Tool For Functional Analysis Research Articles

Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with Next-Generation Sequencing data.

This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.

In-process evaluation of layer defects and surface topography on material extruded parts through a novel point cloud functional analysis

Abstract Material extrusion (MEX) is one of the most used additive manufacturing technologies, thanks to its appealing in various industrial fields. Despite its numerous advantages, process quality remains an issue when compared to other, more established technologies. In-process monitoring has become fundamental to meet the tight requirements of precision industries. In this work, a novel MEX monitoring methodology based on point-cloud functional analysis was tested and discussed. Using a blue-laser line profilometer integrated into a consumer 3D printer, the point cloud of each layer was obtained immediately after its completion and subsequently analysed. Functional analysis tools were employed to characterize the surface topography and assess the distribution of material and voids to detect defects. Common defective conditions in the form of excess or lack of deposited material, referred to as overfill and underfill, respectively, were induced by varying printing parameters and the specimen’s cross-section. After slicing the layer surface according to specified height thresholds, functional parameters such as the projected percentage area (PPA), void and material volume percentage and void and material mean thickness were computed and analysed. Results indicated that the PPA values effectively depicted the overall surface conditions and served as a suitable initial step for enabling corrective actions through the contour extraction of defective regions. Meanwhile, the percentage volume and mean thickness of voids and material, referred to as functional parameters, provided more detailed insights into the morphology of the layer surface and the detected defects.

Topo-Geometric Analysis of Variability in Point Clouds Using Persistence Landscapes.

Topological data analysis provides a set of tools to uncover low-dimensional structure in noisy point clouds. Prominent amongst the tools is persistence homology, which summarizes birth-death times of homological features using data objects known as persistence diagrams. To better aid statistical analysis, a functional representation of the diagrams, known as persistence landscapes, enable use of functional data analysis and machine learning tools. Topological and geometric variabilities inherent in point clouds are confounded in both persistence diagrams and landscapes, and it is important to distinguish topological signal from noise to draw reliable conclusions on the structure of the point clouds when using persistence homology. We develop a framework for decomposing variability in persistence diagrams into topological signal and topological noise through alignment of persistence landscapes using an elastic Riemannian metric. Aligned landscapes (amplitude) isolate the topological signal. Reparameterizations used for landscape alignment (phase) are linked to a resolution parameter used to generate persistence diagrams, and capture topological noise in the form of geometric, global scaling and sampling variabilities. We illustrate the importance of decoupling topological signal and topological noise in persistence diagrams (landscapes) using several simulated examples. We also demonstrate that our approach provides novel insights in two real data studies.

T-Minkowski Noncommutative Spacetimes II: Classical Field Theory

Abstract This paper is the second part of a series that develops the mathematical framework necessary for studying field theories on “T-Minkowski” noncommutative spacetimes. These spacetimes constitute a class of noncommutative geometries, introduced in Part I, that are each invariant under distinct quantum-group deformations of the Poincaré group. All these noncommutative geometries possess certain physically desirable characteristics, which allow me to develop all the tools of differential geometry and functional analysis that are necessary in order to build consistent and T-Poincaré-invariant noncommutative classical field theories.

Systemic Functional Linguistic Based on Semantic Web

This research aims to combine a functional and systematic linguistic approach with Semantic Web technology. The main aim of this research is to develop a framework that can facilitate systematized analysis and language modeling in the context of the semantic web. The research method used is a qualitative approach with focused data analysis. The data collected will involve text and web content relevant to the Semantic Web framework. The analysis was carried out using the principles and tools of systematic functional linguistic analysis to identify language structure, communicative function, and meaning in web content. The results of this research are expected to provide new insights into the understanding of how language can be organized and analyzed regularly in the context of the semantic web. Apart from that, this research is also expected to contribute to the development of Semantic Web technology by using a functional linguistic approach. It is hoped that the conclusions of this research will provide a deeper understanding of the role of language in the context of the semantic web and provide a basis for the development of more sophisticated and effective Semantic Web-based applications and systems.

TCM-ADIP: A Multidimensional Database Linking Traditional Chinese Medicine to Functional Brain Zones of Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex neurodegenerative disorder, with existing therapeutic drugs typically targeting specific disease stages. Traditional Chinese medicine (TCM), known for its multi-target and multi-mechanism therapeutic approach, has demonstrated efficacy in treating various stages of AD. In the present work, through a systematic review of classical Chinese medical texts, the formulae for preventing and treating AD were identified. Meanwhile, the active ingredients within these formulae were extracted and cataloged. A comprehensive bioinformatics analysis of omics data was performed to identify differentially expressed genes across different functional brain zones in AD patients at various stages. Finally, by integrating the multidimensional data, we proposed the first database, TCM-ADIP, dedicated to TCM based AD prevention and treatment, which is freely available at https://cbcb.cdutcm.edu.cn/TCM-ADIP/. TCM-ADIP not only supports interactive searching of multidimensional data, but also provides tools for gene localization and functional enrichment analysis of formulae, herbs, and ingredients for AD intervention in specific brain zones. TCM-ADIP fills a crucial gap in existing databases, offering a comprehensive resource for TCM in the treatment of AD.

Efficient Regeneration of Transgenic Rice from Embryogenic Callus via Agrobacterium-Mediated Transformation: A Case Study Using GFP and Apple MdFT1 Genes

Genetic transformation is a critical tool for gene manipulation and functional analyses in plants, enabling the exploration of key phenotypes and agronomic traits at the genetic level. While dicotyledonous plants offer various tissues for in vitro culture and transformation, monocotyledonous plants, such as rice, have limited options. This study presents an efficient method for genetically transforming rice (Oryza sativa L.) using seed-derived embryogenic calli as explants. Two target genes were utilized to assess regeneration efficiency: green fluorescent protein (eGFP) and the apple FLOWERING LOCUS T (FT)-like gene (MdFT1). Antisense MdFT1 was cloned into a vector controlled by the rice α-amylase 3D (Ramy3D) promoter, while eGFP was fused to Cas9 under the Ubi promoter. These vectors were introduced separately into rice embryogenic calli from two Korean cultivars using Agrobacterium-mediated transformation. Transgenic seedlings were successfully regenerated via hygromycin selection using an in vitro cultivation system. PCR confirmed stable transgene integration in the transgenic calli and their progeny. Fluorescence microscopy revealed eGFP expression, and antisense MdFT1-expressing lines exhibited notable phenotypic changes, including variations in plant height and grain quality. High transformation efficiency and regeneration frequency were achieved for both tested cultivars. This study demonstrated the effective use of seed-derived embryogenic calli for rice transformation, offering a promising approach for developing transgenic plants in monocot species.

Green/WeakCoupling: Implementation of fully self-consistent finite-temperature many-body perturbation theory for molecules and solids

The accurate ab-initio simulation of molecules and periodic solids with diagrammatic perturbation theory is an important task in quantum chemistry, condensed matter physics, and materials science. In this article, we present the WeakCoupling module of the open-source software package Green, which implements fully self-consistent diagrammatic weak coupling simulations, capable of dealing with real materials in the finite-temperature formalism. The code is licensed under the permissive MIT license. We provide self-consistent GW (scGW) and self-consistent second-order Green's function perturbation theory (GF2) solvers, analysis tools, and post-processing methods. This paper summarizes the theoretical methods implemented and provides background, tutorials and practical instructions for running simulations. Program summaryProgram Title:Green/WeakCouplingCPC Library link to program files:https://doi.org/10.17632/2ysyhzww6t.1Developer's repository link:https://github.com/Green-Phys/green-mbptProgramming language:C++17, CUDA, PythonLicensing provisions: MIT LicenseExternal routines/libraries:MPI >= 3.0, BLAS, Eigen >= 3.4.0, cmake >= 3.18, cuBLAS.Nature of problem: The simulation of periodic solids and molecules using diagrammatic perturbation theorySolution method: We present an open-source implementation of the fully self-consistent finite-temperature many-body perturbation theory formalism at the GW and second-order perturbation theory level.

Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts

This paper explores sampled-data control for Markovian switching neural networks (MSNNs) with dynamic output quantization and packet dropouts. The primary goal is to construct a multi-mode, quantized sampled-data controller that ensures stochastic stability and H∞ disturbance-reduction performance of the closed-loop MSNN. A Bernoulli-distributed random variable with uncertain probability is introduced to characterize the incidence of packet dropouts. To describe potential mode inconsistencies that may occur between the MSNN and controller, an exponential hidden Markov model is employed. Furthermore, the quantizer’s dynamic scaling factor is intentionally built as a piecewise function to avoid the potential division-by-zero problem. A sufficient condition for stochastic stability and H∞ disturbance-reduction performance is proposed, utilizing a mode- and time-dependent Lyapunov-type functional and several stochastic analysis tools. Then, through decoupling nonlinearities, a numerically efficient approach for determining the desired controller gains and parameter range associated with the dynamic scaling factor is developed. In order to facilitate comparisons, the situation with no uncertainty in the probability of packet dropouts is studied, and both analysis and design approaches are offered. Finally, two simulation examples are provided to validate the effectiveness and applicability of the developed approaches.

Numerical analysis of the stochastic Stefan problem

The gradient discretisation method (GDM) – a generic framework encompassing many numerical methods – is studied for a general stochastic Stefan problem with multiplicative noise. The convergence of the numerical solutions is proved by compactness method using discrete functional analysis tools, Skorokhod theorem and the martingale representation theorem. The generic convergence results established in the GDM framework are applicable to a range of different numerical methods, including for example mass-lumped finite elements, but also some finite volume methods, mimetic methods, lowest-order virtual element methods, etc. Theoretical results are complemented by numerical tests based on two methods that fit in GDM framework.

