Labeling Technique Research Articles

Morphological analysis of neck muscle nerves and neurons in cats

Previous studies have failed to show morphological differences between neck muscle alpha and gamma motor fibers or alpha and gamma motoneurons. The present study aimed to investigate the morphological features of neck muscle motor nerves and motoneurons in cats. To determine the morphological features of peripheral motor fibers, the value of the outer contours of each fiber was converted into a perfect circle after ganglionectomy to remove sensory fibers, and the fiber diameters were calculated based on their circumferences. The sizes of neck motor fibers in the peripheral nerves had an evident bimodal distribution into small and large fiber groups, as depicted in histograms. The sizes of small and large motor fibers ranged from 2 to 12 µm and from 12 to 40 µm, respectively. The small fiber group is likely to correspond to gamma motor fibers and the large fiber group to alpha motor fibers. The morphological features of neck muscle motoneurons sectioned in the horizontal plane were examined using the horseradish peroxidase (HRP) retrograde labeling technique. The diameters of the biventer cervicis and complexus motoneurons had bimodal distributions. The inflection point between the small and large diameter population was 28 µm for the biventer cervicis and 26 µm for the complexus. We also observed that larger neurons displayed more dendrites. In conclusion, we could identify morphological differences likely to correlate with alpha and gamma motoneurons in both neck muscle peripheral nerves and neck motoneurons.

AGPs as molecular determinants of reproductive development.

Morphogenesis occurs through accurate interaction between essential players to generate highly specialized plant organs. Fruit structure and function are triggered by a neat transcriptional control involving distinct regulator genes encoding transcription factors (TFs) or signalling proteins, such as the C2H2/C2HC zinc-finger NO TRANSMITTING TRACT (NTT) or the MADS-box protein SEEDSTICK (STK), which are important in setting plant reproductive competence, feasibly by affecting cell wall polysaccharide and lipid distribution. Arabinogalactan proteins (AGPs) are major components of the cell wall and are thought to be involved in the reproductive process as important players in specific stages of development. The detection of AGPs epitopes in reproductive tissues of NTT and other fruit development-related TFs, such as MADS-box proteins including SHATTERPROOF1 (SHP1), SHP2 and STK, was the focus of this study. We used fluorescence microscopy to perform immunolocalization analyses on stk and ntt single mutants, on the ntt stk double mutant and on the stk shp1 shp2 triple mutant using specific anti-AGP monoclonal antibodies. In these mutants, the expression levels of selected AGP genes were also measured by quantitative real-time PCR and compared with the respective expression in wild-type (WT) plants. The present immunolocalization study collects information on the distribution patterns of specific AGPs in Arabidopsis female reproductive tissues, complemented by the quantification of AGP expression levels, comparing WT, stk and ntt single mutants, the ntt stk double mutant and the stk shp1 shp2 triple mutant. These findings reveal distinct AGP distribution patterns in different developmental mutants related to the female reproductive unit in Arabidopsis. The value of the immunofluorescence labelling technique is highlighted in this study as an invaluable tool to dissect the remodelling nature of the cell wall in developmental processes.

Fungi in ant-plant interactions: a key to enhancing plant nutrient-acquisition strategies.

This article is a Commentary on Gegenbauer et al. (2023), 238: 2210–2223.

