Search Results (364)

In the Roman Empire, lead tags were used for various purposes, one of which was to label textiles that needed cleaning, repairing or dyeing. So far, these tesserae have been found at over 90 sites in 13 Roman provinces. The cities of Siscia and Carnuntum in Pannonia Superior have the highest number of finds. In 2011, a Roman cesspit was excavated in the civil city of Carnuntum and dated to the mid-2nd century CE. The latrine contained household and food waste, human faeces, pottery shards, pollen, lime, amber and 179 lead tags. The tags bear inscriptions consisting of personal names, prices, and abbreviations of terms relating to garments, colours and services such as cleaning, mending, repairing, fulling, fumigating, perfuming, dyeing, and redyeing. The findings of Roman textiles unearthed in Carnuntum are too degraded to allow a successful dye analysis to be carried out. Therefore, the inscriptions are important sources for drawing conclusions about dyeing materials and techniques. This information was supplemented by ancient written sources as well as archaeobotanical finds of dye plants and dye analyses of archaeological textiles found in Central Europe dating from the same period or earlier. Full article

Accurate delineation of fields is difficult in fragmented landscapes where single-date images provide no seasonal cues and supervised models require labels. We propose a method that explicitly represents phenology to improve zero-shot delineation. Using 22 cloud-free PlanetScope scenes over a 5 × 5 km area, a single harmonic model is fitted to the NDVI per pixel to obtain the phase, amplitude and mean. These values are then mapped into cylindrical colour spaces (Hue–Saturation–Value, Hue–Whiteness–Blackness, Luminance-Chroma-Hue). The resulting recoloured composites are segmented using the Segment Anything Model (SAM), without fine-tuning. The results are evaluated object-wise, object-wise grouped by area size, and pixel-wise. Pixel-wise evaluation achieved up to F1 = 0.898, and a mean Intersection-over-Union (mIoU) of 0.815, while object-wise performance reached F1 = 0.610. HSV achieved the strongest area match, while HWB produced the fewest fragments. The ordinal time-of-day basis provided better parcel separability than the annual radian adjustment. The main errors were over-segmentation and fragmentation. As the parcel size increased, the IoU increased, but the precision decreased. It is concluded that recolouring using harmonic NDVI time series is a simple, scalable, and interpretable basis for field delineation that can be easily improved. Full article

A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive process. In addition, the models are often only applicable to the conditions represented in the training data. In this study, we propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery, applying Segment Anything Model 2.1 (SAM 2.1) for label-free segmentation. To prevent the segmentation of irrelevant objects such as soil or weeds, the model is guided using point prompts, which correspond to local maxima in the canopy height model (CHM). The pipeline was used to measure the crown diameter of approximately 15000 ball-shaped chrysanthemums (Chrysanthemum morifolium (Ramat)) in a 6158 ${\mathrm{m}}^2$ field on two dates. Nearly all plants were successfully segmented, resulting in a recall of 96.86%, a precision of 99.96%, and an ${\mathrm{F}}_1$ score of 98.38%. The estimated diameters showed strong agreement with manual measurements. The results demonstrate the potential of the proposed pipeline for accurate plant trait extraction across varying field conditions without the need for model training or data annotation. Full article

Nearly all eukaryotic proteins are turned over by the ubiquitin (Ub)-26S proteasome system (UPS). Despite its broad cellular roles, only a handful of UPS members, particularly the Ub E3 ligases that specifically recognize a protein for ubiquitylation, have been characterized in plants to date. The challenge arises from the transient recognition and rapid degradation of ubiquitylation substrates by the UPS. To tackle this challenge, the emerging biotinylation-based proximity labeling (PL) offers an exciting tool for enriching transient interactors of Ub E3 ligases. In this study, we examined the efficacy of TurboID in identifying substrates of Arabidopsis Skp1-cullin1-F-box (SCF) ligases. We demonstrate that the Arabidopsis Skp1 Like (ASK)1-TurboID is not fully functioning in planta, which led us to discover a novel antagonism between biotinylation and ubiquitylation in regulating protein stability in vivo. This discovery lowers the effectiveness of PL in ubiquitylome studies. However, using one long-known SCF substrate, phytochrome A, we succeeded to apply its TurboID fusion for complementing the far-red-light response of the phyA-211 null mutant allele, suggesting an efficacy of PL in characterizing single ubiquitylation pathways. This study highlighted a limitation of PL in ubiquitylome studies, discovered a new antagonistic pathway of biotinylation, and developed a theoretical guidance for future PL-based characterization of ubiquitylation pathways. Full article

