Two-step Approach Research Articles

A two-step approach for damage identification in bridge structure using convolutional Long Short-Term Memory with augmented time-series data

This paper presents a novel two-step approach to identifying structural damages in bridge structure through the integration of 1D Convolutional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) networks, enhanced by the augmentation and transformation techniques using Symbolic Aggregate approXimation (SAX) for time-series data analysis. In the first step, the time-series data of the bridge is diversified and quantified by augmentation techniques to make the model more robust and increase its generalization capabilities. After that, SAX is implemented to reduce the volume and categorize time series data through the transformation of continuous time series into discrete symbols, thereby decreasing the size of the data for more efficient training performance. In the second step, an advanced DL model combining 1DCNN and LSTM is proposed to tackle the damage identification problems of the processed data. By leveraging the strengths of CNNs in feature extraction and LSTMs in sequence learning, combined with advanced techniques for data augmentation, our methodology offers a robust solution not only for improving the model's training process but also for enabling it to learn from a more diverse and comprehensive dataset that mimics different damage scenarios, allowing more accurate detection of damages within bridge structures. Validation of the proposed method is conducted using time-series data collected from Chuong Duong Bridge structure. The effectiveness of the proposed method is compared with other models, such as 1DCNN, LSTM, and the combined 1DCNN-LSTM. The results show that the proposed 1DCNN-LSTM-SAX outperforms the other methods in terms of accuracy and, thus, can be used extensively to deal with the damage identification problems of bridges using time-series data.

Potential site evaluation of fifth-generation district heating and cooling networks under uncertain energy market conditions: A two-step robust design analysis approach

In a fifth-generation district heating and cooling (5GDHC) network, energy demands are satisfied without using fossil fuel-based sources, rather by sharing participants’ energy loads through decentralized consumption and production. However, finding such locations where the complementary energy demands of participants are matched is difficult. The problem becomes more complicated due to uncertain energy markets (equipment investment and operational costs). These uncertain market trends make it difficult to decide whether to build the network. Finding the feasible network temperatures for the 5GDHC network is challenging due to the conflicting participants, as at one-time instance some need cooling and others need heating. The sensitivity analysis using Sobol’ indices is performed to find the most influencing input parameters. Then, a techno-economical assessment of the considered location is performed against influencing inputs. Two different sites Diest and Alpha Cloud (Flanders, Belgium) are evaluated. The systematic techno-economic analysis under the influence of an uncertain energy market and network operating temperatures reveals that building a network might be not feasible due to the high payback period. The methodology offers greater flexibility in identifying key uncertain input parameters and evaluating the robustness of critical performance indicators, overcoming the limitations of current GIS-based approaches. This improved capability assists decision-makers in more accurately determining feasibility.

Bamboo waste derived hard carbon as high performance anode for sodium-ion batteries

Biomass-derived hard carbon stands out as one of the most promising anode materials for advancing the commercial viability of sodium-ion batteries (SIBs). In this study, we harnessed a straightforward and eco-friendly two-step carbonization approach to fabricate high-performance hard carbon materials, ingeniously repurposing industrial bamboo waste. Concurrently, we delved into the impact of carbonization temperature on the microstructure and properties of the hard carbon derived from bamboo waste. The findings revealed that the as-prepared hard carbon at 1400 °C (HCB-1400) showcased the most desirable sodium-ion storage capabilities. The HCB-1400 material not only can deliver a substantial reversible capacity of 328.4 mAh g−1 at 30 mA g−1 in its maiden charge-discharge cycle but also display remarkable cycling stability. Moreover, the HCB-1400//Na3V2(PO4)3 full cell, when assembled, exhibited an impressive energy density of 249.25 Wh kg−1, with a capacity retention rate of 93 % after enduring 200 cycles at a current density of 1.0 C. This research underscores the potential of transforming biomass waste into high-performance hard carbon, heralding a sustainable path for SIB applications.

Fully Dynamic Zoom ADC with Energy-Efficient Residue Feedforward and Two-Step Summation

This paper proposes a fully dynamic zoom ADC based on residue feedforward and correlated level shifting (CLS)-assisted floating inverter amplifier (FIA) technique with 200× bandwidth/power scalability, by only changing the clock frequency. A CLS-assisted FIA which achieves 65 dB DC gain is employed to reduce errors from finite FIA gain. An energy-efficient residue feedforward path extracted from the input of the SAR ADC’s comparator minimizes the leakage of the SAR ADC’s quantization noise into the band. A novel two-step summation approach is proposed to minimize capacitor areas compared to a traditional passive switched-capacitor adder. The post-simulated results show the prototype ADC with near-constant energy efficiency, which scales power from 5μW to 822μW, achieves high resolution (>100dB) during the scalable bandwidth. At 20kHz BW, it achieves 106.2dB DR, 102.0dB SNDR, leading to FoMDR of 180.0dB and FoMSNDR of 175.8dB.

Automatic registration of large-scale building point clouds with high outlier rates

Point cloud registration plays a crucial role in processing large-scale building point cloud data. However, existing registration algorithms face challenges in effectively handling outliers in descriptor-based correspondence. This paper presents an automatic registration method for large-scale building point clouds that is capable of achieving swift and accurate registration without the need for initial guessing. The method employs a two-step matching optimization approach: coarse (two-point)-to-fine (three-point), selecting matches based on two-point reliability and three-point consistency. Spatial transformation parameters are broken down into rotations and translations. A progressively optimized kernel function is proposed for estimating rotation, while a clustering confidence algorithm computes translation. Comprehensive experiments were conducted using real-world data. The results indicate that the approach swiftly and accurately estimates optimal outcomes when processing large-scale building point clouds with outlier rates up to 99%. Compared to six existing registration methods, the proposed approach reduces rotation error by 6.15% and translation error by 12.83%, while improving efficiency by 2.57%.

A two-step dynamic FEM-FELA approach for seismic slope stability assessment

AbstractThe determination of the factor of safety (FoS) of slopes during seismic excitation can be complex if the relevant effects of pore water pressure accumulation, nonlinear material response and variable shear strength are duly accounted for. A rational two-step approach to tackle this task based on a hydro-mechanically coupled dynamic simulation and finite element limit analyses is henceforth introduced. To ensure accurate transfer of the hydro-mechanical soil state, a mapping concept is presented, accounting for spatial distributions of stresses, excess pore water pressures, inertial forces and shear strength. The proposed approach is compared to limit equilibrium method (LEM) for the case of a large-scale water-saturated open cast mine slope subjected to seismic loading. In comparison with LEM, the new approach to assess seismic slope stability proves to be simpler in its implementation and straightforward, which could be an important asset for practitioners.

Reassessing the Association of Sedentary Behavior and Physical Activity with Ischemic Stroke: A Mendelian Randomization Study

ABSTRACT Purpose Findings from previous Mendelian randomization (MR) studies disagreed with the current scientific consensus regarding the role of physical activity (PA) and sedentary behavior in ischemic stroke (IS). We reassessed these associations with a focus on etiological subtypes of IS and the potential mediating roles of cardiometabolic traits and brain imaging-derived phenotypes (IDPs). Methods We performed MR analyses using summary statistics from genome-wide association studies of sedentary behavior and PA (n = 88,411~608,595), cardiometabolic traits (n = 393,193~694,649), brain IDPs (n = 33,224) and the latest IS data (62,100 cases and 1,234,808 controls). Inverse-variance weighted regression was used as the primary method, complemented by several sensitivity analyses. A two-step MR approach was employed to assess the mediating effects of cardiometabolic traits and brain IDPs. Results Genetic liability to leisure-time moderate-to-vigorous PA (LTMVPA) and higher overall PA (OPA) were associated with reduced risks of IS and small vessel stroke (Benjamini–Hochberg adjusted P < 0.05). Suggestive associations were observed between longer leisure-screen time and higher IS risk and between higher OPA and lower cardioembolic stroke risk (P < 0.05). The isotropic volume fraction in the anterior limb of the left internal capsule (ALIC), as well as some cardiometabolic metrics, partially mediated these associations. There was no evidence for causal effects of overall MVPA, overall light-intensity PA or overall sedentary duration on IS. Conclusions Longer LST, less OPA and not engaging in moderate-to-vigorous PA during leisure time were associated with higher risk of ischemic stroke. The associations between PA and ischemic stroke depended on different subtypes and were mediated by changes in ALIC and cardiometabolic biomarkers.

Machine Learning-Based Software for Predicting Pseudomonas spp. Growth Dynamics in Culture Media

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.

Driving Digital Transformation: The Impact of SME Digitalization on Technology Acceptance and Usage in Small and Medium Enterprises of Yemen

This research investigates the relationships between digitalization, perceived usefulness, attitudes toward using technology, and actual system use of technologies within the context of small and medium-sized enterprises (SMEs) in Yemen. Using a two-step structural equation modelling approach, the research investigates both direct and mediating effects. Data were gathered from 392 SMEs in Yemen by employing a survey research design. The measurement model was estimated to evaluate the constructs' accuracy, while the structural model was analyzed to investigate the proposed relationships. Findings reveal that SME digitalization significantly enhances perceived usefulness, which positively influences users' attitudes toward technology adoption and subsequently drives actual system use. Specifically, the analysis shows that improvements in digitalization practices lead to heightened perceptions of usefulness and favorable attitudes, ultimately resulting in increased actual system use. Moreover, the results indicate that perceived usefulness and attitudes serve as significant mediators between digitalization and system use. This research contributes to understanding how enhancing perceived usefulness and fostering positive attitudes can facilitate technology adoption in SMEs, providing important implications for practitioners, policymakers, and researchers focused on digital transformation. Overall, the findings emphasize the importance of strategic digitalization efforts to improve operational efficiency and promote successful technology acceptance in small and medium enterprises.

Genetically Predicted Immune Cell-Mediated Effect of Lipid Metabolism on Allergic Diseases: A Two-Step, Mediation Mendelian Randomization Study.

An increasing number of studies have demonstrated that dynamic changes in lipid species can affect allergic diseases; however, the causal relationship and mediating role of immune cells remain unclear. We conducted a bidirectional two-sample mendelian randomization (MR) analysis using genome-wide association study (GWAS) data on 179 lipid species (n = 7,174) and three types of allergic diseases including allergic rhinitis (AR) (n = 370,158), allergic asthma (n = 219,753), and allergic conjunctivitis (n = 377,277). The principal model used was the inverse variance-weighted approach, and a series of sensitivity analyses were conducted to ensure the robustness of the results. We used a two-step MR approach to assess whether the causal effect was mediated by immune cells (n = 3,757). Sterol ester and sphingomyelin played pathogenic roles in allergic asthma, AR, and allergic conjunctivitis; however, the effective subtypes differed. Among them, CD45RA- CD4+ mature T cells and CCR2 on CD14+ CD16+ monocytes affected the promoting impact of sterol ester's metabolism on allergic asthma and AR with different mediating proportions, while the role of sphingomyelin may not involve the immune cells. Moreover, we observed that HLA-DR on CD33- HLA DR+ myeloid cells, CD11b on CD66b++ myeloid cells, and IgD+ CD38- B cells played the most mediating effect of phosphatidylethanolamine (O-18:2_20:4) in allergic asthma, phosphatidylinositol (16:0_18:1) in AR, and phosphatidylethanolamine (18:0_18:2) in allergic conjunctivitis. This MR study provides evidence for specific lipid species associated with the risk of allergic diseases, especially sterol esters, and identifies the immune cells that mediate this causal relationship.

A Brief Review of Radiolabelling Nucleic Acid-Based Molecules for Tracking and Monitoring.

The rise of nucleic acid-based therapeutics continues apace. At the same time, the need for radiolabelled oligonucleotides for determination of spatial distribution is increasing. Complex molecular structures with mostly multiple charges and low solubility in organic solvents increase the challenge of integrating radionuclides. In preclinical research, it is important to understand the fate of new drug candidates in biodistribution studies, target binding or biotransformation studies. Depending on a specific question, the selection of a respective radiolabelling strategy is crucial. Radiometals for molecular imaging with positron emission tomography or single-photon computed tomography generally require an attached chelating agent for stable complexation of the metal with the oligonucleotide, whereas labelling using carbon-11/-14 or tritium allows incorporation of the radioisotope into the native structure without altering it. Moreover, the suitability of direct radiolabelling of the oligonucleotide of interest or indirect radiolabelling, for example, by a two-step pretargeting approach, for the study design requires consideration. This review focuses on the challenges of radiolabelling nucleic acid-based molecules with beta-plus, gamma and beta-minus emitters and their use for tracking and monitoring.

Abstract A061: A cost-effective two-step approach for multi-cancer early detection in general population

Abstract In population-wide cancer screening, three key issues need to be focused on: number of cancer cases identified, the issue of false positives, and cost. A high false positive rate can result in a significant waste of healthcare resources. Large-scale screening inevitably leads to significant financial burdens on the healthcare system which is a key factor constraining nationwide screening. OncoSeek is a multi-cancer early detection (MCED) test using a panel of seven plasma protein tumor markers (PTMs) and artificial intelligence (AI). SeekInCare is a MCED test that integrates the seven PTMs and four cancer genomic features (aneuploidy, fragment size, end motifs, and oncogenic viruses) from cell-free DNA (cfDNA) by shallow whole-genome sequencing (sWGS). In a two-step approach, the initial screening is conducted using OncoSeek and SeekInCare is then used as the secondary test for those individuals who tested positive by OncoSeek. In a case-control study (n = 1197) including both OncoSeek and SeekInCare testing results, OncoSeek showed 49.9% sensitivity at 91.0% specificity, while SeekInCare demonstrated 60.0% sensitivity at 98.3% specificity. In the two-step process, specificity significantly increased from 91.0% to 99.3%, while sensitivity decreased from 49.9% to 39.9%. We modeled a screening in five million adults aged ≥ 50 years with a cancer incidence rate of 1.9%. The simulated real-world sensitivities of OncoSeek and two-step were adjusted downward proportionally to 28.0% and 22.4% based on the sensitivity differences observed between GRAIL’s CCGA study (case-control, 51.5%) and PATHFINDER study (prospective, 28.9%). While at 91.0% specificity OncoSeek had 441,450 cases of false positives, using the two-step approach significantly reduced false positives to 34,335 (0.7%). Although Galleri ($949 per test) identified more cancer cases than the two-step MCED, with 27,455 and 21,280 cases respectively, its total cost for the former reached $4,745 million. As the positive predictive value (PPV) of two-step (38.3%) was the same as Galleri (38.3%), it reduced cost 6.6-fold, amounting to a total cost of $713.6 million and a cost of $143 per individual screened. The cost of per cancer case detected using Galleri is $172,828 compared to $33,534 using the two-step MCED, which represents a 5.2-fold difference. In conclusion, the two-step approach not only significantly reduces false positives, but also cuts the screening cost down substantially, making it a cost-effective strategy for population-wide cancer screening. Citation Format: Mao Mao, Geng shuai peng, Li shi yong, Wu Wei, Chang Yin Yin. A cost-effective two-step approach for multi-cancer early detection in general population [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A061.

Motivations for Enrolment and Dropout of First-Year Undergraduate Nursing Students: A Pilot Multimethod Study

Background/Objectives: Higher education institutions must improve the attractiveness and retention of the nursing profession to address the widespread shortage. This pilot multimethod study aimed to preliminarily understand the relationship between motivations for enrolment and dropout among first-year undergraduate nursing students. Methods: A two-step approach was conducted among first-year nursing students from five Italian universities involving: (a) a baseline quantitative online survey collecting their characteristics and motivations for enrolment; (b) a follow-up semi-structured interview qualitative data collection among students who dropped out. Descriptive and inferential statistics were used to describe the motivations for enrolment and differences between universities. Dropout motivations emerged from inductive content analysis, with data categorisation according to Urwin’s framework. Results: A total of 759 students completed the online survey. Primary motivations for enrolment included the desire to be useful (88.8%), help suffering people (84.3%), and find employment (74.2%); 22.3% cited unsuccessful admission to another university as motivation for enrolment. Of the 141 students who discontinued, 31 were interviewed (22%). Eleven categories and three themes were identified. More than half of the participants dropped out due to interest in other courses and lack of aptitude, while a smaller number cited personal circumstances. Other motivations for dropout were related to negative learning environments or feelings and difficulties related to course characteristics. Conclusions: This study provides an initial insight into these complex phenomena that will be instrumental in understanding data from an Italian multicenter cohort study. The findings can inform recommendations and strategies to strengthen the future nursing workforce.

Enhancing Mechanical Properties of Additively Manufactured H13: A Two-Step Approach Integrating Processing Parameters Optimization and a Tailored Heat Treatment

Enhancing Mechanical Properties of Additively Manufactured H13: A Two-Step Approach Integrating Processing Parameters Optimization and a Tailored Heat Treatment

Valorization of White Lupin Straw Through Mild Dilute Acid Hydrothermal Treatment: A Sustainable Route for Monosaccharide and 5-Hydroxymethylfurfural Production

This study investigates the potential use of white lupine straw (WLS), an underutilized agricultural by-product, as a raw material to produce valuable biochemicals such as monosaccharides and 5-hydroxymethylfurfural (5-HMF) through hydrothermal pretreatment. The aim was to optimize mild reaction conditions to maximize the recovery of these products while minimizing degradation. The hydrothermal treatment of WLS in subcritical water with trace amounts of sulfuric acid was performed, followed by a two-step approach to evaluate the yields of hemicellulose and 5-HMF. The highest monosaccharide yield (163 g/kg) was achieved at temperatures between 174 and 181 °C and a holding time of 7–14 min, while the 5-HMF production was 139.9 g/kg at 199–203 °C and after 0.5–4.5 min. These results suggest that optimal 5-HMF production also increases the remaining solid residue. This study highlights the feasibility of WLS as a sustainable, low-cost biomass resource. It highlights the balance between temperature and time to maximize valuable product yields. The results contribute to advancing biorefinery processes by demonstrating that WLS can be effectively converted into bioethanol precursors and industrial chemicals, supporting circular bioeconomy principles and providing an environmentally friendly alternative to burning crop residues.

Highly Active NiRu/C Cathode Catalyst Synthesized by Displacement Reaction for Anion Exchange Membrane Water Electrolysis.

Anion exchange membrane water electrolysis (AEMWE) is highly promising for cost-effective green hydrogen production due to its basic operating conditions facilitating the use of non-noble catalysts. While non-noble Ni/Fe-based catalysts are utilized at the anode, its cathode catalyst still requires precious Pt. Due to the high cost of Pt and the sluggish hydrogen evolution reaction (HER) at the cathode in basic conditions, developing alternative catalysts to replace Pt is highly important. Here, a synthesis procedure for a Ru-based catalyst is reported and its high activity toward the HER in alkaline media is demonstrated in both half-cell and single-cell tests. The catalyst is synthesized in a two-step approach. A highly dispersed Ni catalyst is prepared on carbon support in the first step. In the second step, Ru is deposited on its surface using a galvanic displacement reaction. The uniqueness of this method is that Ru is deposited over the entire electrically conductive surface, resulting in an isotropic and homogeneous Ru distribution within the catalyst powder. It is demonstrated that this material remarkably outperforms state-of-the-art Pt/C catalysts in half-cell and single-cell tests. The single cell only requires 1.73V at 1A cm-2 with an overall PGM content of 0.05mg cm-2.

Sequential Diagnostic Approach Using FIB-4 and ELF for Predicting Advanced Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease

Background and Aims: Multiple non-invasive tests (NITs) for identifying advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) are available, but, due to the limitations of single NITs, the American Association for the Study of Liver Disease (AASLD) guidelines suggest a two-step strategy, combining the Fibrosis-4 Index (FIB-4) score with the Enhanced Liver Fibrosis (ELF) test to improve diagnostic accuracy and minimize unnecessary liver biopsies. However, few real-world studies have used such a sequential approach. We here evaluated the diagnostic accuracy of the ELF test in patients with recently established metabolic dysfunction-associated steatotic liver disease (MASLD) and assessed the clinical utility of applying a two-step strategy, including the ELF test following the FIB-4 score assessment, in patients with MASLD. Methods: We enrolled 153 patients diagnosed with MASLD who underwent liver biopsy at the Dong-A University Hospital between June 2018 and August 2023. The degree of fibrosis was determined based on liver biopsy results. Various NITs were used, including the Aminotransferase-to-Platelet Ratio Index (APRI), FIB-4 score, NAFLD Fibrosis score (NFS) and ELF test. The diagnostic efficacy of these NITs was evaluated based on the area under the receiver operating characteristic curve (AUROC). Additionally, the performance of each test was further examined both when applied individually and in a two-step approach, where FIB-4 was used followed by ELF testing. Key metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were used for this analysis. Results: Overall, 153 patients with MASLD (mean age: 46.62 years; 52.3% men; 28.1% with type 2 diabetes) were included. The performance of the NITs in identifying advanced fibrosis was as follows: the AUROC of the APRI, FIB-4, NFS, and ELF tests were 0.803 (95% confidence interval (CI), 0.713–0.863), 0.769 (95% CI, 0.694–0.833), 0.699 (95% CI, 0.528–0.796), and 0.829 (95% CI, 0.760–0.885), respectively. The combination of the FIB-4 score ≥ 1.30 and the ELF score ≥ 9.8 showed 67.86% sensitivity, 90.40% specificity, a PPV of 75.18%, an NPV of 86.78%, an accuracy of 83.64%, and an AUROC of 0.791 for predicting the diagnosis of advanced fibrosis. This approach excluded 28 patients (71.8%) from unnecessary liver biopsies. Conclusions: Our study demonstrated that ELF testing maintained diagnostic accuracy in assessing liver fibrosis in patients with MASLD in real-world practice. This test was used as a second step in the evaluation, reducing clinically unnecessary invasive liver biopsies and referrals to tertiary institutions. This approach allows assessment of MASLD severity in primary care settings without requiring additional equipment.

Pollution haven or pollution halo in the context of emerging economies: a two-step system GMM approach

Pollution haven or pollution halo in the context of emerging economies: a two-step system GMM approach

Association between gut microbiota, plasma metabolites, and ovarian cancer: A Mendelian randomization study.

Numerous studies have demonstrated a correlation between alterations in gut microbiota (GM) and levels of body metabolites in ovarian cancer (OC). However, the specific causal relationships underlying these associations remain unclear. This study utilized summary statistics of GM from the MiBioGen consortium, along with an unprecedented dataset comprising 1091 blood metabolites and 309 metabolite ratios from the UK Biobank, in conjunction with OC data from the FinnGen Consortium R9 release. We conducted bidirectional Mendelian randomization (MR) analyses to investigate the causal relationships between GM and OC. Additionally, a two-step MR approach was employed to identify potential mediating metabolites. Our analysis revealed significant associations between 6 specific microbiota taxa and OC. Furthermore, we identified several plasma metabolites that act as mediators of the association between GM and OC. In the two-step MR analysis, we observed a negative correlation between 4-methoxyphenol sulfate and pregnenetriol disulfate levels with OC. The genus Lachnospiraceae UCG008 potentially increases the risk of OC by decreasing 4-methoxyphenol sulfate levels, while the genus Howardella may elevate the risk of OC by reducing pregnenetriol disulfate levels, with mediation proportions of 22.35% and 4.23%, respectively. Additionally, levels of dilinoleoyl-GPE (18:2/18:2) and N-acetylkynurenine (2) were positively correlated with OC. The inhibitory effect of the genus Ruminococcus 1 on OC may be mediated through 1,2-dilinoleoyl-GPE (18:2/18:2) and N-acetylkynurenine (2), with mediation proportions of 10.15% and 11.32%, respectively. Our findings highlight the complex relationship among GM, plasma metabolites, and OC. The identified associations and mediation effects offer valuable insights into potential therapeutic approaches targeting GM for the management of OC.

Synthesis of Tetra-ortho-Methoxylated Azobenzene Photoswitches via Sequential Catalytic C-H Activation and Methoxylation.

Functionalized tetra-ortho-methoxyazobenzenes have been prepared in a two-step approach based on palladium-catalyzed C-H ortho bromination of azobenzenes, followed by copper-catalyzed methoxylation. The method has shown a broad tolerance to different functional groups that could not be incorporated by previous strategies. With this two-step transition metal-catalyzed strategy, we achieved overall yields that range from good to excellent and enable the exploitation of these highly coveted photoswitches. The superior robustness of this scaffold for solid phase peptide synthesis (SPPS) applications when compared to its chlorinated counterpart has been demonstrated after extensive treatments with piperidine while bound to a RinkAmide ChemMatrix resin, showcasing their potential for use in the synthesis of red-light-operated peptides.