Unified Analysis Research Articles

A foundation model unlocks unified biomedical image analysis

A foundation model unlocks unified biomedical image analysis

Unified analysis of niche overlap between coyotes (Canis latrans) and gray foxes (Urocyon cinereoargenteus)

Unified analysis of niche overlap between coyotes (Canis latrans) and gray foxes (Urocyon cinereoargenteus)

A unified Jacobi-Ritz-spectral BEM for vibro-acoustic behavior of spherical shell

In order to solve the vibration and acoustic characteristics of spherical shell in light fluid environment, based on Jacobi-Ritz-spectral BEM, a unified analysis formula for acoustic vibration of spherical shell under arbitrary boundary conditions is established. Based on the First-order shear deformation theory (FSDT) and domain decomposition method (DDM), the theoretical model of spherical shell structure is established. The improved Jacobi polynomial is innovatively used to construct the displacement tolerance function of the spherical shell. Based on the spectral Kirchhoff-Helmholtz integral formula, the theoretical model of the acoustic fluid outside the spherical shell is established. The special form of Jacobi polynomial Chebyshev polynomial is used to describe the excitation sound pressure on the spherical shell segment, so as to ensure that the generalized coordinates of the structure can be perfectly matched with the acoustic boundary element nodes. In addition, the integration along the meridian of the shell is used to control the movement of the fluid, which simplifies the surface integration. The CHIEF method is used to solve the problem of non-unique solution of acoustic variables of rotary structure. Compared with the published literature, numerical simulation results and experimental results, the proposed method has higher calculation accuracy. In addition, based on this method, the influence of boundary conditions, geometric dimensions and other factors on the acoustic and vibration characteristics of spherical shells is discussed, which accumulates data for analyzing the acoustic and vibration behavior of spherical shells.

Possession in Categorization across Language and Category

Abstract Menon & Pancheva (2014) conjecture that the roots of all property concepts (words expressing the semantic content of adjectives in English; Dixon 1982, Thompson 1989), independent of syntactic category, have an abstract mass noun semantics in the spirit of Francez & Koontz-Garboden (2015, 2017). According to this conjecture, variation in the morphosemantics of categorization masks this underlying universality, leading to superficial variation in the morphosyntax of predication within this class of words. Supporting this line of analysis, we show that morphologically transparent behavior of nouns, verbs and adjectives in Ulwa, Wá·šiw, and English reveals that despite variation in category, property concepts in these languages receive a unified analysis on the view that mass-denoting roots are categorized by nominalizing, verbalizing, and adjectivizing heads, respectively, that introduce a possessive semantics.

Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea.

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge-discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge-discharge area can be summarized into seven representative profiles.

Dual edge-embedding graph convolutional network for unified aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) mainly involves three elements, i.e., aspect, target, and sentiment. Most existing research focuses on aspect-sentiment or target-sentiment separately, while connection among the three elements is still underexploited. In order to extract three interrelated elements from a sentence simultaneously, we propose a unified end-to-end model, which is based on one aspect via a virtual edge attention mechanism and a graph convolutional network (GCN) to detect related targets and to analyze the corresponding sentiment polarity. The virtual edges are decided by the association intensity between the aspect and the words of the sentence. Instead of naively regarding all connecting edges as binary values, we design a novel GCN with a dual edge-embedding method to help information flow among each word in the sentence and between the aspect category and the sentence. Extensive experiments on two open datasets show that our model outperforms the state-of-the-art models in the aspect-target-sentiment triples detection task and its two subtasks.

Experimental Study on the Optimal Operating Envelops of Ejection Supercharging Technology for Unconventional Natural Gas

Abstract Ejection supercharging technology could make use of the energy of high pressure gas wells to assist the development of low pressure unconventional natural gas. But at present, the process of adaptability analysis is complicated, the period is long, and the process implementation is difficult in process application. In this paper, based on the production situation of unconventional natural gas, experiments are carried out to investigate the impact of structural and operational parameters on the performance of ejection supercharging. According to analyze the relationship among high, middle and low pressure inlet conditions and their influence on operational performance, as well as the dimensionless processing of pressure parameters, the optimal operating envelops of Ejection supercharging technology for unconventional natural gas is established. Furthermore, A chart is proposed with variable parameters under different conditions to determine the target gas wells for this technology, allowing for a rapid analysis and determination of whether gas wells are suitable for Ejection supercharging technology. And it also get the pressure reduction range of the process acting on the low pressure well, which provides a reference for analyzing the application effect of the process. Finally, The results of the case analysis demonstrate a 100% match rate with the chart established in this paper. The research results greatly simplify the process of well selection, and transform the analysis for specific working conditions one by one into a unified analysis for multiple working conditions, consequently lays a solid foundation for the widespread application of the ejection supercharging technology for unconventional natural gas.

Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning.

Protein aggregation (PA) is a critical phenomenon associated with Alzheimer's and Parkinson's disease. Recent studies have suggested that factors like aggregation-prone regions (APRs) and β-strand interactions are crucial in understanding such behavior. While experimental methods have provided valuable insights, there has been a shift towards computational strategies, particularly machine learning, for their efficacy and speed. The challenge, however, lies in effectively incorporating structural information into these models. This study constructs a Graph Convolutional Network (GCN) to predict PA scores with the expanded and refined Protein Data Bank (PDB) and AlphaFold2.0 dataset. We employed AGGRESCAN3D 2.0 to calculate PA propensity and to enhance the dataset, we systematically separated multi polypeptide chains within PDB data into single polypeptide chains, removing redundancy. This effort resulted in a dataset comprising 302 032 unique PDB entries. Subsequently, we compared sequence similarity and obtained 22 774 Homo sapiens data from AlphaFold2.0. Using this expanded and refined dataset, the trained GCN model for PA prediction achieves a remarkable coefficient of determination (R 2) score of 0.9849 and a low mean absolute error (MAE) of 0.0381. Furthermore, the efficacy of the active learning process was demonstrated through its rapid identification of proteins with high PA propensity. Consequently, the active learning approach achieved an MAE of 0.0291 in expected improvement, surpassing other methods. It identified 99% of the target proteins by exploring merely 29% of the entire search space. This improved GCN model demonstrates promise in selecting proteins susceptible to PA, advancing protein science. This work contributes to the development of efficient computational tools for PA prediction, with potential applications in disease diagnosis and therapy.

A Unified Analysis of AdaGrad With Weighted Aggregation and Momentum Acceleration.

Integrating adaptive learning rate and momentum techniques into stochastic gradient descent (SGD) leads to a large class of efficiently accelerated adaptive stochastic algorithms, such as AdaGrad, RMSProp, Adam, AccAdaGrad, and so on. In spite of their effectiveness in practice, there is still a large gap in their theories of convergences, especially in the difficult nonconvex stochastic setting. To fill this gap, we propose weighted AdaGrad with unified momentum and dubbed AdaUSM, which has the main characteristics that: 1) it incorporates a unified momentum scheme that covers both the heavy ball (HB) momentum and the Nesterov accelerated gradient (NAG) momentum and 2) it adopts a novel weighted adaptive learning rate that can unify the learning rates of AdaGrad, AccAdaGrad, Adam, and RMSProp. Moreover, when we take polynomially growing weights in AdaUSM, we obtain its O(log(T)/√T) convergence rate in the nonconvex stochastic setting. We also show that the adaptive learning rates of Adam and RMSProp correspond to taking exponentially growing weights in AdaUSM, thereby providing a new perspective for understanding Adam and RMSProp. Finally, comparative experiments of AdaUSM against SGD with momentum, AdaGrad, AdaEMA, Adam, and AMSGrad on various deep learning models and datasets are also carried out.

A population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases

The gut microbiome has been implicated in various human diseases, though findings across studies have shown considerable variability. In this study, we reanalyzed 6314 publicly available fecal metagenomes from 36 case-control studies on different diseases to investigate microbial diversity and disease-shared signatures. Using a unified analysis pipeline, we observed reduced microbial diversity in many diseases, while some exhibited increased diversity. Significant alterations in microbial communities were detected across most diseases. A meta-analysis identified 277 disease-associated gut species, including numerous opportunistic pathogens enriched in patients and a depletion of beneficial microbes. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.776) and high-risk patients from controls (AUC = 0.825), and it also performed well in external cohorts. These results offer insights into the gut microbiome’s role in common diseases in the Chinese population and will guide personalized disease management strategies.

MIMO Super-Twisting Controller using a passivity-based design

A novel MIMO Super-Twisting Algorithm is proposed in this paper for nonlinear systems with relative degree one, having a time and state-varying uncertain control matrix. The uncertainty is represented by a time and state-varying but unknown left matrix factor. Sufficient conditions for stability with full-matrix control gains are established, in contrast to the usual scalar gains. For this a smooth Lyapunov function, based on a passivity interpretation, is used. Moreover, continuous and homogeneous approximations of the classical discontinuous Super-Twisting algorithm are obtained, using a unified analysis method. Moreover, the proposed Super-Twisting algorithm is of the discontinuous type, in contrast to the usual unitary type.

