Meaningful Information Research Articles

The Biology in the Pattern: Metastatic organotropism and clinical outcome depend on DNA damage response and immune interactions in pancreatic cancer.

Metastatic cancer remains a leading cause of cancer-related mortality, with a 5-year survival rate of just 8% for pancreatic ductal adenocarcinoma (PDAC). Among patients with metastatic PDAC, those with liver metastases experience significantly worse outcomes compared to the rare cases of isolated lung metastases. Recent findings by Link and colleagues reveal that these distinct metastatic patterns reflect underlying biological differences beyond established molecular subtypes. Specifically, the authors curated a primary organotropism (pORG) gene signature that is enriched in the liver cohorts. In detail they found that high-pORG/liver-avid tumors are characterized by high replication stress, enriched DNA repair pathways, and an immunosuppressive microenvironment, whereas low-pORG/lung-avid tumors display stronger immune infiltration, higher T cell density and reduce the richness of TCR repertoire, and better survival outcomes. These insights suggest that the clinical pattern of metastasis provides meaningful information about tumor biology and prognosis, complementing current subtype classifications in PDAC.

Genetically encoded biosensors as gateways to retrograde redox signalling in live plants.

In eukaryotic cells, protein supply to organelles varies depending on the stage of development and, in particular, on the exposure to environmental challenges. Adequate protein supply in terms of quality and quantity relies on sophisticated retrograde signalling systems that enable appropriate responses to the respective stress situations. Among many other retrograde signals, reactive oxygen species, that are being generated during the initial stress response, are thought to be involved in transduction of redox-related signals that may also involve multiple redox pairs such as NAD(P)H/NAD(P)+ and redox-active metabolites such as glutathione. Deciphering such signals requires detailed knowledge of their amplitudes and temporal and spatial dynamics. Genetically encoded biosensors based on fluorescent proteins have been developed for a number of different redox-related physiological parameters and can be monitored in living cells, tissues and even whole plants using a variety of instruments adapted to the respective resolution requirements, thus opening gateways to retrograde signalling in plant cells. This review summarizes and critically evaluates current probes and devices used to monitor sensor fluorescence. It also outlines how biosensors can be used in combination with genetic and pharmacological approaches, to extract meaningful information and dissect the retrograde redox signalling systems in living plants.

Investigating AI technology use in English studies for the explanation and analysis of biomechanical studies’ results

The use of artificial intelligence (AI) technology in English education has promised new possibilities in the explanation and interpretation of technical biomechanical research analysis. This article investigates how artificial intelligence is filling the gap between factual biomechanical data and relevant, meaningful information needed from an educational perspective, by making it possible for different groups to comprehend it. Instructors, educators, and learners are now able to accomplish much more by utilizing visualizations, language processing (NLP), and adaptive learning. This article investigates the technology-related barriers to interdisciplinary communication, analyzes the efficacy of AI-related tools deployed, and presents a case study on the use of AI instruction and guidance on fundamental biomechanical concepts. The results from this study suggest that AI may provide fresh avenues for making biomechanical research more engaging to English learners, thus improving scientific literacy and fostering interdisciplinary collaboration. The work ends with some comments intended for educators and researchers to look for smarter ways of manipulating AI technology in English teaching and learning in a science-focused world.

Semantic approaches for query expansion: taxonomy, challenges, and future research directions

The internet has been inundated with an ocean of information, and hence, information retrieval systems are failing to provide optimal results to the user. In order to meet the challenge, query expansion techniques have emerged as a game-changer and are improving the results of information retrieval significantly. Of late, semantic query expansion techniques have attracted increased interest among researchers since these techniques offer more pertinent and practical results to the users. These allow the user to retrieve more meaningful and useful information from the web. Currently, few research works provide a comprehensive review on semantic query expansion; usually, they cannot provide a full view on recent advances, diversified data application, and practical challenges. Therefore, it is imperative to go deep in review in order to explain these advances and assist researchers with concrete insights for future development. This article represents the comprehensive review of the query expansion methods, with a particular emphasis on semantic approaches. It overviews the recent frameworks that have been developed within a period of 2015–2024 and reviews the limitations of each approach. Further, it discusses challenges that are inherent in the semantic query expansion field and identifies some future research directions. This article emphasizes that the linguistic approach is the most effective and flexible direction for researchers to follow, while the ontology approach better suits domain-specific search applications. This, in turn, means that development of the ontology field may further open new perspectives for semantic query expansion. Moreover, by employing artificial intelligence (AI) and making most of the query context without relying on user intervention, improvements toward the optimal expanded query can be achieved.

