Search Results (1,369)

Different cultures worldwide have attributed particular healing abilities to various plants for a long time. After decades of studies, research has demonstrated that their bioactivity is associated mainly with the presence of natural products, including short protein fragments known as peptides. These molecules may occur naturally in plants or be generated from plant protein through enzyme hydrolysis. In recent years, a growing body of evidence has linked plant-derived peptides to diverse biological activities, underscoring the importance of their structural and physicochemical features in determining functionality. Compared with peptides of animal or microbial origin, plant peptides stand out for their high abundance in sustainable sources, low allergenic potential, and distinctive structural traits- such as enrichment in hydrophobic and aromatic residues- that influence their stability, mechanisms of action, and biological functions. This review compiles and analyzes current literature to provide insights into how amino acid composition, secondary structure, net charge, and hydrophobicity influence peptide bioactivity. In addition, the review highlights the mechanisms of action most frequently described for plant peptides. Finally, the article discusses the current landscape and prospects of peptide-based drugs. Full article

Maintaining the freshness of shrimp is a critical issue in quality and safety control within the food processing industry. Traditional methods often rely on destructive techniques, which are difficult to apply in online real-time monitoring. To address this challenge, this study aims to propose a non-destructive approach for shrimp freshness assessment based on imaging and deep learning, enabling efficient and reliable freshness classification. The core innovation of the method lies in constructing an improved GoogLeNet architecture. By incorporating the ELU activation function, L2 regularization, and the RMSProp optimizer, combined with a transfer learning strategy, the model effectively enhances generalization capability and stability under limited sample conditions. Evaluated on a shrimp image dataset rigorously annotated based on TVB-N reference values, the proposed model achieved an accuracy of 93% with a test loss of only 0.2. Ablation studies further confirmed the contribution of architectural and training strategy modifications to performance improvement. The results demonstrate that the method enables rapid, non-contact freshness discrimination, making it suitable for real-time sorting and quality monitoring in shrimp processing lines, and providing a feasible pathway for deployment on edge computing devices. This study offers a practical solution for intelligent non-destructive detection in aquatic products, with strong potential for engineering applications. Full article

Emerging evidence suggests a potential role of gut dysbiosis in neurodegenerative disorders and, in particular, Alzheimer’s disease (AD) pathology and cognitive decline. However, the role of gut microbiome in the early prodromal stages of AD and particularly in mild cognitive impairment (MCI) remains understudied and has been mostly explored in Asian populations with no representation of European populations. To address this research gap in the literature and to suggest novel microbiome features associated with MCI, we conducted a cross-sectional study in a European population sample and profiled gut microbiota in 99 individuals without dementia through 16s ribosomal RNA (rRNA) sequencing. Individuals were categorized by cognitive status based on standard clinical criteria to cognitively normal (n = 49) or individuals with MCI (n = 50). Differential abundance through Microbiome Multivariable Associations with Linear model (MaAsLin2) and elastic net logistic regression analyses were used to identify gut microbiome features associated with MCI. MCI group was older than the CN group and age was used as covariate in the differential abundance analysis. No differences in alpha and beta diversity were found between the two groups (p > 0.05). At false discovery rate (FDR) < 0.05, we identified specific genera associated with MCI, mostly linked to short chain fatty acids (SCFAs) production (e.g., Candidatus_Soleaferrea q = 0.027, MaAsLin2 coefficient = 1.65, Sellimonas q = 0.017, MaAsLin2 coefficient = −4.45), while we highlight nominal (p < 0.05, q > 0.05) correlations of genera (e.g., Hydrogenoanaerobacterium, Subdoligranulum) with metrics of cognitive assessment. Microbiota was shown to have a fairly good discriminative capacity for MCI status (area under the curve AUC = 0.77), with Rothia genus found as the top predictor for MCI (beta coefficient [95% confidence intervals] = 0.224 [0.216–0.233]). Overall, our findings add to current knowledge reporting gut microbiome alterations in MCI by suggesting novel associated microbiome features; however, larger scale longitudinal studies are needed to further elucidate the underlying biological pathways linked to the disease. Full article

Energy demand is a critical challenge for sustainable infrastructure, yet in road asset management, it is rarely considered a central decision criterion. Most decision frameworks remain focused on financial and structural performance. This study introduces a comparative Energy Balance Analysis (EBA) as a complementary tool to existing life-cycle approaches. A case study is presented in which the only variable is binder composition—conventional 50/70 bitumen versus the same binder modified with 3% styrene–butadiene–styrene (SBS) polymer. The methodology integrates material-level energy demand estimation, laboratory performance testing, and pavement life modeling with HDM-4, and vehicle operational energy analysis. Results show that although SBS modification increases initial binder production energy by 13.3%, it doubles pavement service life and avoids mid-life rehabilitation, leading to a net saving of 110,671.75 MJ over 20 years. These findings confirm that early-stage material improvements can generate long-term energy efficiency gains. The study thus demonstrates the potential of EBA as a practical decision-support tool for sustainable pavement management. Full article

Addressing the carbon footprint in pig production is a fundamental technical basis for achieving carbon neutrality and peak carbon emissions. Only by systematically studying the carbon footprint can the goals of carbon neutrality and peak carbon emissions be effectively realized. This study aims to reduce the carbon footprint through optimized feeding strategies based on minimizing carbon emissions. To this end, this study conducted a full-lifecycle monitoring of the carbon footprint during pig growth from December 2024 to May 2025, optimizing feeding strategies using a real-time pig weight estimation model driven by deep learning to reduce resource consumption and the carbon footprint. We introduce EcoSegLite, a lightweight deep learning model designed for non-contact real-time pig weight estimation. By incorporating ShuffleNetV2, Linear Deformable Convolution (LDConv), and ACmix modules, it achieves high precision in resource-constrained environments with only 1.6 M parameters, attaining a 96.7% mAP50. Based on full-lifecycle weight monitoring of 63 pigs at the Pianguan farm from December 2024 to May 2025, the EcoSegLite model was integrated with a life cycle assessment (LCA) framework to optimize feeding management. This approach achieved a 7.8% reduction in feed intake, an 11.9% reduction in manure output, and a 5.1% reduction in carbon footprint. The resulting growth curves further validated the effectiveness of the optimized feeding strategy, while the reduction in feed and manure also potentially reduced water consumption and nitrogen runoff. This study offers a data-driven solution that enhances resource efficiency and reduces environmental impact, paving new pathways for precision agriculture and sustainable livestock production. Full article

To address the high fossil energy dependency and the low-value utilization of stillage (WDGS) in conventional cassava-based ethanol production—factors that increase greenhouse gas emissions and limit overall sustainability—this study develops an integrated ethanol–biogas–CHP system that valorizes stillage and enhances energy recovery. Three process scenarios were designed and evaluated through life cycle assessment (LCA) and techno-economic analysis: Case-I (WDGS dried and sold as animal feed), Case-II (stillage anaerobically digested for biogas used for heat), and Case-III (biogas further utilized in a combined heat and power system). Process simulation was conducted in Aspen Plus V11, while environmental impacts were quantified with the CML 2001 methodology under a cradle-to-gate boundary across six categories, including global warming potential (GWP) and abiotic depletion potential (ADP). Results show that Case-III achieves the highest environmental and economic performance, with a net GWP of −1515.05 kg CO₂-eq/ton ethanol and the greatest profit of 396.80 USD/ton of ethanol, attributed to internal energy self-sufficiency and surplus electricity generation. Sensitivity analysis further confirms Case-III’s robustness under variations in transportation distance and electricity demand. Overall, valorizing cassava stillage through biogas–CHP integration significantly improves the sustainability of ethanol production, offering a practical pathway toward low-carbon bioenergy with potential for negative emissions. This study fills a gap in previous life cycle research by jointly assessing WDGS utilization pathways with techno-economic evaluation, providing actionable insights for carbon-neutral bioenergy policies in cassava-producing regions. Certain limitations, such as software version and data accessibility, remain to be addressed in future work. Full article

Zebra mussel invasion of North American lakes during the last century may play an important role in the occurrence of toxic cyanobacterial blooms. However, empirical evidence quantifying their influence on cyanobacterial community dynamics at broad spatial scales remains limited. Here, we analyzed data from the U.S. EPA National Lakes Assessment (>1000 lakes) to examine potential linkages among zebra mussels, cyanobacterial community composition, and cyanotoxin levels. The analysis results showed significant differences in cyanobacterial communities between lakes located in areas with and without established zebra mussel populations. The lakes with established zebra mussels exhibited significantly higher microcystin levels and cyanobacterial abundance, but lower phosphorus concentrations. Structural equation modeling was used to confirm and estimate the effect of zebra mussels on microcystin concentrations via different pathways. The results suggest three potential pathways whereby zebra mussels influence microcystin production: (1) altering phosphorus concentration; (2) increasing cyanobacterial abundance; and (3) shifting cyanobacteria community structure. The total effect of zebra mussel establishment resulted in an overall 1.40-fold net increase in microcystin level, which presumably resulted from three contributing factors: (1) a 1.06-fold increase through an increased cyanobacterial abundance; (2) a 1.53-fold increase through a selective force, resulting in increased cyanobacteria toxicity; and (3) a 0.86-fold decrease in microcystin level through total phosphorus decrease. The study highlights the potential role of zebra mussel invasion in altering cyanobacterial composition and influencing microcystin levels in U.S. lakes. Full article

This study examines the growth dynamics of Nassella tenuis (Phil.) Barkworth, a palatable perennial tussock grass, abundant in the natural grasslands of Central Argentina. It focuses on the effects of water regimes and grazing history. Plants were collected from sub-humid and semiarid grasslands with contrasting grazing histories (grazed and ungrazed) and cultivated under controlled conditions. Key growth traits, such as leaf elongation, senescence, and net growth rates, as well as tiller production, were assessed across the growth cycle. The results reveal that sub-humid grasslands favor faster growth rates and higher tiller production, while semiarid grasslands exhibit lower growth rates, potentially reflecting adaptive strategies for water-limited environments. Seasonal analysis revealed distinct life cycle patterns: plants from sub-humid grasslands exhibited higher elongation rates during autumn and spring, whereas growth in semiarid plants remained consistently low across seasons. Grazing history significantly influenced growth patterns, with grazed plants showing reduced tiller numbers and growth rates but lower senescence rates, particularly in semiarid grasslands. These findings underscore the importance of aligning grazing management practices with the growth dynamics of N. tenuis and the water regime of the site to optimize forage production while maintaining grassland resilience. Full article

Over 15 billion tonnes year⁻¹ of biomass is used globally, yet 14% is downcycled for energy, forfeiting billions in potential revenue for higher-value products. Robust metrics that couple cascading use with cradle-to-gate greenhouse gas (GHG) emissions and economic value are essential for identifying superior biomass pathways. The aim of this review is to systematically map biomass utilization indicators published between 2010 and 2025; compare their treatment regarding circularity, climate, and economic value; and introduce the enhanced Bioresource Utilization Index (eBUI). A PRISMA-aligned search of Scopus and Web of Science yielded 80,808 records, of which 33 met the eligibility criteria. Each indicator was scored on cascading, data intensity, and environmental and economic integration, as well as computational complexity and sector scope. The Material Circularity Indicator, Biomass Utilization Efficiency, the Biomass Utilization Factor, and legacy BUI satisfied no more than two criteria simultaneously, and none directly linked mass flows to both GHG emissions and net revenue. The eBUI concept integrates mass balance, lifecycle carbon intensity, and value coefficients into a single 0–1 score. An open-access calculator and data quality checklist accompany the metric, enabling policymakers and industry to prioritize biomass pathways that are circular, climate-smart, and economically attractive. Full article

Although the beneficial effects of silicon on plant resistance to biotic and abiotic stresses are recognized, there is a lack of knowledge regarding its application in field conditions and its direct impact on physiological metabolism, root development, and, most importantly, the economic return of corn production in tropical regions. This study is justified by the need to quantify the effects of foliar silicon application on these variables, providing a scientific and economic basis for optimizing corn productivity and profitability in tropical environments. The objective of this study was to evaluate the effect of silicon on physiological metabolism, root system development, grain yield, and the potential economic return of maize production in a tropical region. The study was conducted under field conditions in two growing seasons (2020 and 2021), using a randomized block design in a 2 × 5 factorial arrangement with four replications. The first factor consisted of the maize growing seasons, and the second factor was foliar silicon fertilization (0 (control), 150, 300, 450, and 600 g ha⁻¹). Foliar fertilization with silicon at a dose of 150 g ha⁻¹ increases transpiration rate by up to 9%, net photosynthetic rate by 13%, and grain yield of maize by 10% after two growing seasons, regardless of the water deficit experienced during the crop cycle. At this dose, silicon application is economically viable, yielding the highest differential profit (USD 97.11 ha⁻¹). In conclusion, foliar fertilization with silicon is an agronomically and economically viable strategy for efficient maize grain production during the second growing season in tropical regions. Full article

Hydrogen is key to achieving a net-zero carbon future, yet current production remains predominantly fossil-based. Biohydrogen derived from agricultural residues represents a sustainable alternative aligned with circular economy principles. While several studies have assessed the bioenergy potential from agricultural residues in various African countries, their potential in Togo remains largely unexplored. This study employed an exploratory mixed-methods approach to quantify residue availability, evaluate production pathways, and estimate potential biohydrogen yields. Secondary data on crop production from the Food and Agriculture Organization (FAO) and theoretical conversion factors were used to assess the availability of agricultural residues from the eight major crops in Togo, resulting in a residue potential of 7.95 million tons per year. Considering ecological and competing aspects of residue utilization, a sustainable share of 3.1 to 6.6 million tons was estimated to be available for biohydrogen production, depending on the residue recoverability assumptions. A multi-criteria decision analysis (MCDA) was used to evaluate different biohydrogen production processes, identifying dark fermentation as the most suitable due to its low energy requirements and decentralized applicability. The theoretical biohydrogen potential was estimated at 20,991–42,293 tons per year (2.5–5.1 PJ per year) based on biochemical residue composition data and stoichiometric calculations. This study established a baseline assessment of biohydrogen potential from agricultural residues in Togo, offering a methodological framework for assessing biohydrogen potential in other regions. The results also underscore the need for site-specific data to reduce uncertainty and support evidence-based energy planning. Full article

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Full article

Solar coatings have become increasingly relevant as a means to enhance the performance and efficiency of photovoltaic (PV) panels, playing a critical role in advancing sustainable solar energy solutions. This study employs the life cycle assessment (LCA) methodology to quantify the greenhouse gas (GHG) emissions associated with the production process of a coating used on solar PV panels. Actual data were collected for the manufacture of the solar coating, constituted by two layers: (i) tetraethyl orthosilicate (TEOS) and (ii) titanium isopropoxide (TTIP). Data on energy and material flows were compiled. The GHG emissions for the TEOS and TTIP coatings were 1.8977 and 6.3204 g CO₂-eq/mL, respectively. With experimental data demonstrating a 4.5% increase in panel efficiency from the coatings, a simulation was carried out to verify the impact of the solar coating on a 16.4 MW solar power plant. The results indicate lifetime avoided emissions of 98,029,294 kg CO₂-eq over 25 years. Sensitivity assessments verified the impact of shorter lifetimes of the coatings, and even with frequent reapplication—down to monthly intervals—the coating continues to provide net environmental benefits. This robustness reinforces the potential of solar coatings as a complementary strategy for decarbonizing PV systems. Full article

As many German SMEs approach the end of their photovoltaic (PV) feed-in tariff period, the challenge of maintaining economic viability for these installations intensifies. This study addresses the integration of intermittent renewable energy sources (iRES) into production processes by proposing a method to identify and exploit industrial flexibility. A detailed case study of a German carpentry factory designed as a Net-Zero Energy Factory (NZEF) illustrates the approach, combining energy monitoring with blockchain technology to enhance transparency and traceability. Flexibility is exploited through a three-layer control system involving passive operator guidance, battery storage, and electric vehicle charging. The installation of a 40 kWh battery raises self-consumption from 50 to 70%, saving approximately EUR 4270 annually. However, this alone does not offset the investment. Blockchain-based transparency adds economic value by enabling premium pricing, potentially increasing revenue by up to 10%. This dual benefit supports the financial case for NZEFs. The framework is replicable and particularly relevant for low-automation industries, offering small and medium enterprises (SMEs) a viable pathway to decarbonization. The results align with the European Clean Industrial Deal, demonstrating how digitalization and industrial flexibility can reinforce competitiveness, sustainability, and digital trust in Europe’s transition to a resilient, low-carbon economy. Full article

Phosphorus (P) is essential for sugarcane growth but often presents low agricultural use efficiency. This research evaluated the effects of Bacillus velezensis UFV 3918 (Bv), applied alone or with monoammonium phosphate (MAP), on sugarcane’s physiological, biochemical, and biomass variables. Six treatments were tested in a completely randomized design: absolute control (AC), commercial control (CC, full MAP dose), Bv alone, and Bv combined with 1/3, 2/3, or full MAP dose. B. velezensis (Bv) and Bv + 1/3 MAP increased soil P availability by 22%, correlating strongly with physiological, biochemical, and shoot biomass variables. These treatments boosted total chlorophyll content (11.4%), electron transport rate (28.5%), and photochemical quenching (16.9%), resulting in higher photosynthetic efficiency. Compared with CC, net CO₂ assimilation, stomatal conductance, and carboxylation efficiency increased by 49.0%, 35.4%, and 72.9%, respectively. Additionally, amino acid content and leaf acid phosphatase activity rose by 12.1% and 13.8%. Key traits associated with biomass production included stomatal density (abaxial face), chlorophyll content, electron transport rate, intercellular CO₂ concentration, and leaf acid phosphatase activity. The results highlight the potential of Bv UFV 3918, particularly with reduced MAP doses, to improve sugarcane photosynthesis and biomass accumulation, offering a sustainable and cost-effective fertilization strategy. Full article