Search Results (12,478)

Agave species are plants with great economic value and multiple possibilities of use as ornamentals, medicinal plants, and fibers, as well as being significant sources of bioethanol. However, their long life cycles hinder their conventional breeding. Therefore, biotechnology tools are the most effective means for clonal propagation and genetic improvement. In vitro micropropagation of A. sisalana via axillary shoot proliferation from bulbil explants was attained using Murashige and Skoog medium (MS) supplemented with cytokinins (CKs), such as 6-benzyladenine (BA), kinetin (KIN), or thidiazuron (TDZ). The optimum significant shoot proliferation (14.67 shoots/explant) was achieved on 1.0 mg L⁻¹ TDZ. The carry-over effect of CKs on subsequent rooting could be detected. Control and KIN treatments could enhance the rooting of shoots on shoot proliferation media. The regenerated plantlets were acclimatized directly with 100% survival. To mitigate this carry-over effect, that causes hindering further root growth and development, and promote healthy growth of roots, subculturing shoots onto a CK-free medium is a recommended practice. The shoots induced on all BA treatments, and TDZ at 0.5 and 1.0 mg L⁻¹ could be rooted after two subcultures on CK-free medium, then they were acclimatized with 100% survival. However, the higher concentrations of TDZ inhibited in vitro rooting even after two subcultures on CK-free medium, and the acclimatization percentage was reduced by increasing the TDZ concentration recorded from 10 to 0%. Full article

Entomopathogenic fungi (EPF) are one of the most environmentally friendly ways to control a plethora of chewing insects such as T. pityocampa, G. mellonella, and A. grisella. Bioassay of EPF on these highly damaging pests is considered important in the face of climate change in order to research alternative solutions that are capable of limiting chemical control, the overuse of which increases insects’ resistance to chemical compounds. In this study, the insecticidal virulence of Metarhizium robertsii isolates, retrieved from forest ecosystems, was tested on second-instar larvae of T. pityocampa, G. mellonella, and A. grisella. Bioassays were carried out in the laboratory, where experimental larvae were sprayed with 2 mL of a six-conidial suspension from each isolate. Mortality was recorded for 144 h after exposure. Mean mortality, lethal concentrations, sporulation percentage, and sporulation time were estimated for each isolate. Metarhizium isolates resulted in the highest mortality (89.2% for G. mellonella and 90.2% for A. grisella). Based on the LC₅₀ estimates determined by the concentration–mortality relationships for the tested fungal isolates, we demonstrated significant virulence on larvae of G. mellonella, A. grisella, and T. pityocampa. Our results indicate that entomopathogenic fungi have the potential to become a very useful tool in reducing chemical applications. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

This study investigates the seasonal effectiveness of high-efficiency particulate air (HEPA) purifiers and window-opening behaviors in three London nurseries, using continuous indoor and outdoor PM_2.5 monitoring, window state and air purifier use, and occupant questionnaire data collected from March 2021 to February 2022. Of the approximately 40–50 nurseries contacted, only three agreed to participate. Results show that HEPA purifiers substantially reduced indoor particulate matter (PM_2.5), with the greatest effect observed during the heating season when windows remained closed for longer periods. Seasonal and behavioral analysis indicated more frequent and longer window opening in the non-heating season (windows were open 41.5% of the time on average, compared to 34.2% during the heating season) driven by both ventilation needs and heightened COVID-19 concerns. Predictive modeling identified indoor temperature as the main driver of window opening, while carbon dioxide (CO₂) had a limited effect. In addition, window opening often increased indoor PM_2.5 under prevailing outdoor air quality conditions, with mean concentrations rising from 2.73 µg/m³ (closed) to 3.45 µg/m³ (open), thus reducing the apparent benefit of air purifiers. These findings underscore the complex interplay between mechanical purification and occupant-controlled ventilation, highlighting the need to adapt indoor air quality (IAQ) strategies to both seasonal and behavioral factors in educational settings. Full article

Sulfur-containing fertilizers increase production costs, which leads to low utilization of this nutrient. Thus, evaluating how the absence of sulfur influences the early development of Urochloa brizantha is essential. Study was conducted in a greenhouse at the Federal University of Rondonópolis in a completely randomized design, with six treatments in a 3 × 2 factorial scheme, and eight replications. Three cultivars of U. brizantha (Marandu, Xaraés and Piatã) were evaluated under two fertilization strategies: with or without sulfur fertilization. Sufur presence increased the number of leaves and forage mass, in which cultivar Xaraés presented the greatest means. Piatã was the cultivar most sensitive to sulfur deficiency at establishment, which reduced forage mass, number of leaves and number of tillers by 42%, 32%, and 45%, respectively. Despite these differences between cultivars, sulfur efficiently increased the forage yield. Sulfur fertilization increased the concentrations of nutrients in the plants without significantly affecting the uptake of nitrogen, phosphorus, potassium, calcium and magnesium. Sulfur omission resulted in increased phosphorus uptake in all grass. In contrast, Marandu grass exhibited the greatest reduction in sulfur uptake. Therefore, the use of sulfur in the fertilization of grasses is recommended, it is important to evaluate the responses of each cultivar to better adjust the fertilization management. Full article

