Search Results (4,199)

Crude fidaxomicin contains difficult-to-separate impurities, and conventional dual-step purification usually requires intermediate concentration and transfer, which increases process complexity and may aggravate product loss or degradation. To address this challenge, this study exploits the complementary selectivity of methanol/water (80/20, v/v) and acetonitrile/water (70/30, v/v) binary mobile phases and proposes two purification processes based on step-gradient twin-column recycling chromatography, namely spatial integration and system integration. In the spatial integration strategy, dual-stage separations that are conventionally performed in separate chromatographic systems are sequentially integrated into a single twin-column recycling system in combination with on-line heart-cutting, thereby eliminating intermediate off-line processing steps. In contrast, the system integration strategy merges the two binary mobile phases in defined proportions to construct a single ternary mobile phase composed of methanol/acetonitrile/water (37.5/37.5/25, v/v/v), enabling one-step complete separation. The results demonstrate that the spatial integration strategy, employing binary mobile-phase switching, produces fidaxomicin with a purity of 99.9%, recoveries ranging from 75.27% to 78.77%, and productivities ranging from 307.22 to 328.82 g·L⁻¹·day⁻¹, regardless of the switching sequence. The system integration strategy, based on one-step elution with the ternary mobile phase, achieves the same product purity of 99.9% without mobile-phase switching, with a recovery of 70.41% and a productivity of 246.33 g·L⁻¹·day⁻¹. These results confirm the applicability and flexibility of both integrated strategies for fidaxomicin purification, while indicating that the spatial integration strategy provides better overall preparative performance and the system integration strategy offers a simpler one-step operation. Full article

Landslides are one of the most common natural hazards in China, and the efficient screening of important factors is crucial for landslide susceptibility mapping. Taking the Zigui–Badong section of the Three Gorges Reservoir Area (TGRA) as the study area, this research initially selected 25 evaluation factors based on topography, geology, hydrology, remote sensing images, and previous studies. Thirteen key factors were obtained through analysis. Three machine learning models—RF, DT, and XGBoost—were then used for landslide susceptibility mapping, with SHAP and LIME employed to interpret the models. Finally, a scoring method was used to rank the six sets of results and compare them with those from the traditional AUC-based Recursive Feature Elimination (AUC-RFE) method. The results showed that the core factor sets screened by interpretable methods outperformed those from AUC-RFE. To further obtain accurate core factor sets, two additional interpretable methods—PI and Explainable Boosting Machine (EBM)—were integrated, ultimately identifying a core factor set consisting of eight factors including Elevation, Slope Height, and Aspect. This set achieved an AUC value of 0.931, only 0.003 lower than that of the 13 filtered factors. The screening method proposed in this paper can significantly improve the efficiency of factor acquisition, reduce the difficulty of factor acquisition, and provide a new approach for the selection of key factors in landslide susceptibility assessment. Full article

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination. Full article

This study investigates how solar and geomagnetic driver selection affects 24 h-ahead global ionospheric vertical total electron content (VTEC) prediction under different geomagnetic conditions. A four-step feature selection strategy involving importance evaluation, redundancy elimination, physical interpretability prioritization, and performance validation was developed to identify five key drivers from candidate solar and geomagnetic factors. Using global ionospheric maps provided by the Center for Orbit Determination in Europe (CODE) from 2014 to 2018, a non-overlapping 90-day temporal block scheme was adopted to reduce the risk of temporal information leakage. Six ablation experiments were conducted to compare the predictive performance of different driver combinations. The results show that the full-factor configuration selected by the proposed strategy achieved the most favorable overall performance among the tested combinations, although the global-average improvement relative to the baseline remained modest. The optimal driver combination varied with geomagnetic disturbance level, and the contribution of external drivers showed clear latitudinal dependence. In addition, the full-factor configuration yielded a more balanced global error distribution and was associated with slower error accumulation over the 24 h horizon. These findings suggest that physically guided driver selection is useful for constructing more physically meaningful driver combinations and for improving long-horizon prediction stability within a unified ConvLSTM-based framework. Full article

