Recognition Of Components Research Articles

Unlocking the secrets of the immunopeptidome: MHC molecules, ncRNA peptides, and vesicles in immune response

The immunopeptidome, a diverse set of peptides presented by Major Histocompatibility Complex (MHC) molecules, is a critical component of immune recognition and response. This review article delves into the mechanisms of peptide presentation by MHC molecules, particularly emphasizing the roles of ncRNA-derived peptides and extracellular vesicles (EVs) in shaping the immunopeptidome landscape. We explore established and emerging insights into MHC molecule interactions with peptides, including the dynamics of peptide loading, transport, and the influence of cellular and genetic variations. The article highlights novel research on non-coding RNA (ncRNA)-derived peptides, which challenge conventional views of antigen processing and presentation and the role of EVs in transporting these peptides, thereby modulating immune responses at remote body sites. This novel research not only challenges conventional views but also opens up new avenues for understanding immune responses. Furthermore, we discuss the implications of these mechanisms in developing therapeutic strategies, particularly for cancer immunotherapy. By conducting a comprehensive analysis of current literature and advanced methodologies in immunopeptidomics, this review aims to deepen the understanding of the complex interplay between MHC peptide presentation and the immune system, offering new perspectives on potential diagnostic and therapeutic applications. Additionally, the interactions between ncRNA-derived peptides and EVs provide a mechanism for the enhanced surface presentation of these peptides and highlight a novel pathway for their systemic distribution, potentially altering immune surveillance and therapeutic landscapes.

Activation of lysophagy by a TBK1-SCFFBXO3-TMEM192-TAX1BP1 axis in response to lysosomal damage

Lysophagy eliminates damaged lysosomes and is crucial to cellular homeostasis; however, its underlying mechanisms are not entirely understood. We screen a ubiquitination-related compound library and determine that the substrate recognition component of the SCF-type E3 ubiquitin ligase complex, SCFFBXO3(FBXO3), which is a critical lysophagy regulator. Inhibition of FBXO3 reduces lysophagy and lysophagic flux in response to L-leucyl-L-leucine methyl ester (LLOMe). Furthermore, FBXO3 interacts with TMEM192, leading to its ubiquitination in LLOMe-treated cells. We also identify TAX1BP1 as a critical autophagic adaptor that recognizes ubiquitinated TMEM192 during lysophagy and find that TBK1 activation is crucial for lysophagy, as it phosphorylates FBXO3 in response to lysosomal damage. Knockout of FBXO3 significantly impairs lysophagy, and its reconstitution with a loss-of-function mutant (V221I) further confirms its essential role in lysophagy regulation. Collectively, our findings highlight the significance of the TBK1-FBXO3-TMEM192-TAX1BP1 axis in lysophagy and emphasize the critical role of FBXO3 in lysosomal integrity.

Development and verification of automatic recognition system for vocal performance styles based on ResNet

This paper studies the automatic recognition system of vocal performance style based on residual network (ResNet). The system can precisely recognize multiple vocal performance styles through automation technology. Firstly, an audio dataset covering multiple vocal styles was constructed based on the publicly available dataset GTZAN, and the data in the dataset was preprocessed to extract key music features. Subsequently, the ResNet model was chosen as the core recognition component of the system, which was trained to learn the complex relationship between audio features and vocal styles. Finally, the construction of the system was carried out. In the verification phase, the dataset was divided into two parts: the training set and the test set. After training the model using the training set, its performance was comprehensively evaluated on the test set. The experiment showed that the recognition system based on ResNet performed well in precision, recall, and accuracy, with specific values of 95.6%, 91.6%, and 96.7%, respectively. Meanwhile, the recognition accuracy of the ResNet-based system for 10 vocal performance styles was 95.96% on average. Applying ResNet to an automatic recognition system for vocal performance styles not only provides an efficient and reliable solution in this field, but also opens up new horizons for the further application of deep learning in the music industry.

The main components of international recognition of national standards, calibration, and measurement capabilities

International recognition of measurement results improves the development of international trade, science, and technology. It helps to reduce technical barriers in international trade, improve product quality, and increase confidence in calibration and testing laboratories. The recognition of national standards is critical for ensuring global metrological consistency between countries and ensuring international confidence in carried out measurements. National standards are subject to comparison with the standards of other countries. National Metrology Institutes (NMIs) and Designated Institutes (DIs) are responsible for establishing and maintaining national standards of units of measurement and ensuring their compliance with international standards. The results of comparisons of national standards, review, and adoption of calibration and measurement capabilities (CMC) of NMIs and DIs are published in the special Key Comparison Database (KCDB) of the International Bureau of Weights and Measures (BIPM). An analysis of the current state of implementation of the Mutual Recognition Agreement (MRA) of the International Committee for Weights and Measures (CIPM) — CIPM MRA was carried out. The basis of this analysis is the results of key and supplementary comparisons of national standards, structured by regional metrological organizations (RMOs), types of measurements, as well as CMC of NMIs and DIs both by RMOs and types of measurements and by countries. The place of Ukraine in the implementation of the CIRM MRA Agreement is shown.

Role of emotional intelligence and emotion recognition ability in romantic relationship satisfaction of adults varying in autistic-like traits

Research suggests that emotional intelligence is important for relationship satisfaction. Some people face challenges with both romantic relationships and aspects of emotional intelligence, for example, autistic people. There has, however, been very limited research into these factors with non-clinical participants with varying levels of autistic like traits. This research aimed to investigate the extent to which both general emotional intelligence (EI) and the specific component of emotion recognition mediated the relationship between autistic-like traits and satisfaction with romantic relationships. A correlational design, with a general population sample, was used in two studies (study 1, n = 139; study 2, n = 183). The results of our studies found that higher emotional intelligence and emotion recognition were associated with higher relationship satisfaction and that those with higher numbers of autistic like traits had lower emotional intelligence and emotion recognition scores. Emotional intelligence, but not emotion recognition, mediated the relationship between autistic like traits and relationship satisfaction. The research provides novel insights into how emotional intelligence and autistic-like traits influence romantic relationship satisfaction and has implications for potential interventions.

KCTD10 p.C124W variant contributes to schizophrenia by attenuating LLPS-mediated synapse formation

KCTD10, a member of the potassium channel tetramerization domain (KCTD) family, is implicated in neuropsychiatric disorders and functions as a substrate recognition component within the RING-type ubiquitin ligase complex. A rare de novo variant of KCTD10, p.C124W, was identified in schizophrenia cases, yet its underlying pathogenesis remains unexplored. Here, we demonstrate that heterozygous KCTD10 C124W mice display pronounced synaptic abnormalities and exhibit schizophrenia-like behaviors. Mechanistically, we reveal that KCTD10 undergoes liquid-liquid phase separation (LLPS), a process orchestrated by its intrinsically disordered region (IDR). p.C124W mutation disrupts this LLPS capability, leading to diminished degradation of RHOB and subsequent excessive accumulation in the postsynaptic density fractions. Notably, neither IDR deletion nor p.C124W mutation in KCTD10 mitigates the synaptic abnormalities caused by Kctd10 deficiency. Thus, our findings implicate that LLPS may be associated with the pathogenesis of KCTD10-associated brain disorders and highlight the potential of targeting RHOB as a therapeutic strategy for diseases linked to mutations in KCTD10 or RHOB.

Automated classification of tree species using graph structure data and neural networks

The classification of tree species in urban contexts is pivotal in assessing ecosystem services and fostering sustainable urban development. This paper explores using graph neural networks (GNNs) on graph structure data derived from quantitative structure models (QSMs) and tree structural measurement for appropriate species classification. The study addresses gaps in existing methods by integrating relationships between tree components, such as branches and cylinders, and considering the entire tree structure in a novel graph data format. The results demonstrate the efficacy of GNNs, particularly the Gated Graph Convolutional Network (GatedGCN), in appropriately classifying urban tree species. It gained an overall classification accuracy and weighted F1 score of 0.84. An analysis of confusion matrices revealed similarities in visual characteristics among several species, including A. platanoides and T. cordata, which pose significant challenges in accurately distinguishing between them. However, certain species, such as A. hippocastanum and P. nigra var. italica, have proved easier to classify than others. Furthermore, the results highlight the importance of relationships between different tree components in species recognition, such as the ratio between branch radius and parent branch radius, the factors often overlooked by previous methods. This underscores the novelty and effectiveness of the proposed approach in this study. Future research could explore integrating additional data sources, such as Leaf Area Density (LAD) calculated from LiDAR and hyperspectral data, to enhance classification accuracy. In conclusion, the evaluation results of the GatedGCN model demonstrated its effectiveness in classifying tree species using a novel data structure format derived from QSM tree characteristics. Advancing urban tree species classification through such methods can enhance future urban tree management using automated AI and robotics solutions.

Enhancing Target Detection: A Fluorescence-Based Streptavidin-Bead Displacement Assay.

Fluorescence-based aptasensors have been regarded as innovative analytical tools for the detection and quantification of analytes in many fields, including medicine and therapeutics. Using DNA aptamers as the biosensor recognition component, conventional molecular beacon aptasensor designs utilise target-induced structural switches of the DNA aptamers to generate a measurable fluorescent signal. However, not all DNA aptamers undergo sufficient target-specific conformational changes for significant fluorescence measurements. Here, the use of complementary 'antisense' strands is proposed to enable fluorescence measurement through strand displacement upon target binding. Using a published target-specific DNA aptamer against the receptor binding domain of SARS-CoV-2, we designed a streptavidin-aptamer bead complex as a fluorescence displacement assay for target detection. The developed assay demonstrates a linear range from 50 to 800 nanomolar (nM) with a limit of detection calculated at 67.5 nM and a limit of quantification calculated at 204.5 nM. This provides a 'fit-for-purpose' model assay for the detection and quantification of any target of interest by adapting and functionalising a suitable target-specific DNA aptamer and its complementary antisense strand.

Promoting emotion understanding in middle childhood: A systematic review of school-based SEL programs

Emotion understanding (EU) describes the ability to identify, interpret, and communicate about emotions, and is often targeted by social-emotional learning (SEL) programs. Still, the theoretical framing of SEL programs and their impact on specific areas of social-emotional development, such as EU, for different age bands is not always transparent. This systematic review synthesized emotion-focused content in SEL programs used in quantitative outcome studies in middle childhood to identify which EU components are targeted and examine content congruence with an integrated EU development model drawing on the Pons EU developmental model and the Crick and Dodge social-information processing (SIP) model that posits emotion identification as fundamental to social decision-making in childhood. A total of 38 programs for Grades 3 to 5 across 54 studies in 20 countries were reviewed. Program aims, lesson topics, and activities were extracted and mapped to a 10-component EU framework integrating the nine Pons model components (‘recognition, external cause, reminder, desire, belief, hiding, regulation, mixed, morality’) with one based on the SIP model (‘decision/action’). At least 87 % of emotion-focused SEL content targeted EU components of recognition, regulation, and social decision-making. Findings indicate a good level of congruence between emotion-focused SEL program content and prevailing EU development models. Many programs emphasized the external causes of emotions, underscoring the importance of scenarios to explain emotions—discussed further in light of cross-cultural variation in emotion socialization. We encourage SEL intervention research to be more transparent in reporting SEL program content and activities to move toward causal explanations of program impact.

Mediations of executive function, social cognitions, and interpersonal conflict in explaining the impact of self‐esteem on disordered eating: structural equation modelling

AbstractThe complexity of psychological mechanisms in disordered eating behaviours was tested using a nonclinical sample (female adults from English speaking countries, N = 334). Concerning the development and maintenance of disturbed cognition and eating behaviour, theories of self‐esteem, executive function, and emotion regulation as well as interpersonal and transdiagnostic theories and theory of mind were applied with covariates of depression, anxiety, family functioning, and demographic variables. Structural Equation Modelling demonstrated seven variables mediated the impact of self‐esteem upon disordered eating. The theory of mind variable, when measured on the emotion recognition component, was a significant mechanism in explaining both anorexic eating and shape concerns, and bulimic symptoms. Meanwhile, thwarted belongingness, social evaluation, social conflict, and executive function—cognitive flexibility—were suggested mechanisms for shape and weight concerns, and restrictive eating. In contrast, emotion regulation and another executive function variable of negative urgency appeared as mechanisms of bulimic symptoms and behaviours.

Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders

Charged particle reconstruction or track reconstruction is one of the most crucial components of pattern recognition in high-energy collider physics. It is known to entail enormous consumption of computing resources, especially when the particle multiplicity is high, which will be the conditions at future colliders, such as the High Luminosity Large Hadron Collider and Super Proton–Proton Collider. Track reconstruction can be formulated as a quadratic unconstrained binary optimization (QUBO) problem, for which various quantum algorithms have been investigated and evaluated with both a quantum simulator and hardware. Simulated bifurcation algorithms are a set of quantum-annealing-inspired algorithms, known to be serious competitors to other Ising machines. In this study, we show that simulated bifurcation algorithms can be employed to solve the particle tracking problem. The simulated bifurcation algorithms run on classical computers and are suitable for parallel processing and usage of graphical processing units, and they can handle significantly large amounts of data at high speed. These algorithms exhibit reconstruction efficiency and purity comparable to or sometimes improved over those of simulated annealing, but the running time can be reduced by as much as four orders of magnitude. These results suggest that QUBO models together with quantum-annealing-inspired algorithms are valuable for current and future particle tracking problems.

A novel alkaline pH-responsive ratiometric probe based on hybrid fluorophores and its applications

The current study describes a novel development in the field of chromogenic and ratiometric fluorescence probes designed for pH sensing purposes. Of particular interest is the incorporation of coumarin as the recognition component in this probe, which was synthesized utilizing hybrid fluorophores containing fluorescein and coumarin. This probe exhibited two distinct fluorescence response modes toward changes in pH: a ratiometric fluorescence response at 328 nm excitation and a fluorescence switch-on response at 514 nm excitation. These responses are attributed to the structure changes induced by coumarin hydrolysis in alkaline conditions above a pH of 9, as confirmed through MS and DFT analysis. Furthermore, the practical utility of the probe was effectively evaluated through visual detection on silica gel plates and bio-imaging with Bacillus subtilis, illustrating its potential for the advancement of alkaline pH-responsive fluorescence probes.

Cross-view motion consistent self-supervised video inter-intra contrastive for action representation understanding

Self-supervised contrastive learning draws on power representational models to acquire generic semantic features from unlabeled data, and the key to training such models lies in how accurately to track motion features. Previous video contrastive learning methods have extensively used spatially or temporally augmentation as similar instances, resulting in models that are more likely to learn static backgrounds than motion features. To alleviate the background shortcuts, in this paper, we propose a cross-view motion consistent (CVMC) self-supervised video inter-intra contrastive model to focus on the learning of local details and long-term temporal relationships. Specifically, we first extract the dynamic features of consecutive video snippets and then align these features based on multi-view motion consistency. Meanwhile, we compare the optimized dynamic features for instance comparison of different videos and local spatial fine-grained with temporal order in the same video, respectively. Ultimately, the joint optimization of spatio-temporal alignment and motion discrimination effectively fills the challenges of the missing components of instance recognition, spatial compactness, and temporal perception in self-supervised learning. Experimental results show that our proposed self-supervised model can effectively learn visual representation information and achieve highly competitive performance compared to other state-of-the-art methods in both action recognition and video retrieval tasks.

Learning generalizable AI models for multi-center histopathology image classification

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA’s potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.

Trademark Text Recognition Combining SwinTransformer and Feature-Query Mechanisms

The task of trademark text recognition is a fundamental component of scene text recognition (STR), which currently faces a number of challenges, including the presence of unordered, irregular or curved text, as well as text that is distorted or rotated. In applications such as trademark infringement detection and analysis of brand effects, the diversification of artistic fonts in trademarks and the complexity of the product surfaces where the trademarks are located pose major challenges for relevant research. To tackle these issues, this paper proposes a novel recognition framework named SwinCornerTR, which aims to enhance the accuracy and robustness of trademark text recognition. Firstly, a novel feature-extraction network based on SwinTransformer with EFPN (enhanced feature pyramid network) is proposed. By incorporating SwinTransformer as the backbone, efficient capture of global information in trademark images is achieved through the self-attention mechanism and enhanced feature pyramid module, providing more accurate and expressive feature representations for subsequent text extraction. Then, during the encoding stage, a novel feature point-retrieval algorithm based on corner detection is designed. The OTSU-based fast corner detector is presented to generate a corner map, achieving efficient and accurate corner detection. Furthermore, in the encoding phase, a feature point-retrieval mechanism based on corner detection is introduced to achieve priority selection of key-point regions, eliminating character-to-character lines and suppressing background interference. Finally, we conducted extensive experiments on two open-access benchmark datasets, SVT and CUTE80, as well as a self-constructed trademark dataset, to assess the effectiveness of the proposed method. Our results showed that the proposed method achieved accuracies of 92.9%, 92.3% and 84.8%, respectively, on these datasets. These results demonstrate the effectiveness and robustness of the proposed method in the analysis of trademark data.

Development and Application of Reversible and Irreversible Covalent Probes for Human and Mouse Cathepsin-K Activity Detection, Revealing Nuclear Activity.

Cathepsin-K (CTSK) is an osteoclast-secreted cysteine protease that efficiently cleaves extracellular matrices and promotes bone homeostasis and remodeling, making it an excellent therapeutic target. Detection of CTSK activity in complex biological samples using tailored tools such as activity-based probes (ABPs) will aid tremendously in drug development. Here, potent and selective CTSK probes are designed and created, comparing irreversible and reversible covalent ABPs with improved recognition components and electrophiles. The newly developed CTSK ABPs precisely detect active CTSK in mouse and human cells and tissues, from diseased and healthy states such as inflamed tooth implants, osteoclasts, and lung samples, indicating changes in CTSK's activity in the pathological samples. These probes are used to study how acidic pH stimulates mature CTSK activation, specifically, its transition from pro-form to mature form. Furthermore, this study reveals for the first time, why intact cells and cell lysate exhibit diverse CTSK activity while having equal levels of mature CTSK enzyme. Interestingly, these tools enabled the discovery of active CTSK in human osteoclast nuclei and in the nucleoli. Altogether, these novel probes are excellent research tools and can be applied in vivo to examine CTSK activity and inhibition in diverse diseases without immunogenicity hazards.

Prompting GPT –4 to support automatic safety case generation

In the ever-evolving field of software engineering, the advent of large language models and conversational interfaces, exemplified by ChatGPT, represents a significant revolution. While their potential is evident in various domains, this paper expands upon our previous research, where we experimented with GPT–4, on its ability to create safety cases. A safety case is a structured argument supported by a body of evidence to demonstrate that a given system is safe to operate in a given environment. In this paper, we first determine GPT–4’s comprehension of the Goal Structuring Notation (GSN), a well-established notation for visually representing safety cases. Additionally, we conduct four distinct experiments using GPT–4 to evaluate its ability to generate safety cases within a specified system and application domain. To assess GPT–4’s performance in this context, we compare the results it produces with the ground-truth safety cases developed for an X-ray system, a machine learning-enabled component for tire noise recognition in a vehicle, and a lane management system from the automotive domain. This comparison enables us to gain valuable insights into the model’s generative capabilities. Our findings indicate that GPT–4 is able to generate moderately accurate and reasonable safety cases.

유아교육기관의 ESG 운영 평가도구 개발 및 타당화 연구

The purpose of this study is to develop an ESG operation evaluation tool for early childhood education institutions and to verify the validity of the developed test tool. To this end, the concept and components of ESG operation of early childhood education institutions were derived through analysis of related literature. Based on the content validity of the components of ESG operation, 53 ESG concept recognition questions and 36 ESG attitude questions were selected. A preliminary survey was conducted on 28 employees at early childhood education institutions on the selected questions. After verifying the content validity of the preliminary survey data, the main survey was conducted on 242 people. Basic analysis, exploratory factor analysis, and confirmatory factor analysis were conducted on main survey data to derive the final test tool. As a result of the study, 17 ESG concept recognition components and 11 ESG attitude components were derived, and a total of 28 questions were developed as an ESG operation evaluation tool for early childhood education institution. The ESG operation evaluation tool for early childhood education institutions derived through this study is significant in that it can provide basic data for proper ESG operation of early childhood education institutions in the future.

The Development of 7th Grade Students’ Algebraic Thinking Through Task-assisted Instruction

This study aims to investigate how the algebraic thinking skills of seventh-grade students develop with the task-assisted teaching approach. The study was conducted in a seventh-grade class at a public school in Istanbul. The tasks were designed to support the basic components of students’ algebraic thinking processes - pattern recognition, writing algebraic expressions, constructing and solving equations. During the implementation, the students in the class were divided into groups of three and four, and a teacher candidate in each group was responsible for implementing the tasks. Teacher candidates were informed about the instructions provided by the researcher, the implementation principles, and possible student errors before each task. The entire implementation process was recorded with the consent of the students. This paper focused on the pattern recognition component of algebraic thinking. Video analysis and students' responses showed that their algebraic thinking processes improved in the pattern recognition component, and furthermore, the pattern recognition component evaluation through qualitative analysis showed that there was an improvement in the students' algebraic thinking skills compared to their previous performance. The results indicate that task-assisted instruction could be an effective method for improving students' algebraic thinking skills and supporting their algebra learning.

Multi-scale sampling attention graph convolutional networks for skeleton-based action recognition

Skeleton-based action recognition has attracted increasing interest in recent years. With the flexibility of modeling long-range dependency of joints, the self-attention module has served as the basic component in skeleton-based action recognition. However, the global receptive field of self-attention is not conducive to the modeling of skeleton locality, and the self-attention model is imbued with less inductive bias, which leads to overfitting. In this paper, we propose an attention graph convolutional network (AGCN) with multi-scale sampling to effectively model the local and global features of the skeleton. Firstly, we propose two extreme sampling strategies for generating and ordering neighboring nodes of root nodes. A local-first sampling method is introduced to construct local graph windows, and a global-first sampling method is proposed to assemble long-range joints for constructing global graph windows. The local-first sampling and global-first sampling introduce more skeleton-specific inductive biases to regularize the model capacity. Secondly, the AGCN combines the self-attention mechanism with graph convolution operation, which alleviates the over-smoothing of graph convolution and preserves the translation invariant. Based on the multi-scale sampling strategy, the AGCN can effectively model the locality and non-locality of the skeleton. Finally, by coupling the aforementioned proposals, we develop a two-pathway model for multi-scale feature fusion. Extensive experiments demonstrate that our model could achieve comparable performance with state-of-the-art works on the NTU RGB+D 60, NTU RGB+D 120, the UAV-HUMAN and NW-UCLA datasets.