Microbial carbon fixation and its influencing factors in saline lake water

Microbial carbon fixation in saline lakes constitutes an important part of the global lacustrine carbon budget. However, the microbial inorganic carbon uptake rates in saline lake water and its influencing factors are still not fully understood. Here, we studied in situ microbial carbon uptake rates under light-dependent and dark conditions in the saline water of Qinghai Lake using a carbon isotopic labeling (14C-bicarbonate) technique, followed by geochemical and microbial analyses. The results showed that the light-dependent inorganic carbon uptake rates were 135.17–293.02 μg C L−1 h−1 during the summer cruise, while dark inorganic carbon uptake rates ranged from 4.27 to 14.10 μg C L−1 h−1. Photoautotrophic prokaryotes and algae (e.g. Oxyphotobacteria, Chlorophyta, Cryptophyta and Ochrophyta) may be the major contributors to light-dependent carbon fixation processes. Microbial inorganic carbon uptake rates were mainly influenced by the level of nutrients (e.g., ammonium, dissolved inorganic carbon, dissolved organic carbon, total nitrogen), with dissolved inorganic carbon content being predominant. Environmental and microbial factors jointly regulate the total, light-dependent and dark inorganic carbon uptake rates in the studied saline lake water. In summary, microbial light-dependent and dark carbon fixation processes are active and contribute significantly to carbon sequestration in saline lake water. Therefore, more attention should be given to microbial carbon fixation and its response to climate and environmental changes of the lake carbon cycle in the context of climate change.

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Objective. Confusion is the primary epistemic emotion in the learning process, influencing students’ engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what electroencephalography (EEG) signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science. Approach. First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network) and end-to-end (Long Short Term Memory, Residual Network, and EEGNet) machine learning methods. Main results. Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states. Significance. The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.

Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm

Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.

Key mechanism of enzymatic dehairing technology for leather-making: permeation behaviors of protease into animal hide and the mechanism of charge regulation

Enzymes have been widely used as alternatives to conventional chemicals in cleaner leather processes due to their advantages of meeting increasing environmental demands. Especially, enzymatic unhairing based on protease has been applied to leather-making for a long time, however, it still has the key problem of slow permeation in the animal hide, resulting in slow unhairing rate, poor hair removal effect, excessive proteolysis of hide collagen, and decreased leather quality. Aiming at the key problem of bio-unhairing technology, fluorescent labeling technique and confocal laser scanning microscopy were used to investigate the protease permeation behaviors into the animal hide based on well-prepared labeled proteases, as well as the quantitative analysis of the protease amount in different hide layers. The results show that the protease mainly permeates the bovine hide through transfollicular routes from the hair side, and although the intercellular-interfibrillar pathway also exists, it does not play an important role. Additionally, the protease permeation behaviors into the hide are greatly impacted by the charge states of the hide and protease proteins, depending on the isoelectric points (pI) of the proteins and solution pH values. When the solution pH is not between the pI values of the two proteins, the similarly charged protease can quickly and deeply penetrate the hide because of the electrostatic repulsion. The established mechanism provides a theoretical basis for developing an efficient enzymatic unhairing technology for leather-making, and this can also be applied to other processes involving the enzyme permeation into the hide or leather.Graphical

(p,q)-biclique counting and enumeration for large sparse bipartite graphs.

In this paper, we study the problem of (, )-biclique counting and enumeration for large sparse bipartite graphs. Given a bipartite graph and two integer parameters p and q, we aim to efficiently count and enumerate all (, )-bicliques in G, where a (, )-biclique B(L,R) is a complete subgraph of G with , , , and . The problem of (, )-biclique counting and enumeration has many applications, such as graph neural network information aggregation, densest subgraph detection, and cohesive subgroup analysis. Despite the wide range of applications, to the best of our knowledge, we note that there is no efficient and scalable solution to this problem in the literature . This problem is computationally challenging, due to the worst-case exponential number of (, )-bicliques. In this paper, we propose a competitive branch-and-bound baseline method, namely BCList, which explores the search space in a depth-first manner, together with a variety of pruning techniques. Although BCList offers a useful computation framework to our problem, its worst-case time complexity is exponential to . To alleviate this, we propose an advanced approach, called BCList++. Particularly, BCList++ applies a layer-based exploring strategy to enumerate (, )-bicliques by anchoring the search on either U or V only, which has a worst-case time complexity exponential to either p or q only. Consequently, a vital task is to choose a layer with the least computation cost. To this end, we develop a cost model, which is built upon an unbiased estimator for the density of 2-hop graph induced by U or V. To improve computation efficiency, BCList++ exploits pre-allocated arrays and vertex labeling techniques such that the frequent subgraph creating operations can be substituted by array element switching operations. We conduct extensive experiments on 16 real-life datasets, and the experimental results demonstrate that BCList++ significantly outperforms the baseline methods by up to 3 orders of magnitude. We show via a case study that (, )-bicliques optimizes the efficiency of graph neural networks. In this paper, we extend our techniques to count and enumerate (, )-bicliques on uncertain bipartite graphs. An efficient method IUBCList is developed on the top of BCList++, together with a couple of pruning techniques, including common neighbor refinement and search branch early termination, to discard unpromising uncertain (, )-bicliques early. The experimental results demonstrate that IUBCList significantly outperforms the baseline method by up to 2 orders of magnitude.

Deep Learning Model for COVID-19 Sentiment Analysis on Twitter.

The COVID-19 pandemic impacted the mood of the people, and this was evident on social networks. These common user publications are a source of information to measure the population's opinion on social phenomena. In particular, the Twitter network represents a resource of great value due to the amount of information, the geographical distribution of the publications and the openness to dispose of them. This work presents a study on the feelings of the population in Mexico during one of the waves that produced the most contagion and deaths in this country. A mixed, semi-supervised approach was used, with a lexical-based data labeling technique to later bring these data to a pre-trained model of Transformers completely in Spanish. Two Spanish-language models were trained by adding to the Transformers neural network the adjustment for the sentiment analysis task specifically on COVID-19. In addition, ten other multilanguage Transformer models including the Spanish language were trained with the same data set and parameters to compare their performance. In addition, other classifiers with the same data set were used for training and testing, such as Support Vector Machines, Naive Bayes, Logistic Regression, and Decision Trees. These performances were compared with the exclusive model in Spanish based on Transformers, which had higher precision. Finally, this model was used, developed exclusively based on the Spanish language, with new data, to measure the sentiment about COVID-19 of the Twitter community in Mexico.

Evans Blue and Fluorescein Isothiocyanate-Dextran Double Labeling Reveals Precise Sequence of Vascular Leakage and Glial Responses after Exposure to Mild-Level Blast-Associated Shock Waves.

Abstract Blast-induced shock waves (BSWs) are responsible for several aspects of psychiatric disorders that are collectively termed mild traumatic brain injury (mTBI). The pathophysiology of mTBI includes vascular leakage resulting from blood-brain barrier (BBB) disruption. In this study, the precise sequence of BBB breakdown was examined using an Evans blue and fluorescein isothiocyanate (FITC)-dextran double labeling technique. Evans blue solution was injected into the tail vein of male C57BL6/J mice just before and 4 h, 1 day, 3 days, and 7 days after a single BSW exposure at as low as 25-kPa peak overpressure. In contrast, the FITC-dextran solution was transcardially injected just before perfusion fixation. Differences in the labeling time-point revealed that BBB breakdown was initiated after approximately 3 h, with significant remodeling by 1 day, and continued until 7 days after BSW exposure. BBB breakdown was upregulated in three distinct regions, namely the brain surface and subsurface areas facing the skull, regions closely associated with capillaries, and the circumventricular organ and choroid plexus. These regions showed distinct responses to BSW; moreover, clusters of reactive astrocytes were closely associated with the sites of BBB breakdown. In severe cases, these reactive astrocytes recruited activated microglia. Our findings provide important insights into the pathogenesis underlying mTBI and indicate that even mild BSW exposure affects the whole brain.

Modeling the Infrared Spectroscopy of Oligonucleotides with 13C Isotope Labels.

The carbonyl stretching modes have been widely used in linear and two-dimensional infrared (IR) spectroscopy to probe the conformation, interaction, and biological functions of nucleic acids. However, due to their universal appearance in nucleobases, the IR absorption bands of nucleic acids are often highly congested in the 1600-1800 cm-1 region. Following the fruitful applications in proteins, 13C isotope labels have been introduced to the IR measurements of oligonucleotides to reveal their site-specific structural fluctuations and hydrogen bonding conditions. In this work, we combine recently developed frequency and coupling maps to develop a theoretical strategy that models the IR spectra of oligonucleotides with 13C labels directly from molecular dynamics simulations. We apply the theoretical method to nucleoside 5'-monophosphates and DNA double helices and demonstrate how elements of the vibrational Hamiltonian determine the spectral features and their changes upon isotope labeling. Using the double helices as examples, we show that the calculated IR spectra are in good agreement with experiments and the 13C isotope labeling technique can potentially be applied to characterize the stacking configurations and secondary structures of nucleic acids.

A new deep learning-based technique for rice pest detection using remote sensing.

Agriculture plays a vital role in the country's economy and human society. Rice production is mainly focused on financial improvements as it is demanding worldwide. Protecting the rice field from pests during seedling and after production is becoming a challenging research problem. Identifying the pest at the right time is crucial so that the measures to prevent rice crops from pests can be taken by considering its stage. In this article, a new deep learning-based pest detection model is proposed. The proposed system can detect two types of rice pests (stem borer and Hispa) using an unmanned aerial vehicle (UAV). The image is captured in real time by a camera mounted on the UAV and then processed by filtering, labeling, and segmentation-based technique of color thresholding to convert the image into greyscale for extracting the region of interest. This article provides a rice pests dataset and a comparative analysis of existing pre-trained models. The proposed approach YO-CNN recommended in this study considers the results of the previous model because a smaller network was regarded to be better than a bigger one. Using additional layers has the advantage of preventing memorization, and it provides more precise results than existing techniques. The main contribution of the research is implementing a new modified deep learning model named Yolo-convolution neural network (YO-CNN) to obtain a precise output of up to 0.980 accuracies. It can be used to reduce rice wastage during production by monitoring the pests regularly. This technique can be used further for target spraying that saves applicators (fertilizer water and pesticide) and reduces the adverse effect of improper use of applicators on the environment and human beings.

RNA localization mechanisms transcend cell morphology.

RNA molecules are localized to specific subcellular regions through interactions between RNA regulatory elements and RNA binding proteins (RBPs). Generally, our knowledge of the mechanistic details behind the localization of a given RNA is restricted to a particular cell type. Here, we show that RNA/RBP interactions that regulate RNA localization in one cell type predictably regulate localization in other cell types with vastly different morphologies. To determine transcriptome-wide RNA spatial distributions across the apicobasal axis of human intestinal epithelial cells, we used our recently developed RNA proximity labeling technique, Halo-seq. We found that mRNAs encoding ribosomal proteins (RP mRNAs) were strongly localized to the basal pole of these cells. Using reporter transcripts and single-molecule RNA FISH, we found that pyrimidine-rich motifs in the 5' UTRs of RP mRNAs were sufficient to drive basal RNA localization. Interestingly, the same motifs were also sufficient to drive RNA localization to the neurites of mouse neuronal cells. In both cell types, the regulatory activity of this motif was dependent on it being in the 5' UTR of the transcript, was abolished upon perturbation of the RNA-binding protein LARP1, and was reduced upon inhibition of kinesin-1. To extend these findings, we compared subcellular RNAseq data from neuronal and epithelial cells. We found that the basal compartment of epithelial cells and the projections of neuronal cells were enriched for highly similar sets of RNAs, indicating that broadly similar mechanisms may be transporting RNAs to these morphologically distinct locations. These findings identify the first RNA element known to regulate RNA localization across the apicobasal axis of epithelial cells, establish LARP1 as an RNA localization regulator, and demonstrate that RNA localization mechanisms cut across cell morphologies.

Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching.

Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be shown in-situ, thereby enabling real-time visual comparisons and object search in real-life user tasks, such as comparing products and looking up scores in a sports game. While there have been studies on designing AR interfaces for situated information retrieval, there has only been limited research on AR object labeling for visual search tasks in the spatial environment. In this article, we identify and categorize different design aspects in AR label design and report on a formal user study on labels for out-of-view objects to support visual search tasks in AR. We design three visualization techniques for out-of-view object labeling in AR, which respectively encode the relative physical position (height-encoded), the rotational direction (angle-encoded), and the label values (value-encoded) of the objects. We further implement two traditional in-view object labeling techniques, where labels are placed either next to the respective objects (situated) or at the edge of the AR FoV (boundary). We evaluate these five different label conditions in three visual search tasks for static objects. Our study shows that out-of-view object labels are beneficial when searching for objects outside the FoV, spatial orientation, and when comparing multiple spatially sparse objects. Angle-encoded labels with directional cues of the surrounding objects have the overall best performance with the highest user satisfaction. We discuss the implications of our findings for future immersive AR interface design.

Multiscale feature cross‐layer fusion remote sensing target detection method

AbstractTarget detection based on remotely sensed images, which has recently attracted much attention, is a fundamental but challenging task. In remote sensing images, the problem of difficult recognition of small targets or targets with a large aspect ratio arises because the targets have the characteristics of small proportion, dense distribution, and multidirectionality. To address the above problems, this article proposes an improved multiscale feature cross‐layer fusion remote sensing target detector based on YOLOv5. First, this method introduces the circular smooth label technique, using YOLOv5 as a rotation detector to solve the angular boundary condition and angle prediction problem for large aspect ratio targets. Second, the explicit visual centre module is introduced to solve the problem of missed detection in target‐dense distribution tasks. Finally, a multiscale feature cross‐layer fusion structure (S‐160) is proposed based on YOLOv5, which improves the detection accuracy of each scale target by fusing shallow and deep feature information and introduces new large‐scale features for small target detection to solve the problem that ultrasmall targets in remote sensing images cannot be recognised. Our experiments were conducted on three public remote sensing datasets, DOTA, DIOR‐R, and HRSC2016, and the average accuracy (mAP) on the datasets was 76.50%, 70.34%, and 97.68%, respectively, demonstrating the substantial detection performance of the proposed method.

Labeling with 15N as Compared with Homoarginine Suggests a Lower Prececal Digestibility of Casein in Pigs , ,

Calculations of prececal protein digestibility based on the stable isotope 15N and the chemical label homoarginine were compared, using casein doubly-labeled with both markers. After food was withheld overnight 24 miniature pigs were given a meal containing 15 g/100 g casein, including 4 g/100 g doubly-labeled protein, and chromic oxide as an indigestible marker. The intestine of eight animals each was removed 3, 6 or 12 h later, divided into 3 sections of equal length, and chyme was collected. Kjeldahl-N, 15N and homoarginine were determined in diet and chyme. Digestibility of casein in the distal third of the small intestine was 93.5 +/- 0.5% and 97.6 +/- 0.3% (P < 0.05) according to 15N and homoarginine label, respectively. Potential causes for this systematic difference were assessed. The data suggest that incorporation of 15N into endogenous proteins and re-entry into the intestinal lumen via secreta and desquamations is the major cause for the 4.2 +/- 0.4% lower digestibility based on the 15N as compared with the homoarginine labeling technique. A preferential occurrence of homoarginine in more easily digestible sections of the protein, faster release during the digestive process and absorption of homoarginine, or incorporation of 15N into proteins of intestinal bacteria are less likely to cause this difference.

Cholesterol Regulation of Membrane Proteins Revealed by Two-Color Super-Resolution Imaging.

Cholesterol and phosphatidyl inositol 4,5-bisphosphate (PIP2) are hydrophobic molecules that regulate protein function in the plasma membrane of all cells. In this review, we discuss how changes in cholesterol concentration cause nanoscopic (<200 nm) movements of membrane proteins to regulate their function. Cholesterol is known to cluster many membrane proteins (often palmitoylated proteins) with long-chain saturated lipids. Although PIP2 is better known for gating ion channels, in this review, we will discuss a second independent function as a regulator of nanoscopic protein movement that opposes cholesterol clustering. The understanding of the movement of proteins between nanoscopic lipid domains emerged largely through the recent advent of super-resolution imaging and the establishment of two-color techniques to label lipids separate from proteins. We discuss the labeling techniques for imaging, their strengths and weakness, and how they are used to reveal novel mechanisms for an ion channel, transporter, and enzyme function. Among the mechanisms, we describe substrate and ligand presentation and their ability to activate enzymes, gate channels, and transporters rapidly and potently. Finally, we define cholesterol-regulated proteins (CRP) and discuss the role of PIP2 in opposing the regulation of cholesterol, as seen through super-resolution imaging.

Long range inter-chromosomal interaction of Oct4 distal enhancer loci regulates ESCs pluripotency

Nuclear architecture underlies the transcriptional programs within the cell to establish cell identity. As previously demonstrated, long-range chromatin interactions of the Oct4 distal enhancer (DE) are correlated with active transcription in naïve state embryonic stem cells. Here, we identify and characterize extreme long-range interactions of the Oct4 DE through a novel CRISPR labeling technique we developed and chromosome conformation capture to identify lethal giant larvae 2 (Llgl2) and growth factor receptor-bound protein 7 (Grb7) as putative functional interacting target genes in different chromosomes. We show that the Oct4 DE directly regulates expression of Llgl2 and Grb7 in addition to Oct4. Expression of Llgl2 and Grb7 closely correlates with the pluripotent state, where knock down of either result in loss of pluripotency, and overexpression enhances somatic cell reprogramming. We demonstrated that biologically important interactions of the Oct4 DE can occur at extreme distances that are necessary for the maintenance of the pluripotent state.

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion.

Type three secretion systems (T3SSs) enable gram-negative bacteria to inject a battery of effector proteins directly into the cytosol of eukaryotic host cells. Upon entry, the injected effector proteins cooperatively modulate eukaryotic signaling pathways and reprogram cellular functions, enabling bacterial entry and survival. Monitoring and localizing these secreted effector proteins in the context of infections provides a footprint for defining the dynamic interface of host-pathogen interactions. However, labeling and imaging bacterial proteins in host cells without disrupting their structure/function is technically challenging. Constructing fluorescent fusion proteins does not resolve this problem, because the fusion proteins jam the secretory apparatus and thus are not secreted. To overcome these obstacles, we recently employed a method for site-specific fluorescent labeling of bacterial secreted effectors, as well as other difficult-to-label proteins, using genetic code expansion (GCE). This paper provides a complete step-by-step protocol to label Salmonella secreted effectors using GCE site-specifically, followed by directions for imaging the subcellular localization of secreted proteins in HeLa cells using direct stochastic optical reconstruction microscopy (dSTORM) Recent findings suggest that the incorporation of non-canonical amino acids (ncAAs) via GCE, followed by bio-orthogonal labeling with tetrazine-containing dyes, is a viable technique for selective labeling and visualization of bacterial secreted proteins and subsequent image analysis in the host. The goal of this article is to provide a straightforward and clear protocol that can be employed by investigators interested in conducting super-resolution imaging using GCE to study various biological processes in bacteria and viruses, as well as host-pathogen interactions.

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

The presence of Deformable Linear Objects (DLOs) such as wires, cables or ropes in our everyday life is massive. However, the applicability of robotic solutions to DLOs is still marginal due to the many challenges involved in their perception. In this letter, a methodology to generate datasets from a mixture of synthetic and real samples for the training of DLOs segmentation approaches is thus presented. The method is composed of two steps. First, key-points along a real-world DLO are labeled by employing a VR tracker operated by a user. Second, synthetic and real-world datasets are mixed for the training of semantic and instance segmentation deep learning algorithms to study the benefit of real-world data in DLOs segmentation. To validate this method a user study and a parameter study are conducted. The results show that the VR tracker labeling is usable as other labeling techniques but reduces the number of clicks. Moreover, mixing real-world and synthetic DLOs data can improve the IoU score of a semantic segmentation algorithm by circa 5%. Therefore, this work demonstrates that labeling real-world data via a VR tracker can be done quickly and, if the real-world data are mixed with synthetic data, the performances of segmentation algorithms for DLOs can be improved.