Search Results (1,037)

Monascus pigments (MPs) and monacolin K (MK) are important secondary metabolites produced by Monascus spp. This study aimed to investigate the effect of soybean protein isolate (SPI) on the biosynthesis of MPs and MK based on the analysis of physiological indicators, transcriptomes, and metabolomes. The results indicated that the growth, yellow MPs, and MK production of Monascus pilosus MS-1 were significantly enhanced by SPI, which were 8.20, 8.01, and 1.91 times higher than that of the control, respectively. The utilization of a nitrogen source, protease activity, the production and utilization of soluble protein, polypeptides, and free amino acids were also promoted by SPI. The transcriptomic analysis revealed that the genes mokA, mokB, mokC, mokD, mokE, mokI, and mokH which are involved in MK biosynthesis were significantly up-regulated by SPI. Moreover, the glycolysis/gluconeogenesis, pyruvate metabolism, fatty acid degradation, tricarboxylic acid (TCA) cycle, and amino acid metabolism were effectively up-regulated by SPI. The metabolomic analysis indicated that metabolisms of amino acid, lipid, pyruvate, TCA cycle, glycolysis/gluconeogenesis, starch and sucrose, and pentose phosphate pathway were significantly disturbed by SPI. Thus, MPs and MK production promoted by SPI were mainly attributed to the increased biomass, up-regulated gene expression level, and more precursors and energies. Full article

Single-pixel imaging (SPI) is an alternative method for obtaining images using a single photodetector, which has numerous advantages over the traditional matrix-based approach. However, most experimental SPI realizations provide relatively low resolution compared to matrix-based imaging systems. Here, we show a simple yet effective experimental method to scale up the resolution of SPI. Our imaging system utilizes patterns based on Hadamard matrices, which, when reshaped to a variable aspect ratio, allow us to improve resolution along one of the axes, while sweeping of patterns improves resolution along the second axis. This work paves the way towards novel imaging systems that retain the advantages of SPI and obtain resolution comparable to matrix-based systems. Full article

Transglutaminase (TGase)-catalyzed crosslinking has gained substantial traction as a novel strategy for reducing allergenic risk in food proteins, particularly within the realm of hypoallergenic food production. This study explored the impact of TGase crosslinking on conformational changes in a binary protein system composed of soy protein isolate (SPI) and sodium caseinate (SC) at varying mass ratios (10:0, 7:3, 5:5, 3:7 (w/w)). Specifically, the immunoglobulin E (IgE) binding capacity of soy proteins within this system was examined. Prolonged TGase crosslinking (ranging from 0 h to 15 h) resulted in a gradual reduction in IgE reactivity across all SPI-SC ratios, with the order of IgE-binding capability as follows: SPI > SPI5-SC5 > SPI7-SC3 > SPI3-SC7. These alterations in protein conformation following TGase crosslinking, as demonstrated by variable intrinsic fluorescence, altered surface hydrophobicity, increased ultraviolet absorption and reduced free sulfhydryl content, were identified as the underlying causes. Additionally, ionic bonds were found to play a significant role in maintaining the structure of the dual-protein system after crosslinking, with hydrophobic forces and hydrogen bonds serving as supplementary forces. Generally, the dual-protein system may exhibit enhanced efficacy in reducing the allergenicity of soy protein. Full article

In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R² value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R² of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management. Full article

The present study aimed to investigate the properties of calcium-rich soy protein isolate (SPI) gels (14% SPI; 100 mM CaCl₂), the effects of incorporating different concentrations locust bean gum (LBG) (0.1–0.3%, w/v) to the systems and the stability of the obtained gels. Also, the incorporation of solid lipid microparticles (SLMs) was tested as an alternative strategy to improve the system’s stability and, therefore, potential to be applied as a product prototype. The gels were evaluated regarding their visual aspect, rheological properties, water-holding capacities (WHCs) and microstructural organizations. The CaCl₂-induced gels were self-supported but presented low WHC (40.0% ± 2.2) which was improved by LBG incorporation. The obtained mixed system, however, presented low stability, with high syneresis after 10 days of storage, due to microstructural compaction. The gels’ stability was improved by SLM incorporation, which decreased the gelled matrices’ compaction and syneresis for more than 20 days. Even though the rheological properties of the emulsion-filled gels (EFGs) were very altered due to the ageing process (which may affect the sensory perception of a future food originated from this EFG), the incorporation of SLMs increased the systems potential to be applied as a calcium-rich product prototype. Full article

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors. Full article

Microalgae-based wastewater treatment offers an eco-friendly opportunity for simultaneous nutrient recovery and biomass generation, aligning with the circular bioeconomy concept. This approach aims to utilize the nutrients of potato industry wastewater (PIW) for algal growth while mitigating the environmental impact of this industrial byproduct. This study focused on cultivating three cyanobacterial strains, Anabaena oryzae, Nostoc muscorum, and Spirulina platensis, in PIW and synthetic media for 30 days to assess feasibility. Growth performance was monitored by measuring chlorophyll content, dry weight (DW), optical density (OD), and pH at 3-day intervals. The high-performing cyanobacterial biomass from the laboratory findings was formulated into a biofertilizer, which was then evaluated in a controlled greenhouse experiment on celery and lettuce plants. The biofertilizer replaced conventional NPK mineral fertilizers at different levels (25%, 50%, and 75%), while a control group received 100% chemical fertilizer. The results showed favourable growth of all three cyanobacteria strains and their mixture in PIW throughout the experiment. The mixed cyanobacteria followed by Spirulina platensis exhibited the highest growth rates, achieving chlorophyll contents of 3.75 and 2.30 µg·mL⁻¹, DWs of 1.79 g·L⁻¹ and 1.63 g·L⁻¹, and ODs of 0.41 and 0.38, respectively, surpassing the other treatments. The formulated biofertilizers, Spi-PIW (Spirulina platensis + potato industry wastewater) and Cyano-PIW (mixed culture+ potato industry wastewater), significantly enhanced plant height, root and stem lengths, and the number of leaves per plant in celery and lettuce compared to the control group. These biofertilizer treatments also improved chlorophyll contents, as well as macro- and micronutrient levels, in the two crops. Additionally, the application of these biofertilizers improved certain sandy soil properties, i.e., pH, total organic matter, total nitrogen, phosphorus, and potassium. In conclusion, utilizing PIW as a substrate for cultivating cyanobacteria strains and producing high-quality liquid bio-organic fertilizers holds potential for reducing recommended NPK fertilizer doses by 25–50% in celery and lettuce growth, providing an environmentally friendly approach. Full article

Understanding the propagation characteristics and driving factors from meteorological drought to hydrological drought is essential for alleviating drought and for early warning systems regarding drought. This study focused on the Weihe River basin (WRB) and its two subregions (the Jinghe River (JRB) and the middle reaches of the Weihe River (MWRB)), utilizing the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) to characterize meteorological and hydrological drought, respectively. Based on Copula theory and conditional probability, a quantification model for the propagation time (PT) of meteorological–hydrological drought was constructed. The dynamic characteristics of PT on annual and seasonal scales were explored. Additionally, the influences of different seasonal meteorological factors and underlying surface factors on the dynamic changes in PT were analyzed. The main conclusions were as follows: (1) The PT of meteorological–hydrological drought was characterized by faster propagation during the hot months (June–September) and slower propagation during the cold months (December to March of the following year); (2) Under the same level of hydrological drought, as the level of meteorological drought increases, the PT of the drought shortens. The propagation thresholds of meteorological to hydrological drought in the WRB, the JRB, and the MWRB are −0.69, −0.81, and −0.78, respectively. (3) In the dynamic changes in PT, the WRB showed a non-significant decrease; however, both the JRB and the MWRB exhibited a significant increase in PT across different drought levels. (4) The influence of the water and heat status during spring, summer, and winter on PT was more pronounced, while in autumn, the impact of the basin’s water storage and discharge status was more significant in the JRB and the MWRB. Full article

In recent decades, the main commercial crops of Mato Grosso, such as soybeans, corn, and cotton, have been undergoing transformations regarding the adoption of new technologies to increase production. However, regardless of the technological level, the climate of the region, including the rainfall regime, can influence the success of crops and facilitate, or not, the maximum production efficiency. This study aimed to define the behavior of the variability in monthly and annual rainfall and its probability of monthly occurrence and calculate the drought index for the northwestern region of Mato Grosso, in the southern region of the Brazilian Amazon. To carry out the study, daily rainfall records were collected, calculating the totals for each month of the historical series for each of the four National Water and Sanitation Agency (ANA) rain gauge stations, Aripuanã (1985–2020), Colniza (2001–2020), Cotriguaçu (2004–2020), and Juína (1985–2020), representing the northwestern region. The annual distribution of rainfall during the periods studied ranged from 1376.2 to 3017.3 mm. The monthly distribution indicated a typical water shortage in the months of June, July, and August. The probability of rainfall near the average for each month was more than 50%. The monthly SPI-1 index revealed a total of 56 months affected by very dry events and 34 extreme dry events. The annual SPI-12 index pointed to seven very dry years and five extremely dry years. Therefore, the region presented high rainfall rates in most years; however, a significant process of drought was also observed, including in rainy months, which are the periods with the greatest demand for the main agricultural crops. Full article

Certain impacts of climate change could potentially be linked to alterations in rainfall patterns, including shifts in rainfall intensity or drought occurrences. Hence, predicting droughts can provide valuable assistance in mitigating the detrimental consequences associated with water scarcity, particularly in agricultural areas or densely populated urban regions. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applied an Auto-Machine-Learning approach to deploy Artificial Neural Network models, aiming to predict the Standardized Precipitation Index in four regions of Zacatecas, Mexico. Climatological time-series data spanning from 1979 to 2020 were utilized as predictive variables. The best models were found using performance metrics that yielded a Mean Squared Error, Mean Absolute Error, and Coefficient of Determination ranging from 0.0296 to 0.0388, 0.1214 to 0.1355, and 0.9342 to 0.9584, respectively, for the regions under study. As a result, the Auto-Machine-Learning approach successfully developed and tested Artificial Neural Network models that exhibited notable predictive capabilities when estimating the monthly Standardized Precipitation Index within the study region. Full article

This study aimed to evaluate the effects of fruit position, light exposure and fruit surface temperature (FST) on apple fruit colour development and fruit quality at harvest, including sunburn damage severity. This was achieved by undertaking two experiments in a high-density planting of the dark-red apple ANABP 01 in Tatura, Australia. In the 2020–2021 growing season an experiment was conducted to draw relationships between fruit position and fruit quality parameters. Here, sample fruit position and level of light exposure were respectively determined using a static LiDAR system and a portable quantum photosynthetically active radiation (PAR) sensor. At harvest the sample fruit were analysed for percentage red colour coverage, objective colour parameters (L*, a*, b*, hue angle and chroma), sunburn damage, fruit diameter (FD), soluble solids concentration (SSC), flesh firmness (FF) and starch pattern index (SPI). A second experiment was conducted in the 2021–2022 growing season and focused on how fruit shading, light exposure and the removal of ultraviolet (UV) radiation affected the FST, colour development and harvest fruit quality. Five treatments were distributed among sample fruit: fully shaded with aluminium umbrellas, shaded for one month and then exposed to sunlight until harvest, exposed for one month and then shaded until harvest, covered with a longpass UV filter and a control treatment. The development of colour in this dark-red apple cultivar was highly responsive to aspects of fruit position, and the intensity and quality of light exposure. The best-coloured fruit were exposed to higher quantities of PAR, exposed to both PAR and UV radiation simultaneously and located higher in the tree canopy. Fruit that were fully exposed to PAR and achieved better colour development also displayed higher FST and sunburn damage severity. Full article

Background: The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS. Methods: The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan–Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice. Results: FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2. Conclusions: Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS. Full article

In this study, future precipitation and drought in the Ca river basin, Vietnam, were projected based on an ensemble of 27 CMIP6 models for four climate change scenarios. The impact of climate change on precipitation and drought was investigated. Monthly precipitation observation data were adjusted using the bias correction method. To detect drought events, the Standard Precipitation Index (SPI) was employed. Changes in drought were assessed using SPI3, SPI6, and SPI12. Although the amount of annual total precipitation slightly increased, the drought events may become more severe. There is a high likelihood of increased drought intensity and severity in Vietnam due to climate change. The frequency of droughts is likely to change depending on the location and climate change scenario. We found that the frequency and severity of droughts may be altered depending on the window size of SPI. The short-term drought events will be more frequent and severe, and long-term drought events will become more severe in the Ca river basin. Full article

In this study, soy protein isolate (SPI) films incorporating quercetin-grafted dialdehyde starch (DAS-QR) and DAS/QR, respectively, were developed. The structural, physical, and functional properties of the composite films were determined. The results suggested that DAS-QR and DAS/QR formed hydrogen bonding with the SPI matrix, which improved the structural properties of the films. The light-blocking capacity, thermal stability, hydrophobicity, tensile strength, elongation at break, and antioxidant and antibacterial abilities of SPI films were improved by DAS-QR and DAS/QR. Notably, SPI films incorporated with DAS-QR exhibited better performance than those with DAS/QR in terms of antioxidant (SPI/DAS-QR: 79.8% of DPPH and 62.1% of ABTS scavenging activity; SPI/DAS/QR: 71.4% of DPPH and 56.0% of ABTS scavenging activity) and antibacterial abilities against S. aureus (inhibition rate: 92.7% for SPI/DAS-QR, 83.4% for SPI/DAS/QR). The composite coating film SPI/DAS-QR effectively maintained appearance quality, delayed the loss of weight and total soluble solids, postponed malondialdehyde accumulation, and decreased peroxidase activity and microbial contamination in fresh-cut potatoes. These good performances highlight SPI/DAS-QR as a promising active packaging material for fresh-cut product preservation. Full article

Freezing affects texture and induces the loss of gel quality. This study investigated the effects of methylcellulose (MC) (0.2%, 0.4%, 0.6%) and sodium hexametaphosphate (SHMP) (0.15%, 0.3%) on the gel textural and structural properties of SPI gels before and after freezing, and explores the synergistic enhancement of gel texture and the underlying mechanisms resulting from the simultaneous addition of SHMP and MC to SPI gels. It was revealed that MC improved the strength of SPI gels through its thickening properties, but it could not inhibit the reduction of SPI gels after freezing. The 0.4% MC-SPI gel exhibited the best gel strength (193.2 ± 2.4 g). SHMP inhibited gel reduction during freezing through hydrogen bonding and ionic interactions; it enhanced the freezing stability of SPI gels. The addition of 0.15% SHMP made the water-holding capacity in SPI gels reach the highest score after freezing (58.2 ± 0.32%). The synergistic effect of MC and SHMP could improve the strength and the freezing stability of SPI gels. MC facilitated the release of ionizable groups within SPI, causing negatively charged SHMP groups to aggregate on the SPI and inhibit the freezing aggregation of proteins. These results provide a strong basis for the improvement of cryogenic soy protein gel performance by SHMP and MC. Full article