How nanoinformatics could pave the way to safer design of engineered nanomaterials?

Applications of nanotechnology have rapidly expanded across various fields, including materials, energy, medicine, and the environment. The toxicity of nanomaterials (NMs) and nanotechnology to living organisms, as well as potential negative environmental impacts, poses significant challenges. Laboratory approaches for assessing the negative impacts of NMs are costly, time-consuming, and frequently fall behind the development of novel materials. Therefore, intelligent systems of informatics to forecast their toxicity potentials are a possible alternative option. Research on nanotoxicology has generated extensive and diverse datasets. However, data alone does not equate to information. Since little is known about how to extract meaningful information from large streams of data, a broad discussion of potential concerns still exists. In this regard, we present a perspective on how the “big data” can change the paradigm toward data-centric computational materials research to decipher the toxicity mechanisms of NMs for their efficient risk assessment management to provide safe-by-design standards for the sustainable advancement of nanotechnology. We will also discuss the challenges of the current data-driven research into the nano realm.

GcDUO: an open-source software for GC × GC-MS data analysis.

Comprehensive 2D gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful analytical technique. However, the complexity and volume of data generated pose significant challenges for data processing and interpretation, limiting a broader adoption. Chemometric approaches, particularly multiway models like Parallel Factor Analysis (PARAFAC), have proven effective in addressing these challenges by enabling the extraction of meaningful chemical information from multi-dimensional datasets. However, traditional PARAFAC is constrained by its assumption of data tri-linearity, which may not be valid in all cases, leading to potential inaccuracies. To overcome these limitations, we present GcDUO, an open-source software implemented in R, designed specifically for the processing and analysis of GC × GC-MS data. GcDUO integrates advanced chemometric methods, including both PARAFAC and PARAFAC2, for a more accurate and comprehensive analysis. PARAFAC is particularly useful for deconvoluting overlapping peaks and extracting pure chemical signals, while PARAFAC2 relaxes de tri-linearity constraint, allowing the alignment between samples. The software is structured into six modules-data import, region of interest (ROI) selection, deconvolution, peak annotation, data integration, and visualization-facilitating comprehensive and flexible data processing. GcDUO was validated against the gold-standard software for comprehensive GC, demonstrating a high correlation (R2 = 0.9) in peak area measurements, confirming its effectiveness and reliability. GcDUO provides a valuable, open-source platform for researchers in metabolomics and related fields, enabling more accessible and customizable GC × GC-MS data analysis.

Development of a Biomechanical Diagnosis and Analysis System Using a Textile Elbow Angle Sensor: Integrating Inverse Dynamics and Multi-Layer Perceptron Techniques

The recent development of algorithms through artificial intelligence and the ability to measure the human body through soft textile sensors has enabled the provision of meaningful information to the wearer. In this study, a sensor sleeve using a textile elbow angle sensor that can measure the bending and relaxation of the elbow was manufactured and measured. In addition, biomechanical data from Biomechanical of Bodies (BoB)-4, a software capable of inverse dynamics that can optimally calculate the load on human joints and segments during exercise, was collected. A continuous system of resistance angle and angle biomechanical data was designed with an artificial intelligence multilayer perceptron (MLP) algorithm, and the accuracy and output results were checked. Consequently, the accuracy of MLP1 and MLP2 is exceedingly high, at approximately 0.80 and 1.00, respectively. The biomechanical data of the system is comparable to that of BoB, rendering it suitable for providing reliable information to the wearer. Based on this study, it is possible to develop algorithms and systems that can perform biomechanical analysis for various exercise movements in the future.

An Exploration of the Content and Language Used in Publicly Available National Health Service Patient Information Leaflets for People Considering Shoulder Replacement Surgery: A Qualitative Study.

The decision to undergo total shoulder replacement surgery is a major one and should be a joint one between the patient and surgeon. It is important that patients are provided with accessible, meaningful and appropriate information to enable an informed decision. The aim of this study was to explore the content and language used within publicly available information leaflets produced by UK National Health Service (NHS) Trusts for people considering shoulder replacement surgery and to consider how this may influence surgical decision making. An online search of publicly available NHS shoulder replacement patient information leaflets (PIL) was undertaken. The text within the PIL was analysed using reflexive thematic analysis. Thirty-eight PIL were identified. The volume of information and content varied greatly. All PIL discussed the clinical problem, mainly within a biomedical framework and from a clinician's perspective in which normal shoulder anatomy was contrasted with shoulder pathology. Only a minority of the PIL discussed non-surgical treatments and of those that did, such approaches were predominantly portrayed as a temporary management option only, whilst surgery was frequently portrayed as the optimum treatment. There is variation in the content of NHS shoulder replacement PIL. The content and language used may not adequately support people in making an informed decision about whether surgery is the right treatment option for them. We need to better understand the information needs of people considering shoulder replacement surgery, and provide information that is accessible, culturally sensitive, and capable of facilitating shared decision making.

Can Growth Monitoring and Promotion Accurately Diagnose or Screen for Inadequate Growth of Individual Children? ACritical Review of the Epidemiologic Foundations.

Can Growth Monitoring and Promotion Accurately Diagnose or Screen for Inadequate Growth of Individual Children? ACritical Review of the Epidemiologic Foundations.

Modeling the Impact of Traffic Density on Critical Gap Distribution at Unsignalized Intersections

This study constructs mathematical models to evaluate the connection between critical gap dimensions and traffic flow volumes at traffic intersections without signals. Drivers need critical gap as the briefest gap between vehicles to safely move through the road. Traditional theoretical models maintain fixed traffic density patterns even though actual conditions from urban roads typically exhibit changing densities. Including fluctuating traffic density in this model will improve accuracy when studying how changing traffic conditions influence critical gap measurements. The model applies exponential distribution to compute acceptance probabilities of different gap sizes while traffic density adjusts. In more congested traffic conditions drivers accept shorter gaps because the critical gap becomes smaller. The conclusion matches what existing traffic flow theories have previously demonstrated. This model delivers meaningful information regarding traffic management procedures and road intersection design standards particularly for city areas experiencing shifting traffic congestion throughout the day.

Quantification of the Available Information of Hydrological Datasets: Stabilizing the Estimator of Multivariate Mutual Information

Abstract The Available Information contained in a hydrological dataset represents the meaningful information per timestep that enters a rainfall-runoff model. To estimate this quantity, Independent Component Analysis (ICA) must be performed to transform the original observations into statistically independent signals. However, ICA algorithms may detect many sets of nearly independent signals suitable for this transformation, posing the question of which set is the optimal one. In the present paper, it is proposed that the components of the optimal set must share the least total pairwise mutual information among all sets since the mutual information of a pair of components is a measure of their statistical independence. This novel approach of estimating the available information of a dataset is applied to five basins in Thessaly, Greece and it is compared to the standard approach of taking the average value occurring from multiple ICA runs. It is illustrated that discarding ICA solutions with high total pairwise mutual information shared between their components stabilizes the estimator of Available Information and increases its precision. This comes with the cost of additional computation time since multiple evaluations of bivariate mutual information are needed.

On author self-citations as carriers of scientific topic structure information

Abstract Author self-citations are a somewhat controversial phenomenon. Some scholars maintain they are a normal, even indispensable, part of scientific referencing practice, while others claim they are frequently an expression of vanity and self-promotion. Citations are the basic data for citation network clustering, an important approach to creating bottom-up, data-driven, global taxonomic systems of research publications. Thus the topical information content of self-citations is of particular interest in this context. Since it is not yet known how author self-citations affect such systems, we study the question of their influence on cluster solution topic quality in a citation network by experimentally re-weighting self-citation edges by increasing and decreasing their link weights according to self-citation status and strength. As a case study, we investigate data on the field of astronomy and astrophysics. We assess the effects of self-citation manipulations by evaluating the quality of the resulting clustering solutions using diverse external data containing meaningful topic-structural information, namely topical journal special issues, co-usage data from scientific literature database search logs, grant funding data, and intellectual paper-level classification assignments. We find that we can reliably improve clustering solution quality by emphasizing self-citation link weights.

Exploring the role of AI in shaping social behavior: An Intersectional psychological perspective on financial risk assessment through digital platforms

Artificial intelligence analytics in digital finance platforms is important in the modern digital world. AI can conduct analytics quickly and provide the outcomes for the system users to make informed, data-driven conclusions. AI can scan through large datasets and provide meaningful information on social media platforms, historical quantitative transactions, and finances to give critical findings, unlike traditional systems. This review article assessed previous research articles on financial risk evaluation using AI analytics in the finance industry and digital finance platforms. The outcomes outlined the capabilities of financial risks evaluated with the help of AI in digital finance platforms. The key identified risks were credit risks, market risks, operational risks, fraud risks, and compliance risks. The study outlined the key capabilities of AI in shielding firms against such risks through predictive analytics, anomaly detection, sentiment analysis, and credit scoring. The AI systems should be hosted on the cloud to have access to large datasets to give accurate, data-driven conclusions. The identified challenges are algorithm bias, data privacy, regulatory compliance (especially across platforms and countries), and skill gaps in the market. In conclusion, using AI in digital finance platforms has increased the efficiency in making informed decisions for sustainability and strategic growth.

COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell-type annotation. By transferring knowledge between scRNA-seq and ST data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets. In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments. The COME is freely available in GitHub (https://github.com/cindyway/COME).

Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.

Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by pathologists in the diagnosis and grading of many tumors, particularly malignant tumors. Large datasets such as TCGA and the Human Protein Atlas, in combination with emerging machine learning and statistical modeling methods, such as feature extraction and deep learning techniques, can be used to extract meaningful knowledge from images of nuclei, particularly from cancerous tumors. Here, we describe a new technique based on the mathematics of optimal transport for modeling the information content related to nuclear chromatin structure directly from imaging data. In contrast to other techniques, our method represents the entire information content of each nucleus relative to a template nucleus using a transport-based morphometry (TBM) framework. We demonstrate that the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types. In particular, we demonstrate morphological differences capable of distinguishing nuclear features along the spectrum from benign to malignant categories of tumors across different cancer tissue types, including tumors derived from liver parenchyma, thyroid gland, lung mesothelium, and skin epithelium. We believe these proof-of-concept calculations demonstrate that the TBM framework can provide the quantitative measurements necessary for performing meaningful comparisons across a wide range of datasets and cancer types that can potentially enable numerous cancer studies, technologies, and clinical applications and help elevate the role of nuclear morphometry into a more quantitative science.

EmoBERTa-X: Advanced Emotion Classifier with Multi-Head Attention and DES for Multilabel Emotion Classification

The rising prevalence of social media turns them into huge, rich repositories of human emotions. Understanding and categorizing human emotion from social media content is of fundamental importance for many reasons, such as improvement of user experience, monitoring of public sentiment, support for mental health, and enhancement of focused marketing strategies. However, social media text is often unstructured and ambiguous; hence, extracting meaningful emotional information is difficult. Thus, effective emotion classification needs advanced techniques. This article proposes a novel model, EmoBERTa-X, to enhance performance in multilabel emotion classification, particularly in informal and ambiguous social media texts. Attention mechanisms combined with ensemble learning, supported by preprocessing steps, help in avoiding issues such as class imbalance of the dataset, ambiguity in short texts, and the inherent complexities of multilabel classification. The experimental results on the GoEmotions dataset indicate that EmoBERTa-X has outperformed state-of-the-art models on fine-grained emotion-detection tasks in social media expressions with an accuracy increase of 4.32% over some popular approaches.

