Multiple Labels Research Articles

Quantitative Analysis of Protein-Protein Equilibrium Constants in Cellular Environments Using Single-Molecule Localization Microscopy.

Current methods for determining equilibrium constants often operate in three-dimensional environments, which may not accurately reflect interactions with membrane-bound proteins. With our technique, based on single-molecule localization microscopy (SMLM), we directly determine protein-protein association (Ka) and dissociation (Kd) constants in cellular environments by quantifying associated and isolated molecules and their interaction area. We introduce Kernel Surface Density (ks-density,) a novel method for determining the accessible area for interacting molecules, eliminating the need for user-defined parameters. Simulation studies validate our method's accuracy across various density and affinity conditions. Applying this technique to T cell signaling proteins, we determine the 2D association constant of T cell receptors (TCRs) in resting cells and the pseudo-3D dissociation constant of pZAP70 molecules from phosphorylated intracellular tyrosine-based activation motifs on the TCR-CD3 complex. We address challenges of multiple detection and molecular labeling efficiency. This method enhances our understanding of protein interactions in cellular environments, advancing our knowledge of complex biological processes.

Identifying and Addressing Errors in Anatomical Pathology: A Survey Analysis

Abstract Introduction/Objective To get an update on potential sources of pre-analytical, analytical, post-analytical and other sources of errors in anatomic pathology and strategies to mitigate such errors. Methods/Case Report The authors designed a 5-question survey in Google software and shared it widely on social media platforms. Results (if a Case Study enter NA) The survey, which garnered 43 responses from staff across various areas of the anatomic pathology laboratory, revealed significant insights. Labeling and fixation/preservation errors emerged as the most prevalent pre-analytical errors, while discrepancies between gross and microscopic examinations were common analytical errors. Specimen handling, inadequate clinical information, and transportation were the most frequently perceived other potential sources of errors. The implications of these findings are clear: robust protocols with multiple identifiers, staff training, and barcode labeling can significantly reduce patient identification errors. Similarly, standardized collection instructions, pre-labeled containers, and meticulous verification processes can effectively minimize such errors. Validated tests, standardized scoring systems, and proper investigation of quality control discrepancies are key to reducing analytical errors. Documented procedures, staff training, regular equipment calibration, and reagent testing are also crucial. Conclusion This study contributes to the literature on the current state of potential sources of errors in anatomic pathology. The study findings and recommendations on strategies to address such errors would improve the performance of the anatomic pathology laboratory and eventually improve patient care.

Role of delay in brain dynamics

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and M multiple delays between successive layers, thereby generating a polynomial time-series outputs with M. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with M outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.

Application of Label Correlation in Multi-Label Classification: A Survey

Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-label classification, each instance in Multi-Label Classification corresponds to multiple labels, and there is a correlation between these labels, which contains a wealth of information. Therefore, the ability to effectively mine and utilize the complex correlations between labels has become a key factor in Multi-Label Classification methods. In recent years, research on label correlations has shown a significant growth trend internationally, reflecting its importance. Given that, this paper presents a survey on the label correlations in Multi-Label Classification to provide valuable references and insights for future researchers. The paper introduces multi-label datasets across various fields, elucidates and categorizes the concept of label correlations, emphasizes their utilization in Multi-Label Classification and associated subproblems, and provides a prospect for future work on label correlations.

A multi-label legal charge prediction method based on summarization generation

Legal Charge Prediction (LCP) is a task of predicting charge labels for which a criminal should be charged based on the factual case descriptions. In recent years, the advancement of deep learning networks has led to the emergence of effective approaches for LCP through the Fine-tuning of pre-trained language models. While existing methods primarily treat it as multiple binary classifications, excelling in the single charge label given factual case descriptions, they still exhibit substantial room for improvement in multi-label LCP. This is because single-label classification methods do not effecitively deal with the relationship between multiple labels, mainly determining the likelihood of a factual description’s association to each individual charge label. Recognizing the need to enhance the relationship among multi-label charges in Fine-tuning-based discriminative charge prediction, this paper introduces a multi-label LCP method based on summarization generation. This method reconstructs the multi-label classification problem as a summarization generation task, capturing the relationship among multiple labels within the summarization content. Empirical findings from the Chinese legal domain’s CAIL2018 dataset demonstrate the method’s capacity to discern the relationship between multiple legal charge labels with Micro-F1 and Macro-F1 enhancements, compared to the fine-tuning-based approaches.

Explaining results of path queries on graphs: Single-path results for context-free path queries

Many graph query languages use, at their core, path queries that yield node pairs (m,n) that are connected by a path of interest. For the end-user, such node pairs only give limited insight as to why this result is obtained, as the pair does not directly identify the underlying path of interest.In this paper, we propose the single-path semantics to address this limitation of path queries. Under single-path semantics, path queries evaluate to a single path connecting nodes m and n and that satisfies the conditions of the query. To put our proposal in practice, we provide an efficient algorithm for evaluating context-free path queries using the single-path semantics. Additionally, we perform a short evaluation of our techniques that shows that the single-path semantics is practically feasible, even when query results grow large.In addition, we explore the formal relationship between the single-path semantics we propose the problem of finding the shortest string in the intersection of a regular language (representing a graph) and a context-free language (representing a path query). As our formal results show, there is a distinction between the complexity of the single-path semantics for queries that use a single edge label and queries that use multiple edge labels: for queries that use a single edge label, the length of the shortest path is linearly upper bounded by the number of nodes in the graph; whereas for queries that use multiple edge labels, the length of the shortest path has a worst-case quadratic lower bound.

Dissecting the immune infiltrate of primary luminal B-like breast carcinomas in relation to age.

The impact of aging on the immune landscape of luminal breast cancer (Lum-BC) is poorly characterized. Understanding the age-related dynamics of immune editing in Lum-BC is anticipated to improve the therapeutic benefit of immunotherapy in older patients. To this end, here we applied the 'multiple iterative labeling by antibody neo-deposition' (MILAN) technique, a spatially resolved single-cell multiplex immunohistochemistry method. We created tissue microarrays by sampling both the tumor center and invasive front of luminal breast tumors collected from a cohort of treatment-naïve patients enrolled in the prospective monocentric IMAGE (IMmune system and AGEing) study. Patients were subdivided into three nonoverlapping age categories (35-45 = 'young', n = 12; 55-65 = 'middle', n = 15; ≥70 = 'old', n = 26). Additionally, depending on localization and amount of cytotoxic T lymphocytes, the tumor immune types 'desert' (n = 22), 'excluded' (n = 19), and 'inflamed' (n = 12) were identified. For the MILAN technique we used 58 markers comprising phenotypic and functional markers allowing in-depth characterization of T and B lymphocytes (T&B-lym). These were compared between age groups and tumor immune types using Wilcoxon's test and Pearson's correlation. Cytometric analysis revealed a decline of the immune cell compartment with aging. T&B-lym were numerically less abundant in tumors from middle-aged and old compared to young patients, regardless of the geographical tumor zone. Likewise, desert-type tumors showed the smallest immune-cell compartment and were not represented in the group of young patients. Analysis of immune checkpoint molecules revealed a heterogeneous geographical pattern of expression, indicating higher numbers of PD-L1 and OX40-positive T&B-lym in young compared to old patients. Despite the numerical decline of immune infiltration, old patients retained higher expression levels of OX40 in T helper cells located near cancer cells, compared to middle-aged and young patients. Aging is associated with important numerical and functional changes of the immune landscape in Lum-BC. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Local Mean Suppression Filter for Effective Background Identification in Fluorescence Images.

We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub.

Three-way decision-based label integration for crowdsourcing

In crowdsourcing learning, label integration is often used to infer instances’ integrated labels from their multiple noisy labels. However, almost all existing label integration algorithms apply the same strategy to infer different instances’ integrated labels, which limits their performance. This paper argues that different instances should enjoy different label integration strategies alone. Thanks to the three-way decision theory, a three-way decision-based label integration (TDLI) algorithm is proposed. In TDLI, we at first evaluate the label qualities of each instance and its K-nearest neighbors, and then utilize them to divide the whole crowdsourced datasets into three disjoint subsets, called positive set, boundary set and negative set, respectively. For each instance in the positive set, we directly apply the simplest majority voting (MV) to infer its integrated label. For each instance in the boundary set, we absorb its K-nearest neighbors’ multiple noisy labels to infer its integrated label by the weighted MV. For each instance in the negative set, we merge the positive and boundary sets to train a classifier to infer its integrated label by fusing its own multiple noisy label distribution and the predicted label distribution. Extensive experiments demonstrate that TDLI distinctly outperforms all the other existing label integration algorithms used to compare.

Multi-scale locality preserving projection for partial multi-view incomplete multi-label learning

Amidst advancements in feature extraction techniques, research on multi-view multi-label classifications has attracted widespread interest in recent years. However, real-world scenarios often pose a challenge where the completeness of multiple views and labels cannot be ensured. At present, only a handful of techniques have attempted to address the complex issue of partial multi-view incomplete multi-label classification, and the majority of these approaches overlook the significance of manifold structures between instances. To tackle these challenges, we propose a novel partial multi-view incomplete multi-label learning model, termed MSLPP. Differing from existing studies, MSLPP emphasizes retaining the effective inherent structure of data during the feature extraction process, thereby facilitating a richer semantic information extraction. Specifically, MSLPP captures and integrates four types of information: the distance and similarity information in the original feature space, and the distance and similarity information in the extracted feature space. Further, by adopting the graph embedding technique, it simultaneously preserves the intrinsic structure with multi-scale information through a constraint term. Moreover, taking into account the negative impact of the missing views on the model and the possible impact of missing views on the data inherent structure, we further propose a shielding strategy for missing views, which not only eliminates the negative effects of missing views on the model but also more accurately captures the inherent data structure. The experimental results on five widely recognized datasets indicate that the model performs better than many excellent methods.

Total sugar content of pre-packaged beverages available in Saudi Arabia

Beverages are a major source of sugar; therefore, efforts to reduce sugar consumption focus on limiting sugary drinks. This is a descriptive observational analysis study of pre-packaged beverages. The analysis covered 12 beverage categories and 28 types of commonly available sugars in beverages. The beverages' median total sugar was determined and assessed based on the World Health Organization (WHO) recommended upper limit of sugar intake and the multiple traffic light labeling (MTL) criteria. Different analyses were performed including frequencies and median to determine the categories with high sugar content and non-parametric tests to compare the median of the total sugar among categories. The median total sugar content of the 1400 analyzed beverages was 11 g 100 mL-1; fruit juices and soft drinks had the highest median of total sugar (12 – 11 g 100 mL-1). The top sugar terms found are White sugar, Sucrose, High-fructose corn syrup, and Glucose syrup. The median total sugar in our study amounts to 20 % of the WHO upper limit recommendation, and 46.22 % of the beverages received a ‘red’ label for sugar content per 100 mL based on the MTL criteria. Sugar content in the pre-packaged beverages is high compared to the recommended daily limit of sugar.

Interplay of multiple labels and willingness to pay

PurposeThere are calls for irradiated food labelling due to a significant need for food safety and extensive procedures to safeguard consumer health. Additionally, there is a strong push from producers for mandatory Country of Origin (COO) labelling. The study examines how the COO and Radura labels influence consumer behaviour and shows the interplay between these influences. Using Attitude-Behavior-Context theory, a conceptual model is proposed and tested to evaluate these factors. The use of multiple labels allows for more choices. How the information presented on labels corresponds to consumers' pre-purchase information search is tested on regular label users.Design/methodology/approachStructural Equation Modelling (SEM), Generalised Linear Modelling and Cluster Analysis were used to analyse the data on a sample of 322 Australian respondents.FindingsThe study revealed that COO labelling had a significant positive relationship with attitudes but a negative relationship with WTP, acting as a suppressed mediator between attitudes and WTP. Interestingly, while knowledge was not found significant, label confidence emerged as a significant factor. Furthermore, the research suggests that regular users may prioritize COO labels over Radura labels.Originality/valueThis research contributes novelty by being the first to address the interplay between COO and irradiated labels, complementing the growing body of literature on irradiation labelling. It also offers valuable insights for retail practitioners, providing an understanding that can facilitate the delivery of high-value multiple labels at the point of purchase.

Determinants of cerebral blood flow and arterial transit time in healthy older adults.

Cerebral blood flow (CBF) and arterial transit time (ATT), markers of brain vascular health, worsen with age. The primary aim of this cross-sectional study was to identify modifiable determinants of CBF and ATT in healthy older adults (n = 78, aged 60-81 years). Associations between cardiorespiratory fitness and CBF or ATT were of particular interest because the impact of cardiorespiratory fitness is not clear within existing literature. Secondly, this study assessed whether CBF or ATT relate to cognitive function in older adults. Multiple post-labelling delay pseudo-continuous arterial spin labelling estimated resting CBF and ATT in grey matter. Results from multiple linear regressions found higher BMI was associated with lower global CBF (β = -0.35, P = 0.008) and a longer global ATT (β = 0.30, P = 0.017), global ATT lengthened with increasing age (β = 0.43, P = 0.004), and higher cardiorespiratory fitness was associated with longer ATT in parietal (β = 0.44, P = 0.004) and occipital (β = 0.45, P = 0.003) regions. Global or regional CBF or ATT were not associated with processing speed, working memory, or attention. In conclusion, preventing excessive weight gain may help attenuate age-related declines in brain vascular health. ATT may be more sensitive to age-related decline than CBF, and therefore useful for early detection and management of cerebrovascular impairment. Finally, cardiorespiratory fitness appears to have little effect on CBF but may induce longer ATT in specific regions.

Multi-Label and Evolvable Dataset Preparation for Web-Based Object Detection

In this paper, we focus on the emerging field of web-based object detection, which has gained considerable attention due to its ability to utilize large amounts of web data for training, thus eliminating the need for labor-intensive manual annotations. However, the noisy and ever-evolving nature of web data poses challenges in preparing high-quality datasets for web-based object detection. To address these challenges, we propose a fully automatic dataset preparation method in this paper. Our proposed method incorporates a hierarchical clustering module that assigns multiple precise labels to each image. This module is based on our observation that web image data exhibits different distributions at varying granularities. Furthermore, an evolutionary relabeling module ensures the adaptability of both the prepared dataset and trained detection models to the ever-evolving web data. Extensive experiments demonstrate that our method outperforms other web-based methods, and achieves a comparable performance to those manually labeled benchmark datasets.

Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning

Multivariate machining quality prediction of thin-walled parts with multiple machining features is a complex problem due to different data distribution between training and unlabelled test samples. Traditional quality prediction methods ignore the correlation of multiple quality labels and do not consider changes in data distribution, resulting in low accuracy of multivariate quality prediction. Therefore, a multivariate quality prediction method using multi-task parallel deep transfer learning is proposed to solve this problem. Specifically, a multi-output quality prediction model of cross-machining features is constructed through the joint design of multi-task parallel learning and deep transfer learning. Furthermore, a domain matcher is designed to form multiple transfer strategies, which can be used for dynamic matching of multi-source and multi-target machining features with multiple quality labels. The domain invariant data features through dynamic domain adaptation are extracted to deal with data distribution discrepancy between the source and target domains. Finally, the results of multiple comparison experiments show that the proposed method can effectively achieve the accurate quality prediction of the target domain with unlabelled labels and different distributions. Compared with the traditional methods, the proposed method has improved by 8.34%, 7.14%, and 9.09%, respectively, in MAE, RMSE, and score on average.

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing views or some uncertain labels. Recently, many approaches have been proposed for incomplete data, but few of them can handle the case of both missing views and labels. Moreover, these few existing works commonly ignore potentially valuable information about unknown labels or do not sufficiently explore latent label information. Therefore, in this paper, we propose a label semantic-guided contrastive learning method named LSGC for the dual incomplete multi-view multi-label classification problem. Concretely, LSGC employs deep neural networks to extract high-level features of samples. Inspired by the observation of exploiting label correlations to improve the feature discriminability, we introduce a graph convolutional network to effectively capture label semantics. Furthermore, we introduce a new sample-label contrastive loss to explore the label semantic information and enhance the feature representation learning. For missing labels, we adopt a pseudo-label filling strategy and develop a weighting mechanism to explore the confidently recovered label information. We validate the framework on five standard datasets and the experimental results show that our method achieves superior performance in comparison with the state-of-the-art methods.

A semantic guidance-based fusion network for multi-label image classification

Multi-label image classification (MLIC), a fundamental task assigning multiple labels to each image, has been seen notable progress in recent years. Considering simultaneous appearances of objects in the physical world, modeling object correlations is crucial for enhancing classification accuracy. This involves accounting for spatial image feature correlation and label semantic correlation. However, existing methods struggle to establish these correlations due to complex spatial location and label semantic relationships. On the other hand, regarding the fusion of image feature relevance and label semantic relevance, existing methods typically learn a semantic representation in the final CNN layer to combine spatial and label semantic correlations. However, different CNN layers capture features at diverse scales and possess distinct discriminative abilities. To address these issues, in this paper we introduce the Semantic Guidance-Based Fusion Network (SGFN) for MLIC. To model spatial image feature correlation, we leverage the advanced TResNet architecture as the backbone network and employ the Feature Aggregation Module for capturing global spatial correlation. For label semantic correlation, we establish both local and global semantic correlation. We further enrich model features by learning semantic representations across multiple convolutional layers. Our method outperforms current state-of-the-art techniques on PASCAL VOC (2007, 2012) and MS-COCO datasets.

A consistency-aware deep capsule network for hierarchical multi-label image classification

Hierarchical classification is a significant challenge in computer vision due to the logical order and interconnectedness of multiple labels. This paper presents HD-CapsNet, a novel neural network architecture based on deep capsule networks, specifically designed for hierarchical multi-label classification(HMC). By incorporating a tree-like hierarchical structure, HD-CapsNet is designed to leverage the inherent ontological order within the hierarchical label tree, thereby ensuring classification consistency across different levels. Additionally, we introduce a specialized loss function that promotes accurate hierarchical relationships while penalizing inconsistencies. This not only enhances classification performance but also strengthens the network’s robustness. We rigorously evaluate HD-CapsNet’s efficacy by benchmarking it against existing HMC methods across six diverse datasets: Fashion-MNIST, Marine-Tree, CIFAR-10, CIFAR-100, Caltech-UCSD Birds-200-2011, and Stanford Cars. Our results conclusively demonstrate that HD-CapsNet excels in learning hierarchical relationships and significantly outperforms the competition in various image classification tasks. Our implementation is available at https://github.com/tasrif-khondaker/HD-CapsNet.

Memetic multilabel feature selection using pruned refinement process

With the growing complexity of data structures, which include high-dimensional and multilabel datasets, the significance of feature selection has become more emphasized. Multilabel feature selection endeavors to identify a subset of features that concurrently exhibit relevance across multiple labels. Owing to the impracticality of performing exhaustive searches to obtain the optimal feature subset, conventional approaches in multilabel feature selection often resort to a heuristic search process. In this context, memetic multilabel feature selection has received considerable attention because of its superior search capability; the fitness of the feature subset created by the stochastic search is further enhanced through a refinement process predicated on the employed multilabel feature filter. Thus, it is imperative to employ an effective refinement process that frequently succeeds in improving the target feature subset to maximize the benefits of hybridization. However, the refinement process in conventional memetic multilabel feature selection often overlooks potential biases in feature scores and compatibility issues between the multilabel feature filter and the subsequent learner. Consequently, conventional methods may not effectively identify the optimal feature subset in complex multilabel datasets. In this study, we propose a new memetic multilabel feature selection method that addresses these limitations by incorporating the pruning of features and labels into the refinement process. The effectiveness of the proposed method was demonstrated through experiments on 14 multilabel datasets.

Assessment of consumer preferences in the context of multiple labels: the case of fishery and aquaculture products

Assessment of consumer preferences in the context of multiple labels: the case of fishery and aquaculture products