Labeling Tool Research Articles

DNA Labeling Using DNA Methyltransferases.

DNA methyltransferases (MTases) uniquely combine the ability to recognize and covalently modify specific target sequences in DNA using the ubiquitous cofactor S-Adenosyl-L-methionine (AdoMet). Although DNA methylation plays important roles in biological signaling, the transferred methyl group is a poor reporter and is highly inert to further biocompatible derivatization. To unlock the biotechnological power of these enzymes, extended cofactor AdoMet analogs have been developed that enable targeted MTase-directed attachment of larger moieties containing functional or reporter groups onto DNA. As the enlarged cofactors are not always compatible with the active sites of native MTases, steric engineering of the active site has been employed to optimize their alkyltransferase activity. In addition to the described cofactor analogs, recently discovered atypical reactions of DNA cytosine-5 MTases involving non-cofactor-like compounds can also be exploited for targeted derivatization and labeling of DNA. Altogether, these approaches offer new powerful tools for sequence-specific covalent DNA labeling, leading to a variety of useful techniques in DNA research, diagnostics and nanotechnologies, and have already proven practical utility for optical DNA mapping and high-throughput epigenome studies.

Selective and Nongenetic Peroxidase Tag of Membrane Protein: a Nucleic Acid Tool for Proximity Labeling

The protein nanoenvironment on the plasma membrane is intimately linked to cellular biological functions. Elucidation of the protein nanoenvironment contributes to understanding the pathological mechanism and discovery of disease biomarkers. However, methods enabling characterization of the protein nanoenvironment in the endogenous biological environment have been rarely developed. Toward this end, we created a nucleic acid tool called Apt-Gq/h for proximity labeling to decipher the endogenous protein nanoenvironment. Here, the aptamer acts as an anchor for binding the protein of interest (POI). The G-quadruplex/hemin complex induces proximity labeling of POI via catalyzing the conversion of inert small-molecule substrates into short-lived reactive species. The labeled proteins enable the subsequent affinity-based enrichment and proteomic analysis. We first characterized Apt-Gq/h-mediated POI labeling in vitro and tested its utility by interrogating the protein nanoenvironment of POI in living cells. Taking advantage of the nongenetic, multiple reaction sites, and rapid proximity labeling, Apt-Gq/h was further utilized to imaging the cell-cell connection and amplification detection of biomarkers in living cells and tissue sections. We believe that Apt-Gq/h will be a potential tool for basic science and clinical applications.

TagLab: AI‐assisted annotation for the fast and accurate semantic segmentation of coral reef orthoimages

AbstractSemantic segmentation is a widespread image analysis task; in some applications, it requires such high accuracy that it still has to be done manually, taking a long time. Deep learning‐based approaches can significantly reduce such times, but current automated solutions may produce results below expert standards. We propose agLab, an interactive tool for the rapid labelling and analysis of orthoimages that speeds up semantic segmentation. TagLab follows a human‐centered artificial intelligence approach that, by integrating multiple degrees of automation, empowers human capabilities. We evaluated TagLab's efficiency in annotation time and accuracy through a user study based on a highly challenging task: the semantic segmentation of coral communities in marine ecology. In the assisted labelling of corals, TagLab increased the annotation speed by approximately 90% for nonexpert annotators while preserving the labelling accuracy. Furthermore, human–machine interaction has improved the accuracy of fully automatic predictions by about 7% on average and by 14% when the model generalizes poorly. Considering the experience done through the user study, TagLab has been improved, and preliminary investigations suggest a further significant reduction in annotation times.

Analysis of Chinese Consumers’ Nutrition Facts Table Use Behavior Based on Knowledge-Attitude-Practice Model

The nutrition facts table is a nutrition labeling tool designed to help consumers understand the nutritional content of food and make informed food choices. However, the utilization rate of the nutrition facts table has been low in China since its introduction in 2013. This study employed structural equation modeling to investigate consumers’ knowledge, attitudes, and practices related to the nutrition facts table by using an online survey of 930 valid samples from 31 provinces in China based on the knowledge, attitude, practice (KAP) model. The analysis revealed that most respondents had a positive attitude, but not a good level of knowledge or practice in their use of the labeling. Respondents’ knowledge about the nutrition facts table directly affected their attitude, through which it could then affect their practice, whereas knowledge played an indirect role in nutrition labeling use. The results confirmed that the KAP model is suitable for explaining consumers’ use of nutrition labels in China, and that the first step to promoting labeling usage is to enrich consumers’ knowledge on the nutrition facts table.

Handy Thioethers: Biotin-Derived Sulfimides as Protein and DNA Labeling Tools

Handy Thioethers: Biotin-Derived Sulfimides as Protein and DNA Labeling Tools

Proximity labeling: an enzymatic tool for spatial biology

In this Forum, we highlight how cutting-edge, proximity-dependent, enzymatic labeling tools, aided by sequencing technology developments, have enabled the extraction of spatial information of proteomes, transcriptomes, genome organization, and cellular networks. We also discuss the potential applications of proximity labeling in the unexplored field of spatial biology in live systems.

Rapid data annotation for sand-like granular instance segmentation using mask-RCNN

Image processing, as an efficient and accurate technology, has been widely applied to characterize granular object morphology in many fields, such as construction engineering, material science, agriculture, etc. Traditional static image processing is not autonomous because it cannot automatically segment contacting particles. In contrast, the current deep-learning-based algorithms can achieve high degree of autonomy in instance segmentation given it is well trained. However, lack of training data is a common pain point as it requires extensive manual labour using the conventional labelling tools. In this study, using sand as an example, we proposed a mask labelling methodology that can establish a large and diverse training set without manual labelling. The trained Mask-RCNN demonstrates excellent performance on a densely packed particle image. Using the data labelling method proposed in this study and the deep-learning algorithms, fully automated image processing can be realized for granular materials without massive manual labelling workload.

Synthesis of O 6-alkylated preQ1 derivatives.

A naturally occurring riboswitch can utilize 7-aminomethyl-O6-methyl-7-deazaguanine (m6preQ1) as cofactor for methyl group transfer resulting in cytosine methylation. This recently discovered riboswitch-ribozyme activity opens new avenues for the development of RNA labeling tools based on tailored O6-alkylated preQ1 derivatives. Here, we report a robust synthesis for this class of pyrrolo[2,3-d]pyrimidines starting from readily accessible N2-pivaloyl-protected 6-chloro-7-cyano-7-deazaguanine. Substitution of the 6-chloro atom with the alcoholate of interest proceeds straightforward. The transformation of the 7-cyano substituent into the required aminomethyl group turned out to be challenging and was solved by a hydration reaction sequence on a well-soluble dimethoxytritylated precursor via in situ oxime formation. The synthetic path now provides a solid foundation to access O6-alkylated 7-aminomethyl-7-deazaguanines for the development of RNA labeling tools based on the preQ1 class-I riboswitch scaffold.

Pharmaceutical Applications of Quantum Dots.

Nanotechnology has been utilized in developing novel drug formulations with minimal adverse effects. Nanoparticles in a lower size range with great surface area, increased potency, and easy permeability could be an approach for the treatment of cancer and other diseases. Unlike other nanoparticles, quantum dots have specific functional groups, have charges over their surface, and are extremely small in size (2-10nm), which makes them more permeable through tight junctions. Quantum dots are interesting materials that offer diagnosis and treatment concurrently. Quantum dots are reported to have several applications in pharmaceuticals as well as drug delivery, diagnosis, immunolabeling, and cell labeling tools. However, the existence of heavy metals in quantum dots such as cadmium poses a potential challenge for future medical applications, where quantum dots may be deliberately injected into the body. In this review, we are focusing on various pharmaceutical applications of quantum dots. Graphical Abstract.

Ways and Challenges of Product Labelling for the Visually Impaired

The conceptualizing process plays an important role in assisting designers’ creativity in form and styling development. It contributes to representing the cultural elements before product transformation, which has a limited investigation. This research aims to identify the metaphorical form element that conveys the brain impaired as factors of selection and defining form development of the humanoid robot embodiment. Design Protocol Analysis obtains to into design linguistic interpretations and synthesizing design based on perceptual product experience. Findings have outlined the theory of metaphorical form element selection and identification that could represent brain impaired product in assisting humanoid robotic acceptance among autism. Keywords: braille; visually impaired; tactile; labelling tool. eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI5.2933

