Search Results (664)

Mycotoxins are secondary metabolites produced primarily by certain species of the genera Aspergillus, Fusarium, Penicillium, Alternaria, and Claviceps. Toxigenic fungi and mycotoxins are prevalent in staple foods, resulting in significant economic losses and detrimental impacts on public health and food safety. These fungi demonstrate remarkable adaptation to water and heat stress conditions associated with climate change, and the use of synthetic antifungals can lead to the selection of resistant strains. In this context, the development of novel strategies for their prevention and control of food is a priority objective. This review synthesizes the extant knowledge concerning the antifungal and anti-mycotoxin potential of the primary metal nanoparticles (silver, copper) and metal oxide nanoparticles (copper oxide and zinc oxide) studied in the literature. It also considers synthesis methods and the lack of consensus on technical definitions and regulations. Despite methodological gaps and the scarcity of publications analyzing the effect of these NPs on fungal growth and mycotoxin production simultaneously, it can be concluded that these NPs present high reactivity, stability, and the ability to combat these food risks. However, aspects related to their biosafety and consumer acceptance remain major challenges that must be addressed for their implementation in the food industry. Full article

The global population is increasing rapidly and, according to the United Nations (UN), it is expected to reach 9.8 billion by 2050. The demand for food is also increasing with a growing population. Food shortages, land scarcity, resource depletion, and climate change are significant issues raised due to an increasing population. Meat is a vital source of high-quality protein in the human diet, and addressing the sustainability of meat production is essential to ensuring long-term food security. To cover the meat demand of a growing population, meat scientists are working on several meat alternatives. Bacteria, fungi, yeast, and algae have been identified as sources of microbial proteins that are both effective and sustainable, making them suitable for use in the development of meat analogs. Unlike livestock farming, microbial proteins produce less environmental pollution, need less space and water, and contain all the necessary dietary components. This review examines the status and future of microbial proteins in regard to consolidating and stabilizing the global food system. This review explores the production methods, nutritional benefits, environmental impact, regulatory landscape, and consumer perception of microbial protein-based meat analogs. Additionally, this review highlights the importance of microbial proteins by elaborating on the connection between microbial protein-based meat analogs and multiple UN Sustainable Development Goals. Full article

The ecological drivers of wildlife crop-foraging behavior—whether as a compensatory response to natural resource scarcity or as opportunistic exploitation of anthropogenic food sources—remain poorly understood in human–wildlife conflict research. Traditional methodologies, which primarily rely on direct observation and morphological identification, have limitations in comprehensively quantifying wildlife dietary composition, particularly in accurately distinguishing between morphologically similar plant species and conducting precise quantitative analyses. This study utilized DNA metabarcoding technology (rbcL gene markers) to identify and quantify plant dietary components through fecal sample analysis, systematically investigating the dietary composition and patterns of agricultural resource utilization of wild rhesus macaques (Macaca mulatta) in human–wildlife interface zones of southwestern China. A total of 29 rhesus macaque fecal samples were analyzed (15 from spring and 14 from winter), identifying 142 plant genera, comprising 124 wild plant genera, and 18 crop genera. The results revealed distinct seasonal foraging patterns: crops accounted for 32.11% of the diet in winter compared to 7.66% in spring. Notably, rhesus macaques continued to consume crops even during spring when wild resources were relatively abundant, challenging the traditional hypothesis driven by resource scarcity and suggesting that crop-foraging behavior may reflect an opportunistic, facultative resource selection strategy. This study demonstrates the significant value of DNA metabarcoding technology in wildlife foraging behavior research, providing scientific evidence for understanding human–primate conflict ecology and developing effective management strategies. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