Enhanced PolSAR Image Segmentation with Polarization Channel Fusion and Diffusion-Based Probability Modeling

With the advancement of polarimetric synthetic aperture radar (PolSAR) imaging technology and the growing demand for image interpretation, extracting meaningful land cover information from PolSAR images has become a key research focus. To address the segmentation challenge, we propose an innovative method. First, features from co-polarization and cross-polarization channels are separately used as dual inputs, and a cross-attention mechanism effectively fuses these features to capture correlations between different polarization information. Second, a diffusion framework is employed to jointly model target features and class probabilities, aiming to improve segmentation accuracy by learning and fitting the probabilistic distribution of target labels. Finally, experimental results demonstrate that the proposed method achieves superior performance in PolSAR image segmentation, effectively managing complex polarization relationships while offering robustness and broad application potential.

Mapping AI ethics narratives: evidence from Twitter discourse between 2015 and 2022

The ethical issues that arise in the development of AI technologies are closely linked to public engagement. Although Twitter, as an online public sphere, provides a platform for exploring AI ethics discourse, it is difficult for current research to effectively extract fine-grained but meaningful information from the vast amount of social media data. To address this challenge, this paper proposes a research framework for the fine-grained exploration of AI ethics discourse on Twitter. The framework consists of two main parts: (1) combining neural networks with large-scale language models to construct a hierarchically structured topic framework that not only extracts popular topics of public interest, but also highlights smaller, yet significant voices; (2) using narrative metaphors to achieve the integration of fragmented information across levels and topics, ultimately presenting a complete story to help the public better understand the evolution of topics within AI ethics discourse. Our research has revealed that the most significant concern in the current AI ethics discourse is the lag in AI-related laws and ethical guidelines. It also shows that the integration of AI technology with the humanities is essential to promote a good public society. Through cross-level fine-grained mining, this study uncovers information hidden beneath the noise interference, which helps policymakers make targeted adjustments or improvements to policies. In addition, this research framework provides a reference for fine-grained mining of other specific issues in social media data.

Improving Data Security in Cloud Computing Using Data Fragmentation

Cloud computing has revolutionized data storage and processing by offering scalability, flexibility, and cost efficiency. However, it introduces significant security challenges, particularly concerning data privacy and protection against unauthorized access. Traditional encryption methods, while effective, are not always sufficient to mitigate sophisticated attacks such as data breaches or insider threats. Data fragmentation emerges as a robust supplementary approach to enhance data security in cloud environments. This paper explores the concept of data fragmentation, which involves dividing data into smaller fragments and distributing them across multiple storage nodes or regions. By storing fragmented data in disparate locations and combining it with encryption, the risk of unauthorized data access is significantly reduced. Even if one fragment is compromised, the attacker cannot reconstruct meaningful information without access to all fragments and decryption keys.

Exploring trajectories in supply chain integration: a systematic analysis of emerging themes

Abstract The purpose of this research is to holistically examine the development of supply chain integration (SCI) domain over the preceding economic cycle to project emerging themes that will shape the trajectory of future research. We conduct a systematic literature review to explore the trajectories in SCI domain with articles retrieved from aggregating databases. A total of 152 articles, which satisfied the evaluation and selection process, were analysed. The analysis reveals the intricate correlation between SCI and performance and highlights the progressively pivotal role of blockchain, artificial intelligence, and environmental exigency in SCI adoption. Our findings suggest the need for further research into the impact of SCI on corporate social responsibility performance and on SCI adoption practice in specific industries. Tiered sample collection research is encouraged in this field. However, this paper only included high–ranking peer-reviewed publications. Future SLR papers can incorporate wider sources of data to include meaningful information. By identifying and elucidating key themes, this study offers valuable insights that can inform and guide future research directions in the field of SCI.