Labeling Approach Research Articles

Review of ultrasound-guided labeling: exploring its potential in teaching cadaveric ligaments during anatomical dissection courses.

This scoping review aimed to give an overview of the existing literature about ultrasound-guided labeling techniques of human cadaver ligaments and tried to work out the possibilities of integrating ultrasound into dissection courses. A literature review was carried out on the 3rd of January 2023, with relevant studies discovered in the following databases: MEDLINE, EMBASE, CENTRAL, BIOSIS Previews and Web of Science Core Collection. Grey literature was also considered. The reference lists of all relevant papers were scanned. Only ultrasound studies on human cadaver ligaments were included. The included studies' general characteristics and ultrasound-guided approaches to label the ligaments were taken from them and examined. The search found 8899 matches, but only 96 of them met the criteria. The transverse carpal ligament (15.62%) and the annular pulleys (19.79%) were the ligaments that had received the greatest research attention. Twenty-three studies are included in the methodological analysis. Both the marking substrate and the injected volume were diverse. Although 65% of the included studies achieved 100% accuracy using the ultrasound directed labeling approaches. Ultrasound-guided labeling techniques achieve a high accuracy. Therefore, this methodology could be a potential teaching tool for students during the dissection course. But caution is advised in drawing general conclusions because of the small sample sizes and different methodologies in the studies. Future larger-scale research is necessary.

A Collaborative Visual Sensing System for Precise Quality Inspection at Manufacturing Lines

Visual sensing has been widely adopted for quality inspection in production processes. This paper presents the design and implementation of a smart collaborative camera system, called BubCam , for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system which leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system which additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction, compared with the manual inspection approach.

Surface Proteome of Extracellular Vesicles and Correlation Analysis Reveal Breast Cancer Biomarkers

Breast cancer (BC) is the second most frequently diagnosed cancer and accounts for approximately 25% of new cancer cases in Canadian women. Using biomarkers as a less-invasive BC diagnostic method is currently under investigation but is not ready for practical application in clinical settings. During the last decade, extracellular vesicles (EVs) have emerged as a promising source of biomarkers because they contain cancer-derived proteins, RNAs, and metabolites. In this study, EV proteins from small EVs (sEVs) and medium EVs (mEVs) were isolated from BC MDA-MB-231 and MCF7 and non-cancerous breast epithelial MCF10A cell lines and then analyzed by two approaches: global proteomic analysis and enrichment of EV surface proteins by Sulfo-NHS-SS-Biotin labeling. From the first approach, proteomic profiling identified 2459 proteins, which were subjected to comparative analysis and correlation network analysis. Twelve potential biomarker proteins were identified based on cell line-specific expression and filtered by their predicted co-localization with known EV marker proteins, CD63, CD9, and CD81. This approach resulted in the identification of 11 proteins, four of which were further investigated by Western blot analysis. The presence of transmembrane serine protease matriptase (ST14), claudin-3 (CLDN3), and integrin alpha-7 (ITGA7) in each cell line was validated by Western blot, revealing that ST14 and CLDN3 may be further explored as potential EV biomarkers for BC. The surface labeling approach enriched proteins that were not identified using the first approach. Ten potential BC biomarkers (Glutathione S-transferase P1 (GSTP1), Elongation factor 2 (EEF2), DEAD/H box RNA helicase (DDX10), progesterone receptor (PGR), Ras-related C3 botulinum toxin substrate 2 (RAC2), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), Aconitase 2 (ACO2), UTP20 small subunit processome component (UTP20), NEDD4 binding protein 2 (N4BP2), Programmed cell death 6 (PDCD6)) were selected from surface proteins commonly identified from MDA-MB-231 and MCF7, but not identified in MCF10A EVs. In total, 846 surface proteins were identified from the second approach, of which 11 were already known as BC markers. This study supports the proposition that Evs are a rich source of known and novel biomarkers that may be used for non-invasive detection of BC. Furthermore, the presented datasets could be further explored for the identification of potential biomarkers in BC.

Mitochondria-Associated Transcriptome Profiling via Localizable Aggregation-Induced Emission Photosensitizers in Live Cells.