Mixed material point method formulation, stabilization, and validation for a unified analysis of free-surface and seepage flow

This paper presents a novel stabilized mixed material point method (MPM) designed for the unified modeling of free-surface and seepage flow. The unified formulation integrates the Navier-Stokes equation with the Darcy-Brinkman-Forchheimer equation, effectively capturing flows in both non-porous and porous domains. In contrast to the conventional Eulerian computational fluid dynamics (CFD) solver, which solves the velocity and pressure fields as unknown variables, the proposed method employs a monolithic displacement-pressure formulation adopted from the mixed-form updated-Lagrangian finite element method (FEM). To satisfy the discrete inf-sup stability condition, a stabilization strategy based on the variational multiscale method (VMS) is derived and integrated into the proposed formulation. Another distinctive feature is the implementation of blurred interfaces, which facilitate a seamless and stable transition of flows between free and porous domains, as well as across two distinct porous media. The efficacy of the proposed formulation is verified and validated through several benchmark cases in 1D, 2D, and 3D scenarios. Conducted numerical examples demonstrate enhanced accuracy and stability compared to analytical, experimental, and other numerical solutions.

Can changes in corporate income tax rate affect corporate innovation?

The relationship between corporate income tax rates and innovation is a critical international issue. Existing studies often overlook the potential mechanisms of market competition and productivity heterogeneity. This paper addresses these gaps by integrating these elements into a unified mathematical analysis framework. Using China's corporate income tax reform as a quasi-natural experiment, this study empirically tests the model. The findings reveal that decreases in tax rates significantly foster innovation, while increases have the opposite effect. The underlying mechanism involves market competition, with the impact of tax changes being more pronounced in firms with higher productivity. This study deepens the understanding of the nexus between corporate income tax and innovation, providing new insights to refine tax policies that promote innovative economic development globally.

Towards more sustainable, nutritious, and affordable plant‐based milk alternatives: A critical review

AbstractThe consumer market has seen a surge in various types of plant‐based milk alternatives (PBMAs), driven by factors such as sustainability, ethics, and the availability of diverse options. However, the global adoption of PBMAs is still hindered by potential nutritional concerns and cost‐ineffectiveness. It is therefore paramount to understand the status and challenges of these alternatives, encompassing production, stabilization strategies, nutrition and health, digestion, and environmental impacts. This review provides a comprehensive examination of these aspects. We begin by scrutinizing the production processes and formulation strategies of PBMAs, followed by an exploration of fundamental stabilization aspects, including homogenization, emulsification, thickening, and quantitative measurements of stabilization. Additionally, we discuss their nutrition facts and potential health benefits, as well as potential differences in the digestion of nutrients from PBMAs. Importantly, their challenges and future directions are discussed individually, highlighting the need for a fundamental understanding and effective food technologies to develop sustainable, nutritious, tasty, and affordable PBMAs. For example, several critical aspects involve maximizing the use of plant materials (e.g., proteins, fibers, and bioactive compounds) and using reliable and unified life cycle analyses to guide the development. This review offers valuable insights for researchers, food technologists, and consumers seeking to navigate and develop the landscape of PBMAs effectively.

The cell rejuvenation atlas: leveraging network biology to identify master regulators of rejuvenation strategies.

Current rejuvenation strategies, which range from calorie restriction to in vivo partial reprogramming, only improve a few specific cellular processes. In addition, the molecular mechanisms underlying these approaches are largely unknown, which hinders the design of more holistic cellular rejuvenation strategies. To address this issue, we developed SINGULAR (Single-cell RNA-seq Investigation of Rejuvenation Agents and Longevity), a cell rejuvenation atlas that provides a unified system biology analysis of diverse rejuvenation strategies across multiple organs at single-cell resolution. In particular, we leverage network biology approaches to characterize and compare the effects of each strategy at the level of intracellular signaling, cell-cell communication, and transcriptional regulation. As a result, we identified master regulators orchestrating the rejuvenation response and propose that targeting a combination of them leads to a more holistic improvement of age-dysregulated cellular processes. Thus, the interactive database accompanying SINGULAR is expected to facilitate the future design of synthetic rejuvenation interventions.

Effects of the social environment on movement-integrated habitat selection

BackgroundMovement links the distribution of habitats with the social environment of animals using those habitats. Despite the links between movement, habitat selection, and socioecology, their integration remains a challenge due to lack of shared vocabulary across fields, methodological gaps, and the implicit (rather than explicit) historical development of theory in the fields of social and spatial ecology. Given these challenges can be addressed, opportunity for further study will provide insight about the links between social, spatial, and movement ecology. Here, our objective was to disentangle the roles of habitat selection and social association as drivers of movement in caribou (Rangifer tarandus).MethodsTo accomplish our objective, we modelled the relationship between collective movement and selection of foraging habitats using socially informed integrated step selection function (iSSF). Using iSSF, we modelled the effect of social processes, i.e., nearest neighbour distance and social preference, and movement behaviour on patterns of habitat selection.ResultsBy unifying social network analysis with iSSF, we identified movement-dependent social association, where individuals took shorter steps in lichen habitat and foraged in close proximity to more familiar individuals.ConclusionsOur study demonstrates that social preference is context-dependent based on habitat selection and foraging behaviour. We therefore surmise that habitat selection and social association are drivers of collective movement, such that movement is the glue between habitat selection and social association. Here, we put these concepts into practice to demonstrate that movement is the glue connecting individual habitat selection to the social environment.

Euclid preparation

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the Euclid era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the Euclid combined cluster and weak-lensing pipeline (COMB-CL). Notably, COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five recent, independent lensing surveys (CHFTLenS, DES SV1, HSC-SSP S16a, KiDS DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the Planck PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with the results in the literature collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO-detected clusters in the HSC-SSP XXL-North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If this control continues into Stage IV, Euclid will be able to measure the weak lensing masses of around 13 000 (considering shot noise only) or 3000 (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than three.

Kid-Sized Dentistry: A Pioneering First-of-Its-Kind Study on Customizing Dental Tools and Technology for Pediatric Care.

Introduction At the core of pediatric dentistry lies a profound understanding of child development, behavior management, and tailored treatment modalities. Similarly, tailoring dental tools and technology to address the distinct requirements of children is an essential aspect of pediatric dental practice. Kid-sized dentistry is the customization of dental tools and technology to effectively meet the unique needs of children. This study is the first to comprehensively explore all dimensions of resizing dental tools and technology for children, synthesizing scattered literature on the topic into a unified analysis, a trailblazing contribution to the field. Therefore, the aim of this article is to address these research gaps by conducting both a comprehensive analysis and a cross-sectional study. Methods To gather information for the subject, we conducted searches using both free-text terms and specific MeSH terms. We searched for articles published in English up to July 2024 across various electronic databases. Utilizing keywords, we initially obtained approximately 25-30 relevant literature studies, from which we selected 15-20 which were more topic-specific. We conducted an original survey to assess the knowledge, attitude, and practices of pediatric and general dentists regarding kid-sized dentistry in the Jabalpur zone. This survey employed a validated, structured questionnaire designed to collect data comprehensively. Results Despite high awareness, there remains a gap in the actual use of these tools and technology, particularly among general dentists. Conclusion In the realm of dentistry, every specialty brings a unique lens through which patient care is viewed. Within this mosaic, the pediatric dentist offers a particularly invaluable perspective, one that is grounded in the delicate nuances of treating children. Pediatric dentistry, in particular, focuses on the specialized needs of children, with an emphasis on understanding child development, behavior management, and customized treatment approaches. Central to this field is the adaptation of dental tools and technologies to suit pediatric patients. Insights from pediatric clinicians have contributed to advancements in dental equipment, improving its suitability for children. Increasing awareness of these innovations among both general and pediatric dentists can enhance this progress. This study underscores the need for ongoing education and advocacy to ensure all dental professionals have access to the best tools for treating young patients.

MBCN: A novel reference database for Effcient Metagenomic analysis of human gut microbiome

Metagenomic shotgun sequencing data can identify microbes and their proportions. But metagenomic shotgun data profiling results obtained from multiple projects using different reference databases are difficult to compare and apply meta-analysis. Our work aims to create a novel collection of human gut prokaryotic genomes, named Microbiome Collection Navigator (MBCN). 2379 human gut metagenomic samples are screened, and 16,785 metagenome-assembled genomes (MAGs) are assembled using a standardized pipeline. In addition, MAGs are combined with the representative genomes from public prokaryotic genomes collections to cluster, and pan-genomes for each cluster's genomes are constructed to build Kraken2 and Bracken databases. The databases built by MBCN are more comprehensive and accurate for profiling metagenomic reads comparing with other collections on simulated reads and virtual bio-projects. We profile 1082 human gut metagenomic samples with MBCN database and organize profiles and metadata on the web program. Meanwhile, using MBCN as a reference database, we also develop a unified, standardized, and systematic metagenomic analysis pipeline and platform, named MicrobiotaCN (http://www.microbiota.cn) and common statistical and visualization tools for microbiome research are integrated into the web program. Taken together, MBCN and MicrobiotaCN can be a valuable resource and a powerful tool that allows researchers to perform metagenomic analysis by a unified pipeline efficiently.

Computational Analysis of Marker Genes in Alzheimer’s Disease across Multiple Brain Regions

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies.