Search Results (8,781)

This study focuses on biogas production within lab-scale semi-batch bioreactors using agro-industrial wastes and dry biomass of an invasive aquatic species. In particular, the primary objective is to increase the yield of anaerobic digestion processes, with a specific focus on reducing CO₂ emissions associated with the degradation of biomass, by co-digesting different raw biomasses and agro-industrial wastes. In detail, the experiments concerned the pulp of Brewery’s Spent Grain (BSGp), consisting of the residual of Brewery’s Spent Grain after fiber deconstruction with ionic liquids–based treatment, and Lemna minor L. (LM). The two biomasses were studied separately and then co-digested. Co-digestion was carried out using a 1:1 (VS basis) mixture of Lemna minor and Brewery’s Spent Grain pulp. Due to the lack of organic nitrogen, BSGp showed low biogas production if compared with untreated BSG (1.14 × 10⁻³ vs. 1.71 × 10⁻³ Nm³/gVS). Differently, LM has a high nitrogen content and, when digested alone, produced 9.79 × 10⁻⁴ Nm³/gVS. The co-digestion tests allowed us to reach the highest performance: 2.94 × 10⁻³ Nm³/gVS. In terms of bioenergy production, the two biomasses showed high synergy when used in co-digestion. The amount of energy produced was calculated using a lower heating value (LHV) of CH₄ equal to 52 MJ. The results showed that co-digestion yielded 64.9 ± 0.6 MJ/kgVS, followed by BSG (43.3 ± 5.3 MJ/kgVS), BSGp (25.6 ± 0.3 MJ/kgVS), and LM (19.3 ± 1.0 MJ/kgVS). In addition, in terms of CO₂ avoided, the following results were achieved: 0.38–0.40 gCO₂/gVS with BSGp, 0.73–0.8 gCO₂/gVS with LM. Conversely, co-digestion tests allowed for the avoidance of 1.68–1.91 gCO₂/gVS. In conclusion, co-digesting BSGp with Lemna minor yields more methane and less CO₂ per unit processed, providing an effective way to convert readily available waste and biomass into bioenergy. Full article

Confounding factors in olfactory aroma data, such as high humidity levels, substantially affect sensor outputs, masking subtle volatile organic compound (VOC) patterns and hindering generalizable machine learning models. Traditional representation learning methods often require large datasets to mitigate confounder-induced variance, a resource unavailable in specialized sensor applications with limited data. This study presents Confounder-Invariant Representation Learning (CIRL), a method designed to mitigate confounding influences in data-scarce settings by leveraging explicit confounder information, such as relative humidity. CIRL enhances learned representations by reducing confounder effects, improving data purity and model robustness. Applied to three breath aroma datasets—acetone, ketosis, and peppermint-oil breath, all affected by high humidity—CIRL was integrated with standard autoencoder models. Evaluated within the same framework, CIRL improved generalization performance by 10–15% in classification accuracy across all three datasets. These results demonstrate CIRL’s potential to advance reliable artificial olfaction for applications like breath-based diagnostics in challenging real-world conditions. Full article

Background: Non-transected and partially transected peripheral nerve injuries (neuromas-in-continuity) are relatively common but understudied. Their optimal surgical management and expected outcomes remain unclear. We conducted a literature review of surgical repairs in such lesions and illustrate a case to guide decision-making. Systematic searches of PubMed and Google Scholar identified 70 eligible reports (Level I = 2, Level II = 5, Level III = 37, Level IV = 20, Level V = 4). Across studies, neurolysis of NAP-positive lesions often restored antigravity strength, while direct repair or grafting of nonconductive segments yielded meaningful recovery in ~75%. After neurolysis or reconstruction, ~77–92% of brachial plexus/axillary neuromas-in-continuity reached LSUHSC Grade ≥3. Median/ulnar lesions treated with neurolysis, biologic/vascularized coverage, or reconstruction showed reliable pain relief but variable sensory/motor recovery. Radial/PIN lesions improved in some series irrespective of NAPs. Earlier intervention, shorter gaps, distal sites, and younger age correlated with superior outcomes. Meanwhile, prolonged observation risking end-organ atrophy degraded results. Adjuncts such as electrical stimulation and wraps may aid reinnervation or reduce scarring, though high-quality evidence is limited. Conclusions: For non-transected and partially transected PNIs, a pragmatic approach emerges: Observe low-grade injuries with serial examinations. Explore early if recovery stalls (≈3–6 months). Use NAP-guided neurolysis for conductive lesions. Perform tension-free repair or grafting for nonconductive segments, adding anti-adhesive coverage when appropriate. Standardized reporting and prospective trials are needed to refine timing, technique selection, and patient-reported outcomes. Full article

Electrochemiluminescence (ECL) is a chemiluminescence phenomenon triggered by electrochemical reactions at the electrode surface, which has gradually become a high-sensitivity detection technology due to its low background, simple instrumentation, and high sensitivity. Therein, potential-resolved ECL refers to the generation of two or more ECL signals with distinct potentials and wavelengths during an electrochemical process. This unique capability enables simultaneous multi-signal outputs, making potential-resolved ECL particularly promising for self-calibration and multiplexed detection strategies. In this review, we focus on two critical aspects: on the one hand, the advancement of traditional ECL luminophores and potential-resolved ECL systems was reviewed, which were classified, respectively, into three categories to be introduced in detail (inorganic, organic and nanomaterial-based ECL luminophores or potential-resolved ECL of metal–organic complexes, layer-by-layer-modified electrodes, and nanomaterials). On the other hand, we summarized ECL detection strategies based on potential-resolved ECL systems and the application of these protocols in disease biomarker detection, which results in two categories (self-calibration strategies and multi-target strategies) for discussion. In this work, we aim to inspire investigators to explore novel ECL luminophores and design detection strategies with high performance, which could provide strong support for precision medicine, personalized assessment, portable medical devices, and the digital transformation of healthcare. Full article

Enzymatic browning markedly reduces the processing quality and economic value of potatoes. This study evaluated the effects of copper sulfate (CuSO₄) on potato growth, yield, and enzymatic browning. Quantitative analysis of browning-related enzymes, organic acids, free amino acids, and the browning index (BI) was conducted using high-performance liquid chromatography and non-contact colorimetric technology. Moderate Cu²⁺ (0.078–0.157 mmol·L⁻¹) supply enhanced photosynthetic capacity, biomass, and starch accumulation, whereas excessive Cu induced oxidative stress, increased PPO activity, phenolic accumulation, and BI. Metabolic profiling revealed that Cu²⁺ activates PPO via its copper-binding sites and reprograms amino acid and organic acid metabolism—upregulating arginine and proline while downregulating isoleucine, leucine, phenylalanine, lysine, citrate, and chlorogenic acid, all strongly correlated with BI. These findings highlight the dual role of copper in yield formation and enzymatic browning, introducing metabolic reprogramming as a potential mechanism for optimizing Cu fertilization to balance productivity and postharvest quality. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

African Swine Fever (ASF) control is severely limited by a diagnostic gap, as laboratory-based quantitative polymerase chain reaction (qPCR) is highly sensitive but slow, whereas field-deployable immunochromatographic assays (ICAs) are rapid but unreliable. To address this limitation, this study evaluated a novel, rapid isothermal assay, RNase hybridization-assisted amplification (RHAM), as a high-sensitivity, point-of-need diagnostic solution. This study compared the performance of RHAM and a conventional p72-based ICA against the qPCR reference standard using 106 diverse clinical field samples, including oral swabs, blood, serum, and organs, collected from suspected ASF cases in Thailand. The ICA exhibited markedly low diagnostic performance, achieving only 56.76% sensitivity and showing moderate agreement (κ = 0.421) with qPCR, highlighting the need for a more reliable alternative. In contrast, the RHAM assay achieved 94.59% sensitivity and 96.88% specificity, providing results rapidly within 35 min. This statistically superior performance (McNemar’s test, p < 0.0001) demonstrated almost perfect agreement (κ = 0.891) with the qPCR reference standard, missing only four samples with very high Ct values (>30). In conclusion, RHAM is a powerful, accurate, and field-deployable diagnostic tool that effectively bridges the diagnostic gap, offering qPCR-like sensitivity for the rapid containment of ASF outbreaks. Full article

Photo(electro)-catalysis has increasingly attracted attention from researchers due to its wide applications in green chemical transformation, including organic synthesis and environmental remediation. As a promising candidate, the n-type semiconductor WO₃ possesses a suitable bandgap (~2.6 eV), good visible-light response, high chemical stability, and multi-electron transfer capability, thus endowing it with enormous potential in heterogeneous photocatalysis (PC) and photoelectrocatalysis (PEC) to address environment and energy issues. In this review, the recent research progress of WO₃-based photo(electro)-catalysts is examined and systematically summarized with regard to construction strategies and various application scenarios. To start with, the research background, functionalization methods and possible reaction mechanisms for WO₃ are introduced in depth. Key influencing factors, including light absorption capacity, charge carrier separation, and reusability, are also analyzed. Then, diverse applications of WO₃ for the elimination of organic pollutants (e.g., persistent organic pollutants and polymeric wastes) and green organic synthesis (i.e., oxidation, reduction, and other reactions) are intentionally discussed to underscore their vast potential in photo(electro)-catalytic performance. Finally, future challenges and insightful perspectives are proposed to explore effective WO₃-based materials. This comprehensive review aims to offer profound insights into innovative exploration of high-performance WO₃ semiconductor catalysts and guide new researchers in this field to better understand their vital roles in green organic synthesis and hazardous pollutants removal. Full article

In this paper, spherical-shaped catalytic materials with needle-like stacking structures were synthesized in situ on the foam nickel substrate using the hydrothermal method, resulting in the NiM (M = Co, Mn, W, Zn)-MOF series. Furthermore, the catalyst with the best performance was obtained by adjusting the ratio of metal elements. Electrochemical tests show that NiCo-MOF (Ni: Co = 1:2) has the best electrocatalytic performance. During the UOR process, NiCo-MOF exhibits the optimal performance in 1 M KOH and 0.5 M urea solution, with a potential of only 1.33 V at a current density of 10 mA/cm². The improvement in the activity of NiCo-MOF can be attributed to the synergistic effect between the Ni and Co bimetals, which leads to an increase in the electron transfer rate, the exposure of active sites, and an improvement in conductivity. Moreover, metal–organic framework materials are widely used as electrocatalysts due to their compositional diversity, rich pore structures, and high specific surface areas. Meanwhile, NiCo-MOF was used as a UOR and HER catalyst to assist the overall water decomposition with urea, and it showed relatively excellent performance. Only a voltage of 1.56 V was required to drive the current density of 10 mA/cm² of the UOR || HER system. Therefore, the synthesized NiCo-MOF catalyst plays an important role in improving the efficiency of hydrogen production from water electrolysis and has promising sustainable application prospects. Full article

Hydroxamic acids are emerging as versatile chiral ligands for metal-catalyzed asymmetric oxidations due to their tunable electronic and steric environments. In this study, we systematically compared the catalytic behavior of C₂- and C₁-symmetric hydroxamic acid ligands in the vanadium-catalyzed asymmetric epoxidation of allylic alcohols. A series of chiral hydroxamic acids (HA1–HA7) was synthesized and evaluated under varied conditions to elucidate the influence of ligand symmetry on enantioinduction and reactivity. The results demonstrate that C₂-symmetric bishydroxamic acids generate a highly organized chiral environment, leading to high enantioselectivity but often limited conversion, consistent with the Sabatier principle. Conversely, certain C₁-symmetric ligands—particularly HA3—produced notable enantioselectivity (up to 71% e.e.) and full conversion under optimized conditions with VO(OiPr)₃ in CH₂Cl₂. A quadrant-based stereochemical model is proposed to rationalize the differential performance of these ligands. These findings highlight the critical role of ligand desymmetrization in modulating the chiral environment around vanadium centers, providing valuable design principles for next-generation hydroxamic acid-based catalysts in asymmetric synthesis. The optimized system (VO(OiPr)₃/HA3 in CH₂Cl₂) afforded >99% conversion and 71% e.e., providing a basis for extending hydroxamic acid scaffolds to diverse allylic alcohols. Full article

Background: A significant concern today is the dependence on low-quality water sources, such as saline water, in hydroponic systems, especially due to the scarcity of freshwater. Halophytes and salt-tolerant species have emerged as viable candidates for cultivation in saline hydroponics. However, their agronomic performance and physiological responses within hydroponic systems require further investigation. Objectives: This research aims to explore the potential of edible halophytes grown in saline nutrient solutions within hydroponic systems within salt-tolerant ranges, focusing on their metabolic profiles and mineral accumulation. Methods: Plantago coronopus (L.), Portulaca oleracea (L.), and Salsola komarovii (Iljin) were grown in walk-in controlled environment chambers in deep water culture hydroponic systems, at 0, 50, 100, 150, and 200 mM·L⁻¹ NaCl salinity; 16h, 250 µmol m⁻² s⁻¹, and wide LED spectrum lighting was maintained. Results: A significant decrease in organic acids, and fresh and dry weight under high saltinity was observed in Plantago coronopus and Portulaca oleracea, but not in Salsola komarovii. An increase in hexoses, particularly glucose, violaxanthin and β-carotene, P⁺ and Zn²⁺, along with a decrease in lutein, K⁺ and Ca²⁺ levels across salinity levels from 0 to 200 mM NaCl was observed in all treated halophytes. Increased salinity did not significantly affect total protein accumulation. Conclusions: These findings reveal that different shifts in osmolytes, mineral elements, and biomass accumulation in tested halophytes indicate species-dependent osmotic adjustment to increased salinity and may be attributed to the morphological differences among halophytic grasses, dicot halophytes, and those with succulent leaves or stems. The PCA score scatterplot results excluded the response of Plantago coronopus from other tested halophytes; also, it demonstrated that Portulaca oleracea was more sensitive to the hydroponic solution salinity compared to Salsola komarovii and Plantago coronopus. Full article

Therapeutic strategies targeting “tumor-educated platelets” (TEPs) and platelet–tumor interactions by key signaling pathways (ITAM, P2Y12) may reduce metastasis and cancer. Using a TEP gene expression dataset originally created to study swarm intelligence-enhanced detection of lung cancer cells (GSE89843), we did perform extensive transcriptome analysis to integrate these data with directed protein–protein interactions and build a TEP-specific signaling network. We analyze network topology and controllability and identify critical and indispensable nodes, as well as high-weight, usually high-score nodes. We reconstruct (pharmacological) controllable subnetworks of TEP signaling, which we then explore for drugs targets. We found 111 upregulated and 108 downregulated genes compared to control platelets, enriched in pathways related to extracellular matrix interactions, cytoskeleton organization, immune signaling, and platelet activation. Ribosomal function, apoptosis, and immune signaling were among the downregulated processes, highlighting unique TEP profiles in non-small-cell lung cancer (NSCLC). Our integrative analysis of TEPs in NSCLC reveals key transcriptional and network-based alterations harmful for the cancer patient. Using four complementary strategies, we identified five high-confidence genes (Gene symbols always given throughout the paper), ITGA2B, FLNA, GRB2, FCGR2A, and APP, as central to TEP signaling. These can be targeted by FDA-approved drugs. Fostamatinib, an SYK inhibitor, emerged as the top candidate drug to disrupt ITAM-mediated platelet activation selectively; metastasis-promoting metalloprotease and cytoskeletal targets influencing adhesion were also identified. A low-dose combination therapy of fostamatinib, Aducanumab, and acetylsalicylic acid (aspirin) may control TEP effects. In conclusion, our preclinical in silico approach revealed FDA-approved drugs that allow therapeutic targeting of metastasis-promoting TEPs and target NSCLC at the same time. Full article

The presence of organic matter can alter the dewatering characteristics of river-dredged silt and affect the dewatering efficiency. This study systematically compared the dewatering effects of cationic polyacrylamide (CPAM), ferric chloride (FeCl₃), and composite flocculant (CPAM + FeCl₃) for sludge with different organic matter contents by using the combined flocculation–electro-osmotic dewatering technology. The results show that the presence of organic matter significantly hinders the dewatering of silt. After the combined treatment of low-, medium-, and high-organic-matter river-dredging sludge with composite flocculants and electro-osmotic treatment, the final water content was 39.53%, 45.08%, and 47.28%, respectively. Compared with the use of CPAM alone, its dewatering efficiency increased by 66.98%, 5.39%, and 13.72%, respectively. Three-dimensional fluorescence spectroscopy analysis (3D-EEM) indicates that the combined dewatering of flocculation and electro-osmosis can improve the dewatering performance of sludge by promoting the transformation of organic matter. Scanning electron microscopy (SEM) analysis shows that under the action of the composite flocculant, the sludge particles aggregate significantly, and after electro-osmosis, the structure becomes more compact and channels are formed, which further improves the sludge dewatering efficiency. This study provides a theoretical basis for the optimization of dewatering processes for dredged silt with different organic matter contents. Full article

In Eritrea, efforts are being made to tackle the widespread land degradation and promote natural resources and the agricultural sector. However, these efforts lack digital resources assessment, mapping, planning and monitoring. Thus, we developed soil organic carbon (SOC) predictor models for the Western Lowlands of the country, employing 6 machine learning models with different input variables (36, 27, 15, and 08) obtained following these variables selection strategies: (1) all proposed SOC predictor variables; (2) very high multicollinearity (≥0.900 **) reduction; (3) high multicollinearity (≥0.700 **) reduction; (4) the Boruta feature selection algorithm. The results revealed that SOC levels were generally low (mean = 0.43%). Grazing lands, rainfed croplands, and irrigated farmlands all exhibited similarly low SOC values, attributed to unsustainable land management practices that deplete soil nutrients. In contrast, natural forestlands exhibited significantly higher SOC concentrations, highlighting their potential for soil carbon sequestration. Among the tested models, the XGBoost algorithm using 27 covariates achieved the highest predictive performance (RMSE = 0.118, R² = 0.758, RPD = 2.252), whereas the multiple linear regression (MLR) model with 8 variables yielded the lowest performance (RMSE = 0.141, R² = 0.742, RPD = 1.883). Compared to the Boruta-based feature selection, the MLR, PLS, XGBoost, Cubist, and GB models showed performance improvements of 10.41%, 10.06%, 6.72%, 6.50%, and 3.15%, respectively. Rainfall emerged as the most influential predictor of SOC spatial variability in the study area. Other important predictors included temperature, soil taxonomy, SWIR2 and NIR bands from Landsat 8 imagery, as well as sand and clay contents. We conclude that reducing very high multicollinearity is essential for improving model performance across all tested algorithms, while reducing moderate multicollinearity is not consistently necessary. The developed SOC prediction models demonstrate robust predictive capabilities and can serve as effective tools for supporting soil fertility management, land restoration planning, and climate change mitigation strategies in the Western Lowlands of Eritrea. Full article

The widespread replacement of cellulose paper with polypropylene (PP)-based synthetic paper has been hindered by the relatively low stiffness and modulus of PP. Conventional approaches that incorporate rigid inorganic fillers can enhance the modulus but typically compromise processability and mechanical performance. In this work, we propose a dual strategy by introducing high-modulus organic hydrogenated resin fillers (C9) and applying a biaxial stretching force field. The biaxial stretching process not only promotes PP crystallization but also significantly improves the uniform dispersion of C9 fillers. As a result, a composite paper with ultrafine C9 dispersion and a crystalline self-reinforced structure was successfully fabricated. The composite exhibits a modulus that is 38% higher than that of biaxially stretched neat PP and 218% higher than that of unstretched neat PP. Furthermore, under biaxial stretching, the C9 fillers impart a toughening effect, effectively overcoming the conventional stiffness–toughness trade-off. This work therefore provides a promising strategy for the scalable fabrication of high-performance PP-based synthetic paper. Full article