CrackLabel: A Thresholding-Based Crack Labeling Tool for Asphalt Pavement Images

In an image classification system based on deep learning, a training dataset is a set of labelled images and is often composed of a large number of images. Image labelling tool is usually used to facilitate in creating the training dataset used by the classifier during the learning phase. This paper presents a new image labelling tool called CrackLabel that can automatically label the cracks in the asphalt pavement images. A specially designed image thresholding method called the Global and Lower Quartile Average Intensity (GLQAI) method is utilised. In this study, the training dataset is developed by using real pavement images that resized to 1024×768 resolution. First, crack images are automatically segmented into 768 small patches with 32×32 resolution (pixel). Then, a threshold-based method is applied to automatically segment these patches into two classes which are crack and non-crack patches. The image thresholding method based on the average of global average intensity (GAI) and lower quartile intensity (LQI), namely GLQAI is proposed for this task. Next, the labelling process is performed by assigning patches associated with the crack and background into the crack and non-crack folder, respectively. Finally, the performance of CrackLabel is benchmarked by comparing the results with the manual label crack images by human experts, and three commonly used thresholding methods; Otsu, Kapur and Kittler-Illingworth thresholding. Experimental results show that the proposed thresholding method achieved the best classification rate among various thresholding methods with 94.50%, 93.60% 94.00% and 94.05% for recall, precision, accuracy, and F-score respectively. In conclusion, it is observed that the proposed method using the newly threshold algorithm is very effective in label images into the crack and non-crack patches to maximize the training performance.

AI 기반의 용접로봇 3D 비전모듈 및 시스템

This paper proposes an AI-based welding robot 3D vision module and manipulator control system for welding automation. With conventional welding robots, welding is performed only at the designated welding points through robot teaching; however, the difficulty with this method is that the position of the welding point changes due to the tolerance (gap) of the welding object. To solve this problem, we automatically recognized the welding points using a deep learning-based object detection model called YOLOv4. The time required for labeling was thus reduced through a deep learning labeling tool that uses an existing learned weights file. To control the robot with the recognized welding point, a hand/eye calibration method calculating the homogeneous transformation matrix between the robot and the camera was adopted. Additionally, to correct the final robot control error caused due to calibration and camera depth errors, the control precision at the welding point was improved through interpolation method. The system has been verified by conducting sufficient number of experiments in a non-destructive formwork welding environment.

CONSTRAINTS TO INTERMEDIARIES ROLE IN VALUE ADDITION AND QUALITY IMPROVEMENT FOR FRESH FRUITS AND VEGETABLES SUPPLY CHAIN IN DAR ES SALAAM CITY, TANZANIA

Despite the critical role played by intermediaries in value addition and quality improvement in the context of urban fresh fruits and vegetable supply chain, this same role has not been fully realised in the context of urban fruits and vegetable supply chain in Dar es Salaam, Tanzania. This paper explores the intermediaries’ role on value addition and quality improvement in the context of urban fresh fruits and vegetable (FFVs) supply chain in Dar es Salaam city to understand impediments to value addition and quality improvement. Qualitative approach was adopted and data were collected using documentary review, in-depth interviews, and Focus Group Discussion (FGD) and non-participant observations based on the purposive sample size of 60 respondents. Thematic data analysis strategy was adopted and data were analysed using MAXQDA software. Findings showed that the intermediaries’ inability to value addition and quality improvement for urban fresh fruits and vegetables were determined by a number of factors. The inability to facilitate packaging and packing was related to inadequate knowledge and awareness of packaging facilities, limited financial capital to buy modern packaging facilities and lack of quality and value assurance regulatory authorities. Impediments to branding and labelling rotated around inadequate branding and labelling technology and tools, inadequate knowledge and skills, financial difficulties by the intermediaries. Impediments to grading and sorting emanated from lack of proper grading and sorting tools and lack of knowledge and awareness on value addition and quality improvement. Impediments to collection were caused by lack of modern harvesting and collection tools, lack of modern storage facilities as well as climate changes and variation. Lastly, impediments to inspection and arrangement revolved around poor timing for conducting inspection, lack of enough fund to buy inspection tools and poor coordination of inspection. Strategies for overcoming the impediments include introduction and enforcement of relevant laws, regulatory mechanisms, appropriate policies and regulations that would effectively govern issues of fruits and vegetable value addition and quality improvement, sensitizing intermediaries on proper knowledge and skills, provision of financial assistance to intermediaries to facilitate packing and packaging of the FFV as well as improvement of marketing and selling strategies.

An Ionic Liquid Medium Enables Development of a Phosphine-Mediated Amine-Azide Bioconjugation Method.

