and interval motion features from the silhouette frames globally. Simultaneously, an inter‐frame motion excitation (IME) module is proposed to enhance the unique motion expression of an individual, which remains unchanged regardless of clothing variations. The spatio‐temporal features extracted from the TFE and IME modules are then weighted and concatenated by an adaptive aggregator network for recognition. Through the experiments over CASIA‐B and mini‐OUMVLP datasets, the proposed method has shown the comparable performance (as 98%, 95%, and 84.9% in the normal walking, carrying a bag or packbag, and wearing coats or jackets categories in CASIA‐B, and 89% in mini‐OUMVLP) to the other state‐of‐the‐art approaches. Extensive experiments conducted on the CASIA‐B and mini‐OUMVLP datasets have demonstrated the comparable performance of our proposed method compared to other state‐of‐the‐art approaches.

Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.

As novel biomarkers for gastroenteropancreatic neuroendocrine tumors (GEPNET) are in demand, we aimed to validate the clinical value of the NETest in Japanese patients. Between 2021 and 2023, blood and clinical data were collected from patients with GEPNET. Among 35 patients (median age: 59 [49–66] years), 27 cases originated from the pancreas and eight from the gastrointestinal tract. Of 69 samples sent to the laboratory, 56 (81.2%) underwent NETest. The diagnostic sensitivity was 97.1%. Among three patients who underwent R0 resection and four treated with peptide receptor radionuclide therapy, the changes in NETest scores closely correlated with disease progression. The NETest demonstrated high diagnostic efficacy and accurate therapeutic monitoring capabilities in a Japanese population.

Abstract Background Sonic Hedgehog (Shh), extensively researched for its role in early neurogenesis and brain development, has recently been recognized for its neuroprotective potential following neuronal injuries. This study examines the immediate impact of early administered Shh on the local inflammatory response post-acute spinal cord injury in rats. Methods Thirty-four female Wistar rats underwent either sham surgery (laminectomy; n = 10) or clip compression/contusion spinal cord injury (SCI) at the T9 level. This was followed by implantation of an osmotic pump and a subdural catheter for continuous intrathecal delivery of Shh (n = 12) or placebo (NaCl; n = 12). Locomotor function was assessed at 3- and 7-days post-injury (dpi) using the Basso, Beattie, and Bresnahan (BBB) score and the Gridwalk test. Animals were euthanized after 3 or 7 days for immunohistochemical analysis of the local inflammatory reaction and immune cell migration. Results Shh-treated rats demonstrated significant hindlimb movement and coordination improvements at 7 days post-injury, compared to controls. This enhancement was accompanied by a significant reduction in both immune cell presence and blood plasma products within spinal cord lesions, suggesting Shh’s dual role in modulating immune cell migration and maintaining the integrity of the blood-spinal cord barrier. Separately, these Shh-treated rats also showed an increase in M(IL-4) polarization of macrophages, further underlining the potential therapeutic impact of Shh in post-injury recovery. Notably, these effects were not evident at three days post-injury. Conclusion Shh application at 7 days post-injury showed immunomodulatory effects, possibly via enhanced blood-spinal cord barrier integrity, reduced immune cell migration, and increased anti-inflammatory immune cell differentiation. These mechanisms collectively contribute to enhanced locomotor recovery.

The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly. Genomic selection offers a potential solution to improve efficiency, but accurately predicting plant disease resistance remains a challenge. In this study, we evaluated eight different machine learning (ML) methods, including random forest classification (RFC), support vector classifier (SVC), light gradient boosting machine (lightGBM), random forest classification plus kinship (RFC_K), support vector classification plus kinship (SVC_K), light gradient boosting machine plus kinship (lightGBM_K), deep neural network genomic prediction (DNNGP), and densely connected convolutional networks (DenseNet), for predicting plant disease resistance. Our results demonstrate that the three plus kinship (K) methods developed in this study achieved high prediction accuracy. Specifically, these methods achieved accuracies of up to 95% for rice blast (RB), 85% for rice black-streaked dwarf virus (RBSDV), and 85% for rice sheath blight (RSB) when trained and applied to the rice diversity panel I (RDPI). Furthermore, the plus K models performed well in predicting wheat blast (WB) and wheat stripe rust (WSR) diseases, with mean accuracies of up to 90% and 93%, respectively. To assess the generalizability of our models, we applied the trained plus K methods to predict RB disease resistance in an independent population, rice diversity panel II (RDPII). Concurrently, we evaluated the RB resistance of RDPII cultivars using spray inoculation. Comparing the predictions with the spray inoculation results, we found that the accuracy of the plus K methods reached 91%. These findings highlight the effectiveness of the plus K methods (RFC_K, SVC_K, and lightGBM_K) in accurately predicting plant disease resistance for RB, RBSDV, RSB, WB, and WSR. The methods developed in this study not only provide valuable strategies for predicting disease resistance, but also pave the way for using machine learning to streamline genome-based crop breeding.

Abstract Background Esophageal squamous cell carcinoma (ESCC) is often diagnosed at advanced stages due to the inherent limitations of current screening methodologies. Central to evaluating tumor invasion and prognostic assessment in ESCC is the integrity of the basement membrane (BM). However, current research on the implications of BM-related genes (BMRGs) in diagnosing ESCC remains sparse. Methods We performed a comprehensive analysis using single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, alongside gene expression profiles acquired from GEO and The Cancer Genome Atlas (TCGA) databases. This identified differentially expressed BMRGs in ESCC. Employing LASSO, RF, and SVM-RFE, we selected potential BM biomarkers and crafted a diagnostic nomogram for ESCC, validated by ROC curves and AUC values. We also explored immune infiltration and biological mechanisms through consensus clustering and GSVA, and utilized single cell trajectory analysis and GSCALite to study gene distributions and pathways. In vitro experiments further elucidated the role of these genes in ESCC carcinogenesis. Results Here, we discovered that ESCC cell types exhibited markedly elevated BM-related scores. Our analysis pinpointed seven BM genes upregulated and linked to immune infiltration, showcasing unique gene expression profiles and varying immune cell densities across the BM-related subtypes. Furthermore, a robust positive correlation was observed between these genes expression and EMT activity. The knockdown of BGN significantly suppressed cell proliferation, migration, invasion, while also augmenting cell viability following chemotherapy drug treatment. Conclusion Our study identified seven key BMRGs (BGN, LAMB3, SPARC, MMP1, LUM, COL4A1, and NELL2) and established a diagnostic nomogram for ESCC. Of noteworthy significance is the discovery of BGN as a promising drug target, indicating a novel strategy for future clinical combination therapies in ESCC.

Abstract Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO2 presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO2, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development. Graphical Abstract

Abstract Wearable ultrasound imaging technology has become an emerging modality for the continuous monitoring of deep-tissue physiology, providing crucial health and disease information. Fast volumetric imaging that can provide a full spatiotemporal view of intrinsic 3D targets is desirable for interpreting internal organ dynamics. However, existing 1D ultrasound transducer arrays provide 2D images, making it challenging to overcome the trade-off between the temporal resolution and volumetric coverage. In addition, the high driving voltage limits their implementation in wearable settings. With the use of microelectromechanical system (MEMS) technology, we report an ultrasonic phased-array transducer, i.e., a 2D piezoelectric micromachined ultrasound transducer (pMUT) array, which is driven by a low voltage and is chip-compatible for fast 3D volumetric imaging. By grouping multiple pMUT cells into one single drive channel/element, we propose an innovative cell–element–array design and operation of a pMUT array that can be used to quantitatively characterize the key coupling effects between each pMUT cell, allowing 3D imaging with 5-V actuation. The pMUT array demonstrates fast volumetric imaging covering a range of 40 mm × 40 mm × 70 mm in wire phantom and vascular phantom experiments, achieving a high temporal frame rate of 11 kHz. The proposed solution offers a full volumetric view of deep-tissue disorders in a fast manner, paving the way for long-term wearable imaging technology for various organs in deep tissues.

Abstract Background Accurately predicting the walking independence of stroke patients is important. Our objective was to determine and compare the performance of logistic regression (LR) and three machine learning models (eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest (RF)) in predicting walking independence at discharge in stroke patients, as well as to explore the variables that predict prognosis. Methods 778 (80% for the training set and 20% for the test set) stroke patients admitted to China Rehabilitation Research Center between February 2020 and January 2023 were retrospectively included. The training set was used for training models. The test set was used to validate and compare the performance of the four models in terms of area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results Among the three ML models, the AUC of the XGBoost model is significantly higher than that of the SVM and RF models (P

Abstract Advancements in human-engineered heart tissue have enhanced the understanding of cardiac cellular alteration. Nevertheless, a human model simulating pathological remodeling following myocardial infarction for therapeutic development remains essential. Here we develop an engineered model of myocardial repair that replicates the phased remodeling process, including hypoxic stress, fibrosis, and electrophysiological dysfunction. Transcriptomic analysis identifies nine critical signaling pathways related to cellular fate transitions, leading to the evaluation of seventeen modulators for their therapeutic potential in a mini-repair model. A scoring system quantitatively evaluates the restoration of abnormal electrophysiology, demonstrating that the phased combination of TGFβ inhibitor SB431542, Rho kinase inhibitor Y27632, and WNT activator CHIR99021 yields enhanced functional restoration compared to single factor treatments in both engineered and mouse myocardial infarction model. This engineered heart tissue repair model effectively captures the phased remodeling following myocardial infarction, providing a crucial platform for discovering therapeutic targets for ischemic heart disease.

Abstract Otopetrin 1 (OTOP1) is a proton-activated channel crucial for animals’ perception of sour taste. Despite its significance, the gating mechanism of OTOP1 remains poorly understood. Here, we demonstrate that carvacrol activates the mouse OTOP1 (mOTOP1) channel under neutral and acidic conditions. Functional analysis showed that carvacrol enhances pH fluorescence signals in OTOP1-expressing cells, with reduced efficacy at lower pH levels. Carvacrol selectively activates mOTOP1, while mOTOP2, mOTOP3, and Chelonia mydas OTOP1 (CmOTOP1) are insensitive to carvacrol activation under neutral pH. Through chimera and point mutation experiments, swapping S134 in transmembrane segment 3 (TM3) and T247 in the TM5-6 linker abolished carvacrol activation of mOTOP1 and conferred activation on CmOTOP1, suggesting these two residues are critical for carvacrol sensitivity. These findings highlight TM3 and TM5-6 linker as pivotal gating apparatus of OTOP1 channels and potential docking sites for drug design.

The present study systematically investigated the influence of forging strategies on the microstructures and deformation behaviors of a Ti–5Al–5Mo–5V–1Cr–1Fe (Ti-55511) near β-titanium alloy. Microstructure analysis revealed that the as-cast alloy exhibited a basket-weave structure predominantly comprised of lamellar α phase. Three different forging methods were used to adjust the morphology of the primary α phase during the final two forging steps: 1) dual-phase (825 °C) + dual-phase forging (DD sample), 2) dual-phase + β single-phase (900 °C) forging (DS sample), and 3) β single-phase + dual-phase forging (SD sample). After standard heat treatment, the DD samples exhibited equiaxed αp due to sufficient deformation in dual-phase region, while DS samples transformed into lamellar shape due to αp reforming during the cooling process of the final β single-phase forging. In contrast, the SD samples displayed short-rod αp due to reforming in the single-phase region and insufficient deformation in the dual-phase region. All these αp and βt phases can hinder dislocation motion to enhance the strength. The DS sample exhibited the best combination of strength, ductility, and impact toughness. During tensile deformation, planar dislocation slip and {101‾1} twinning were dominant in the αp, while wave slip occurred in the βt phase. The fracture behavior transitioned from brittle to ductile after forging and heat treatment. Overall, this study offers a practical and effective method for optimizing the mechanical properties of the Ti-55511 alloy.

Abstract Background The reference values of eNO have certain differences among people of different countries and races. We aimed to obtain the reference value of eNO in healthy children and adolescents (6–18 years old) in China and to explore the associations between the reference values with ages, gender, heights, BMI, and regions. Methods We measured FeNO50 levels in 5949 healthy Chinese children and adolescents, FeNO200 and CaNO levels in 658 participants from 16 provinces of 7 administrative areas in China aged 6–18. All persons were studied after obtaining informed consent from children and their parents. Results The mean FeNO50 of 5949 Chinese children and adolescents aged 6–18 years was 14.1 ppb, with a 95% confidence interval of 1-38.1 ppb. The mean FeNO200 of 658 persons was 6.9 ppb with a 95% upper confidence interval of 15.0 ppb, and the mean CaNO was 3.0 ppb with a 95% upper confidence interval of 11.2 ppb. In the 6–11 age group, age and height were correlated with the logarithm of FeNO50 (P 0.05). The logarithm of CaNO was correlated with gender (P

Abstract Background Acute kidney injury (AKI) is a major complication following cardiac surgery. We explored the clinical utility of iron metabolism indexes for identification of patients at risk for AKI after cardiac surgery. Methods This prospective observational study included patients who underwent cardiac surgery between March 2023 and June 2023. Iron metabolism indexes were measured upon admission to the intensive care unit. Multivariable logistic regression analyses were performed to explore the relationship between iron metabolism indexes and cardiac surgery-associated AKI (CSA-AKI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of iron, APACHE II score and the combination of the two indicators. Restricted cubic splines (RCS) was used to further confirm the linear relationship between iron and CSA-AKI. Results Among the 112 recruited patients, 38 (33.9%) were diagnosed with AKI. Multivariable logistic regression analysis indicated that APACHE II score (odds ratio [OR], 1.208; 95% confidence interval [CI], 1.003–1.455, P = 0.036) and iron (OR 1.069; 95% CI 1.009–1.133, P = 0.036) could be used as independent risk factors to predict CSA-AKI. ROC curve analysis showed that iron (area under curve [AUC] = 0.669, 95% CI 0.572–0.757), APACHE II score (AUC = 0.655, 95% CI 0.557–0.744) and iron and APACHE II score combination (AUC = 0.726, 95% CI 0.632–0.807) were predictive indicators for CSA-AKI. RCS further confirmed the linear relationship between iron and CSA-AKI. Conclusions Elevated iron levels were independently associated with higher risk of CSA-AKI, and there was a linear relationship between iron and CSA-AKI.

Abstract The olfactory system can generate unique sensory memories of various odorous molecules, guiding emotional and cognitive decisions. However, most existing electronic noses remain constrained to momentary concentration, failing to trigger specific memories for different smells. Here, we report an artificial olfactory memory system utilizing conductive metal-organic frameworks (Ce-HHTP) that integrates sensing and memory and exhibits short- and long-term memory responses to alcohols and aldehydes. Experiments and theoretical calculations show that distinct memories are derived from the specific combinations of Ce-HHTP with O atoms in different guest. An unmanned aircraft equipped with this system realized the sensory memories in established areas. Moreover, the fusion of portable detection boxes and wearable flexible electrodes demonstrated the immense potential in off-site pollution monitoring and health management. This work represents an artificial olfactory memory system with two specific sensory memories under simultaneous conditions, laying the foundation for bionic design with qualities of human olfactory memory.

Highlights Comprehensively reviewed the progress in research on graphene electrode-based triboelectric nanogenerators (TENGs) from two dimensions, including precision processing methods of graphene electrodes and applications of TENGs. Discussed the various applications of graphene electrode-based TENGs in different scenarios, as well as the ways in which graphene electrodes enhance the performance of TENGs. Offered a prospective discussion on the future development of graphene electrode-based TENGs, with the aim of promoting continuous advancements in this field.

Abstract Recent advances in cancer research have highlighted the pivotal role of tertiary lymphoid structures (TLSs) in modulating immune responses, particularly in breast cancer (BRCA). Here, we performed an integrated analysis of bulk transcriptome data from over 6000 BRCA samples using biological network-based computational strategies and machine learning (ML) methods, and identified LGALS2 as a key marker within TLSs. Single-cell sequencing and spatial transcriptomics uncover the role of LGALS2 in TLS-associated dendritic cells (DCs) stimulation and reveal the complexity of the tumor microenvironment (TME) at both the macro and micro levels. Elevated LGALS2 expression correlates with prolonged survival, which is associated with a robust immune response marked by diverse immune cell infiltration and active anti-tumor pathways leading to a ‘hot’ tumor microenvironment. The colocalization of LGALS2 with TLS-associated DCs and its role in immune activation in BRCA were confirmed by hematoxylin-eosin (HE), immunohistochemistry (IHC), and in vivo validation analyses. The identification of LGALS2 as a key factor in BRCA not only highlights its therapeutic potential in novel TLS-directed immunotherapy but also opens new avenues in patient stratification and treatment selection, ultimately improving clinical management.

The Ta2AlC material is designed to enhance the performance of Ag-based electrical contact materials as a reinforcement phase. With a work function of 6.7192 eV, Ta2AlC demonstrated significantly higher values than reinforcement phase materials such as SnO2, ZrO2, and the commonly used MAX phase materials. Consequently, the arc erosion performance of an Ag–Ta2AlC composite was investigated under air conditions. Gas breakdown mainly occurred due to electron avalanches, with the observation of a streamer breakdown mechanism in a strongly nonuniform field. The arc exhibited concentrated erosion on the surface of the Ag–20 vol% Ta2AlC material, resulting in a higher arc energy. As the volume fraction of the Ta2AlC material increased to 30% and 40%, the eroded area became more dispersed. In particular, Ag–30 vol% Ta2AlC displayed the lowest arc energy (3.395 kJ) and shortest arcing time (33.26 ms). Among the four tested components, the Ag–30 vol% Ta2AlC composite demonstrated superior arc erosion resistance. Oxides of Ag2O, AgO, Ta2O5, and Al2O3 were formed through the interaction of ionized Ag and Ta2AlC particles. By combining the electromagnetic force and plasma flow force, sputtered particles and bulges were generated on the eroded surface. These research findings contribute to broadening the applications of Ag–MAX materials in the realm of electrical contacts.