In recent decades, there has been increasing interest in studying mitochondria through transcriptomic research. Various exogenous fusion protein-based proximity labeling methods have been reported that focus on the site of one particular protein/peptide and might also influence the corresponding localization or interactome. To enable unbiased and high spatial-resolution profiling of mitochondria-associated transcriptomes in live cells, a flexible RNA proximity labeling approach was developed using aggregation-induced emission (AIE) type photosensitizers (PSs) that possess great mitochondria-targeting capabilities. Their accumulation in an enclosed mitochondrial environment tends to enhance the fluorescence emission and reactive oxygen species generation. By comparing the in vitro optical properties, photosensitization processes, as well as the in cellulo mitochondrial specificity and RNA labeling performance of four AIE PSs, high-throughput sequencing analysis was conducted using TFPy-mediated RNA proximity labeling in live HeLa cells. This approach successfully captured a comprehensive list of transcripts, including mitochondria-encoded RNAs, as well as some nuclear-derived RNAs located at the outer mitochondrial membrane and interacting organelles. This small molecule-based proximity labeling method bypasses complex genetic manipulation and transfection steps, making it readily applicable for diverse research purposes.

Intracellular RNA Labeling Technologies for the Analysis of RNA Biology†

Comprehensive SummaryAs a cornerstone of the central dogma of molecular biology, RNA plays vital roles in living organisms. Over the past few decades, many RNA labeling technologies have been developed to elucidate the biological function of RNA. These technologies have significantly advanced our understanding of RNA secondary structure, localization, and turnover. Additionally, taking advantage of these innovative RNA labeling approaches, plenty of tool kits have been devised for the regulation of RNA‐related biological process, such as gene expression and gene editing. In this review, we primarily focus on an array of intracellular RNA labeling methods, encompassing chemical probes‐based labeling, metabolic labeling, and proximity‐dependent labeling. We also provide a brief overview of their applications in the research of RNA biology. Finally, the perspectives of RNA labeling are also discussed.Key Scientists4SU was first used in1978 as a safer way to directly examine RNA synthesis. However, the enrichment of 4SU‐labeled RNA seriously relied on mercurated cellulose columns, which are toxic. In 2005, Boothroyd et al. introduced a novel covalent‐modification method of 4SU based on thiol exchange reaction. Then, Salic et al. applied 5EU, which is clickable, for RNA metabolic labeling in animals. Later, Spitale et al. designed 5EC and 2’AzU, which enabled cell specific RNA metabolic labeling under the help of specific enzyme. In 2013, directly chemical RNA labeling in living cell for RNA structure mapping was realized by Chang and Kool et al. using SHAPE reagent, which was further modified with an azide handle in 2015. Weissman, Bevilacqua and Assmann et al. introduced DMS probe that targets adenine and cytosine of RNA for RNA structure mapping in 2014. Then, Chang et al. exploited psoralen for RNA duplex mapping in 2016. Later, He, Zhou and Zhang et al. described another clickable probe N3‐kethoxal targeting guanine for RNA secondary structure mapping. Besides, Chang and Ting et al. described a proximity‐dependent RNA labeling method based on APEX in 2019, revealing subcellular RNA localization. Meanwhile, Zou and Wang et al. achieved proximity‐dependent RNA labeling using miniSOG oxidase, which provided an alternative method to investigate the spatial transcriptome. Spitale et al. utilizing photosensitizer dibromofluorescein (DBF) to realize proximity‐dependent RNA labeling and sequencing.

OS-FCM: A semi-supervised clustering approach to investigating consumers’ usage patterns of contactless shopping-delivery (S-D) channel

Investigating the factors that contribute to consumers’ usage patterns of contactless shopping-delivery (S-D) channel is important for e-commerce retailers and logistics operators. However, in the data as collected by a questionnaire survey, distinct usage patterns are not always easily observed due to the existence of outliers, which places a challenge for the investigation. Hence, this study develops a labelling approach, named OS-FCM, to determine the usage patterns. As a semi-supervised clustering approach, OS-FCM integrates One-Class Support Vector Machine and Fuzzy C-Means, which enables it to make a trade-off between subjective judgement and statistical evidence when identifying patterns and to effectively recognize the patterns of outliers. The dataset with the usage patterns is used to train Random Forest, upon which Shapley Additive exPlanations (SHAP) approach is applied to measure the contributions of each factor. The data as collected by a questionnaire survey in Singapore are used for a case study. By using OS-FCM, the participants are clustered into five patterns with each pattern involving three variables, namely, current usage frequency, current behavioral habit, and future continuance intention. The contributions of each potential factor to the patterns are measured by SHAP values. In the case study, two regular patterns and three outlier patterns are identified. It is found that consumers’ past behavior stands out to be the most important factor to the pattern formation from an aggregated perspective. Factors such as fear appeal and behavior maintenance/change motivations exert differentiated impacts on the formation of the five patterns. Besides, as compared to commonly used clustering methods, OS-FCM has better clustering performance and the patterns determined by OS-FCM are more reasonable and rational, which provides a solid basis for better understanding the usage patterns.

Site-Specific Anchoring a Luminescent Tag in a Protein with Non-Emissive Iridium(III) Complex.

Site-specific modification of proteins with synthetic fluorescent tag effectively improves the resolution of imaging, and such a labeling method with negligible three-dimensional structural perturbations and minimal impact on the biological functions of proteins is of high interest to dissect the high-resolution activities of biomolecules in complex systems. To this end, several non-emissive iridium(III) complexes [Ir(C-N)2 (H2 O)2 ]+ OTF- (C-N denotes various cyclometalated ligands) were designed and synthesized. These complexes were tested for attaching a protein by coordinating to H/X (HisMet, HisHis, and HisCys) that are separated by i and i+4 in α-helix. Replacement of the two labile water ligands in the iridium(III) complex by a protein HisHis pair increases the luminescent intensity up to over 100 folds. This labeling approach has been demonstrated in a highly specific and efficient manner in a number of proteins, and it is also feasible for labeling target proteins in cell lysates.

Bioorthogonal click labeling of an amber-free HIV-1 provirus for in-virus single molecule imaging

Structural dynamics of human immunodeficiency virus 1 (HIV-1) envelope (Env) glycoprotein mediate cell entry and facilitate immune evasion. Single-molecule FRET using peptides for Env labeling revealed structural dynamics of Env, but peptide use risks potential effects on structural integrity/dynamics. While incorporating noncanonical amino acids (ncAAs) into Env by amber stop-codon suppression, followed by click chemistry, offers a minimally invasive approach, this has proved to be technically challenging for HIV-1. Here, we develope an intact amber-free HIV-1 system that overcomes hurdles of preexisting viral amber codons. We achieved dual-ncAA incorporation into Env on amber-free virions, enabling single-molecule Förster resonance energy transfer (smFRET) studies of click-labeled Env that validated the previous peptide-based labeling approaches by confirming the intrinsic propensity of Env to dynamically sample multiple conformational states. Amber-free click-labeled Env also enabled real-time tracking of single virion internalization and trafficking in cells. Our system thus permits in-virus bioorthogonal labeling of proteins, compatible with studies of virus entry, trafficking, and egress from cells.

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment.

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields-isotope, isotopolog, isotopolog label, labeling approach, and gradient position-and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Fluorescent, phosphorescent, magnetic resonance contrast and radioactive tracer labelling of extracellular vesicles.

This review focusses on the significance of fluorescent, phosphorescent labelling and tracking of extracellular vesicles (EVs) for unravelling their biology, pathophysiology, and potential diagnostic and therapeutic uses. Various labeling strategies, such as lipid membrane, surface protein, luminal, nucleic acid, radionuclide, quantum dot labels, and metal complex-based stains, are evaluated for visualizing and characterizing EVs. Direct labelling with fluorescent lipophilic dyes is simple but generally lacks specificity, while surface protein labelling offers selectivity but may affect EV-cell interactions. Luminal and nucleic acid labelling strategies have their own advantages and challenges. Each labelling approach has strengths and weaknesses, which require a suitable probe and technique based on research goals, but new tetranuclear polypyridylruthenium(II) complexes as phosphorescent probes have strong phosphorescence, selective staining, and stability. Future research should prioritize the design of novel fluorescent probes and labelling platforms that can significantly enhance the efficiency, accuracy, and specificity of EV labeling, while preserving their composition and functionality. It is crucial to reduce false positive signals and explore the potential of multimodal imaging techniques to gain comprehensive insights into EVs.

To click or not to click for short pulse-labeling of the bacterial cell wall.

A method of choice to study the spatio-temporal dynamics of bacterial cell growth and division is to analyze the localization of cell wall synthesis regions by fluorescence microscopy. For this, nascent cell wall biopolymers need to be labeled with fluorescent reporters, like fluorescent d-alanines (FDAs) that can be incorporated into the peptidoglycan. To achieve high spatial and temporal resolution, dense, high-intensity fluorescence labeling must be obtained in the shortest possible time. However, modifications carried by d-Ala can hinder their uptake by the enzymes that incorporate them into the peptidoglycan, such as the d,d-transpeptidases. Conversely, these modifications can impede the elimination of the incorporated d-Ala derivatives by d,d-carboxypeptidases, making the labeling more persistent. In this context, we synthesized clickable d-Alas and tested their incorporation into the peptidoglycan using different labeling approaches, prior or after their conjugation to clickable fluorescent dyes through SPAAC reaction. Our data allow ranking of the d-Ala derivatives in terms of their ease of incorporation and resistance to trimming during one-step, "one-pot" two-step or sequential two-step labeling strategies. We further show that a hybrid "one-step" approach, in which a FDA is used in combination with clickable choline and fluorescent dye, enables two-color co-labeling of peptidoglycan and teichoic acids. Finally, we identify a strategy compatible with the cell fixation required for super-resolution microscopy, by combining one-step labeling with FDA and sequential two-step labeling with clickable choline and fluorescent dye, allowing to obtain two-color high-resolution images of peptidoglycan and teichoic acid synthesis regions.

Simple generation of cleavable labels for multiplexed imaging

We present a simple and inexpensive approach for the amine-targeted labeling of biomolecules with fluorophores so that the linker between them can be easily cleaved by a mild reducing agent....

Identification of Neighboring Proteins of Endosomal Regulators by Using TurboID-Based Proximity Labeling.

In the endomembrane system, multivesicular bodies (MVBs) play a crucial role in sorting ubiquitinated membrane proteins into intraluminal vesicles for degradation upon fusion with vacuoles or lysosomes. This process involves regulations by multiprotein complexes, including endosomal sorting complex required for transport (ESCRT) I-III, and accessory proteins. Although many organellar proteomes have been identified in plant cells, the information of specific proteomes associated with regulators engaged in MVB biogenesis remains limited. Here, using the ESCRT component FREE1 as an example, we describe a method to identify neighboring proteins of endosomal regulators by using an approach of TurboID-based proximity labeling.

A Tip-Based Workflow for Sensitive IMAC-Based Low Nanogram Level Phosphoproteomics.

Analyzing the phosphoproteome at nanoscale poses a significant challenge, mainly due to the substantial sample loss from nonspecific surface adsorption during the enrichment of low stoichiometric phosphopeptides. Here, we describe a tandem tip-based phosphoproteomics sample preparation method capable of sequential sample cleanup and enrichment without the need for additional sample transfer, thereby minimizing sample loss. Integration of this method to our recently developed SOP (surfactant-assisted one-pot sample preparation) and iBASIL (improved boosting to amplify signal with isobaric labeling) approaches creates a streamlined workflow, enabling sensitive, high-throughput nanoscale phosphoproteomics measurements.

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Reaction rate constant: a theoretical description from local temperature.

Application of various descriptors based on electron density and its associated quantities to quantify chemical reactivity within the conceptual density functional theory has recently come into spotlight. Among others and particularly relevant to our study, local temperature based on electron density as well as kinetic energy density, as a measure of the kinetic energy of an electron moving in the Kohn-Sham potential of systems, should be mentioned. In this work, we propose to use the local temperature for describing the reaction rate constant, where our main idea originates from the point that the smaller the local temperature at the reaction center, the easier the electron removal, leading to a larger rate constant. On the basis of theoretical considerations, it is proved that the rate constant variations caused by the substituent effects can well be proportional to the local temperature at the reaction center. In order to numerically validate our proposed approach, we have taken the phenol derivatives with the available experimental rate constants of their O-methylation reaction as working models. The reason for this choice is that one of the most versatile approaches for labeling biologically active compounds with the 11C nuclide for positron emission tomography (PET) is methylation by methyl iodide including 11C nuclide, [11C]MeI, where methylation of phenolic oxygen with [11C]MeI is utilized to label some important tracers for PET studies. Our results unveil that the local temperature changes at the reaction center of the aforementioned reaction are reasonably correlated with the rate constant variations. Hopefully, incorporating the proposed correlations between the local temperature and the kinetics data into a computer control algorithm not only provides a simple tool for predicting the rate constant of the O-methylation reaction for other substituted phenols, but also, as a part of the chemical artificial intelligence, the optimum [11C]MeI labeling conditions for a wide variety of phenol derivatives can be controlled.

Self-Supervised Autoregressive Domain Adaptation for Time Series Data.

Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series data due to the following reasons. First, they mainly rely on the large-scale dataset (i.e., ImageNet) for source pretraining, which is not applicable for time series data. Second, they ignore the temporal dimension on the feature space of the source and target domains during the domain alignment step. Finally, most of the prior UDA methods can only align the global features without considering the fine-grained class distribution of the target domain. To address these limitations, we propose a SeLf-supervised AutoRegressive Domain Adaptation (SLARDA) framework. In particular, we first design a self-supervised (SL) learning module that uses forecasting as an auxiliary task to improve the transferability of source features. Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependence of both source and target features during domain alignment. Finally, we develop an ensemble teacher model to align class-wise distribution in the target domain via a confident pseudo labeling approach. Extensive experiments have been conducted on three real-world time series applications with 30 cross-domain scenarios. The results demonstrate that our proposed SLARDA method significantly outperforms the state-of-the-art approaches for time series domain adaptation. Our source code is available at: https://github.com/mohamedr002/SLARDA.

Active Gaze Labeling: Visualization for Trust Building.

Areas of interest (AOIs) are well-established means of providing semantic information for visualizing, analyzing, and classifying gaze data. However, the usual manual annotation of AOIs is time-consuming and further impaired by ambiguities in label assignments. To address these issues, we present an interactive labeling approach that combines visualization, machine learning, and user-centered explainable annotation. Our system provides uncertainty-aware visualization to build trust in classification with an increasing number of annotated examples. It combines specifically designed EyeFlower glyphs, dimensionality reduction, and selection and exploration techniques in an integrated workflow. The approach is versatile and hardware-agnostic, supporting video stimuli from stationary and unconstrained mobile eye tracking alike. We conducted an expert review to assess labeling strategies and trust building.

Identification of Proteomic Biomarkers of Acetaminophen-Induced Hepatotoxicity Using Stable Isotope Labeling.

Quantitative proteomics approaches based on stable isotopic labeling and mass spectrometry have been widely applied to disease research, drug target discovery, biomarker identification, and systems biology. One of the notable stable isotopic labeling approaches is trypsin-catalyzed 18O/16O labeling, which has its own advantages of low sample consumption, simple labeling procedure, cost-effectiveness, and absence of chemical reactions that potentially generate by-products. In this chapter, a protocol for 18O/16O labeling-based quantitative proteomics approach is described with an application to the identification of proteomic biomarkers of acetaminophen (APAP)-induced hepatotoxicity in rats. The protocol involves first the extraction of proteins from liver tissues of control and APAP-treated rats and digestion into peptides by trypsin. After cleaning of the peptides by solid-phase extraction, equal amounts of peptides from the APAP treatment and the control groups are then subject to trypsin-catalyzed 18O/16O labeling. The labeled peptides are combined and fractionated by off-line strong cation exchange liquid chromatography (SCXLC), and each fraction is then analyzed by nanoflow reversed-phase LC coupled online with tandem mass spectrometry (RPLC-MS/MS) for identification and quantification of differential protein expression between APAP-treated rats and controls. The protocol is applicable to quantitative proteomic analysis for a variety of biological samples.

Fine-Tuned Convolutional Neural Networks for Feature Extraction and Classification of Scanned Document Images using Semi-Automatic Labelling Approach

Fine-Tuned Convolutional Neural Networks for Feature Extraction and Classification of Scanned Document Images using Semi-Automatic Labelling Approach