Introduction: Osteoarthritis (OA) is a chronic degenerative joint disease with limited treatment options focused primarily on symptom management. Emerging evidence suggests that dietary interventions may influence inflammation and pain through modulation of the gut microbiome and metabolome. Methods: We conducted a 4-week open-label pilot trial evaluating the effects of an anti-inflammatory dietary intervention (ITIS diet) in 20 patients with knee OA (ClinicalTrials.gov ID: NCT05559463, registered prior to enrollment; sponsor: University of California, San Diego; responsible party: Monica Guma; study start date: 1 October 2021). The following were assessed before and after the intervention: (1) clinical outcomes; (2) gut and salivary microbiomes; and (3) salivary, stool, and plasma metabolomes. Responders were defined as patients achieving ≥30% reduction in Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain scores. Results: The ITIS diet was well-tolerated, with good adherence (66.2%) and a significant improvement in clinical outcomes, including reduced pain and improved overall health measured with the visual analog scale (VAS). Responders (n = 8) showed distinct gut microbiome and metabolome profiles compared to non-responders (n = 12). Notably, taxa within the Lachnospiraceae family exhibited dynamic, bidirectional shifts post-intervention: Anaerostipes and Limivivens were enriched among responders and negatively correlated with pain scores, while Oliverpabstia and Fusicatenibacter were depleted following dietary intervention. These taxa also showed strong correlations with anti-inflammatory metabolites, including hydroxydecanoic acid derivatives and pyridoxine. Furthermore, subsequent network analysis revealed more structured and selective microbiome–metabolome interactions in responders, specifically post-intervention. Conclusions: This pilot study shows that a short-term anti-inflammatory dietary intervention was associated with meaningful changes in the gut microbiome and metabolome. Members of the Lachnospiraceae family emerged as key taxa associated with pain reduction and anti-inflammatory metabolite production. Our findings suggest that specific microbial responses—rather than global diversity changes—may underlie dietary responsiveness in OA. Although exploratory and limited by sample size, our results support further investigation into personalized, microbiome-informed nutritional strategies for OA management. Full article

Date palm (Phoenix dactylifera L.) is a vital crop cultivated primarily in developing regions, playing a strategic role in global food security through its significant contribution to nutrition, economy, and livelihoods. Global and regional production trends revealed increasing demand and expanded cultivation areas, underpinning the fruit’s importance in national food security policies and economic frameworks. The date fruit’s rich nutritional profile, encompassing carbohydrates, dietary fiber, minerals, and bioactive compounds, supports its status as a functional food with health benefits. Postharvest technologies and quality preservation strategies, including temperature-controlled storage, advanced drying, edible coatings, and emerging AI-driven monitoring systems, are critical to reducing losses and maintaining quality across diverse cultivars and maturity stages. Processing techniques such as drying, irradiation, and cold plasma distinctly influence sugar composition, texture, polyphenol retention, and sensory acceptance, with cultivar- and stage-specific responses guiding optimization efforts. The cold chain and innovative packaging solutions, including vacuum and modified atmosphere packaging, along with biopolymer-based edible coatings, enhance storage efficiency and microbial safety, though economic and practical constraints remain, especially for smallholders. Microbial contamination, a major challenge in date fruit storage and export, is addressed through integrated preservation approaches combining thermal, non-thermal, and biopreservative treatment. However, gaps in microbial safety data, mycotoxin evaluation, and regulatory harmonization hinder broader application. Date fruit derivatives such as flesh, syrup, seeds, press cake, pomace, and vinegar offer versatile functional roles across food systems. They improve nutritional value, sensory qualities, and shelf life in bakery, dairy, meat, and beverage products while supporting sustainable waste valorization. Emerging secondary derivatives like powders and extracts further expand the potential for clean-label, health-promoting applications. This comprehensive review underscores the need for multidisciplinary research and development to advance sustainable production, postharvest management, and value-added utilization of date palm fruits, fostering enhanced food security, economic benefits, and consumer health worldwide. Full article

