Search Results (3,087)

Orange allergy is estimated to account for up to 3–4% of food allergies. Major allergens identified in orange (Citrus sinensis) include Cit s 1 (germin-like protein) and Cit s 2 (profilin), while Cit s 3 (non-specific lipid transfer protein, nsLTP) and Cit s 7 (gibberellin-regulated protein) have also been described. The objective of this study was to investigate the presence and IgE-binding capacity of germin-like proteins in citrus fruits other than oranges. We describe five patients with immediate allergic reactions after orange ingestion. All patients underwent skin prick tests (SPT) to aeroallergens and common food allergens, prick-by-prick testing with orange, lemon, and mandarin (pulp, peel, seeds), total IgE, specific IgE (sIgE), anaphylaxis scoring (oFASS), and the Food Allergy Quality of Life Questionnaire (FAQLQ-AF). Protein extracts from peel and pulp of orange, lemon, and mandarin were analyzed by Bradford assay, SDS-PAGE, and IgE immunoblotting using patient sera. Selected bands were identified by peptide mass fingerprinting. A 23 kDa band was recognized by all five patients in orange (pulp and peel), lemon (peel), and mandarin (peel). This band was consistent with Cit s 1, a germin-like protein already annotated in the IUIS allergen database for orange but not for lemon or mandarin. Peptide fingerprinting confirmed the germin-like identity of the 23 kDa bands in all three citrus species. Germin-like proteins of approximately 23 kDa were identified as IgE-binding components in peel extracts of orange, lemon, and mandarin, and in orange pulp. These findings suggest a potential shared allergen across citrus species that may contribute to allergic reactions independent of LTP sensitization. Full article

In the context of the valorization of agri-food by-products, tomato (Solanum lycopersicum L.) seeds represent a protein-rich matrix containing potential bioactives. The aim of the present work is to develop a biochemical pipeline for (i) achieving high protein recovery from tomato seed, (ii) optimizing the hydrolysis with different proteases, and (iii) characterizing the resulting peptides. This approach was instrumental for obtaining and selecting the most promising peptide mixture to test for antifungal activity. To this purpose, proteins from an alkaline extraction were treated with bromelain, papain, and pancreatin, and the resulting hydrolysates were assessed for their protein/peptide profiles via SDS-PAGE, SEC-HPLC, and RP-HPLC. Bromelain hydrolysate was selected for antifungal tests due to its greater quantity of peptides, in a broader spectrum of molecular weights and polarity/hydrophobicity profiles, and higher DPPH radical scavenging activity, although all hydrolysates exhibited antioxidant properties. In vitro assays demonstrated that the bromelain-digested proteins inhibited the growth of Fusarium graminearum and F. oxysporum f.sp. lycopersici in a dose-dependent manner, with a greater effect at a concentration of 0.1 mg/mL. The findings highlight that the enzymatic hydrolysis of tomato seed protein represents a promising strategy for converting food by-products into bioactive agents with agronomic applications, supporting sustainable biotechnology and circular economy strategies. Full article

Glutamate dehydrogenase (GDH) is ubiquitous in organisms and crucial for amino acid metabolism, energy production, and redox balance. The gdhA and gudB genes encoding GDH were identified in Bacillus altitudinis AS19 and shown to be regulated by iron. However, their functions remain unclear. In this study, gdhA and gudB were analyzed using bioinformatics tools, such as MEGA, Expasy, and SWISS-MODEL, expressed with a prokaryotic expression system, and the induction conditions were optimized to increase the yield of soluble proteins. Phylogenetic analysis revealed that GDH is evolutionarily conserved within the genus Bacillus. GdhA and GudB were identified as hydrophobic proteins, not secreted or membrane proteins. Their structures were primarily composed of irregular coils and α-helices. SWISS-MODEL predicts GdhA to be an NADP-specific GDH, whereas GudB is an NAD-specific GDH. SDS-PAGE analysis showed that GdhA was expressed as a soluble protein after induction with 0.2 mmol/L IPTG at 24 °C for 16 h. GudB was expressed as a soluble protein after induction with 0.1 mmol/L IPTG at 16 °C for 12 h. The proteins were confirmed by Western blot and mass spectrometry. The enzyme activity of recombinant GdhA was 62.7 U/mg with NADPH as the coenzyme. This study provides a foundation for uncovering the functions of two GDHs of B. altitudinis AS19. Full article

Early printed books, particularly incunabula, are invaluable archives of the beginnings of modern educational systems. However, their complex layouts, antique typefaces, and page degradation caused by bleed-through and ink fading pose significant challenges for automatic transcription. In this work, we present a modular pipeline that addresses these problems by combining modern layout analysis and language modeling techniques. The pipeline begins with historical layout-aware text segmentation using Kraken, a neural network-based tool tailored for early typographic structures. Initial optical character recognition (OCR) is then performed with Kraken’s recognition engine, followed by post-correction using a fine-tuned ByT5 transformer model trained on manually aligned line-level data. By learning to map noisy OCR outputs to verified transcriptions, the model substantially improves recognition quality. The pipeline also integrates a preprocessing stage based on our previous work on bleed-through removal using robust statistical filters, including non-local means, Gaussian mixtures, biweight estimation, and Gaussian blur. This step enhances the legibility of degraded pages prior to OCR. The entire solution is open, modular, and scalable, supporting long-term preservation and improved accessibility of cultural heritage materials. Experimental results on 15th-century incunabula show a reduction in the Character Error Rate (CER) from around 38% to around 15% and an increase in the Bilingual Evaluation Understudy (BLEU) score from 22 to 44, confirming the effectiveness of our approach. This work demonstrates the potential of integrating transformer-based correction with layout-aware segmentation to enhance OCR accuracy in digital humanities applications. Full article

Rice seed storage proteins (SSPs) play a critical role in determining the nutritional quality, cooking properties, and digestibility of rice. To enhance seed quality, CRISPR/Cas9 genome editing was applied to modify SSP composition by targeting genes encoding 13 kDa prolamins and type A glutelins. Three CRISPR/Cas9 constructs were designed: one specific to the 13 kDa prolamin subfamily and two targeting conserved GluA glutelin regions. Edited T₀ and T₁ lines were generated and analyzed using InDel analysis, SDS-PAGE, Bradford assay, and RP-HPLC. Insertions were more frequent than deletions, accounting for 56% and 74% of mutations in prolamin and glutelin genes, respectively. Editing efficiency varied between sgRNAs. All lines with altered protein profiles contained InDels in target genes. SDS-PAGE confirmed the absence or reduction in bands corresponding to 13 kDa prolamins or GluA subunits, showing consistent profiles among lines carrying the same construct. Quantification revealed significant shifts in SSP composition, including increased albumin and globulin content. Prolamin-deficient lines showed reduced prolamins, while GluA-deficient lines exhibited increased prolamins. Total protein content was significantly elevated in all edited lines, suggesting enrichment in lysine-rich fractions. These findings demonstrate that CRISPR/Cas9-mediated editing of SSP genes can effectively reconfigure the rice protein profile and enhance its nutritional value. Full article

This study explores the sustainable valorization of Annurca apple by-products by examining the polyphenolic content and antioxidant activity of peel, flesh, and core at two ripening stages. Ripening significantly enhanced the concentration of bioactive compounds, particularly in the peel, where total polyphenols increased from 124.4 to 423.3 mg of CAE/100 g FW, flavonoids from 18.2 to 51.3 mg of quercetin equivalents, and ortho-diphenols from 11.9 to 36.1 mg of CAE. The flesh and core showed more moderate increases. Antioxidant activity, assessed using five in vitro assays (DPPH, ABTS, FRAP, TAC, and H₂O₂), was consistently highest in the peel, especially in the ABTS assay. Although the flesh had fewer phenolics, it showed a 1.5-fold increase during ripening, accompanied by improved antioxidant performance. The core also proved notable antioxidant potential, particularly in ripe samples. UHPLC-MS/MS analysis identified 11 phenolic compounds, showing tissue- and ripening-specific distribution. SDS-PAGE revealed a ripening-related increase in Thaumatin-like Protein 1a (TLP1a), especially in the core and flesh. Its association with tissues showing high antioxidant ability suggests a possible role in enhancing the bioactivity of polyphenol-rich extracts. From an agri-food waste valorization perspective, the peel and core represent promising sources of bioactive compounds for developing functional foods and nutraceuticals. Full article

This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article

Co-infections pose significant challenges not only clinically, but also in terms of simultaneous diagnoses. The development of sensitive, multiplexed analytical platforms is critical for accurately detecting viral co-infections, particularly in complex biological environments. In this study, we present a mass spectrometry (MS)-based detection strategy employing a target-triggered hybridization chain reaction (HCR) to amplify signals and in situ photocleavable mass tags (PMTs) for the simultaneous detection of multiple targets. Hairpin DNAs modified with PMTs and immobilized loop structures on magnetic particles (Loop@MPs) were engineered for each target, and their hybridization and amplification efficiency was validated using native polyacrylamide gel electrophoresis (PAGE) and laser desorption/ionization MS (LDI-MS), with silica@gold core–shell hybrid (SiAu) nanoparticles being employed as an internal standard to ensure quantitative reliability. The system exhibited excellent sensitivity, with a detection limit of 415.12 amol for the hepatitis B virus (HBV) target and a dynamic range spanning from 1 fmol to 100 pmol. Quantitative analysis in fetal bovine serum confirmed high accuracy and precision, even under low-abundance conditions. Moreover, the system successfully and simultaneously detected multiple targets, i.e., HBV, human immunodeficiency virus (HIV), and hepatitis C virus (HCV), mixed in various ratios, demonstrating clear PMT signals for each. These findings establish our approach as a robust and reliable platform for ultrasensitive multiplexed detection, with strong potential for clinical and biomedical research. Full article

In the search for alternative protein sources, single cell proteins have gained increasing attention in recent years. Among them, proteins derived from yeast represent a promising but still underexplored option. To enable their application in food product design, their techno-functional properties must be understood. In order to investigate the impact of precipitation pH on their emulsion-stabilizing properties, yeast proteins from Saccharomyces cerevisiae were isolated via precipitation at different pH (pH 3.5 to 5) after cell disruption in the high-pressure homogenizer. Emulsions containing 5 wt% oil and ~1 wt% protein were analyzed for stability based on their droplet size distribution. Proteins precipitated at pH 3.5 stabilized the smallest oil droplets and prevented partitioning of the emulsion, outperforming proteins precipitated at higher pH values. It is hypothesized that precipitation under acidic conditions induces protein denaturation and thereby exposes hydrophobic regions that enhance adsorption at the oil–water interface and the stabilization of the dispersed oil phase. To investigate the stabilization mechanism, the molecular weight of the proteins was determined using SDS-PAGE, their solubility using Bradford assay, and their aggregation behavior using static laser scattering. Proteins precipitated at pH 3.5 possessed larger molecular weights, lower solubility, and a strong tendency to aggregate. Overall, the findings highlight the potential of yeast-derived proteins as bio-surfactants and suggest that pH-controlled precipitation can tailor their functionality in food formulations. Full article

The integration of Building Information Modeling (BIM) and 3D Geographic Information System (3D GIS) models provides high-precision spatial data for digital twin watersheds. To tackle the challenges of large data volumes and rendering latency in integrated models, this study proposes a three-step framework that uses Industry Foundation Classes (IFCs) as the base model and Open Scene Graph Binary (OSGB) as the target model: (1) geometric optimization through an angular weighting (AW)-controlled Quadric Error Metrics (QEM) algorithm; (2) Level of Detail (LOD) hierarchical mapping to establish associations between the IFC and OSGB models, and redesign scene paging logic; (3) coordinate registration by converting the IFC model’s local coordinate system to the global coordinate system and achieving spatial alignment via the seven-parameter method. Applied to the Santun River Basin digital twin project, experiments with 10 water gate models show that the AW-QEM algorithm reduces average loading time by 15% compared to traditional QEM, while maintaining 97% geometric accuracy, demonstrating the method’s efficiency in balancing precision and rendering performance. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

Molds readily grow on wet books, documents, and other library materials where they ruin them chemically, mechanically, and aesthetically. Poor maintenance of libraries, failures of Heating, Ventilation, and Air Conditioning (HVAC) systems, roof leaks, and storm damage leading to flooding can all result in accelerated fungal growth. Moreover, when fungal spores are present at high concentrations in the air, they can be linked to severe respiratory conditions and possibly to other adverse health effects in humans. Climate change and the accompanying storms and floods are making the dual potential of fungi to biodegrade library holdings and harm human health more common. This essay is intended for microbiologists without much background in mycology who are called in to help librarians who are dealing with mold outbreaks in libraries. Our goal is to demystify aspects of fungal taxonomy, morphology, and nomenclature while also recommending guidelines for minimizing mold contamination in library collections. Full article

Biographies of Max Wilson and Gunnar Jungner were published in 2017 and 2020. An in-depth appreciation of the Wilson and Jungner principles, and the publication they were presented in, ‘Principles and Practice of Screening for Disease’, published as nr. 34 in the Public Health Paper-series of the World Health Organisation (W.H.O), called PHP-34 hereafter, was not published as yet. Here an analysis is given of PHP-34 and the ten screening principles, focusing on three subjects. First, by careful analysis of PHP-34, the literature published in the peer reviewed scientific literature, and other sources, the historical background and origin of the ten principles is determined. Second, the precise composition of PHP-34 is described, as parts of the monograph were derived from other seminal works published between roughly 1950 and 1965. Third, it is determined what the contributions of both authors of the monograph were. Results together are discussed in relation to the time PHP-34 was conceptualized and the importance of PHP-34 and the ten principles in the current era. Results show that in the 15 years preceding the publication of PHP-34, many principles of screening were published by authors in the United States of America, a selection of which ended up in PHP-34. Secondly, about 33% of the 145 pages of PHP-34 are drawn from other publications and studies on screening. Thirdly, the case can be made that the actual writing of PHP-34 was done (almost) entirely by Wilson. Regardless, Wilson and Jungner to this day should be applauded for their work. It is a testimony to the value of PHP-34 that we are still reflecting upon, discussing and seeking to intelligently apply the screening principles almost 60 years after their original publication. Full article

This study investigated quality changes in seaweed–yak patties before and after freeze–thaw by varying seaweed addition levels (10–70%). Macroscopically, the effects on water-holding capacity, textural properties, and oxidative indices of restructured yak patties were evaluated. Microscopically, the impact of seaweed-derived bioactive ingredients on patty microstructure and myofibrillar protein characteristics was examined. LF-NMR and MRI showed that 40% seaweed addition most effectively restricted water migration, reduced thawing loss, and preserved immobilized water content. Texture profile analysis (TPA) revealed that moderate seaweed levels (30–40%) enhanced springiness and minimized post-thaw hardness increases. SEM confirmed that algal polysaccharides formed a denser protective network around the muscle fibers. Lipid oxidation (MDA), free-radical measurements, and non-targeted metabolomics revealed a significant reduction in oxidative damage at 40% seaweed addition, correlating with increased total phenolic content. Protein analyses (particle size, zeta potential, hydrophobicity, and SDS-PAGE) demonstrated a cryoprotective effect of seaweed on myofibrillar proteins, reducing aggregation and denaturation. These findings suggest that approximately 40% seaweed addition can improve the physicochemical stability and antioxidant capacity of frozen seaweed–yak meat products. This work thus identifies the optimal seaweed addition level for enhancing freeze–thaw stability and functional quality, offering practical guidance for the development of healthier, high-value restructured meat products. Full article

Handwritten Keyword Spotting (KWS) remains a challenging task, particularly in segmentation-free scenarios where word images must be retrieved and ranked based on their similarity to a query without relying on prior page-level segmentation. Traditional KWS methods primarily focus on visual similarity, often overlooking the underlying semantic relationships between words. In this work, we propose a novel NLP-driven re-ranking approach that refines the initial ranked lists produced by state-of-the-art KWS models. By leveraging semantic embeddings from pre-trained BERT-like Large Language Models (LLMs, e.g., RoBERTa, MPNet, and MiniLM), we introduce a relevance feedback mechanism that improves both verbatim and semantic keyword spotting. Our framework operates in two stages: (1) projecting retrieved word image transcriptions into a semantic space via LLMs and (2) re-ranking the retrieval list using a weighted combination of semantic and exact relevance scores based on pairwise similarities with the query. We evaluate our approach on the widely used George Washington (GW) and IAM collections using two cutting-edge segmentation-free KWS models, which are further integrated into our proposed pipeline. Our results show consistent gains in Mean Average Precision (mAP), with improvements of up to $2.3\%$ (from $94.3\%$ to $96.6\%$) on GW and $3\%$ (from $79.15\%$ to $82.12\%$) on IAM. Even when mAP gains are smaller, qualitative improvements emerge: semantically relevant but inexact matches are retrieved more frequently without compromising exact match recall. We further examine the effect of fine-tuning transformer-based OCR (TrOCR) models on historical GW data to align textual and visual features more effectively. Overall, our findings suggest that semantic feedback can enhance retrieval effectiveness in KWS pipelines, paving the way for lightweight hybrid vision-language approaches in handwritten document analysis. Full article