Search Results (19,866)

Food authentication significantly impacts consumer health and the credibility of Food Business Operators (FBOs). As European regulations mandate the verification of food authenticity and supply chain integrity, competent authorities require access to innovative analytical methods to identify and prevent food fraud. This study utilizes the DNA metabarcoding approach on meat preparations, sampled during an official control activity. It assesses animal and plant composition by amplifying DNA fragments of the 12S rRNA and trnL (UAA) genes, respectively. The results not only confirmed the declared species but also revealed undeclared and unexpected taxa in products labelled as containing a single animal species and various unspecified plant species. Notable findings such as the presence of Murinae, Sus scrofa, Ovis aries, and Pisum sativum could raise public health concerns, compromise consumer choices made for ethical or religious reasons, and reflect the hygienic conditions of the processing plant. This study demonstrates that the DNA metabarcoding approach looks to be a promising support tool for official control authorities to ensure food authenticity and safety, and to develop risk profiles along the supply chain. Full article

Food labels and storytelling are marketing tools used by the food industry to highlight and communicate important product characteristics to consumers. By using these tools, food companies can influence consumers’ attitudes toward the product and potentially the likelihood of purchase. In the present study, we investigated how storytelling about an innovative and sustainable organic beef production system influenced participants’ preference and acceptance of a veal steak product and, further, if some information characteristics were more important than others for consumer satisfaction. Without being aware that the samples were identical, participants (n = 224) tasted two veal steak samples: one steak sample was presented with information about the production system, and the other without information. Results showed that when the steak sample was presented with product information, compared to without information, it received significantly higher hedonic ratings (overall liking, liking of flavor, and liking of texture). This was likewise reflected in a greater preference for the steak sample when presented with product information. Furthermore, product information was found to positively impact the participants’ satisfaction with the steak sample regardless of their preference. Overall, our results suggest that the use of storytelling about the innovative and sustainable product system for veal steaks can positively influence consumers’ attitudes toward the product. Full article

Alternaria brown spot (ABS), caused by Alternaria alternata, is one of the main citrus diseases that causes heavy production losses and reductions in fruit quality worldwide. The application of chemical fungicides has a key role in the management of ABS. In this study, 48 isolates of A. alternata collected from citrus orchards since 2014 were tested in vitro for their sensitivity to pyraclostrobin and fludioxonil, the latter being temporarily registered in Italy since 2020. Pyraclostrobin sensitivity was determined using spore germination and mycelial growth assays. The effective concentration inhibiting 50% of fungal growth (EC₅₀) was determined for each isolate. The sensitivity assays showed that the majority of A. alternata isolates tested were sensitive to pyraclostrobin. EC₅₀ values of fludioxonil in a mycelial growth assay indicated that 100% of isolates were sensitive to this fungicide. The analysis of the cytochrome b gene showed that none of the 40 isolates with a different sensitivity profile had the G143A mutation, and the subgroup of 8 isolates analyzed by real-time PCR did not carry the G137R and F129L mutations. A subset of four more sensitive and two reduced-sensitive isolates was chosen to assess sensitivity on detached citrus leaves treated with pyraclostrobin at the maximum recommended label rate. Disease incidence and symptom severity were significantly reduced, with a small reduction reported in leaves inoculated with the reduced-sensitive isolates. Furthermore, there was no correlation between sensitivity and fitness parameters evaluated in vitro (mycelium growth and sporulation rate). These findings help the development of monitoring resistance programs and, consequently, set up effective anti-resistance strategies for managing ABS on citrus orchards. Full article

Background. In high-income countries, shopping for non-prescription multivitamin–multimineral supplements has tremendously increased. Objective and Methods. The purpose of this labeling analysis is to inform on the daily elemental iron (with or without vitamin C) supply provided by multivitamin–multimineral supplements sold online by Amazon in Western and Southern Europe (amazon.es^®, amazon.de^®, amazon.it^®, and amazon.fr^®). Results. We identified 298 iron-containing multivitamin–multimineral preparations sold by Amazon marketplaces: 153 preparations sourced from amazon.de^®, 68 from amazon.fr^®, 54 from amazon.it^®, and 23 from amazon.es^®. The daily iron dose provided by these preparations was 14 [5–14] mg (median and interquartile range), with no differences among the marketplaces. Approximately 90% (n = 265) of the preparations contained ferrous iron. Moreover, 85% (n = 253) of the preparations were fortified with vitamin C in a dose of 80 [40–100] mg daily. Conclusions. The median supply of iron (about 14 mg) and vitamin C (80 mg) in iron-containing multivitamin–multimineral preparations offered on Amazon platforms in Western and Southern Europe falls below that currently recommended for iron deficiency in review articles, namely 100 mg of iron and 500 mg of vitamin C per day. The iron supply of iron-containing multivitamin–multimineral preparations falls also below the dose of 30–60 mg advocated to prevent iron deficiency in menstruating women. Full article

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5. Full article

Oxalate decarboxylase is an Mn- and O₂-dependent enzyme in the bicupin superfamily that catalyzes the redox-neutral disproportionation of the oxalate monoanion to form carbon dioxide and formate. Its best-studied isozyme is from Bacillus subtilis where it is stress-induced under low pH conditions. Current mechanistic schemes assume a monodentate binding mode of the substrate to the N-terminal active site Mn ion to make space for a presumed O₂ molecule, despite the fact that oxalate generally prefers to bind bidentate to Mn. We report on X-band ¹³C-electron nuclear double resonance (ENDOR) experiments on ¹³C-labeled oxalate bound to the active-site Mn(II) in wild-type oxalate decarboxylase at high pH, the catalytically impaired W96F mutant enzyme at low pH, and Mn(II) in aqueous solution. The ENDOR spectra of these samples are practically identical, which shows that the substrate binds bidentate (κO, κO’) to the active site Mn(II) ion. Domain-based local pair natural orbital coupled cluster singles and doubles (DLPNO-CCSD) calculations of the expected ¹³C hyperfine coupling constants for bidentate bound oxalate predict ENDOR spectra in good agreement with the experiment, supporting bidentate bound substrate. Geometry optimization of a substrate-bound minimal active site model by density functional theory shows two possible substrate coordination geometries, bidentate and monodentate. The bidentate structure is energetically preferred by ~4.7 kcal/mol. Our results revise a long-standing hypothesis regarding substrate binding in the enzyme and suggest that dioxygen does not bind to the active site Mn ion after substrate binds. The results are in agreement with our recent mechanistic hypothesis of substrate activation via a long-range electron transfer process involving the C-terminal Mn ion. Full article

Dog breeds are grouped based on scientific agreement, whether for traditional reasons or specific tasks during their domestication. Discrepancies may occur between public views of breed behaviour and actual evidence. This research aims to investigate differences in five behavioural traits (aggression towards people, aggression towards animals, fearfulness, responsiveness to training, and activity/excitability) by using the Dog Personality Questionnaire (DPQ) across six conventional groups/clusters of dog breeds (herding, hunting, guarding, companion dogs, potentially aggressive breeds, and mixed-breed dogs) and to assess hypotheses derived from common public presumptions. A cohort of 1309 dog owners sourced through diverse online platforms took part in the study. Contrary to stereotypes, the findings indicate that breeds labelled as “potentially aggressive” display lower levels of aggression compared to guarding breeds (χ² (5) = 3.657, p = 0.041) and mixed-breeds (χ² (5) = 3.870, p = 0.002). Additionally, mixed-breed dogs exhibited the highest levels of fearfulness among the six conventional clusters. In terms of aggression and gender, males demonstrated higher aggression levels towards both humans and animals compared to females (p = 0.001). These results challenge established assumptions and emphasise the necessity of evidence-based methodologies in the assessment of canine behaviour. Full article

Unsupervised domain adaptation (UDA) methods, based on adversarial learning, employ the means of implicit global and class-aware domain alignment to learn the symmetry between source and target domains and facilitate the transfer of knowledge from a labeled source domain to an unlabeled target domain. However, these methods still face misalignment and poor target generalization due to small inter-class domain discrepancy and large intra-class discrepancy of target features. To tackle these challenges, we introduce a novel adversarial learning-based UDA framework named Coding Rate Reduction Adversarial Domain Adaptation (CR2ADA) to better learn the symmetry between source and target domains. Integrating conditional domain adversarial networks with domain-specific batch normalization, CR2ADA learns robust domain-invariant features to implement global domain alignment. For discriminative class-aware domain alignment, we propose the global and local coding rate reduction methods in CR2ADA to maximize inter-class domain discrepancy and minimize intra-class discrepancy of target features. Additionally, CR2ADA combines minimum class confusion and mutual information to further regularize the diversity and discriminability of the learned features. The effectiveness of CR2ADA is demonstrated through experiments on four UDA datasets. The code can be obtained through email or GitHub. Full article

Vibrational spectroscopic techniques, such as Fourier transform infrared (FTIR) absorption and Raman spectroscopy (RS), offer unique and detailed biochemical fingerprints by detecting specific molecular vibrations within samples. These techniques provide profound insights into the molecular alterations induced by ionising radiation, which are both complex and multifaceted. This paper reviews the application of rapid and label-free vibrational spectroscopic methods for assessing biological radiation responses. These assessments span from early compartmentalised models such as DNA, lipid membranes, and vesicles to comprehensive evaluations in various living biological models, including tissues, cells, and organisms of diverse origins. The review also discusses future perspectives, highlighting how the field is overcoming methodological limitations. RS and FTIR have demonstrated significant potential in detecting radiation-induced biomolecular alternations, which may facilitate the identification of radiation exposure spectral biomarkers/profiles. Full article

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes. Full article

Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer’s feature extraction method, which constructs sparse region-indexing relations and performs feature extraction only in key regions, thereby facilitating more effective capture of flame characteristics. Additionally, we introduce a feature screening method based on a domain-adversarial neural network (DANN) to minimize misclassification by accurately determining feature domains. Furthermore, a feature discrimination method utilizing a Graph Convolutional Network (GCN) is proposed, enabling the model to capture label correlations more effectively and improve performance by constructing a label correlation matrix. This model enhances cross-domain generalization capability and improves recognition performance in fire scenarios. In the experimental phase, we developed a comprehensive dataset by integrating multiple fire-related public datasets, and conducted detailed comparison and ablation experiments. Results from the tenfold cross-validation demonstrate that the proposed model significantly improves recognition of multi-labeled images in fire scenarios. Compared with the baseline model, the mAP increased by 4.426%, CP by 4.14% and CF1 by 7.04%. Full article

Oligoamines in cellular metabolism carry extremely diverse biological functions (i.e., regulating Ca²⁺-influx, neuronal nitric oxide synthase, membrane potential, Na⁺, K⁺-ATPase activity in synaptosomes, etc.). Furthermore, they also act as longevity agents and have a determinative role in autophagy, cell growth, proliferation, and death, while oligoamines dysregulation is a key in a variety of cancers. However, many of their mechanisms of actions have just begun to be understood. In addition to the numerous biosensing methods, only a very few simple small molecule-based tests are available for their selective but reversible tracking or fluorescent labeling. Motivated by this, we present herein a new fluorescent bis(acridino)-crown ether as a sensor molecule for biogenic oligoamines. The sensor molecule can selectively distinguish oligoamines from aliphatic mono- and diamino-analogues, while showing a reversible 1:2 (host:guest) complexation with a stepwise binding process accompanied by a turn-on fluorescence response. Both computational simulations on molecular docking and regression methods on titration experiments were carried out to reveal the oligoamine-recognition properties of the sensor molecule. The new fluorescent chemosensor molecule has a high potential for molecular-level functional studies on the oligoamine systems in cell processes (cellular uptake, transport, progression in cancers, etc.). Full article

The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we propose an unsupervised cross-domain model (UCDT) for TIR tracking. Our approach leverages labeled training samples from the RGB domain (source domain) to train a general feature extraction network. We then employ a cross-domain model to adapt this network for effective target feature extraction in the TIR domain (target domain). This cross-domain strategy addresses the challenge of limited TIR training samples effectively. Additionally, we utilize an unsupervised learning technique to generate pseudo-labels for unlabeled training samples in the source domain, which helps overcome the limitations imposed by the scarcity of annotated training data. Extensive experiments demonstrate that our UCDT tracking method outperforms existing tracking approaches on the PTB-TIR and LSOTB-TIR benchmarks. Full article

A nanoliter liquid chromatography–high resolution mass spectrometry-based method was developed for quantitative proteomics analysis of COVID-19 vaccines. It can be used for simultaneous qualitative and quantitative analysis of target proteins and host cell proteins (HCPs) in vaccine samples. This approach can directly provide protein information at the molecular level. Based on this, the proteomes of 15 batches of COVID-19 inactivated vaccine samples from two companies and 12 batches of COVID-19 recombinant protein vaccine samples from one company were successfully analyzed, which provided a significant amount of valuable information. Samples produced in different batches or by different companies can be systematically contrasted in this way, offering powerful supplements for existing quality standards. This strategy paves the way for profiling proteomics in complex samples and provides a novel perspective on the quality evaluation of bio-macromolecular drugs. Full article

Crude palm oil (CPO) is the most produced vegetable oil globally, with Brazil contributing only 0.74% of global production. Pará and Bahia account for more than 82% of Brazil’s output. Despite its widespread use in the food industry after refining, there is little research on CPO consumption and perception in Brazil, particularly regarding its nutritional aspects. This study, conducted between March and July 2022, explored Brazilians’ perceptions and the potential for CPO consumption. The results show that most participants are unfamiliar with CPO but view its nutrients favorably. Less than half regularly purchase CPO. Refined palm oil (RPO) is even less known, with many unaware that refining CPO can produce carcinogenic substances. The respondents showed little concern about RPO in their foods, rarely noticing its presence on labels. Despite limited knowledge, participants understand that refining reduces CPO’s health benefits, leading to a greater preference for crude oil over refined oil. This study highlights the need for better dissemination of information about CPO in Brazil, emphasizing its nutritional benefits and the importance of adhering to daily lipid intake limits. Adding CPO at the end of cooking or consuming it raw to preserve thermosensitive compounds is also recommended. Full article