Diabetic kidney disease (DKD) affects approximately 40% of patients with diabetes mellitus and remains a leading cause of end-stage renal disease worldwide. Early diagnosis and identification of therapeutic targets are critical for improving patient outcomes, yet reliable biomarkers are lacking. This study integrated transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE96804, GSE30528, and GSE142025) with machine learning algorithms and Mendelian randomization (MR) to identify diagnostic biomarkers for DKD. Differentially expressed genes (DEGs) were identified and intersected with key modules from weighted gene co-expression network analysis (WGCNA). Four machine learning methods—least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XGBoost)—were applied for feature selection. Five hub genes (SPP1, CD44, VCAM1, C3, and TIMP1) were identified at the intersection of these approaches. Two-sample MR analysis using eQTL data from the eQTLGen Consortium and kidney function GWAS from the CKDGen Consortium provided evidence supporting potential causal associations between SPP1, C3, and TIMP1 expression and estimated glomerular filtration rate decline. Immune infiltration analysis via CIBERSORT estimated elevated proportions of M1 macrophages and activated CD4${}^{+}$ memory T cells in DKD samples, with all five hub genes showing correlations with macrophage infiltration. A diagnostic model based on these five genes achieved a cross-validated area under the receiver operating characteristic curve (CV-AUC) of 0.938 in the discovery dataset and AUC values of 0.917 and 0.889 in two independent external validation cohorts. Drug–gene interaction analysis identified 10 candidate compounds targeting the hub genes. These findings provide a computational framework for identifying candidate diagnostic biomarkers and generating hypotheses regarding potential therapeutic targets for DKD; however, all results are derived from in silico analyses and require experimental validation—including qPCR, immunohistochemistry, and prospective clinical cohort studies—before clinical applicability can be established. Full article

Neuroblastoma remains a formidable pediatric malignancy characterized by profound metabolic plasticity and limited therapeutic responsiveness in high-risk disease. Emerging evidence positions the interplay between Reactive Oxygen Species (ROS) and the metabolic sentinel AMP-activated protein kinase (AMPK) as a critical regulator of tumor metabolic stress and apoptotic susceptibility, with additional implications in the systemic pathology of Cancer Cachexia. Building on our previous work demonstrating that 1,1-Diethoxyethane (1,1-DEE; Biosacetalin), a volatile aroma compound inhibits mitochondrial complex I, induces ROS production, and activates AMPK-PGC1α-mediated mitochondrial biogenesis accompanying enhancement of aerobic respiration, leading to anti-Warburg effect. We identify 1,1-DEE as a previously unrecognized metabolic modulator with potent antitumor activity. 1,1-DEE triggers ROS-induced AMPK activation, leading to apoptotic elimination of neuroblastoma cells (SH-SY5Y), robust suppression of tumor growth, and significant prolongation of survival (median survival 77 days) in tumor-bearing NSG mice. Strikingly, 1,1-DEE simultaneously alleviates cancer-associated cachexia by preserving body weight. Mechanistically, our findings reveal a ROS–AMPK–centered signaling axis through which 1,1-DEE integrates tumor-selective cytotoxicity with systemic metabolic protection, highlighting a unified therapeutic strategy for targeting both tumor progression and cachexia in neuroblastoma. Full article

Deep-sea mining operations demand continuous, drift-free positioning over multi-day missions—a requirement that traditional acoustic dead-reckoning systems struggle to meet due to cumulative error accumulation and frequent DVL bottom-lock loss in sediment plume environments. Inspired by Google Cartographer’s 2D grid mapping paradigm, we present a prior map-based visual localization framework that decouples offline mapping from real-time localization, fundamentally eliminating drift through absolute image registration against pre-built seabed mosaics. By integrating adaptive keyframe selection, Multi-Scale Retinex (MSR) enhancement, and the AD-LG deep feature matching architecture, our system constructs globally consistent seabed maps for absolute positioning. The framework leverages deformable convolutions and LightGlue to effectively mitigate challenges such as low texture and non-rigid distortion. Quantitative validation on tank simulation datasets demonstrates significant superiority over IMU-only and standard fusion schemes; qualitative deployment on real Pacific CCZ imagery confirms near-real-time operational feasibility on an embedded Jetson Orin NX platform. This system establishes visual navigation as a viable backup to acoustic systems, addressing a critical gap in deep-sea mining vehicle autonomy. Full article