While a diverse set of design strategies have produced various chemical tools for biomolecule labeling in aqueous media, the development of nonaqueous, biomolecule-compatible media for bioconjugation has significantly lagged behind. In this report, we demonstrate that an aprotic ionic liquid serves as a novel reaction solvent for protein bioconjugation without noticeable loss of the biomolecule functions. The ionic liquid bioconjugation approach led to discovery of a novel triphenylphosphine-mediated amine-azide coupling reaction that forges a stable tetrazene linkage on unprotected peptides and proteins. This strategy of using untraditional media would provide untapped opportunities for expanding the scope of chemical approaches for bioconjugation.

Probing the Role of Chirality in Phospholipid Membranes.

Nucleotides, amino acids, sugars, and lipids are almost ubiquitously homochiral within individual cells on Earth. While oligonucleotides and proteins exist as one natural chirality throughout the tree of life, two stereoisomers of phospholipids have separately emerged in archaea and bacteria, an evolutionary divergence known as "the lipid divide". Within this review, we focus on the emergence of phospholipid homochirality and compare the stability of synthetic homochiral and heterochiral membranes in vitro. We discuss chemical probes designed to study the stereospecific interactions of lipid membranes in vitro. Overall, we aim to highlight studies that help elucidate the determinants of stereospecific interactions between lipids, peptides, and small molecule ligands. Continued work in understanding the drivers of favorable interactions between chiral molecules and biological membranes will lead to the design of increasingly selective chemical tools for bioorthogonal labeling of lipid membranes and safer membrane-associating pharmaceuticals.

The Role of Peptides in the Design of Electrochemical Biosensors for Clinical Diagnostics.

Peptides represent a promising class of biorecognition elements that can be coupled to electrochemical transducers. The benefits lie mainly in their stability and selectivity toward a target analyte. Furthermore, they can be synthesized rather easily and modified with specific functional groups, thus making them suitable for the development of novel architectures for biosensing platforms, as well as alternative labelling tools. Peptides have also been proposed as antibiofouling agents. Indeed, biofouling caused by the accumulation of biomolecules on electrode surfaces is one of the major issues and challenges to be addressed in the practical application of electrochemical biosensors. In this review, we summarise trends from the last three years in the design and development of electrochemical biosensors using synthetic peptides. The different roles of peptides in the design of electrochemical biosensors are described. The main procedures of selection and synthesis are discussed. Selected applications in clinical diagnostics are also described.

Aptamer C10.36 Reveals a Ribonucleoprotein Complex on the Surface of Non-Hodgkin Lymphoma Cells Providing Candidates for Multi-Target Therapeutics

Introduction: Aptamers are valuable tools for identifying novel therapeutic targets due to their high affinity and specificity. The relative ease of selection of aptamers binding to complex targets and their lack of immunogenicity have led to the incorporation of aptamers into drug transport vehicles and cell labeling tools. However, their potential as direct anti-cancer therapies remains to be explored. Recent advances in aptamer therapeutics have underscored their potential to meet the need for better therapies in B-cell non-Hodgkin lymphomas (NHL). For example, the aptamer AS1411 binds nucleolin and inhibits the proliferation of multiple leukemia and lymphoma cancer cell lines. The DNA aptamer C10.36 has been shown to selectively bind to Ramos Burkitt lymphoma cells and to be internalized via clathrin-dependent endocytosis. Here we identify the molecular target of C10.36 and explore its potential role as a targeted therapy in NHL.Methods: The protein targets of C10.36 on the surface of tumor cells were identified by LC-MS/MS following affinity purification using biotin labeled aptamers. Direct molecular targets of C10.36 were identified by aptamer-based affinity labeling of target proteins after photo-crosslinking and subsequent affinity purification. Bands were excised from polyacrylamide gels, reduced, alkylated with an Iodo-TMT126/129 label, digested, and analyzed by LC-MS/MS. Targets were confirmed by the presence of the precursor mass of the peptide with the TMT-SDAD label and both the 126 and 129 reporter ions in the MS/MS spectrum. To confirm extracellular expression of proteins on intact cells, we carried out selective biotin labeling of surface proteins followed by subsequent purification and LC-MS/MS detection. In vitro anti-proliferative activity of C10.36 was assessed by CellTiter-Glo® assay. Preferential binding and internalization of C10.36 were assessed by flow cytometry and immunofluorescence, respectively.Results: C10.36 affinity pull-downs revealed that it associates with a ribonucleoprotein complex on the surface of Ramos cells. Gene Ontology enrichment analysis revealed proteins over-represented in chromatin organization and RNA metabolic process gene sets. Covalent crosslinking identified heterogeneous nuclear ribonucleoprotein U (hnRNP U) as a direct binding target of C10.36 within the complex. We next surveyed additional lymphoma and leukemia cell lines for C10.36 binding. Interestingly, C10.36 binding to MEC-1 and SU-DHL-1 cells was readily observed, whereas binding to a normal B cell derivative, SKW6.4, was greatly reduced. Although hnRNP U was detected on the surface of all four cell lines by cell surface protein labeling, affinity purification of hnRNP U by C10.36 was only observed in the fully transformed cell lines and not in SKW6.4 cells. This observation indicates that C10.36 binding may be determined by conformation, context or post-translational modification. Treatment with C10.36 led to a loss of viability and IC50 values for sensitive cell lines ranged from 100 to 400 nM. Importantly, a single point mutation in C10.36 (G24A) abrogated both binding and anti-proliferative activity.Conclusion: The present study identifies cell surface hnRNP U and its ribonucleoprotein interaction partners as potential therapeutic targets for NHL and highlights the potential for the development of C10.36 as a novel anti-B cell lymphoma targeted therapy. DisclosuresTonapi:Caris Life Sciences: Employment. Duncan:Caris Life Sciences: Employment. Vaishali:Caris Life Sciences: Employment. Rosenow:Caris Life Sciences: Employment. Richards:Caris Life Sciences: Employment. Tinder:Caris Life Sciences: Employment. O'Neill:Caris Life Sciences: Employment. Miglarese:Caris Life Sciences: Employment. Spetzler:Caris Life Sciences: Employment. Famulok:Caris Life Sciences: Consultancy. Mayer:Caris Life Sciences: Consultancy.

Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs.

ObjectivesThis study aimed to investigate the effectiveness of deep convolutional neural network (CNN) in the diagnosis of interproximal caries lesions in digital bitewing radiographs.Methods and materialsA total of 1,000 digital bitewing radiographs were randomly selected from the database. Of these, 800 were augmented and annotated as “decay” by two experienced dentists using a labeling tool developed in Python programming language. The 800 radiographs were consisted of 11,521 approximal surfaces of which 1,847 were decayed (lesion prevalence for train data was 16.03%). A CNN model known as you only look once (YOLO) was modified and trained to detect caries lesions in bitewing radiographs. After using the other 200 radiographs to test the effectiveness of the proposed CNN model, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) were calculated.ResultsThe lesion prevalence for test data was 13.89%. The overall accuracy of the CNN model was 94.59% (94.19% for premolars, 94.97% for molars), sensitivity was 72.26% (75.51% for premolars, 68.71% for molars), specificity was 98.19% (97.43% for premolars, 98.91% for molars), PPV was 86.58% (83.61% for premolars, 90.44% for molars), and NPV was 95.64% (95.82% for premolars, 95.47% for molars). The overall AUC was measured as 87.19%.ConclusionsThe proposed CNN model showed good performance with high accuracy scores demonstrating that it could be used in the diagnosis of caries lesions in bitewing radiographs.Clinical significanceCorrect diagnosis of dental caries is essential for a correct treatment procedure. CNNs can assist dentists in diagnosing approximal caries lesions in bitewing radiographs.

Recent advances in protein modifications techniques for the targeting N‐terminal cysteine

AbstractN‐terminal modifications of proteins have garnered the attention of chemical biologists because of their critical roles in numerous cellular processes, including protein translocation, protein structural and metabolic stability, and the regulation of the half‐life of proteins by a process known as “N‐end rule pathway.” In addition, other posttranslational modification processes also depend on the N‐terminal signal sequences. Chemical probes are powerful tools that can be used to investigate N‐terminal modifications, to gain structural and functional insights into protein modification, protein‐protein interactions, protein localization, and protein dynamics. Most modifications target cysteine and lysine residues because of their high nucleophilicity. However, due to multiple occurrences of these residues in a protein at a given time, regioselective labeling can be quite difficult to accomplish. N‐terminal cysteine presents itself as a unique practical option to overcome regioselectivity and site‐specificity challenges. This review provides an overview of N‐terminal cysteine labeling tools, including N‐terminal cysteine condensation with aldehydes, cyanobenzothiazoles, cyclopropenones, and trans‐thioesterification. The review also highlights the technical challenges of each of the techniques, which need to be addressed to broaden the scope of these approaches.

Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology

In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection.