Search Results (28)

The immunofluorescence assay is widely used for cellular biology and diagnosis applications. Such an antigen–antibody detection system enables the assessment and visualization of the expression and localization of target proteins. In the classical indirect immunofluorescence assay, secondary antibodies are conjugated to fluorophores. However, conventional secondary antibodies have limited applications due to their large size (150 kDa). Moreover, as animal-derived products, secondary antibodies are associated with ethical concerns and batch-to-batch variability. In this study, we developed fluorescence-labeled recombinant nanobodies as secondary antibodies by utilizing previously established anti–mouse and anti–rabbit IgG secondary nanobodies in combination with the self-labeling SNAP-tag. Nanobodies, which are significantly smaller (15 kDa), are capable to detect primary antibodies produced in mice and rabbits. The SNAP-tag (20 kDa) enables site-specific binding of various O⁶-benzylguanine (BG)-modified fluorophores to the recombinant nanobodies. These recombinant nanobodies were produced using mammalian cell expression system, and their specific binding to mouse or rabbit antibodies was validated using flow cytometry and multi-color fluorescence microscopy. The low cost, easy of expression, purification and site-specific conjugation procedures for these anti–mouse and anti–rabbit IgG secondary nanobodies make them an attractive alternative to traditional secondary antibodies for indirect immunofluorescence assays. Full article

In order to identify carcinoembryonic antigen (CEA) in serum samples, an innovative smartphone-based, label-free electrochemical immunosensor was created without the need for additional labels or markers. This technology presents a viable method for on-site cancer diagnostics. The novel smartphone-integrated, label-free immunosensing platform was constructed by nanostructured materials that utilize the layer-by-layer (LBL) assembly technique, allowing for meticulous control over the interface. Detection relies on direct interactions without extra tagging agents, where ordered graphene oxide (GO), carbon nanotubes (CNTs), and copper oxide nanoparticles (CuONPs) were sequentially deposited onto a screen-printed carbon electrode (SPCE), designated as CuONPs/CNTs/GO/SPCE. This significantly amplifies the electrochemical signal, allowing for the detection of low concentrations of target molecules of CEA. The LBL approach enables the precise construction of multi-layered structures on the sensor surface, enhancing their activity and optimizing the electrochemical performance for CEA detection. These nanostructured materials serve as efficient carriers to significantly increase the surface area, conductivity, and structural support for antibody loading, thus improving the sensitivity of detection. The detection of carcinoembryonic antigen (CEA) in this electrochemical immunosensing transducer is based on a decrease in the current response of the [Fe(CN)₆]^3−/4− redox probes, which occurs in proportion to the amount of the immunocomplex formed on the sensor surface. Under the optimized conditions, the immunosensor exhibited good detection of CEA with a linear range of 0.1–5.0 ng mL⁻¹ and a low detection limit of 0.08 ng mL⁻¹. This label-free detection approach, based on signal suppression due to immunocomplex formation, is highly sensitive and efficient for measuring CEA levels in serum samples, with higher recovery ranges of 101% to 112%, enabling early cancer diagnosis. The immunosensor was successfully applied to determine CEA in serum samples. This immunosensor has several advantages, including simple fabrication, portability, rapid analysis, high selectivity and sensitivity, and good reproducibility with long-term stability over 21 days. Therefore, it has the potential for point-of-care diagnosis of lung cancer. Full article

Identifying vulnerabilities in Smart Contracts (SCs) is crucial, as they can lead to significant financial losses if exploited. Although various SC vulnerability identification methods exist, selecting the most effective approach remains challenging. This article examines these challenges and introduces solutions to enhance SC vulnerability identification. It introduces MultiTagging, a modular SC multi-labeling framework designed to overcome limitations in existing SC vulnerability identification approaches. MultiTagging automates SC vulnerability tagging by parsing analysis reports and mapping tool-specific tags to standardized labels, including SC Weakness Classification (SWC) codes and Decentralized Application Security Project (DASP) ranks. Its mapping strategy and the proposed vulnerability taxonomy resolve tool-level labeling inconsistencies, where different tools use distinct labels for identical vulnerabilities. The framework integrates an evaluation module to assess SC vulnerability identification methods. MultiTagging enables both tool-based and vote-based SC vulnerability labeling. To improve labeling accuracy, the article proposes Power-based voting, a method that systematically defines voter roles and voting thresholds for each vulnerability. MultiTagging is used to evaluate labeling across six tools: MAIAN, Mythril, Semgrep, Slither, Solhint, and VeriSmart. The results reveal high coverage for Mythril, Slither, and Solhint, which identified eight, seven, and six DASP classes, respectively. Tool performance varied, underscoring the impracticality of relying on a single tool to identify all vulnerability classes. A comparative evaluation of Power-based voting and two threshold-based methods—AtLeastOne and Majority voting—shows that while voting methods can increase vulnerability identification coverage, they may also reduce detection performance. Power-based voting proved more effective than pure threshold-based methods across all vulnerability classes. Full article

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study. Full article

Traditional approaches to modular construction progress monitoring and quality control with stringent and tight tolerances for on-site and off-site assembly processes are usually based on 3D laser scanning, but the high equipment costs associated with acquiring point clouds have economic ramifications. This paper provides the details of a new and inexpensive method through the integration of AprilTags and an ordinary phone. By using AprilTags instead of QR codes to label modules, progress management is achieved through the rapid identification and association of multiple modules based on a single image. Moreover, a virtual multi-view vision algorithm based on AprilTags is proposed to generate 3D reverse models of the construction site; the quality result can be acquired by comparing the offset and rotation values of the reverse model and the BIM model. Finally, all the algorithms are validated through comparing the reverse models with the reference models made with 3D printing and 3D laser scanning, which verifies the accuracy and efficiency of the proposed method. Full article

In the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgrounds of annotators and the significant cost associated with procuring extensive labeled datasets. To address these challenges, this paper introduces a novel multi-label classification method called updating Correlation-enhanced Feature Learning (uCeFL), which extracts label correlations directly from the data instances, circumventing the dependency on potentially incomplete label sets. uCeFL initially computes a revised label matrix by multiplying the incomplete label matrix with the label correlations extracted from the data matrix. This revised matrix is then utilized to enrich the original data features, enabling a neural network to learn correlation-enhanced representations that capture intricate relationships between data features, labels, and their interactions. Notably, label correlations are not static; they are dynamically updated during the neural network’s training process. Extensive experiments carried out on various datasets emphasize the effectiveness of the proposed approach. By leveraging label correlations within data instances, along with the hierarchical learning capabilities of neural networks, it offers a significant improvement in multi-label classification, even in scenarios with incomplete labels. Full article

The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods could achieve superior performance in the segmentation task of the DeepFish dataset. We also expanded the labeling of specific fish species classification tags on the basis of the original semantic segmentation dataset of DeepFish and completed the multi-class instance segmentation task of fish based on the newly labeled tags. Based on the above two achievements, we propose a general and flexible self-iterative fish identification and segmentation standard workflow that can effectively improve the efficiency of fish surveys. Full article

Diapause, an adaptative strategy for survival under harsh conditions, is a dynamic multi-stage process. Bombus terrestris, an important agricultural pollinator, is declining in the wild, but artificial breeding is possible by imitating natural conditions. Mated queen bees enter reproductive diapause in winter and recover in spring, but the regulatory mechanisms remain unclear. Herein, we conducted a comparative 4D label-free proteomic analysis of queen bees during artificial breeding at seven timepoints, including pre-diapause, diapause, and post-diapause stages. Through bioinformatics analysis of proteomic and detection of substance content changes, our results found that, during pre-diapause stages, queen bees had active mitochondria with high levels of oxidative phosphorylation, high body weight, and glycogen and TAG content, all of which support energy consumption during subsequent diapause. During diapause stages, body weight and water content were decreased but glycerol increased, contributing to cold resistance. Dopamine content, immune defense, and protein phosphorylation were elevated, while fat metabolism, protein export, cell communication, signal transduction, and hydrolase activity decreased. Following diapause termination, JH titer, water, fatty acid, and pyruvate levels increased, catabolism, synaptic transmission, and insulin signaling were stimulated, ribosome and cell cycle proteins were upregulated, and cell proliferation was accelerated. Meanwhile, TAG and glycogen content decreased, and ovaries gradually developed. These findings illuminate changes occurring in queen bees at different diapause stages during commercial production. Full article

The advancement of stem cell therapy has offered transformative therapeutic outcomes for a wide array of diseases over the past decades. Consequently, stem cell tracking has become significant in revealing the mechanisms of action and ensuring safe and effective treatments. Fluorescence stands out as a promising choice for stem cell tracking due to its myriad advantages, including high resolution, real-time monitoring, and multi-fluorescence detection. Furthermore, combining fluorescence with other tracking modalities—such as bioluminescence imaging (BLI), positron emission tomography (PET), photoacoustic (PA), computed tomography (CT), and magnetic resonance (MR)—can address the limitations of single fluorescence detection. This review initially introduces stem cell tracking using fluorescence imaging, detailing various labeling strategies such as green fluorescence protein (GFP) tagging, fluorescence dye labeling, and nanoparticle uptake. Subsequently, we present several combinations of strategies for efficient and precise detection. Full article

Facing fast-increasing electronic documents in the Digital Media Age, the need to extract textual features of online texts for better communication is growing. Sentiment classification might be the key method to catch emotions of online communication, and developing corpora with annotation of emotions is the first step to achieving sentiment classification. However, the labour-intensive and costly manual annotation has resulted in the lack of corpora for emotional words. Furthermore, single-label semantic corpora could hardly meet the requirement of modern analysis of complicated user’s emotions, but tagging emotional words with multiple labels is even more difficult than usual. Improvement of the methods of automatic emotion tagging with multiple emotion labels to construct new semantic corpora is urgently needed. Taking Twitter short texts as the case, this study proposes a new semi-automatic method to annotate Internet short texts with multiple labels and form a multi-labelled corpus for further algorithm training. Each sentence is tagged with both the emotional tendency and polarity, and each tweet, which generally contains several sentences, is tagged with the first two major emotional tendencies. The semi-automatic multi-labelled annotation is achieved through the process of selecting the base corpus and emotional tags, data preprocessing, automatic annotation through word matching and weight calculation, and manual correction in case of multiple emotional tendencies are found. The experiments on the Sentiment140 published Twitter corpus demonstrate the effectiveness of the proposed approach and show consistency between the results of semi-automatic annotation and manual annotation. By applying this method, this study summarises the annotation specification and constructs a multi-labelled emotion corpus with 6500 tweets for further algorithm training. Full article

Somatic heterozygous mutations in the active site of the enhancer of zeste homolog 2 (EZH2) are prevalent in diffuse large B-cell lymphoma (DLBCL) and acute myeloid leukemia (AML). The methyltransferase activity of EZH2 towards lysine 27 on histone H3 (H3K27) and non-histone proteins is dysregulated by the presence of gain-of-function (GOF) and loss-of-function (LOF) mutations altering chromatin compaction, protein complex recruitment, and transcriptional regulation. In this study, a comprehensive multi-omics approach was carried out to characterize the effects of differential H3K27me3 deposition driven by EZH2 mutations. Three stable isogenic mutants (EZH2^Y641F, EZH2^A677G, and EZH2H^689A/F667I) were examined using EpiProfile, H3K27me3 CUT&Tag, ATAC-Seq, transcriptomics, label-free proteomics, and untargeted metabolomics. A discrete set of genes and downstream targets were identified for the EZH2 GOF and LOF mutants that impacted pathways involved in cellular proliferation, differentiation, and migration. Disruption of protein networks and metabolic signatures able to sustain aberrant cell behavior was observed in response to EZH2 mutations. This systems biology-based analysis sheds light on EZH2-mediated cell transformative processes, from the epigenetic to the phenotypic level. These studies provide novel insights into aberrant EZH2 function along with targets that can be explored for improved diagnostics/treatment in hematologic malignancies with mutated EZH2. Full article

Massive and high-quality in situ data are essential for Earth-observation-based agricultural monitoring. However, field surveying requires considerable organizational effort and money. Using computer vision to recognize crop types on geo-tagged photos could be a game changer allowing for the provision of timely and accurate crop-specific information. This study presents the first use of the largest multi-year set of labelled close-up in situ photos systematically collected across the European Union from the Land Use Cover Area frame Survey (LUCAS). Benefiting from this unique in situ dataset, this study aims to benchmark and test computer vision models to recognize major crops on close-up photos statistically distributed spatially and through time between 2006 and 2018 in a practical agricultural policy relevant context. The methodology makes use of crop calendars from various sources to ascertain the mature stage of the crop, of an extensive paradigm for the hyper-parameterization of MobileNet from random parameter initialization, and of various techniques from information theory in order to carry out more accurate post-processing filtering on results. The work has produced a dataset of 169,460 images of mature crops for the 12 classes, out of which 15,876 were manually selected as representing a clean sample without any foreign objects or unfavorable conditions. The best-performing model achieved a macro F1 (M-F1) of 0.75 on an imbalanced test dataset of 8642 photos. Using metrics from information theory, namely the equivalence reference probability, resulted in an increase of 6%. The most unfavorable conditions for taking such images, across all crop classes, were found to be too early or late in the season. The proposed methodology shows the possibility of using minimal auxiliary data outside the images themselves in order to achieve an M-F1 of 0.82 for labelling between 12 major European crops. Full article

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field. Full article

Music plays a crucial role in our lives, with growing consumption and engagement through streaming services and social media platforms. However, caution is needed for children, who may be exposed to explicit content through songs. Initiatives such as the Parental Advisory Label (PAL) and similar labelling from streaming content providers aim to protect children from harmful content. However, so far, the labelling has been limited to tagging the song as explicit (if so), without providing any additional information on the reasons for the explicitness (e.g., strong language, sexual reference). This paper addresses this issue by developing a system capable of detecting explicit song lyrics and assessing the kind of explicit content detected. The novel contributions of the work include (i) a new dataset of 4000 song lyrics annotated with five possible reasons for content explicitness and (ii) experiments with machine learning classifiers to predict explicitness and the reasons for it. The results demonstrated the feasibility of automatically detecting explicit content and the reasons for explicitness in song lyrics. This work is the first to address explicitness at this level of detail and provides a valuable contribution to the music industry, helping to protect children from exposure to inappropriate content. Full article

Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively. Full article