Search Results (599)

Most research on domestic dog (Canis familiaris) behavior has focused on pets with restricted movement. However, free-ranging dogs exist in diverse cultural contexts globally, and their interactions with humans are less understood. Tourists can facilitate unrestricted dog movement into wilderness areas, where they may negatively impact wildlife. This study investigated which stimuli—namely, voice, touch, or food—along with inherent factors (age, sex, sociability) motivate free-ranging dogs to follow a human stranger. We measured the distance (up to 600 m) of 129 free-ranging owned and stray dogs from three villages in southern Chile as they followed an experimenter who presented them one of the above stimuli or none (control). To evaluate the effect of dog sociability (i.e., positive versus stress-related or passive behaviors), we performed a 30 s socialization test (standing near the dog without interacting) before presenting a 10 s stimulus twice. We also tracked whether the dog was in the company of other dogs. Each focus dog was video-recorded and tested up to three times over five days. Generalized linear mixed-effects models revealed that the food stimulus significantly influenced dogs’ motivation to follow a stranger, as well as a high proportion of sociable behaviors directed towards humans and the company of other dogs present during the experiment. Juveniles tended to follow a stranger more than adults or seniors, but no effects were found for the dog’s sex, whether an owner was present, the repetition of trials, the location where the study was performed, or for individuals as a random variable. This research highlights that sociability as an inherent factor shapes dog–stranger interactions in free-ranging dogs when food is given. In the context of wildlife conservation, we recommend that managers promote awareness among local communities and tourists to avoid feeding dogs, especially in the context of outdoor activities close to wilderness. Full article

Situated in the southern hilly-mountain belt of China’s “Three Zones and Four Belts Strategy”, Gannan region is a critical ecological shelter belt for the Ganjiang River. Decades of intensive mineral extraction and irrational agricultural development have rendered it into an ecologically fragile area. Consequently, multiple restoration initiatives have been implemented in the region over recent decades. However, it remains unclear how relationships among ecosystem services have evolved under these interventions and how future ecosystem management should be optimized based on these changes. Thus, in this study, we simulated and assessed the spatiotemporal dynamics of five key ESs in Gannan region from 1990 to 2020. Through integrated correlation, clustering, and redundancy analyses, we quantified ES interactions, tracked the evolution of ecosystem service bundles (ESBs), and identified their socio-ecological drivers. Despite a 31% decline in water yield, ecological restoration initiatives drove substantial improvements in key regulating services: carbon storage increased by 6.9 × 10¹² gC while soil conservation rose by 4.8 × 10⁸ t. Concurrently, regional habitat quality surged by 45% in mean scores, and food production increased by 2.1 × 10⁵ t. Critically, synergistic relationships between habitat quality, soil retention, and carbon storage were progressively strengthened, whereas trade-offs between food production and habitat quality intensified. Further analysis revealed that four distinct ESBs—the Agricultural Production Bundle (APB), Urban Development Bundle (UDB), Eco-Agriculture Transition Bundle (ETB), and Ecological Protection Bundle (EPB)—were shaped by slope, forest cover ratio, population density, and GDP. Notably, 38% of the ETB transformed into the EPB, with frequent spatial interactions observed between the APB and UDB. These findings underscore that future ecological restoration and conservation efforts should implement coordinated, multi-service management mechanisms. Full article

Extracellular vesicles (EVs), nanoscale membrane-enclosed particles, are natural carriers of proteins and nucleic acids. Microalgae are widely used as a source of bioactive substances in the food and cosmetic industries and definitely have a potential to be used as the producers of EVs for biomedical applications. In this study, the extracellular vesicles isolated from the culture medium of two unicellular microalgae, Chlamydomonas reinhardtii (Chlamy-EVs) and Parachlorella kessleri (Chlore-EVs), were characterized by atomic force microscopy (AFM), cryo-electronic microscopy (cryo-EM), and nanoparticle tracking analysis (NTA). The biocompatibility with human cells in vitro (HEK-293T, DF-2 and A172) and biodistribution in mouse organs and tissues in vivo were tested for both microalgal EVs. An exogenous therapeutic protein, human heat shock protein 70 (HSP70), was successfully loaded to Chlamy- and Chlore-EVs, and its efficient delivery to human glioma and colon carcinoma cell lines has been confirmed. Additionally, in order to search for potential therapeutic biomolecules within the EVs, their proteomes have been characterized. A total of 105 proteins were identified for Chlamy-EVs and 33 for Chlore-EVs. The presence of superoxide dismutase and catalase in the Chlamy-EV constituents allows for considering them as antioxidant agents. The effective delivery of exogenous cargo to human cells and the possibility of the particle yield optimization by varying the microalgae growth conditions make them favorable producers of EVs for biotechnology and biomedical application. Full article

Background/Objectives: The objective of this study was to validate a new parent-reported scale for tracking Pediatric Acute-onset Neuropsychiatric Syndrome (PANS). PANS is a condition characterized by a sudden and severe onset of neuropsychiatric symptoms. To meet diagnostic criteria, an individual must present with either obsessive–compulsive disorder (OCD) or severely restricted food intake, accompanied by at least two additional cognitive, behavioral, or emotional symptoms. These may include anxiety, emotional instability, depression, irritability, aggression, oppositional behaviors, developmental or behavioral regression, a decline in academic skills such as handwriting or math, sensory abnormalities, frequent urination, and enuresis. The onset of symptoms is usually triggered by an infection or an abnormal immune/inflammatory response. Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) is a subtype of PANS specifically linked to strep infections. Methods: We developed a 101-item PANS/PANDAS and Related Inflammatory Brain Disorders Treatment Evaluation Checklist (PTEC) designed to assess changes to a patient’s symptoms over time along 10 subscales: Behavior/Mood, OCD, Anxiety, Food intake, Tics, Cognitive/Developmental, Sensory, Other, Sleep, and Health. The psychometric quality of PTEC was tested with 225 participants. Results: The internal reliability of the PTEC was excellent (Cronbach’s alpha = 0.96). PTEC exhibited adequate test–retest reliability (r = 0.6) and excellent construct validity, supported by a strong correlation with the Health subscale of the Autism Treatment Evaluation Checklist (r = 0.8). Conclusions: We hope that PTEC will assist parents and clinicians in the monitoring and treatment of PANS. The PTEC questionnaire is freely available at neuroimmune.org/PTEC. Full article

Microorganisms play a crucial role in food processes, safety, and quality through their dynamic interactions with other organisms. In recent years, biosensors have become essential tools for monitoring these processes in the dairy, meat, and fresh produce industries. This review highlights how microbial diversity, starter cultures, and interactions, such as competition and quorum sensing, shape food ecosystems. Diverse biosensor platforms, including electrochemical, optical, piezoelectric, thermal, field-effect transistor-based, and lateral flow assays, offer distinct advantages tailored to specific food matrices and microbial targets, enabling rapid and sensitive detection. Biosensors have been developed for detecting pathogens in real-time monitoring of fermentation and tracking spoilage. Control strategies, including bacteriocins, probiotics, and biofilm management, support food safety, while decontamination methods provide an additional layer of protection. The integration of new techniques, such as nanotechnology, CRISPR, and artificial intelligence, into Internet of Things systems is enhancing precision, particularly in addressing regional food safety challenges. However, their adoption is still hindered by complex food matrices, high costs, and the growing challenge of antimicrobial resistance. Looking ahead, intelligent systems and wearable sensors may help overcome these barriers. Although gaps in standardization and accessibility remain, biosensors are well-positioned to revolutionize food microbiology, linking ecological insights to practical solutions and paving the way for safer, high-quality food worldwide. Full article

Microalgal biomass has emerged as a valuable and nutrient-rich source of novel plant-based foods of the future, with several demonstrated benefits. In addition to their green and health-promoting characteristics, these foods exhibit bioactive properties that contribute to a range of physiological benefits. Photoautotrophic microalgae are particularly important as a source of food products due to their ability to biosynthesize high-value compounds. Their photosynthetic efficiency and biosynthetic activity are directly influenced by light conditions. The primary goal of this study is to track the changes in the light requirements of various high-value microalgae species and use advanced systems to regulate these conditions. Artificial intelligence (AI) and machine learning (ML) models have emerged as pivotal tools for intelligent microalgal cultivation. This approach involves the continuous monitoring of microalgal growth, along with the real-time optimization of environmental factors and light conditions. By accumulating data through cultivation experiments and training AI models, the development of intelligent microalgae cell factories is becoming increasingly feasible. This review provides a concise overview of the regulatory mechanisms that govern microalgae growth in response to light conditions, explores the utilization of microalgae-based products in plant-based foods, and highlights the potential for future research on intelligent microalgae cultivation systems. Full article

It is projected that the world population will quadruple over the next century, and to meet future food demands, agricultural production will need to increase by 70%. Therefore, there has been a transition from traditional farming methods to autonomous modern agriculture. One such modern technique is aeroponic farming, in which plants are grown without soil under controlled and hygienic conditions. In aeroponic farming, plants are significantly less affected by climatic conditions, infectious diseases, and biotic and abiotic stresses, such as pest infestations. Additionally, this method can reduce water, nutrient, and pesticide usage by 98%, 60%, and 100%, respectively, while increasing the yield by 45–75% compared to traditional farming. In this study, a three-dimensional industrial design of an innovative aeroponic plant growth chamber was presented for use by individuals, researchers, and professional growers. The proposed chamber design is modular and open to further innovation. Unlike existing chambers, it includes load cells that enable real-time monitoring of the fresh weight of the plant. Furthermore, cameras were integrated into the chamber to track plant growth and changes over time and weight. Additionally, RGB power LEDs were placed on the inner ceiling of the chamber to provide an optimal lighting intensity and spectrum based on the cultivated plant species. A customizable chamber design was introduced, allowing users to determine the growing tray and nutrient nozzles according to the type and quantity of plants. Finally, system models were developed for temperature control of the chamber. Temperature control was implemented using a proportional-integral-derivative controller optimized with particle swarm optimization, radial movement optimization, differential evolution, and mayfly optimization algorithms for the gain parameters. The simulation results indicate that the temperatures of the growing and feeding chambers in the cabinet reached a steady state within 260 s, with an offset error of no more than 0.5 °C. This result demonstrates the accuracy of the derived model and the effectiveness of the optimized controllers. Full article

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability. Full article

The valorization of agro-industrial by-products for their use in animal feed leads to a reduction in inputs, creating the opportunity to optimize the sustainability of the agri-food chain, a priority of the SDG 2030 strategy; it also leads to a reduction in production costs. The objective of this study was to examine the changes that occur during the silage process of the pomegranate varieties Mollar de Elche (PDO) and Wonderful in terms of their nutritional and antioxidant characteristics for subsequent use in ruminant feed. Microsilos were created with the by-products of these two different pomegranate varieties. Two different microsilos for each variety were monitored on days 0 (raw material), 14, 35, 60, and 180. The variables studied included microbiology tracks, fermentation products, pH, dry matter (DM), macronutrient composition, organic acid and sugar contents, and antioxidant activity. The results show that, for both varieties, the silage process was successful; the stability of the fermentation process was determined by day 35, and its viability was ensured for a minimum period of 6 months. Furthermore, the nutritional characteristics of the raw material were preserved in the ensiled product. An evaluation of the total phenols and antioxidant capacity (ABTS and DPPH) showed that they remained stable throughout the monitoring period, despite the decrease in bioactive compounds (total phenols) at the end of the study period. It was concluded that silage is an effective preservation method for the by-products of Mollar de Elche and Wonderful pomegranate varieties, and its outcome presents valuable potential as a sustainable nutritional resource for ruminants. Full article