Diabetes mellitus is a health issue that is rapidly increasing worldwide, and it affects more than 347 million people globally. It is important to note that the disease can be successfully detected in its early stages, enabling physicians to avoid complications and improve patient outcomes. Despite the fact that machine learning (ML) has been extensively used in diabetes classification, the available solutions tend to place little or no emphasis on feature selection and ensembles, which limits prediction accuracy and generalizability. In this study, we introduce a hybrid framework that is based on three feature-selection algorithms, specifically, genetic algorithm (GA), correlation-based feature selection (CFS) and recursive feature elimination (RFE), in single and hybrid forms, and three classifiers, namely, multi-layer perceptron (MLP), support vector machine (SVM) and random forest (RF), to achieve a greater predictive robustness with the aid of soft voting. Experimental findings obtained from a benchmark diabetes dataset indicate that the RFE + CFS + SVM combination achieves the best performance, with an accuracy of 98.0%, sensitivity of 97.43%, specificity of 99.03%, precision of 99.51% and F1-score of 98.72%. These results indicate that the suggested hybrid feature-selection and ensemble learning model can offer a robust and highly effective approach for early-stage diabetes diagnosis, one which clinicians may use to make timely and accurate decisions. Full article

Background: Pre-pectoral direct-to-implant breast reconstruction is increasingly adopted after mastectomy because it avoids pectoralis major dissection, reduces postoperative pain, and eliminates animation deformity. However, reconstruction in patients with large or markedly ptotic breasts remains challenging because of skin envelope management, nipple–areola complex (NAC) viability, and implant stability. This study evaluated batwing skin-reducing mastectomy with immediate pre-pectoral polyurethane-coated implant reconstruction. Methods: We conducted a retrospective single-center study of consecutive patients who underwent batwing skin-reducing mastectomy with immediate pre-pectoral polyurethane-coated implant reconstruction between November 2022 and January 2025. Demographic, oncologic, operative, postoperative, and BREAST-Q data were collected. Primary outcomes included complications, oncologic events, and 12-month patient-reported outcomes. Results: Thirteen patients underwent reconstruction, accounting for 18 breasts, with a mean follow-up of 12.85 months. Mean age was 54.5 ± 9.7 years, mean body mass index was 27.0 ± 3.4 kg/m², and mean Regnault ptosis grade was 3.46 ± 0.52. No seromas or oncologic recurrences were observed. One hematoma and one late infection requiring implant removal occurred. Superficial NAC/central flap epidermolysis developed in four patients and resolved conservatively; no full-thickness NAC necrosis occurred. BREAST-Q scores improved significantly in all domains at 12 months, including satisfaction with breasts, psychosocial well-being, physical well-being, and sexual well-being (all p < 0.05). Conclusions: Batwing skin-reducing mastectomy with immediate pre-pectoral polyurethane implant reconstruction appears safe and reproducible in selected patients with advanced ptosis, with acceptable complication rates and significant improvement in patient-reported outcomes. Full article

Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs. The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. Full article

Background: n-3 Docosapentaenoic acid (DPA; 22:5 n-3) is increasingly viewed as a distinct long-chain omega-3 fatty acid with biological activities that are not fully captured by eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA). However, progress remains limited by restricted access to high-purity DPA: most commercial sources contain DPA as a minor component, and published isolation strategies often yield only enriched mixtures or require multi-step workflows that are difficult to scale in standard laboratories. Objectives: We aimed to establish a robust, laboratory-accessible purification workflow to obtain DPA ethyl ester at high purity while preserving oxidative quality. Methods: Candidate lipid sources were screened to select an optimal DPA-containing feedstock. Oils were stabilized with antioxidants and pre-fractionated by cold crystallization (−20 °C) to reduce saturated lipids and oxidation by-products. Preparative separation used a stacked C18 flash system (15 μm + 45 μm in series) operated isocratically (methanol/water 92:8, v/v) at 120 mL/min. Fractions were analyzed by GC and iteratively reinjected to progressively enrich the DPA window. Solvent was recovered by distillation and reused. Results: Omegavie^® 4020EE (5.4% n-3 DPA) was identified as the best starting material. Pretreatment eliminated detectable TBARS-derived malondialdehyde. The isocratic purification-loop strategy produced tens of grams of DPA ethyl ester at >98% purity (GC–FID) defined as n-3 DPA area% of total identified fatty acid methyl esters by GC–FID, with per-cycle DPA recovery of 91–95%, overall recovery of 76% from the starting DPA content, and >90% solvent recycling. The workflow is scalable at the gram-to-tens-of-grams level for research laboratories, although solvent burden and column maintenance remain practical constraints for larger-scale implementation. Identity and purity were confirmed by GC–MS and ^1H NMR, and oxidation indices remained low (peroxide value < 0.2 meq/kg; p-anisidine < 3). Conclusions: This scalable, solvent-conscious protocol enables reliable access to high-purity DPA and should be adaptable to other low-abundance polyunsaturated fatty acids. Full article

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods. Full article

The stable application of Partial Nitritation-Anammox (PN-A) in municipal wastewater treatment is primarily hindered by the ineffective suppression of Nitrite-Oxidizing Bacteria (NOB). This study systematically evaluated PN-A stability by comparing a Sequencing Batch Reactor (SBR) with two distinct Membrane Bioreactor (MBR) configurations. Results indicated that the SBR achieved superior performance through natural hydraulic selective washout, which efficiently eliminated NOB and fostered a robust AOB-AnAOB symbiotic biofilm. In contrast, MBRs were inherently susceptible to NOB proliferation due to their non-selective membrane retention. However, this study demonstrates that an intermittently aerated MBR (MBR-I) can effectively mitigate these disadvantages. By tailoring aeration control, the MBR-I successfully optimized the competitive kinetics for nitrite, suppressing NOB activity and achieving a robust total nitrogen removal rate (TNRR) of 76.38%. This work highlights that tailored aeration serves as a crucial synergistic strategy to bridge the inherent gap between membrane-based systems and conventional washout-driven reactors, providing a potential pathway for implementing PN-A within hybrid MBBR-MBR systems. Full article

Mid-high-frequency ultrasound (375 kHz) and anodic oxidation at low current intensity (<50 mA, NaCl as the supporting electrolyte) were employed to treat sulfonamide antibiotics (sulfamethoxazole—SMX and sulfacetamide—SAM). The sonodegradation involved HO•, while electrogenerated HClO was mainly responsible for the antibiotics’ elimination in the electrochemical process. A comparison of the processes evidenced that the degradation of SMX by ultrasound was faster due to its higher hydrophobicity. In contrast, in the electrochemical system, the SAM degradation was more efficient, which was associated with a higher reactivity of its acetamide moiety toward HClO. Interestingly, SMX was selectively sonodegraded in synthetic hospital wastewater and seawater, whereas the matrix components strongly accelerated the electrochemical degradation but affected the process performance in the hospital wastewater. On the other hand, theoretical analyses of atomic charge indicated that the central S-N bond, the N and aromatic ring in the aniline moiety, the C=C bond, and methyl groups in the isoxazole groups on SMX are the most susceptible moieties to the attacks by HO• and HClO. Furthermore, for the typical byproducts, calculations of the probability of being active against bacteria were slightly lower than that of the parent pharmaceutical, even being much lower for the byproducts from the electrochemical treatment. Full article

This study presents a combined process of sulfide precipitation followed by hydrogen peroxide leaching for rhenium recovery from copper smelting waste acid under ambient temperature and pressure. The process first removed copper through selective sulfide precipitation, then achieved co-precipitation of rhenium and arsenic to obtain a rhenium-rich precipitate. Subsequently, exploration of rhenium-containing precipitate leaching using H₂O₂ solution was conducted under isothermal conditions at 20 °C. The effects of H₂O₂ concentration, liquid-to-solid ratio, acidity, and leaching time rhenium extraction efficiency were examined systematically. The optimal leaching conditions were determined as: H₂O₂ concentration of 150 g/L, liquid-to-solid ratio of 5:1 mL/g, stirring speed of 350 r/min, and leaching time of 30 min. Under these conditions, the leaching conversions of rhenium and arsenic reached 96.0% and 93.8%, respectively. Through characterization of precipitate and leaching residue using ICP, SEM-EDS, XRD, and XPS analyses, the process and related reactions were elucidated. Results demonstrated that low-valence rhenium oxides and sulfides serve as the main reactive species during H₂O₂ leaching, whereas organic sulfur, high-valence oxides, and copper sulfide remained stable and resistant to leaching. Selective precipitation of copper effectively eliminated insoluble metal sulfides from rhenium-containing precipitates, thereby enabling efficient separation of rhenium under mild conditions. Full article