Analysis Of Datasets Research Articles

Roodmus: a toolkit for benchmarking heterogeneous electron cryo-microscopy reconstructions.

Conformational heterogeneity of biological macromolecules is a challenge in single-particle averaging (SPA). Current standard practice is to employ classification and filtering methods that may allow a discrete number of conformational states to be reconstructed. However, the conformation space accessible to these molecules is continuous and, therefore, explored incompletely by a small number of discrete classes. Recently developed heterogeneous reconstruction algorithms (HRAs) to analyse continuous heterogeneity rely on machine-learning methods that employ low-dimensional latent space representations. The non-linear nature of many of these methods poses a challenge to their validation and interpretation and to identifying functionally relevant conformational trajectories. These methods would benefit from in-depth benchmarking using high-quality synthetic data and concomitant ground truth information. We present a framework for the simulation and subsequent analysis with respect to the ground truth of cryo-EM micrographs containing particles whose conformational heterogeneity is sourced from molecular dynamics simulations. These synthetic data can be processed as if they were experimental data, allowing aspects of standard SPA workflows as well as heterogeneous reconstruction methods to be compared with known ground truth using available utilities. The simulation and analysis of several such datasets are demonstrated and an initial investigation into HRAs is presented.

Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristics

This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale experiment dataset (N = 1,206) indicate that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism. Furthermore, age and income are closely connected to cognitive gaps in understanding discriminatory AI outcomes. These findings highlight the importance of promoting digital literacy skills and enhancing digital self-efficacy to maintain trust in AI and beliefs in AI usefulness and safety. The findings also suggest that the disparities in understanding problematic AI outcomes may be aligned with economic inequalities and generational gaps in society. Overall, this study sheds light on the socio-technological system in which complex interactions occur between social hierarchies, divisions, and machines that reflect and exacerbate the disparities.

Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models.

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models.

Over-expression of Transmembrane Protein 158 Predicts Aggressive Tumor Behavior and Poor Prognosis in Lung Cancer.

Transmembrane protein 158 (TMEM158) has emerged as a potential contributor to cancer progression. While TMEM158 has been studied in various cancer types, its role in lung cancer remains unclear. Kaplan-Meier curves were used to evaluate the association between TMEM158 expression and overall survival rates. Gene set enrichment analysis (GSEA) was performed to explore pathways related to TMEM158, and sequence analysis was conducted to identify putative hypoxia-responsive element (HRE) sites in the TMEM158 promoter region. Hypoxic conditions were induced, and TMEM158 expression levels were measured by qPCR. The effect of hypoxia-inducible factor-1α (HIF-1α) depletion on TMEM158 expression was also examined. Additionally, lung cancer cells with either over-expressed or reduced TMEM158 levels were analyzed for epithelial-mesenchymal transition (EMT) and cell migration. Analysis of clinical datasets revealed elevated TMEM158 expression in tumors, particularly in patients with advanced stages. High TMEM158 expression was correlated with lower overall survival. TMEM158 was associated with EMT, hypoxia, and other tumor-promoting pathways. Under hypoxic conditions, TMEM158 expression was at least partially induced in a HIF-1α-dependent manner. Functional studies showed that over-expression of TMEM158 promoted EMT and increased lung cancer cell migration, while depletion of TMEM158 reduced these effects, indicating its role in aggressive tumor behavior. TMEM158 is highly expressed in lung cancer, is associated with hypoxia, and promotes EMT and cell migration. These findings suggest TMEM158 as a potential target for lung cancer therapies.

Numerical Methods and Computation in Financial Mathematics: Solving PDEs, Monte Carlo Simulations, and Machine Learning Applications

Abstract. This paper delves into three fundamental numerical methods and computational techniques in financial mathematics: Finite Difference Methods (FDM), Monte Carlo Simulations (MCS), and Machine Learning (ML) applications. Finite Difference Methods are widely utilized for solving partial differential equations (PDEs) in option pricing, with various schemes offering different stability and convergence properties. Monte Carlo Simulations provide a powerful approach for pricing complex derivatives and risk management, addressing the challenges of high-dimensionality and computational complexity. Machine Learning has revolutionized predictive modeling in finance, enabling sophisticated analysis of large datasets to uncover hidden patterns and enhance trading strategies. Through a detailed examination of these methods, including specific examples and data, this paper highlights their theoretical foundations, practical implementations, and the advancements they bring to computational finance. By bridging theoretical approaches with practical applications, we aim to offer insights into the future directions and challenges in financial mathematics.

The future of physiological research: A greater understanding of female master athletes and aging?

High caliber master athletes provide a valuable model for studying inherent physiological aging and performance capacity, without the confounding factor of physical inactivity. Despite the remarkable achievements of female master athletes, their participation rates remain significantly lower than those of their male counterparts, particularly at more advanced ages. This review examines the biological sex gap in sports participation among master athletes and the subsequent disparity in empirical research, thereafter exploring possible contributing factors. It highlights the importance of studying female master athletes to better understand the aging process and offers recommendations to address current evidence gaps. The need for more comprehensive mechanistic data on highly trained older women, novel cataloguing and analysis of real-world datasets, case studies/series, and longitudinal research are also emphasized. Although analyzing the records of female master athletes as a surrogate to determine age-related physiological and performance changes is a common approach, the process may be hindered by the considerably lower participation rates of women. Therefore, an important step toward bridging these gaps is the longitudinal, integrative study of female athletes engaged in lifelong exercise. Such analyses would improve our understanding of senescence in women and may inform interventions targeting the promotion of physical function in older adults.

How Do Satellite Precipitation Products Affect Water Quality Simulations? A Comparative Analysis of Rainfall Datasets for River Flow and Riverine Nitrate Load in an Agricultural Watershed

Excessive nitrate loading from agricultural runoff leads to substantial environmental and economic harm, and although hydrological models are used to mitigate these effects, the influence of various satellite precipitation products (SPPs) on nitrate load simulations is often overlooked. This study addresses this research gap by evaluating the impacts of using different satellite precipitation products—ERA5, IMERG, and gridMET—on flow and nitrate load simulations with the Soil and Water Assessment Tool Plus (SWAT+), using the Tar-Pamlico watershed as a case study. Although agricultural activities are higher in the summer, this study found the lowest nitrate load during this season due to reduced runoff. In contrast, the nitrate load was higher in the winter because of increased runoff, highlighting the dominance of water flow in driving riverine nitrate load. This study found that although IMERG predicts the highest annual average flow (120 m3/s in Pamlico Sound), it unexpectedly results in the lowest annual average nitrate load (1750 metric tons/year). In contrast, gridMET estimates significantly higher annual average nitrate loads (3850 metric tons/year). This discrepancy underscores the crucial impact of rainfall datasets on nitrate transport predictions and highlights how the choice of dataset can significantly influence nitrate load simulations.

Multi-relation spatiotemporal graph residual network model with multi-level feature attention: A novel approach for landslide displacement prediction

Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters. While most existing prediction methods focus on time-series forecasting for individual monitoring points, there is limited research on the spatiotemporal characteristics of landslide deformation. This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention (MFA-MRSTGRN) that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion. This model integrates internal seepage factors as data feature enhancements with external triggering factors, allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset. The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules: multi-level feature attention, temporal-residual decomposition, spatial multi-relational graph convolution, and spatiotemporal fusion prediction. This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets, facilitating adaptive exploration of the evolving multi-relational, multi-dimensional spatiotemporal complexities in landslides. When applying this model to predict the displacement of the Liangshuijing landslide, we demonstrate that the MFA-MRSTGRN model surpasses traditional models, such as random forest (RF), long short-term memory (LSTM), and spatial temporal graph convolutional networks (ST-GCN) models in terms of various evaluation metrics including mean absolute error (MAE = 1.27 mm), root mean square error (RMSE = 1.49 mm), mean absolute percentage error (MAPE = 0.026), and R-squared (R2 = 0.88). Furthermore, feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models. This research provides an advanced and reliable method for landslide displacement prediction.

Innovation’s false spring: U.S. export controls and Chinese patent quality

This paper evaluates the effect of U.S. export restrictions, enacted in 2018, on China’s patent production. An analysis of patent-level datasets indicates a significant surge in Chinese patent filings in sanctioned technological domains. This quantitative growth, however, is accompanied by a decrease in patent quality, evidenced by reduced citation counts and market worth. Specifically, state-owned enterprises (SOEs) have witnessed a substantial rise in patent filings post-2018, while private firms exhibit a more modest increase. Despite the surge in patent quantity, the overall quality of patents—particularly those originating from SOEs—has deteriorated significantly. This study contributes to the body of research concerning the economic consequences of the U.S.-China trade dispute and offers original insights into the implications of trade sanctions on innovation quality.

A beginner's guide to supervised analysis for mass cytometry data in cancer biology.

Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.

Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e., macaques) relative to multivariate features. We find that integrating multiple data modes with MFs helps remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including an experimentally validated B cell depletion that induced network-wide shifts without reducing vaccine protection.

