Secondary Labeling Research Articles

Copper Deposited on Reduced Titania as Catalyst for the Production of CH4 from Sunlight and Air

AbstractAtmospheric CO2 and H2O adsorbed on the photocatalyst surface undergo sunlight‐assisted conversion to solar products that bridge the gaps between artificial and natural photosynthesis. Herein, we report a Lewis acid‐base interaction derived photocatalyst, Cu deposited on reduced titania, that harvests CO2 and H2O from the air and transforms them to CH4. Photocatalyst surface studies confirm that coordinately unsaturated Cu atoms and oxygen vacancies are formed that facilitate CO2 and H2O adsorption. The mechanistic studies, combined with tandem secondary ion mass spectroscopy and isotopic labelling, confirm the CH4 origin from atmosphere‐adsorbed CO2 and H2O. The contributing factors to photocatalyst instability are explored. We expect that this study will have an impact on the widespread application and economic viability of photocatalytic CO2 reduction.

Multiple distresses detection for Asphalt Pavement using improved you Only Look Once Algorithm based on convolutional neural network

ABSTRACT Leveraging the YOLOv7 object detection framework, this study introduces YOLOv7-CSP, a refined algorithm tailored for identifying asphalt pavement distress with enhanced precision. Utilizing advanced image processing for dataset preprocessing, including data augmentation and denoising, YOLOv7-CSP integrates the CSPNeXt structure and CA attention mechanism for improved detection accuracy and efficiency. The algorithm optimizes anchor box selection through Kmeans clustering and employs a secondary labeling method to enhance learning efficiency and dataset quality. Comparative tests reveal YOLOv7-CSP's superior performance, with significant improvements in mAP, F1 score, GFLOPS, and FPS metrics, demonstrating its effectiveness in detecting various pavement distresses. This innovative approach marks a significant advancement in automatic pavement distress recognition, offering a robust solution for highway maintenance decision-making.

Gas Phase Transformations in Carbon-11 Chemistry.

The short-lived positron-emitter carbon-11 (t1/2 = 20.4 min; β+, 99.8%) is prominent for labeling tracers for use in biomedical research with positron emission tomography (PET). Carbon-11 is produced for this purpose with a cyclotron, nowadays almost exclusively by the 14N(p,α)11C nuclear reaction, either on nitrogen containing a low concentration of oxygen (0.1-0.5%) or hydrogen (~5%) to produce [11C]carbon dioxide or [11C]methane, respectively. These primary radioactive products can be produced in high yields and with high molar activities. However, only [11C]carbon dioxide has some utility for directly labeling PET tracers. Primary products are required to be converted rapidly and efficiently into secondary labeling synthons to provide versatile radiochemistry for labeling diverse tracer chemotypes at molecular positions of choice. This review surveys known gas phase transformations of carbon-11 and summarizes the important roles that many of these transformations now play for producing a broad range of labeling synthons in carbon-11 chemistry.

Economic valuation of the proper management of municipal solid waste in an educational space

The correct management of Municipal Solid Waste (MSW) in any population center is essential to guarantee the quality of life of its inhabitants and the surrounding environment. The study carried out involved the quantification of MSW generated in specific areas of the Juan Pablo II campus of the University of Guanajuato, with the use of two complete recycling stations with primary and secondary labeling, graphically specifying a subclassification with infographic type posters. For 5 weeks we separated and sold the most commercially valuable solids in Celaya, delivering the proceeds to the corresponding university agency, and the lids of the different food containers were donated to an association that supports children with cancer. With the amount obtained, an economic feasibility analysis was carried out using the financial evaluation criteria Net Present Value (NPV) and Internal Rate of Return (IRR) to know the feasibility in monetary terms; to complement this, the equilibrium point (Q) and the sensitivity analysis were used; trying to exploit the maximum recycling capacity and move to the circular economy.

Active relation discovery: Towards general and label-aware open relation extraction

Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discriminate between known and novel relations. When extending conventional test settings to a more general setting where test data might also come from seen classes, existing approaches have a significant performance decline. (2) Secondary labeling must be performed before practical application. Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. Extensive experiments on three real-world datasets show that ARD significantly outperforms previous state-of-the-art methods on both conventional and our proposed general OpenRE settings. The source code and datasets will be available for reproducibility.

Motor neuron loss and neuromuscular transmission failure following chemogenetic inhibition of BDNF/TrkB signaling in adult rats

Brain derived neurotrophic factor (BDNF) signaling through its high-affinity tropomyosin receptor kinase B (TrkB) is known to have potent effects on motor neuron survival and morphology during development and in neurodegenerative diseases. However, the specific effects of reduced BDNF/TrkB signaling on neuromuscular structure and function in adult mammals are not as well known. In this study, we employed a novel 1NMPP1 sensitive TrkBF616 rat model to evaluate the effect of 14 days inhibition of BDNF/TrkB signaling on the number and morphology of phrenic motor neurons (PhMNs) and diaphragm muscle (DIAm) neurotransmission in adult animals. We hypothesized that reduced BDNF/TrkB signaling results in PhMN loss and impaired DIAm neuromuscular transmission. Adult female and male TrkBF616 rats were divided into 1NMPP1 or vehicle treated groups. Three days prior to treatment, PhMNs in both groups were labeled via intrapleural injection of Alexa-Fluor647 cholera toxin B (CTB). After 11 days of treatment, retrograde axonal transport was assessed by secondary labeling of PhMNs by intrapleural injection of Alexa-Fluor488 CTB. After 14 days of treatment, the spinal cord was excised and 100 μm thick spinal sections containing PhMNs were imaged in 3D using two-channel confocal microscopy. Phrenic nerve and DIAm was excised en bloc, and the extent of neuromuscular transmission failure (NMTF) was assessed by comparing DIAm isometric forces evoked by nerve (40 Hz in 330 ms duration trains repeated each s) compared to direct muscle stimulation (40 Hz 330 ms duration train) superimposed every 15 s. Presynaptic (synaptophysin immunoreactivity) and postsynaptic (α-Bungarotoxin) compartments of neuromuscular junctions (NMJs) were labeled and imaged in 3D by confocal microscopy and the extent of pre- and postsynaptic apposition assessed. 1NMPP1-induced BDNF/TrkB inhibition reduced the total number of PhMNs by ~16%, reduced the mean PhMN somal surface areas by ~25%, reduced PhMN dendritic surface area by ~38%, impaired secondary CTB uptake by 2.5-fold and impaired neuromuscular transmission by ~33%, with no overt evidence of NMJ derangement. We conclude that inhibition of BDNF/TrkB signaling in adult TrkBF616 rats is sufficient to lead to PhMN loss, morphological degeneration, deficits in retrograde axonal transport and impaired DIAm neurotransmission. Supported by NIH: R01-AG44615 and R01-HL146114 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Chemogenetic inhibition of TrkB signalling reduces phrenic motor neuron survival and size

Brain derived neurotrophic factor (BDNF) signalling through its high-affinity tropomyosin receptor kinase B (TrkB) is known to have potent effects on motor neuron survival and morphology during development and in neurodegenerative diseases. Here, we employed a novel 1NMPP1 sensitive TrkBF616 rat model to evaluate the effect of 14 days inhibition of TrkB signalling on phrenic motor neurons (PhMNs). Adult female and male TrkBF616 rats were divided into 1NMPP1 or vehicle treated groups. Three days prior to treatment, PhMNs in both groups were initially labeled via intrapleural injection of Alexa-Fluor-647 cholera toxin B (CTB). After 11 days of treatment, retrograde axonal uptake/transport was assessed by secondary labeling of PhMNs by intrapleural injection of Alexa-Fluor-488 CTB. After 14 days of treatment, the spinal cord was excised 100 μm thick spinal sections containing PhMNs were imaged using two-channel confocal microscopy. TrkB inhibition reduced the total number of PhMNs by ∼16 %, reduced the mean PhMN somal surface areas by ∼25 %, impaired CTB uptake 2.5-fold and reduced the estimated PhMN dendritic surface area by ∼38 %. We conclude that inhibition of TrkB signalling alone in adult TrkBF616 rats is sufficient to lead to PhMN loss, morphological degeneration and deficits in retrograde axonal uptake/transport.

Usability of Discarded Lignocellulosic Fibers in Paper for Secondary Green Packaging and Labeling

Packaging and labels are used for a variety of products and have become an indispensable part of daily life, while products without labels or packaging cause uncertainty among consumers. The global trend is to reduce the amount of packaging waste by recycling and reusing the same material or using other available waste raw materials. With large quantities of stalks remaining discarded in the fields after harvest each year, cereal straw is emerging as an alternative source of lignocellulosic fibers for secondary green packaging and labels. In this study, the usability of printed papers with discarded lignocellulosic fibers by offset and gravure printing processes for secondary green packaging and labels was observed based on the qualitative parameters of reproduction and ink penetration into the printing substrate. From the obtained results, it can be concluded that gravure prints have greater penetration of the ink into the printing substrate, resulting in more uneven surface coverage with printing ink, compared to offset prints, where the viscosity of the ink and the printing process itself have the greatest influence. Therefore, these substrates with discarded lignocellulosic fibers can be used for secondary green packaging and labeling printed by the offset printing process, while gravure printing requires an additional coating or a larger amount of filler in the paper structure.

Electrokinetic Active Particles for Motion-Based Biomolecule Detection.

Detection of biomolecules is essential for patient diagnosis, disease management, and numerous other applications. Recently, nano- and microparticle-based detection has been explored for improving traditional assays by reducing required sample volumes and assay times as well as enhancing tunability. Among these approaches, active particle-based assays that couple particle motion to biomolecule concentration expand assay accessibility through simplified signal outputs. However, most of these approaches require secondary labeling, which complicates workflows and introduces additional points of error. Here, we show a proof-of-concept for a label-free, motion-based biomolecule detection system using electrokinetic active particles. We prepare induced-charge electrophoretic microsensors (ICEMs) for the capture of two model biomolecules, streptavidin and ovalbumin, and show that the specific capture of the biomolecules leads to direct signal transduction through ICEM speed suppression at concentrations as low as 0.1 nM. This work lays the foundation for a new paradigm of rapid, simple, and label-free biomolecule detection using active particles.

Abstract P2-11-01: Secondary endocrine resistance and high Ki-67 labeling index (≥20%) in patients with low genomic risk identified by 21-gene recurrence score

Abstract Introduction: Although breast cancer with high Ki-67 index are likely to have high genomic risk, some of these might have low genomic risk. In ER+HER2- breast cancer patients with low genomic risk identified by the 21-gene assay, we addressed a clinical significance of Ki-67 index in terms of recurrence-free survival (RFS). Methods: We retrospectively identified 2,295 patients with ER+HER2- breast cancer who underwent the 21-gene assay (Oncotype DX®). High Ki-67 LI was defined as 20% or greater, and low genomic risk was defined by recurrence score (RS) from the assay (< 26). Recurrence after 3 year was regarded as secondary endocrine resistance, which is defined as recurrence while on 2 years of adjuvant endocrine treatment. Results: In all patients, Ki67 index assigned 870 (38%) as the high and 1,425 (62%) as the low groups. Thirty percent (263/870) of the high Ki67 index had high genomic risk, whereas 84/1425 (6%) of the low Ki67 did. Among the patients with low genomic risk who did not receive chemotherapy (n=1,807), high Ki67 index was an independent poor risk factor for recurrence. Furthermore, recurrence after 3 years differed significantly according to Ki-67 index (P=0.003), whereas recurrence within 3 years did not differ (P=0.900). Multivariable analysis demonstrated high Ki67 index as a risk factor for recurrence after 3 years (hazard ratio 3.12; 95% confidence interval, 1.28–7.66). Conclusions: In ER+HER2- breast cancer with low genomic risk identified by 21-gene RS, high Ki67 index is associated with secondary endocrine resistance. Novel approach with CDK4/6 inhibitors is demanded to overcome secondary endocrine resistance in tumors with high Ki67/low genomic profile. Citation Format: Janghee Lee, Hakyoung Kim, Soong June Bae, Jong Won Lee, Byung Ho Son, Sei Hyun Ahn, Joon Jeong, Sae Byul Lee, Sung Gwe Ahn. Secondary endocrine resistance and high Ki-67 labeling index (≥20%) in patients with low genomic risk identified by 21-gene recurrence score [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-11-01.

Charge-Dependent Signal Changes for Label-Free Electrochemiluminescence Immunoassays.

Label-free electrochemiluminescence (ECL) immunoassays (lf-ECLIA), based on biomarker-induced ECL signal changes, have attracted increasing attention due to the simple, rapid, and low-cost detection of biomarkers without secondary antibodies and complicated labeling procedures. However, the interaction rule and mechanism between analytical interfaces and biomarkers have rarely been explored. Herein, the interactions between biomarkers and analytical interfaces constructed by assembly of a nanoluminophore and antibody-functionalized gold nanoparticles on an indium tin oxide electrode were studied. The nanoluminophore was synthesized by mixing Cu2+/l-cysteine chelate and N-(4-Aminobutyl)-N-ethylisoluminol-bifunctionalized gold nanoparticles with chitosan. It was found that positively charged biomarkers increased the ECL intensity, whereas negatively charged biomarkers decreased the ECL intensity. The assembly pH influenced the biomarker charges, which determined the ECL enhancement or inhibition. The detection pH only affected the ECL intensity but not the ECL changing trends. Based on the ECL signal changes, a charge-dependent lf-ECLIA was established, which exhibited inhibition responses to negatively charged human immunoglobulin G and copeptin and enhancement responses to positively charged cardiac troponin I, heart-type fatty acid binding protein, brain natriuretic peptide, and SARS-CoV-2 N protein. The linear range was 0.1-1000 pg/mL, and the detection limits were distributed in 0.024-0.091 pg/mL. Besides, a mechanism of the charge-dependent ECL enhancement and inhibition effects is proposed, which is very important for the development of new lf-ECLIA methodologies.

Quantum dots-labeled polymeric scaffolds for in vivo tracking of degradation and tissue formation

The inevitable gap between in vitro and in vivo degradation rate of biomaterials has been a challenging factor in the optimal designing of scaffold's degradation to be balanced with new tissue formation. To enable non-/minimum-invasive tracking of in vivo scaffold degradation, chemical modifications have been applied to label polymers with fluorescent dyes. However, the previous approaches may have limited expandability due to complicated synthesis processes. Here, we introduce a simple and efficient method to fluorescence labeling of polymeric scaffolds via blending with near-infrared (NIR) quantum dots (QDs), semiconductor nanocrystals with superior optical properties. QDs-labeled, 3D-printed PCL scaffolds showed promising efficiency and reliability in quantitative measurement of degradation using a custom-built fiber-optic imaging modality. Furthermore, QDs-PCL scaffolds showed neither cytotoxicity nor secondary labeling of adjacent cells. QDs-PCL scaffolds also supported the engineering of fibrous, cartilaginous, and osteogenic tissues from mesenchymal stem/progenitor cells (MSCs). In addition, QDs-PCL enabled a distinction between newly forming tissue and the remaining mass of scaffolds through multi-channel imaging. Thus, our findings suggest a simple and efficient QDs-labeling of PCL scaffolds and minimally invasive imaging modality that shows significant potential to enable in vivo tracking of scaffold degradation as well as new tissue formation.

Detecting Semantic Attack in SCADA System: A Behavioral Model Based on Secondary Labeling of States-Duration Evolution Graph

By violating semantic constraints that the control process impose, the semantic attack leads the Industry Control Systems (ICS) into an undesirable state or critical state. The spread of semantic attack has caused huge economic losses and casualties to critical infrastructure. Therefore, detecting semantic attack is referred to an urgent and critical task. However, few existing detecting techniques can achieve satisfactory effects in detecting semantic attack of ICS, due to the high requirements of complete critical state-based semantic behavior features description, joint detection on multivariate type state variables, and validity of field states datasets under semantic attacks. In an effort to deal with above challenges, We label device states databases with temporal characteristics and divide impacts on states of field devices under semantic attacks into three categories, including absent in states set, confused sequences, irregular frequency. On this basis, we establish a behavioral model based on secondary labeling of states-duration evolution graph (BMSLS), then implement an adaptive secure state-based semantic attack detection framework furtherly. Compared with the traditional Auto Regression (AR) algorithm, the newer time series correlation graph model, and other five deep learning algorithms, our proposed framework demonstrates the superior effect on the detection of semantic attack.

Zwitterionic Polymer Electroplating Facilitates the Preparation of Electrode Surfaces for Biosensing.

Surface chemistry critically affects the diagnostic performance of biosensors. An ideal sensor surface should be resistant to nonspecific protein adsorption, yet be conducive to analytical responses. Here a new polymeric material, zwitterionic polypyrrole (ZiPPy), is reported to produce optimal surface condition for biosensing electrodes. ZiPPy combines two unique advantages: the zwitterionic function that efficiently hydrates electrode surface, hindering nonspecific binding of hydrophobic proteins; and the pyrrole backbone, which enables rapid (<7min), controlled deposition of ZiPPy through electropolymerization. ZiPPy-coated electrodes show lower electrochemical impedance and less nonspecific protein adsorption (low fouling), outperforming bare and polypyrrole-coated electrodes. Moreover, affinity ligands for target biomarkers can be immobilized together with ZiPPy in a single-step electropolymerization. ZiPPy-coated electrodes are developed with specificity for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The prepared sensor detects SARS-CoV-2 antibodies in human saliva down to 50ng mL-1 , without the need for sample purification or secondary labeling.

Multielectrode Spectroscopy Enables Rapid and Sensitive Molecular Profiling of Extracellular Vesicles.

Detecting protein markers in extracellular vesicles (EVs) is becoming a useful tool for basic research and clinical diagnoses. Most EV protein assays, however, require lengthy processes—conjugating affinity ligands onto sensing substrates and affixing EVs with additional labels to maximize signal generation. Here, we present an iPEX (impedance profiling of extracellular vesicles) system, an all-electrical strategy toward fast, multiplexed EV profiling. iPEX adopts one-step electropolymerization to rapidly functionalize sensor electrodes with antibodies; it then detects EV proteins in a label-free manner through impedance spectroscopy. The approach streamlines the entire EV assay, from sensor preparation to signal measurements. We achieved (i) fast immobilization of antibodies (<3 min) per electrode; (ii) high sensitivity (500 EVs/mL) without secondary labeling; and (iii) parallel detection (quadruple) in a single chip. A potential clinical utility was demonstrated by directly analyzing plasma samples from glioblastoma multiforme patients.

Enzymatic Immunohistochemistry.

Immunohistochemistry (IHC) is a powerful laboratory technique that is widely used in a variety of settings, including clinical medicine and research. Enzymatic immunohistochemistry refers to the process of detecting cell surface proteins or antigens using enzyme-labeled antibodies. The site and specificity of the antibody binding may be identified using direct labeling techniques or through secondary labeling methods. The antibody-antigen interaction is ultimately visualized using a chromogenic detection process, in which the enzyme conjugated to the antibody cleaves a substrate to produce a stable, colored precipitate.

Oligopaint DNA FISH reveals telomere-based meiotic pairing dynamics in the silkworm, Bombyx mori.

Accurate chromosome segregation during meiosis is essential for reproductive success. Yet, many fundamental aspects of meiosis remain unclear, including the mechanisms regulating homolog pairing across species. This gap is partially due to our inability to visualize individual chromosomes during meiosis. Here, we employ Oligopaint FISH to investigate homolog pairing and compaction of meiotic chromosomes and resurrect a classical model system, the silkworm Bombyx mori. Our Oligopaint design combines multiplexed barcoding with secondary oligo labeling for high flexibility and low cost. These studies illustrate that Oligopaints are highly specific in whole-mount gonads and on meiotic squashes. We show that meiotic pairing is robust in both males and females and that pairing can occur through numerous partially paired intermediate structures. We also show that pairing in male meiosis occurs asynchronously and seemingly in a transcription-biased manner. Further, we reveal that meiotic bivalent formation in B. mori males is highly similar to bivalent formation in C. elegans, with both of these pathways ultimately resulting in the pairing of chromosome ends with non-paired ends facing the spindle pole. Additionally, microtubule recruitment in both C. elegans and B. mori is likely dependent on kinetochore proteins but independent of the centromere-specifying histone CENP-A. Finally, using super-resolution microscopy in the female germline, we show that homologous chromosomes remain associated at telomere domains in the absence of chiasma and after breakdown and modification to the synaptonemal complex in pachytene. These studies reveal novel insights into mechanisms of meiotic homolog pairing both with or without recombination.

Application of deep learning-based image recognition technology to asphalt–aggregate mixtures: Methodology

An important factor that affects the performance and durability of asphalt–aggregate mixtures is the size distribution of the aggregate particles. However, there are no accurate and quick methods of obtaining this information currently available. A potential solution to this problem is to use computer-aided image recognition technology, a technique that is being used increasingly more often in the field of pavement engineering. In this paper, asphalt core samples drilled from pavements are used as test objects. The information collected from the side surfaces of these cores is analyzed using neural network technology to help improve the speed and accuracy of the quantitative analysis of the asphalt mixtures. First, a camera is used to make a series of photographic images of the side surface and image reconstruction compositing is adopted to produce an image of the asphalt–aggregate mixtures. This image can be processed using a neural network (U-NET++) to identify the aggregate in the image. The training and recognition effect is improved by using Gaussian blur and secondary labeling methods. The grayscale images can then be binarized, filled, and the aggregate particles are segmented in order to identify holes and areas with adhesion problems. The geometric forms of the particles that appear on the side surfaces of the asphalt mixtures can be accurately obtained. Important geometric parameters (e.g. aspect ratio and roundness) can thus be derived from the image. A method of identifying and analyzing segregation and weakness in the asphalt mixture is proposed. The results show that our composite images are more accurate than traditionally-synthesized panoramic images. Also, the trained U-NET++ is ideal for recognizing and segmenting the aggregate image. The data obtained from the image can thus be used to quickly derive index parameters for the asphalt mixture. Compared with traditional test methods, this method only requires the use of mobile phones and personal computers. It is thus a lightweight and intelligent analysis method for acquiring information about asphalt–aggregate mixtures.

Basic principles of immunohistochemistry and epithelial immunohistochemical markers

Dr. Albert Coons et al in 1941 first initiated immunohistochemistry to identify cellular or tissue constituents by means of antigen and antibody interactions. The site of antibody binding can be identified either by direct labeling of antibody or by use of secondary labeling method. Immunohistochemistry (IHC) involves the use of labeled antibodies to localize the antigens in the tissues through antigen-antibody interactions and are visualized either by fluorescent dye, enzyme, radioactive element or colloidal gold. IHC usage has an obvious advantage over the traditional one, which uses special enzyme staining technique that identify only limited number of proteins, enzymes and certain tissue structure. IHC can be commonly employed to distinguish the tissue of epithelial and mesenchymal differentiation, cellular changes which are normally not visible in regular staining techniques, and earliest changes in transformed tissues. IHC is relatively rapid and simple method to better determine the specific neoplastic tissue origin, the behavior or progression of neoplasm. Hence IHC have gained importance in the medical research and has been considered as a crucial & widely used technique in research laboratories as well as in clinical diagnostics. In present review IHC markers for epithelial origin that can be utilized to diagnose the tumor or to determine the prognosis of the tumor.. Keywords: Immunohistochemistry, Antigen, Antibody labeling, Epithelial origin markers.

Scratch-DKG: A Framework for Constructing Scratch Domain Knowledge Graph

With the rapid development of programming platforms, how to utilize the tremendous amount of data produced by the platforms, such as Scatch, has been a big challenge to researchers. The growing data is not only huge, but also heterogeneous and diverse, leading that the existing tools cannot effectively extract valuable information. In this article, considering particular features of Scratch data, we propose an effective framework about constructing a Scratch Domain Knowledge Graph (Scratch-DKG). Our framework includes four modules which are designed to process the semi-structured data, users profile data, projects data and programming knowledge points, respectively. For webpages, we design a template-based wrapper method to extract triples from the semi-structured data. As for users profile data, we improve DeepDive, which is a useful tool to extract information but with the problem of wrong labeling, to extract knowledge triples by the proposed Secondary Labeling Algorithm. For projects data, we propose an advanced keywords extraction method (S-TextRank) to extract keywords triples. For programming knowledge points, we develop a frequently contiguous block combinations mining algorithm to extract the potential domain information of Scratch. Finally, extensive experiments are carried out to evaluate the performance of our proposed methods. The experimental results show that, compared to other competing methods, our proposal can extract more correct and comprehensive Scratch triples.