Ensuring the safety and quality of perishable agrifood products throughout the supply chain is essential. Key parameters, such as temperature and humidity, must be consistently monitored to prevent spoilage, maintain nutritional value, and minimise health risks. Fluctuations in transportation conditions can compromise product integrity, leading to deterioration and an increased risk of foodborne illness. Monitoring agrifood supply chains is essential, from packaging to last-mile delivery. Distribution methods that rely on non-automated monitoring systems, such as manual temperature measurements, are error-prone due to the failure of manual treatments and increase the likelihood of product deterioration. Emerging sensor technologies and the rapid development of Information and Communication Technologies offer new possibilities for real-time tracking, enabling stakeholders to maintain optimal conditions and monitor aesthetic, physicochemical, and nutritional quality. This paper proposes a cost-effective temperature and humidity traceability system that utilises wireless sensor networks (WSN) and Internet of Things (IoΤ) services to monitor perishable products within the agrifood supply chain ecosystem. It also provides an overview of recent innovations in sensor technologies, along with food quality indicators relevant to real-time monitoring of food quality. The proposed research examines the available sensor technologies and methodologies that enable continuous monitoring of agrifood supply chains. Moreover, the paper presents a pilot full-scale project from both functional and technological perspectives. Full article

This paper presents an approach for integrating data between a production system and its digital twin, focusing on achieving proactivity in production control. Recognizing the unique nature of each production system, this research highlights that a universal, plug-and-play solution is only partially feasible, primarily through general guidelines. The study successfully applied and automated proposed data acquisition methods, resulting in a functional, simulation-based digital twin that adheres to the latest ISO standards. The developed solution incorporates multiple data acquisition strategies, including files containing comma-separated values, a permanent connection to the production control system database, open platform communications unified architecture, and external command files for scenario alteration. The main motivation behind the presented implementation is its application on the shop-floors of small and medium enterprises, where it could provide useful tools for keeping up with the ever-rising competition in the manufacturing sector. This integrated approach allows for affordable and accurate system representation within the proactive simulation concept. The methodology was empirically validated across two distinct production systems: a lab-scale food and beverage line focusing on product tracking, and a sub-assembly line with automated guided vehicle optimization. Despite system variability, the core data acquisition methods demonstrated remarkable adaptability. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. Full article

This study analyzes Brazilian consumer behavior regarding quality and visual cues in fresh red meat and cooked beef. Using eye tracking to collect visual attention metrics and psychological scales to assess food behavior, the research examines how visual attention to beef attributes impacts product choice. A discrete choice method combined nine hypothetical products with varied attributes. Results showed that consumers display different visual behaviors toward cues, influencing their probability of choosing a product. For fresh beef, color was the most significant factor, especially bright red and brown hues. Color influenced both the time to first fixation and total fixation time, while breed also affected total fixation time. Dark-red color and unspecified breed information increased the purchase probability, while Nellore breed and brown color decreased it. Total fixation numbers were significantly impacted by color, breed, marbling, and price. In cooked beef, tenderness, price, and flavor were key visual cues. Tenderness and flavor influenced the time to first fixation, whereas price and flavor impacted the number of fixations. This research contributes to understanding visual cues and attention in food choices, suggesting strategies for enhancing beef labeling and communication to better inform Brazilian consumers. Full article

Objectives: The present study examined cross-sectional and longitudinal associations between dysphagia and a variety of health-related parameters, including physical performance, cognitive function, malnutrition, sarcopenia, disability, frailty, falls, hospitalization, and mortality in a cohort of octogenarians living in the mountainous Sirente region of Central Italy. Methods: Dysphagia was operationalized as the need to modify the diet to facilitate swallowing and/or the exclusive consumption of specific food consistencies due to swallowing difficulties. Physical performance, cognitive function, malnutrition, disability, falls, and hospitalizations were assessed via the Minimum Data Set for Home Care. Sarcopenia was defined as the coexistence of low muscle mass and dynapenia, while frailty was operationalized according to Fried’s phenotype. History of falls and incident falls, as well as disability, were tracked over two years, while survival status was followed for up to ten years. Results: Data of 362 older adults (men age: 85.9 ± 4.8; body mass index: 25.6 ± 4.53; women: 66.9%; multimorbidity: 21.5%; dysphagia: 6.6%) were analyzed. The results indicated that dysphagia was significantly and cross-sectionally associated with poor physical performance and reduced cognitive function. In contrast, no longitudinal associations were observed. Conclusions: Dysphagia appears to be linked to deficits in physical and cognitive domains, underscoring the value of comprehensive geriatric assessment and the development of multidomain intervention strategies. Full article