Although omalizumab is currently approved for a limited number of indications—such as asthma, chronic rhinosinusitis with nasal polyps, and chronic spontaneous urticaria—its potential applications are expanding each year. Owing to its diverse and not yet fully elucidated mechanism of action, including effects on both IgE- and non-IgE-mediated hypersensitivity reactions with delayed onset, this monoclonal antibody may be beneficial in a wide range of allergic and non-allergic conditions. To date, numerous clinical trials and case reports have documented the successful off-label use of omalizumab. It appears particularly promising for patients with difficult-to-treat hypersensitivities, such as food and drug allergies, which continue to pose significant challenges in modern allergology. Even though further research is needed to establish clear indications for its use in these contexts, omalizumab holds considerable potential to enhance the outcomes and clinical efficacy of food immunotherapy and drug desensitization protocols. The aim of this review is to present the current and potential future applications of omalizumab as an adjunctive treatment in food allergy therapy and in desensitization protocols for patients with hypersensitivity to selected drugs. Full article

Boron-containing compounds (BCCs) have emerged as potential drugs. Their drug-like effects are mainly explained by their mechanisms of action in enzymes. Nowadays, some experimental data support the effects of specific BCCs on GPCRs, provided there are crystal structures that show them bound to G protein-coupled receptors (GPCRs). Some BCCs are recognized as potential ligands of GPCRs—the drug targets of many diseases. Objective: The aim of this study was to collecte up-to-date data on the interactions of BCCs with GPCRs. Methods: Data were collected from the National Center of Biotechnology Information, PubMed, Global Health, Embase, the Web of Science, and Google Scholar databases and reviewed. Results: Some experimental reports support the interactions of BCCs with several GPCRs, acting as their labels, agonists, or antagonists. These interactions can be inferred based on in silico and in vitro results if there are no available crystal structures for validating them. Conclusions: The actions of BCCs on GPCRs are no longer hypothetical, as the existing evidence supports BCCs’ interactions with and actions on GPCRs. Full article

The demand for land monitoring information continues to increase, but the range and diversity of the available products to date have made their integrated use challenging and, at times, counterproductive. There has therefore been a growing need to enhance and harmonise the practice of land monitoring on a pan-European level with the formulation of a more consistent and standardised set of modelling criteria. The outcome has been a paradigm shift away from a “paper map”-based world where features are given a single, fixed label to one where features have a rich characterisation which is more informative, flexible and powerful. The approach allows the characteristics to be dynamic so that, over time, a feature may only change part of its description (i.e., a forest can be felled, but it may remain as forestry if replanted) or it can have multiple descriptors (i.e., a forest may be used for both timber production and recreation). The concept proposed by the authors has evolved since 2008 from first drafts to a comprehensive and powerful tool adopted by the European Union’s Copernicus programme. It provides for the semantic decomposition of existing nomenclatures, as well as supports a descriptive approach to the mapping of all landscape features in a flexible and object-oriented manner. In this way, the key move away from classification towards the characterisation of the Earth’s surface represents a novel and innovate approach to handling complex land surface information more suited to the age of distributed databases, cloud computing and object-oriented data modelling. In this paper, the motivation for and technical approach of the EAGLE concept with its matrix and UML model implementation are explained. This is followed by an update of the latest developments and the presentation of a number of experimental and operational use cases at national and European levels, and it then concludes with thoughts on the future outlook. Full article

(1) Background: Changes in the spatial, temporal, and magnitude-related patterns of fires caused by humans are expected to exacerbate with climate change, significantly impacting ecosystems and societies worldwide. However, our understanding of fire regimes in many regions remains limited, largely due to the inherent complexity of fire as an ecological process. Pyrogeography, combined with unsupervised learning methods and the availability of long-term satellite data, offers a robust framework for approaching this problem. The purpose of the study is to identify the pyroregions of the Argentine Gran Chaco, the world’s largest continuous tropical dry forest region. (2) Methods: Using globally available fire occurrence datasets, we computed five fire metrics, related to the extent, frequency, intensity, size, and seasonality of fires at three spatial scales (5, 10, and 25 km). In addition, we tested two widely used cluster algorithms, the K-means algorithm and the Gaussian Mixture Model (GMM). (3) Results and Discussion: The identification of pyroregions was dependent on the clustering algorithm and scale of analysis. The GMM algorithm at a 25 km scale ultimately demonstrated more coherent ecological and spatial distributions. GMM identified six pyroregions, which were labeled based on three metrics in the following order: annual burned area (categorized in low, regular or high), interannual variability of fire (rare, occasional, frequent), and fire intensity (low, moderate, intense). The values were as follows: LRM (22% of study area), ROI (19%), ROM (14%), LOM (10%), ROL (9%), and HFL (4%). (4) Conclusions: Our study provides the most comprehensive delineation of the Argentine Gran Chaco’s Dry Forest pyroregions to date, and highlights both the importance of determining the optimal scale of analysis and the critical role of clustering algorithms in efforts to accurately characterize the diverse attributes of fire regimes. Furthermore, it emphasizes the importance of integrating fire ecology principles and fire management perspectives into pyrogeographic studies to ensure a more comprehensive and meaningful characterization of fire regimes. Full article

The growing demand for more sustainable food systems has driven the development of solutions based on food microbiology, capable of integrating safety, functionality, and environmental responsibility. This paper presents a critical and up-to-date review of the most relevant advances at the interface between microbiology, sustainability, and food innovation. The analysis is structured around three main axes: (i) microbial fermentation, with a focus on traditional practices and precision technologies aimed at valorizing agro-industrial waste and producing functional foods; (ii) microbial biocontrol, including the use of bacteriocins, protective cultures, bacteriophages, and CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats–CRISPR-associated)-based tools as alternatives to synthetic preservatives; and (iii) the development of functional foods containing probiotics, prebiotics, synbiotics, and postbiotics, with the potential to modulate the gut microbiota and promote metabolic, immune, and cognitive health. In addition to reviewing the microbiological and technological mechanisms involved, the paper discusses international regulatory milestones, scalability challenges, and market trends related to consumer acceptance and clean labeling. Finally, emerging trends and research gaps are addressed, including the use of omics technologies, artificial intelligence, and unexplored microbial resources. Food microbiology, by incorporating sustainable practices and advanced technologies, is positioned as a strategic pillar for building a healthy, circular, science-based food model. Full article

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article

The date palm industry is a vital pillar of agricultural economies in arid and semi-arid regions; however, it remains vulnerable to challenges such as pest infestations, post-harvest diseases, and limited access to real-time monitoring tools. This study applied the baseline YOLOv11 model and its optimized variant, YOLOv11-Opt, to automate the detection, classification, and monitoring of date fruit varieties and disease-related defects. The models were trained on a curated dataset of real-world images collected in Saudi Arabia and enhanced through advanced data augmentation techniques, dynamic label assignment (SimOTA++), and extensive hyperparameter optimization. The experimental results demonstrated that YOLOv11-Opt significantly outperformed the baseline YOLOv11, achieving an overall classification accuracy of 99.04% for date types and 99.69% for disease detection, with ROC-AUC scores exceeding 99% in most cases. The optimized model effectively distinguished visually complex diseases, such as scale insert and dry date skin, across multiple date types, enabling high-resolution, real-time inference. Furthermore, a visual analytics dashboard was developed to support strategic decision-making by providing insights into production trends, disease prevalence, and varietal distribution. These findings underscore the value of integrating optimized deep learning architectures and visual analytics for intelligent, scalable, and sustainable precision agriculture. Full article

Background/Objectives: We reviewed Post-Marketing Requirements (PMRs) under the Pediatric Research Equity Act (PREA) for antibiotics approved in adults from 2009 to 2024 to better understand factors associated with PMR study completion. Methods: Initial PMRs, including study design and completion timelines were extracted from Food and Drug Administration (FDA) approval letters. Studies were cross-referenced at clinicaltrials.gov, with follow-up from adult approval to study completion or through 31 December 2024. Results: Eighteen antibiotics were approved in adults from 2009 to 2024, with 53 associated PREA PMRs. A total of nine PMRs were excluded from analysis (six exclusions for projected study completion dates on or after 12/31/2024, one exclusion due to lack of information, and two exclusions because the study type was not categorizable as Phase 1 or Phase 2). Of the 44 remaining PMRs in the analysis set, the median pediatric study follow-up time from adult approval was 5.3 years (range 0.94 to 11.5 years), with a study completion rate of 54.5% (N = 24). Small- and medium-sized companies had a study completion rate of 10% (N = 2/20) over a median of 6.44 years of follow-up, with no pediatric approvals. Large pharmaceutical corporations had a significantly higher study completion rate of 91.6% (N = 22/24; adjusted hazard ratio 20.3 95%CI, 5.02 to 82.4) over a median follow-up time of 4.7 years and achieved pediatric approval with labelling updates for 75% of antibiotics (N = 6/8). Conclusions: Compared to larger organizations, smaller pharmaceutical companies have experienced difficulty in PREA PMR antibiotic study completion, which may be related to financial difficulties in the challenging market for antibiotics. To improve PMR study completion, smaller companies require continued financial support and innovation in study design. For pediatric antibiotic development, the FDA accepts the extrapolation of efficacy from well-conducted randomized adult trials (i.e., pharmacokinetics (PK) and the safety approach). Therefore, sponsors should consider the use of single-arm, non-comparative PK and safety study designs to reduce the size and scope of trials. Sponsors should also assess whether the evaluation of an antibiotic is necessary in adolescents, or if data in a surrogate population of adults (e.g., low-weight adults) may serve as adequate evidence for adolescent approval. Full article

Food waste poses critical environmental, economic, and social challenges, with consumer behavior recognized as a key leverage point for intervention. Packaging plays a vital role in preserving food quality and reducing waste, yet its behavioral influence on household food waste (HFW) remains underexplored. This review systematically examines 52 studies investigating the impact of food packaging—excluding storage/date labeling—on consumer food waste (CFW) behaviors. Using a structured methodology, we classified studies by methodological design, geographic coverage, food types, and focal packaging features. The analysis reveals a dominant reliance on consumer surveys and short-duration diaries, with limited application of rigorous experimental methods. Geographically, the English-language literature is skewed toward high-income countries, particularly Australia and Europe, with notable gaps in regions such as Asia and Africa. Moreover, despite U.S. households discarding approximately 40% of their food, research coverage remains limited. The findings also expose a misalignment between research focus and consumer-perceived importance of packaging features; attributes such as transparency, grip/shape, and dispensing mechanisms are frequently rated as important by consumers but are under-represented in the literature. This review contributes by identifying these gaps, synthesizing behavioral evidence, and offering a roadmap for future research and design innovation. By better aligning packaging functionalities with real-world behaviors, this work supports the development of consumer-informed solutions to mitigate HFW and promote sustainable food systems. Full article