This study investigates farmers’ knowledge, perceptions, and adaptation strategies to climate change in Rwanda’s Eastern Province, integrating social and physical science approaches. Analyzing meteorological data (1981–2021) and surveys from 204 farmers across five districts, we assessed climate trends and adaptation behaviors using statistical methods (descriptive statistics, Chi-square, logistic regression, Regional Kendall test, dynamic linear state-space model). Results show that 85% of farmers acknowledge climate change, with 54% observing temperature increases and 37% noting rainfall declines. Climate data confirm significant rises in annual minimum (+0.76 °C/decade) and mean temperatures (+0.48 °C/decade), with the largest seasonal increase (+0.86 °C/decade) in June–August. Rainfall trends indicate a non-significant decrease in March–May and a slight increase in September–December. Farmers report crop failures, yield reductions, and food shortages as major climate impacts. Common adaptations include agroforestry, crop diversification, and fertilizer use, though financial limitations, information gaps, and input scarcity impede adoption. Despite limited formal education (53.9% primary, 22.3% no formal education), indigenous knowledge aids seasonal prediction. Farm location, group membership, and farming goal are key adaptation enablers. These findings emphasize the need for targeted policies and climate communication to enhance rural resilience by strengthening smallholder farmer support systems for effective climate adaptation. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

The global obesity and metabolic syndrome epidemic have accelerated demand for reduced-sugar food, prompting the food industry to adopt functional sugar alcohols as sucrose substitutes. Threitol is a four-carbon sugar alcohol and an isomer of erythritol. However, there is a scarcity of studies reporting on the edible safety of threitol. This study assessed threitol’s toxicological and metabolic properties. Acute oral administration (10 g/kg) caused no mortality or abnormalities in mice. Repeated 28-day exposure revealed no behavioral or histopathological alterations, with negative outcomes in three genotoxicity tests. Metabolic studies in rats demonstrated that the majority of ingested threitol is excreted in the urine within 24 h. Sensory evaluation indicated threitol’s sweetness equivalence to sucrose, exceeding erythritol and allulose. Notably, 16S rRNA sequencing revealed gut microbiota modulation in threitol-fed mice, indicating potential intestinal health benefits. These integrated findings establish threitol’s preclinical safety and support its development as a novel low-calorie sweetener. Full article

Reusing and recycling treated wastewater is a sustainable approach to meet the growing demand for clean water, ensuring its availability for both current and future generations. Wastewater can be treated in such advanced ways that it can be used for industrial operations, recharging groundwater, irrigation of fields, or even manufacturing drinkable water. This strategy meets growing water demand in water-scarce areas while protecting natural ecosystems. Treated wastewater is both a resource and a challenge. Though it may be nutrient-rich and can increase agricultural output while showing resource reuse and environmental conservation, high treatment costs, public acceptance, and contamination hazards limit its use. Proper treatment can reduce these hazards, safeguarding human health and the environment while enhancing its benefits, including a stable water supply, nutrient-rich irrigation, higher crop yields, economic development, and community resilience. On the one hand, inadequate treatment may lead to soil salinization, environmental degradation, and hazardous foods. Examining the dual benefits and risks of using treated wastewater for agricultural irrigation, this paper investigates the complexities of its use as a valuable resource and as a potential hazard. Modern treatment technologies are needed to address these difficulties and to ensure safe and sustainable use. If properly handled, treated wastewater reuse has enormous potential for reducing water scarcity and expanding sustainable agriculture as well as global food security. Full article

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity. Full article

Water scarcity remains critical in Rwanda’s Eastern Savanna Agroecological Zone due to erratic rainfall, prolonged dry seasons, and rising water demands. This challenge threatens agricultural productivity, food security, and livelihoods. Stormwater harvesting presents a sustainable solution that increases water availability and mitigates the impacts of climate variability. This study utilizes Geographic Information System (GIS) tools and SCS-CN to assess stormwater harvesting potential in the region. The methodology includes analyzing land use, soil type, rainfall data (30 years, from 1994 to 2023), and topography. Key research steps involve delineating catchment areas, estimating runoff volumes, and selecting optimal storage sites using multi-criteria decision analysis. Findings include eight main water reservoirs, each with a unique code (W_R1 to W_R8), geographic coordinates (X and Y), and 10 million cubic meters storage volumes. W_R1 has the smallest volume at 0.242 × 10⁶ m³, while W_R2 has the largest volume at 8.51 × 10⁶ m³. W_R3, W_R5, and W_R7 are additional noteworthy reservoirs with sizable capacities. The findings contribute to policy formulation and Sustainable Development Goals (SDGs) related to clean water, food security, and climate action. This research provides a replicable framework for addressing water scarcity and enhancing long-term resilience in water-stressed regions. Full article

Studies examining the role of chikanda orchids in bolstering rural livelihoods coupled with the associated socio-economic intricacies remain absent, especially in African settings. This study examined the contribution of chikanda orchids in supporting rural livelihoods, taking into account socio-economic influences through a case study of the Mwinilunga District of northwestern Zambia. The study employed a mixed methods approach using 303 semi-structured questionnaires, complemented by three focus group discussions and nine in-depth interviews. Study findings showed that revenue generated from chikanda orchid sales supported rural livelihoods and served a crucial function in addressing food scarcity challenges. Income derived from chikanda orchid sales accounted for 30.8% of total household income and exhibited an income equalization effect of 8% among households. Participation in harvesting chikanda orchids exhibited a significant correlation with gender (χ² = 6; p < 0.05) and marital status (χ² = 8; p < 0.05). This study showed the significance of chikanda orchids in supporting livelihoods, including socio-economic influences, particularly for poorer households that exhibit vulnerability to food deficits. Consequently, the need to develop effective chikanda orchid management strategies that are locally tailored and acknowledge the socio-economic intricacies associated with the chikanda orchids trade is fundamental. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

In arid regions with low precipitation, like most of the Cape Verde islands, irrigation is essential for maintaining agricultural production and food security. However, due to significant investment needs, it is critical to improve irrigation efficiency and reduce water losses. The aim of this study is to evaluate irrigated agriculture in Cape Verde and its relationship with water scarcity through the calculation of key indicators and the analysis of statistical and remote sensing data. Crop production data were collected from the Ministry of Agriculture and Environment, and climatic data from the National Institute of Meteorology and Geophysics of Cape Verde (INMG) and FAO’s WaPOR platform. The aridity index was calculated using the UNEP method based on data from INMG. The island of Sal showed the lowest aridity index value (0.07), while Cachaço (São Nicolau island) had the highest (0.41). Sugarcane is currently the dominant irrigated crop, covering over 3000 hectares, about 62% of irrigated land, despite its high water demands. The expansion of sugarcane threatens long-term water sustainability and food production. Promoting crops with higher water productivity and technical training are key actions to ensure the sustainability of irrigated agriculture in Cape Verde. Findings point to the urgent need to improve irrigation infrastructure, maintenance, and system design. Full article

Plants have historically served as key sources of nutrition and popular medicine, which persists in current applications. The increasing demand for natural bioactive compounds has intensified the incorporation of plant-derived ingredients in both the food and pharmaceutical industries. This narrative review focuses on the fruits of Crataegus monogyna Jacq., Sorbus aria (L.) Crantz, and Prunus spinosa L. (Rosaceae), traditionally utilized in Europe and characterized by a high content of phenolic compounds, flavonoids, and anthocyanins. These metabolites are associated with antioxidant, anti-inflammatory, and cardioprotective properties. The available literature on their phytochemical profiles, biological activities, and integration into the Mediterranean Diet is critically assessed. Evidence supports their potential as functional food components. Despite encouraging in vitro results, the scarcity of in vivo and clinical studies limits the translational potential of these findings. Further research is warranted to validate their efficacy and safety in human health. This review underscores the value of integrating traditional ethnobotanical and ethnopharmacological knowledge with contemporary scientific research to explore novel applications of these underutilized wild fruits. Full article

Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging machine learning techniques. However, the scarcity of comprehensive datasets poses a significant challenge. This study addresses this issue by presenting an open synthetic hand washing dataset, created using 3D computer-generated imagery, comprising 96,000 frames (equivalent to 64 min of footage), encompassing eight gestures performed by four characters in four diverse environments. This synthetic dataset includes RGB images, depth/isolated depth images and hand mask images. Using this dataset, four neural network models, Inception-V3, Yolo-8n, Yolo-8n segmentation and PointNet, were trained for gesture classification. The models were subsequently evaluated on a large real-world hand washing dataset, demonstrating successful classification accuracies of 56.9% for Inception-V3, 76.3% for Yolo-8n and 79.3% for Yolo-8n segmentation. These findings underscore the effectiveness of synthetic data in training machine learning models for hand washing gesture recognition. Full article