This study aimed to clarify the reproductive remodeling mechanism in enhancing production performance and egg quality during the fasting-induced molting process of laying hens. A total of two-hundred and forty 380-days-old Jingfen No. 6 laying hens, with an average laying rate of 78% were divided into four replicates, with 60 hens in each replicate to receive a four-stage molt induction experiment. The stages encompassed the pre-molt stage (T1), the molt stage (T2), the recovery stage (T3), and the second peak-laying stage (T4). The egg-laying rate and egg quality were recorded during all stages, and sample collection (serum, magnum of oviduct, ovary) was conducted at the end of each stage. The length and index of oviduct, the number of hierarchical follicles, and serum reproductive hormone levels were further measured, followed by the transcriptomic sequencing process on the magnum of the oviduct and ovarian tissues at each stage. Results showed that the fasting treatment induced atrophy of the oviducts, the disappearance of large yellow follicles in the ovaries, and the decrease in serum reproductive hormone levels compared to the pre-molt stage. Transcriptomic analysis revealed that differentially expressed genes were notably enriched in cytokine-cytokine receptor interactions, cell adhesion molecules, and the arachidonic acid metabolism signaling pathway during the remodeling phases of oviduct and ovary tissues. Key candidate genes such as BMPR1B, NEGR1, VTN, and CHAD emerged as pivotal in influencing reproductive function remodeling in molt-treated chickens. Additionally, genes associated with steroid biosynthesis showed significant up-regulation in the ovaries of molted hens, correlating positively with egg-laying rates. Furthermore, genes related to collagen and laminin displayed significant positive associations with Albumen height and Haugh unit values. The results indicate that fasting interventions might modulate the remodeling of reproductive functions in laying hens by altering cytokine-cytokine receptor interactions, cell adhesion molecules, and arachidonic acid metabolic pathways. Enhanced ovarian steroid biosynthesis and up-regulation of gene expression, including oviductal collagens post-molting, are crucial for enhancing laying rates and egg quality. These findings could offer novel thinking for refining molting protocols.

Ammonia is a hydrogen carrier that emits no carbon and benefits from a well-established infrastructure for production, storage, and transport, making it an increasingly favored clean fuel. This paper introduces the first exploration and analysis of a Direct Ammonia fueled Solid Oxide Fuel Cell and Gas Turbine system with a Dual-Loop Organic Rankine Cycle (DA-SOFC-GT-ORC). We performed a multi-objective optimization for this system, with emphasis on enhancing both efficiency and economic feasibility. Under optimized conditions, the system achieves an exergy efficiency of 60.45 %, which is 10.64 % higher than that of a methane-fueled system. We analyze factors affecting system performance, including fuel flow rate, air flow rate, and fuel utilization rate. The results demonstrate that elevated temperature and pressure at the SOFC inlet improve system performance. The maximum exergy destruction is observed in the SOFC, heat exchangers, and gas turbine. Adding more cells improves system performance but also increases overall costs, with the minimum production cost per electrical unit achieved at 45,000 cells. The economic efficiency is marginally affected by air flow rate, yet considerably by fuel flow rate. For minor fluctuations in power demand, it is recommended to adjust the air flow rate to maintain high economic efficiency.

Fasting is beneficial to alleviate fatty liver, lose weight and improve reproductive function. However, previous studies have shown that, during fasting, disorders of bile acid metabolism were strongly associated with intestinal inflammation. The physiological and biochemical parameters and gene expression of multiple tissues of chickens at every critical time node were measured by ELISA and qPCR. In addition, association analysis was performed based on liver transcriptome sequencing and cecum metabolome data. At the cellular level, the regulatory effects of cecal metabolites on host bile acid metabolism were verified. During fasting, hepatic FXR-SHP-CYP7A1 and ileum-hepatic FXR-SHP-FGF15/19-FGFR4-CYP7A1 negative feedback pathways were activated to inhibit hepatic bile acid synthesis. The ileum FXR-SHP-ASBT pathways are activated, hindered the ileal bile reflux. At the same time, it promotes the secretion of bile acids and cholesterol in the liver, accelerates the utilization of H2O and CO2, to maintain liver homeostasis during fasting. In addition, enhanced gallbladder contraction and increased hunger were observed in laying hens during fasting. At the cellular level, the correlation between CYP7A1 and L-valine was verified, revealing that cecal metabolites of laying hens was enabled to regulate host bile acid metabolism. This study explored the metabolic patterns of bile acids during fasting and identified the main reasons for the accumulation of bile acids in the cecum, which provides a basis for fasting research and offers a reference for the formulation of fasting protocols.