A Comprehensive Empirical Analysis of Data Sets, Regression-Based Feature Selectors, and Linear SVM Classifiers for Intrusion Detection Systems

A Comprehensive Empirical Analysis of Data Sets, Regression-Based Feature Selectors, and Linear SVM Classifiers for Intrusion Detection Systems

Design and Implementation of a Personalized Text-Based Recommendation System

Abstract: We all know that in the competitive & ever evolving Age of content -less is more and relevancy increases engagementwhich should be focal on UX. In this paper, we describe the designing and implementation of a text-based recommendation system that specializes in offering automatic personalized recommendations for textual content-dependent on user preferences, and behavior. Analyze text data and receive tailored, contextual recommendations using Natural Language Processing (NLP) and machine learning methods. This system combines collaborative, content-based and hybrid filtering techniques along with adaptive learning to improve the accuracy of recommendations over time as user interests change. Analysis of real-world datasets indicates that the system dramatically enhances recommendation precision and user satisfaction. The results highlight the promise of tailored recommendations in areas like commerce and digital media to online education, primarily filtering great volumes of information for a more customized experience

LINC01320 facilitates cell proliferation and migration of ovarian cancer via regulating PURB/DDB2/NEDD4L/TGF-β axis

Ovarian cancer (OC) is one of the most prevalent and lethal malignancies affecting the female reproductive system, due to its tendency for metastasis and recurrence. This study identified the overexpression of LINC01320 (or long intergenic nonprotein coding RNA 1320) in tissues of ovarian cancer through the analysis of patient samples and online datasets. In vitro and in vivo experiments demonstrate that silencing of LINC01320 expression led to inhibition of proliferation and metastasis of OC cells. RNA pull-down followed by liquid chromatography tandem mass spectrometry (RNA pull-down-LC-MS/MS) revealed that LINC01320 interacted with purine-rich element binding protein B (PURB), a transcriptional repressor. Furthermore, the RNA-seq analysis identified damage-specific DNA binding protein 2 (DDB2) as a major common target of LINC01320 and PURB. Mechanistically, LINC01320 could recruit PURB to the promoter region of DDB2 to repress DDB2 transcription; thus, promoting the expression of NEDD4L and impeding the TGF-β/SMAD signaling pathway, and ultimately facilitating the progression of OC. Finally, rescue experiments confirmed the involvement of the DDB2/NEDD4L/TGF-β axis in LINC01320-mediated OC progression. In conclusion, this study unveils for the first time the pivotal function of the LINC01320/PURB/DDB2/NEDD4L/TGF-β axis and explores its prospective clinical implications in OC.

Motif mapping during chickpea germination reveals a complex sequential activation of different proteolytic activities.

Despite the importance of grains and legumes in the human diet, little is known regarding peptide release and the temporal changes of protease activities during seed germination. LC/MS-MS peptidomic analysis of two cultivars of germinating chickpea followed by computational analyses indicated cleavage dominated by proteases with a single position preference (mainly before (P1) or after cleavage (P1'): L at P2 (cysEP-like); R or K at P1 (vignain-like), N or Q at P1 (legumain-like); and previously unidentified K, R, A and S at P1'; A at P2'). While P1 N cleavages were relatively constant, P1' K/R preferences were high in soaked garbanzo (kabuli) seeds, declined by four days, and returned at six days, but were much rarer in the brown (desi) cultivar. Late Embryogenesis Associated (LEA) peptides were markedly released during early germination. Vicilin peptides rich in glutamic acid near their N-termini markedly increased with germination, consistent with strong proteolytic resistance, even to human digestion, as indicated by analyses of separate datasets. Thus, this first peptidomics study of seed germination proteolytic profiles unveils a complex cultivar-specific programme of sequential activation and inactivation of a series of proteases, associated with the differential release of peptides from different protein groups.

C→U transition biases in SARS-CoV-2: still rampant 4 years from the start of the COVID-19 pandemic.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the pandemic and post-pandemic periods has shown a remarkable capacity to adapt and evade human immune responses and increase its human-to-human transmissibility. The genome of SARS-CoV-2 is also increasingly scarred by the effects of multiple C→U mutations from host genome editing as a cellular defense mechanism akin to restriction factors for retroviruses. Through the analysis of large data sets of SARS-CoV-2 isolate sequences collected throughout the pandemic period and beyond, we show that C→U transitions have driven a base compositional change over time amounting to a net loss of C bases and accumulation of U's at a rate of approximately 0.2%-0.25%/decade. Most C→U substitutions occurred in the absence of the preferred upstream-base context or targeting of unpaired RNA bases previously associated with the host RNA editing protein, APOBEC 3A. The analyses provide a series of testable hypotheses that can be experimentally investigated in the future.

Stabilization system for solar loading thermography applied on cultural heritage objects exposed outdoors: the contribution of advanced algorithms and dual-branch U-Net

AbstractThis study investigates the use of solar loading thermography (SLT) for thermal non-destructive testing (TNDT) and image stabilization of cultural heritage objects, specifically focusing on a century-old ancient book. The irregular contours and deteriorated areas of the book posed significant challenges for feature extraction due to non-uniform temperature variations. To address these challenges, a convolutional neural network (CNN) based dual-branch network of U-Net was used to stabilize the dataset across three degrees of freedom with the ancient book. The stabilization process involved tracking feature lines across each frame of the time-domain datasets, correcting for frame misalignment caused by sample movement during prolonged data acquisition. The effectiveness of this stabilization technique was evaluated by comparing the results of principal component analysis (PCA), fast Fourier transform (FFT), and fast iterative filtering (FIF) algorithms before and after stabilization. Significant improvements were observed, particularly in the clarity and accuracy of defect detection, indicating that this technique provides a robust foundation for further analysis and processing of SLT datasets in cultural heritage preservation. This research demonstrates the potential of combining advanced image processing techniques with SLT to enhance the quality and reliability of NDT in preserving valuable historical artifacts.

Multidisciplinarity in Geoscientific Research

The multidisciplinary approach of geoscientific research in the academic setting underscores the increasing necessity of integrating diverse fields of knowledge to tackle complex interconnected issues. This multidisciplinary field seeks to uncover the complex operations of the Earth, encompassing the analysis of its past movements of tectonic plates, historical climates, and valuable natural resources like minerals and hydrocarbons. In this context, geospatial intelligence plays a crucial role by enabling the effective analysis and interpretation of extensive geographic datasets, thus enhancing our understanding of environmental dynamics. Nevertheless, advancements in technology, globalization, and the difficulties posed by environmental and climatic transformations demand an even more profound collaboration among various disciplines, facilitating an expansive viewpoint and yielding more holistic solutions to contemporary predicaments.

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functionally related ALI-associated hub genes in sepsis were identified by MCODE analysis and they were enriched in infection and inflammtory responses, lung and cardiovascular disease pathways. These hub genes stratified ALI-sepsis and sepsis and further stratified two subtypes of sepsis-ALI with differential ALI scores, hub gene expression patterns, and levels of immune cells. A seven-gene signature including TNFRSF1A, NFKB1, FCGR2A, NFE2L2, ICAM1 and SOCS3 and PDCD1 was derived from the hub genes. These genes were significantly implicated in immune and metabolism pathways. They were expressed in six circulatory immune cells based on analysis of a single cell RNA sequencing dataset. Furthermore, the seven-gene signature was corrobarated using by integrating 12 machine learning algorithms. A premium three-gene signature NFE2L2, FCGR2A and PDCD1 for differentiating ALI-sepsis from sepsis were also derived from the seven-gene signature based on analysis of the seven core hub genes by the machine learning algorithms. Furthermore, the expressions of hub genes were verified in sepsis mice models. Therefore, our study provided an avenue to develop a molecular tool for identify and characterize progression of acute lung injury associated with sepsis.

Non-elastic time series fuzzy clustering for efficient analysis of industrial data sets

In line with the green transition and digitalization trends, there is an increasing need for effective performance and health monitoring of production machines. Machine operation footprints resonate in quantities such as supply current and pneumatic line pressure, providing valuable insights when analyzed. Examining these low-level signals allows for monitoring and identification of changes in machine operation over time. A critical step in analyzing machine data is clustering, requiring an effective method for calculating time series cluster centroids. This paper introduces Error in Aligned series (ERAL), a novel method that generates a time series centroid that distills the fundamental shape of the underlying datasets. ERAL employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods. Our analysis shows that existing methods can create artificial spikes and plateaus, which ERAL mitigates; it remains faithful to the original data shape. Additionally, ERAL offers improvements in computational efficiency and prototype quality. We evaluate the method against Dynamic Time Warping (DTW)-based methods across various datasets, and apply it to a time series clustering task using a real-world industrial dataset. In combination with a fuzzy clustering algorithm, ERAL generates visually convincing clusters. By leveraging fuzzy membership concepts, it achieves robust and adaptable clustering outcomes that reflect real-world data complexity and ambiguity.