Recognition Of Components Research Articles

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.

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.

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.

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.

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.

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.

Ceftazidime is a potential drug to inhibit cell proliferation by increasing cellular p27.

The development of new therapeutic uses for existing drugs is important for the treatment of some diseases. Cephalosporin antibiotics stand as the most extensively utilized antibiotics in clinical practice, effectively combating bacterial infections. Here, we found that the antimicrobial drug ceftazidime strongly upregulates p27 protein levels by inhibiting p27 ubiquitination. The p27 protein is a classic negative regulator of thecell cycle. Next, we demonstrated that ceftazidime can impede the cell cycle from G1 to S phase, thus inhibiting cell proliferation. Furthermore, we found that ceftazidime promotes p27 expression and inhibits cell proliferation by reducing Skp2, which is a substrate recognition component of the Skp2-Cullin-F-box (SCF) ubiquitin ligase. Moreover, ceftazidime downregulates transcriptional expression of Skp2. Importantly, we demonstrated that ceftazidime inhibited the proliferation of tumor cells in vivo. These findings reveal ceftazidime-mediated inhibition of cell proliferation through the Skp2-p27 axis, and could provide a potential strategy for anti-tumor therapy.

A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization.

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture.

Environmental (in)justice and the post-political

Food production plays a crucial role for challenging the escalating environmental breakdown. It is also a fertile ground for analysing environmental (in)justice and its components of recognition and participation in environmental decision-making. Scholars of environmental justice have paid limited attention to the post-political and its implications for the ability to challenge the ecologically destructive status quo. This article innovatively combines environmental justice perspective with the literature on the post-political condition, using the case study of pig farming intensification in rural Northern Ireland. Northern Ireland has been driving policy to encourage growth and intensify its meat production, resulting in a sharp rise of intensive farms. The resulting pollution have and continue generating environmental justice concerns. Using qualitative data from a 2-month fieldwork in November-December 2018, the article shows that local community’s ideas around how farming should be organised were not recognised. Their participation in environmental decision-making was also reduced to an empty ritual; formal inclusion did not translate into a genuine impact on the decision-making outcome. In the post-political landscape, environmental justice concerns become harder to address; environmental decision-making becomes a means of serving the operations of capitalism, stifling disputes around the neoliberal growth agenda, and precluding possibilities for a meaningful change of the ecologically destructive status quo.

Innate and learned components of egg recognition in the ant Camponotus floridanus.

Insect societies discriminate against foreigners to avoid exploitation. In ants, helper workers only accept individuals with the familiar chemical cues of their colony. Similarly, unfamiliar eggs may get rejected at their first appearance in the nest. We investigated egg acceptance mechanisms by introducing different types of foreign eggs into worker groups of the ant Camponotus floridanus. Workers from established colonies familiar with queen-laid eggs always accepted eggs from highly fecund queens, but worker-laid eggs only after exposure for several weeks. Workers naive to eggs only rejected worker-laid eggs once they had prior exposure to eggs laid by highly fecund queens, suggesting that prior exposure to such eggs is necessary for discrimination. The general acceptance of eggs from highly fecund queens, irrespective of previous worker egg exposure, suggests an innate response to the queen pheromone these eggs carry. Workers learned to accept queen-laid eggs from different species, indicating high flexibility in learning egg-recognition cues. In incipient colonies with queen-laid eggs that carry a weak queen pheromone, worker-laid eggs were more likely to get accepted than queen-laid eggs from a different species, suggesting that the similarity of egg-recognition cues between the two types of C. floridanus eggs increases acceptance.

Chemical genomic analysis reveals the interplay between iron chelation, zinc homeostasis, and retromer function in the bioactivity of an ethanol adduct of the feijoa fruit-derived ellagitannin vescalagin.

Nature has been a rich source of pharmaceutical compounds, producing 80% of our currently prescribed drugs. The feijoa plant, Acca sellowiana, is classified in the family Myrtaceae, native to South America, and currently grown worldwide to produce feijoa fruit. Feijoa is a rich source of bioactive compounds with anticancer, anti-inflammatory, antibacterial, and antifungal activities; however, the mechanism of action of these compounds is largely not known. Here, we used chemical genetic analyses in the model organism Saccharomyces cerevisiae to investigate the mechanism of action of a feijoa-derived ethanol adduct of vescalagin (EtOH-vescalagin). Genome-wide barcode sequencing analysis revealed yeast strains lacking genes in iron metabolism, zinc metabolism, retromer function, or mitochondrial function were hypersensitive to 0.3 µM EtOH-vescalagin. This treatment increased expression of iron uptake proteins at the plasma membrane, which was a compensatory response to reduced intracellular iron. Likewise, EtOH-vescalagin increased expression of the Cot1 protein in the vacuolar membrane that transports zinc into the vacuole to prevent cytoplasmic accumulation of zinc. Each individual subunit in the retromer complex was required for the iron homeostatic mechanism of EtOH-vescalagin, while only the cargo recognition component in the retromer complex was required for the zinc homeostatic mechanism. Overexpression of either retromer subunits or high-affinity iron transporters suppressed EtOH-vescalagin bioactivity in a zinc-replete condition, while overexpression of only retromer subunits increased EtOH-vescalagin bioactivity in a zinc-deficient condition. Together, these results indicate that EtOH-vescalagin bioactivity begins with extracellular iron chelation and proceeds with intracellular transport of zinc via the retromer complex. More broadly, this is the first report of a bioactive compound to further characterize the poorly understood interaction between zinc metabolism and retromer function.

Exploring Performance Skills in the Occupation of Learning to Read

ABSTRACT Only one-third of U.S. fourth-grade public school students score at or above reading proficiency on national standardized tests. Current language-based reading instruction methods do not appear to meet many students’ needs. This study explores the role of visual perception in word recognition skills amongst typically developing first graders. It is hypothesized that visual perception is a crucial component of word recognition. This quantitative correlational study explored the relationship between visual perception and word recognition skills in first grade students participating in general education. The findings revealed that several statistically significant relationships exist between visual perceptual functions and skills recruited for word recognition. Phonological processing is strongly associated with spatial relations (r = .35) and sequential memory (r = .38). These correlations are significant at the 0.01 level (2-tailed). Form constancy (r = .28) p < .05 is significant at the 0.05 level (2-tailed). These relationships suggest that visual perceptual skills and phonological processing are concurrently developed in these participants and that these skills are likely recruited simultaneously when engaging in occupations, such as learning to read. This study highlights the role of visual perception in early reading development. Educators may consider consulting with school-based occupational therapists to explore the potential impact of visual perception when supporting students who struggle with reading.

Nature-inspired substituted 3-(imidazol-2-yl) morpholines targeting human topoisomerase IIα: Dynophore-derived discovery

The molecular nanomachine, human DNA topoisomerase IIα, plays a crucial role in replication, transcription, and recombination by catalyzing topological changes in the DNA, rendering it an optimal target for cancer chemotherapy. Current clinical topoisomerase II poisons often cause secondary tumors as side effects due to the accumulation of double-strand breaks in the DNA, spurring the development of catalytic inhibitors. Here, we used a dynamic pharmacophore approach to develop catalytic inhibitors targeting the ATP binding site of human DNA topoisomerase IIα. Our screening of a library of nature-inspired compounds led to the discovery of a class of 3-(imidazol-2-yl) morpholines as potent catalytic inhibitors that bind to the ATPase domain. Further experimental and computational studies identified hit compound 17, which exhibited selectivity against the human DNA topoisomerase IIα versus human protein kinases, cytotoxicity against several human cancer cells, and did not induce DNA double-strand breaks, making it distinct from clinical topoisomerase II poisons. This study integrates an innovative natural product-inspired chemistry and successful implementation of a molecular design strategy that incorporates a dynamic component of ligand-target molecular recognition, with comprehensive experimental characterization leading to hit compounds with potential impact on the development of more efficient chemotherapies.