The naturally occurring radioactive gas radon presents a major public health danger mainly affecting people who spend time in poorly ventilated buildings. The periodic table includes radon as a noble gas which forms through uranium decay processes in soil, rock, and water. The accumulation of radon indoors in sealed or poorly ventilated areas leads to dangerous concentrations that elevate human health risks of lung cancer. The research examines environmental variables affecting radon concentration indoors by studying geothermal installations and their drilling activities, which potentially increase radon emissions. The study was conducted in the basement of the plumbing educational building at the Aristotle University of Thessaloniki to assess the potential impact of geothermal activity on indoor radon levels, as the building is equipped with a geothermal heating system. The key findings based on 150 days of continuous data showed that radon levels peak during the cold days, where the concentration had a mean value of 41.5 Bq/m³ and reached a maximum at about 95 Bq/m³. The reason was first and foremost poor ventilation and pressure difference. The lowest concentrations were on days with increased human activity with measures that had a mean value of 14.8 Bq/m³, which is reduced by about 65%. The results that are presented confirm the hypotheses and the study is making clear that ventilation and human activity are crucial in radon mitigation, especially on geothermal and energy efficient structures. Full article

Objectives: We assessed the difference between quiet stance and gait in the spatial distribution and intensity of foot plantar pressures and whether it is possible to estimate the distribution during gait from data obtained during stance. Methods: A total of 60 healthy subjects with a mean age of 31.0 ± 9.4 years performed two trials for quiet stance and four trials for gait on a baropodometric walkway with their eyes open. Foot plantar pressures were recorded from 10 areas of the foot sole. Results: During quiet stance, the highest plantar pressure occurred at metatarsal heads (M2 to M4) and the medial (MH) and lateral halves of the heel (LH). During gait, the profile of plantar pressure values was like that during stance, but significantly higher. The differences concentrated at the big toe (T1), M2 to M4, MH, and LH, whilst toes (T2,3,4,5) and midfoot (MF) showed the smallest difference. A significant positive correlation was found between the corresponding areas of foot pressure during gait and stance. Conclusions: During quiet stance and gait, the overall profile of plantar pressure distribution was similar. During quiet stance, the subjects loaded more on the heels, in keeping with the known position of the center of pressure just in front of the ankles. During gait, higher pressures on the metatarsal areas are related to the forward propulsion of the center of mass. The correlation between the corresponding areas of foot pressure during gait and stance suggests that the pressure distribution during gait can partly be estimated from that during stance. This finding might be useful in most clinical settings when a single sensorized platform rather than a complete walkway is available. Full article

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

This study evaluates the potential use of reclaimed sand from municipal wastewater treatment plants (WWTP), categorized as waste under code 19 08 02, as a full substitute for natural sand in cement mortars. The sand was subjected to alkaline pretreatment using sodium hydroxide (NaOH) at concentrations of 0.5%, 1% and 2% to reduce organic impurities and improve surface cleanliness. All mortar mixes were prepared using CEM I 42.5 R as the binder, maintaining a constant water-to-cement ratio of 0.5. Mechanical testing revealed that mortars produced with 100% WWTP-derived sand, pretreated with 0.5% NaOH, achieved a mean compressive strength of 51.9 MPa and flexural strength of 5.63 MPa after 28 days, nearly equivalent to reference mortars with standardized construction sand (52.7 MPa and 6.64 MPa, respectively). In contrast, untreated WWTP sand resulted in a significant performance reduction, with compressive strength averaging 30.0 MPa and flexural strength ranging from 2.55 to 2.93 MPa. The results demonstrate that low-alkaline pretreatment—particularly with 0.5% NaOH—allows for the effective reuse of WWTP waste sand (code 19 08 02) in cement mortars based on CEM I 42.5 R, achieving performance comparable to conventional materials. Although higher concentrations, such as 2% NaOH, are commonly recommended or required by standards for the removal of organic matter from fine aggregates, the results suggest that lower concentrations (e.g., 0.5%) may offer a better balance between cleaning effectiveness and mechanical performance. Nevertheless, 2% NaOH remains the obligatory reference level in some standard testing protocols for fine aggregate purification. Full article

Background: High-sensitivity cardiac troponin (hs-cTn) is useful for detecting acute myocardial infarction, but chronic hemodialysis patients often have elevated baseline levels that exceed the upper reference limit (URL). This study aimed to determine whether hs-cTnI levels in asymptomatic hemodialysis patients exceed the URL established for the general population, evaluate the impact of high-flux hemodialysis on hs-cTnI concentrations, and examine associations between hs-cTnI levels and subsequent hospitalization or mortality. Methods: A prospective, single-center cohort study was conducted at a tertiary care center from August 2023 to July 2024. Blood samples for hs-cTnI were collected from asymptomatic hemodialysis patients aged ≥ 40 years, measured before and after dialysis within one month. Patients were followed for up to 12 months. Results: Fifty-six patients were enrolled. The mean hs-cTnI levels were 28.4 ng/L pre-dialysis and 27.9 ng/L post-dialysis, with ranges of <6–223 ng/L and <6–187 ng/L, respectively. The mean hs-cTnI delta between pre- and post-dialysis was −0.5 ng/L, with 52% showing a negative delta, 30% no change, and 18% a positive delta. No association was found between baseline hs-cTnI levels and mortality or hospitalization during follow-up. Conclusions: Most asymptomatic hemodialysis patients had hs-cTnI levels in the “gray zone”, thus neither confirming nor excluding acute myocardial infarction. Dialysis did not significantly affect hs-cTnI levels, and elevated baseline hs-cTnI was not linked to increased mortality or hospitalization over 12 months. Full article

The diet of dairy calves can be contaminated with mycotoxins, posing a potential risk to animal health. This case study report aimed to make the first assessment of the presence of multiple mycotoxins in concentrates fed to dairy calves in Brazil. A total of 19 concentrate samples intended for dairy calves were analyzed using liquid chromatography coupled with mass spectrometry. Aflatoxins, deoxynivalenol, and T-2 toxin were not detected in any samples, whereas fumonisins B₁ (FB₁) and B₂ (FB₂) were present in 100% of the samples, with mean concentrations of 2750.1 μg/kg and 834.9 μg/kg, respectively. Zearalenone (ZEN) was detected in 36.8% of samples, with a mean concentration of 929.9 μg/kg. Significant correlations were observed between FB₁ and FB₂ (ρ = 0.978; p < 0.001) and between FB₂ and ZEN (ρ = 0.735; p = 0.05). While the physical form of the concentrate did not influence (p > 0.05) mycotoxin concentrations, a trend was observed for FB₁ (ρ = −0.417; p = 0.07) and FB2 (ρ = −0.395; p = 0.09). These findings highlight the frequent occurrence of Fusarium mycotoxins, likely due to pre-harvest contamination, emphasizing the potential risk of additive or synergistic effects in dairy calves. Full article

Background: The increased incidence of malignant melanoma is observed in patients with Parkinson’s disease. Methods: The anti-proliferative effects of carbidopa and rasagiline on four human malignant melanoma cell lines (A375, SK-MEL28, FM55P and FM55M2) were determined in MTT assay. The interaction profiles of rasagiline in combinations with cisplatin (CDDP) and mitoxantrone (MTX) in four human melanoma cell lines (A375, SK-MEL28, FM55P and FM55M2) were assessed by means of the isobolographic analysis in the MTT test; Results: Rasagiline, but not carbidopa, produced clear-cut anti-proliferative effects on various melanoma cell lines. The median inhibitory concentrations (IC₅₀ values) of rasagiline in the MTT were 280.69 µM for A375, 402.89 µM for SK-MEL28, 349.44 µM for FM55P, and 117.45 µM for FM55M2, respectively. The experimentally-derived selectivity index for rasagiline ranged from 8.22 to 28.18. Flow cytometry assay revealed, in two melanoma cell lines (FM55P and A375), a significant increase in the number of cells in the G0/G1 (up to 76.48% and 75.46% for cell lines, respectively), accompanied by a decrease in the percentage of cells in the S phase (decrease to 9.91% and 10.83% for cell lines, respectively), which may indicate potential cytostatic properties of rasagiline. The combinations of rasagiline with CDDP (at the fixed-ratio of 1:1) exerted either antagonistic interactions (p < 0.05) in the A375 and SK-MEL28, or additive interactions, with a tendency toward antagonism in the FM55P and FM55M2 cell lines in the MTT test. In contrast, the combinations of rasagiline with MTX (ratio of 1:1) produced either synergistic interaction (p < 0.05) in the FM55P cell line or additive interactions with a tendency toward synergy in the FM55M2, SK-MEL28, and A375 cell lines in the MTT test. Conclusions: Rasagiline combined with MTX exerted the most desirable synergistic interactions in relation to the anti-proliferative effects in four malignant melanoma cell lines, as assessed isobolographically. In contrast, rasagiline should not be combined with CDDP during the treatment of malignant melanoma due to the antagonistic interactions in the MTT assay. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article