Analysis of a class of fractal hybrid fractional differential equation with application to a biological model

Recently, the area devoted to fractional calculus has given much attention by researchers. The reason behind such huge attention is the significant applications of the mentioned area in various disciplines. Different problems of real world processes have been investigated by using the concepts of fractional calculus and important and applicable outcomes were obtained. Because, there has been a lot of interest in fractional differential equations. It is brought on by both the extensive development of fractional calculus theory and its applications. The use of linear and quadratic perturbations of nonlinear differential equations in mathematical models of a variety of real-world problems has received a lot of interest. Therefore, motivated by the mentioned importance, this research work is devoted to analyze in detailed, a class of fractal hybrid fractional differential equation under Atangana- Baleanu- Caputo ABC derivative. The qualitative theory of the problem is examined by using tools of non-linear functional analysis. The Ulam-Hyer’s (U-H) type stability criteria is also applied to the consider problem. Further, the numerical solution of the model is developed by using powerful numerical technique. Lastly, the Wazewska-Czyzewska and Lasota Model, a well-known biological model, verifies the results. Several graphical representations by using different fractals fractional orders values are presented. The detailed discussion and explanations are given at the end.

NcStem: a comprehensive resource of curated and predicted ncRNAs in cancer stemness.

Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html.

A Pedagogic Approach Serving Inclusive Design

This paper presents an architecture teaching approach intended to final year architecture students, which is based on a human-centered design perspective, while respecting environment in all its compounds. The present work claims, as a hypothesis, that to design inclusive projects, it is worthy to develop in future architects’ capacity to analyze and reconcile both environment requirements and users’ needs as well. The large array of environment key elements and the complexity of users’ global apprehension, make it crucial for the future architects and urban planners to control the appropriate set of analysis methodologies and put to contribution the most efficient design tools. The pedagogical approach we deal with in this article, educates students in empathy, listening, and developing an open-mindedness that according to this study, allows them design better inclusive spaces. I am adopting such a vision for several years now, and I am experimenting pedagogical strategies and tools accordingly, either within the final year Architectural Design Studio I share with other colleagues, or during the Seminar I offer to the same audience. In trying to subscribe to a perspective of action research and reflexivity, I chose to present in this paper the analysis of four architectural dissertations addressing inclusive design concerns. This analysis aims to fathom how experienced analysis tools, such as SWOT grid, urban transect and qualitative social survey, as well as design tools, such as sequential approach of space, functional analysis and value creation tools, may help final year architecture students better analyze and design inclusive projects.

Analysis of MicroRNA Cargo in Circulating Extracellular Vesicles from HIV-Infected Individuals with Pulmonary Hypertension.

The risk of developing pulmonary hypertension (PH) in people living with HIV is at least 300-fold higher than in the general population, and illicit drug use further potentiates the development of HIV-associated PH. The relevance of extracellular vesicles (EVs) containing both coding as well as non-coding RNAs in PH secondary to HIV infection and drug abuse is yet to be explored. We here compared the miRNA cargo of plasma-derived EVs from HIV-infected stimulant users with (HIV + Stimulants + PH) and without PH (HIV + Stimulants) using small RNA sequencing. The data were compared with 12 PH datasets available in the GEO database to identify potential candidate gene targets for differentially altered miRNAs using the following functional analysis tools: ingenuity pathway analysis (IPA), over-representation analysis (ORA), and gene set enrichment analysis (GSEA). MiRNAs involved in promoting cell proliferation and inhibition of intrinsic apoptotic signaling pathways were among the top upregulated miRNAs identified in EVs from the HIV + Stimulants + PH group compared to the HIV + Stimulants group. Alternatively, the downregulated miRNAs in the HIV + Stimulants + PH group suggested an association with the negative regulation of smooth muscle cell proliferation, IL-2 mediated signaling, and transmembrane receptor protein tyrosine kinase signaling pathways. The validation of significantly differentially expressed miRNAs in an independent set of HIV-infected (cocaine users and nondrug users) with and without PH confirmed the upregulation of miR-32-5p, 92-b-3p, and 301a-3p positively regulating cellular proliferation and downregulation of miR-5571, -4670 negatively regulating smooth muscle proliferation in EVs from HIV-PH patients. This increase in miR-301a-3p and decrease in miR-4670 were negatively correlated with the CD4 count and FEV1/FVC ratio, and positively correlated with viral load. Collectively, this data suggest the association of alterations in the miRNA cargo of circulating EVs with HIV-PH.

Wavelet Feature Screening

An initial screening of which covariates are relevant is a common practice in high-dimensional regression models. The classic feature screening selects only a subset of covariates correlated with the response variable. However, many important features might have a relevant albeit highly nonlinear relation with the response. One screening approach that handles nonlinearity is to compute the correlation between the response and nonparametric functions of each covariate. Wavelets are powerful tools for nonparametric and functional data analysis but are still seldom used in the feature screening literature. We propose a wavelet feature screening method that can be easily implemented, and we prove that, under suitable conditions, it captures the true covariates with high probability. Simulation results also show that our approach outperforms other screening methods in highly nonlinear models. We apply feature screening to two datasets about ozone concentration and epilepsy. In both applications, the proposed method selects features that match findings in the literature of their respective research fields, illustrating the applicability of feature screening. Supplementary material for this article is available online.

A Study of Pupil Response to Light as a Digital Biomarker of Recent Cannabis Use

Introduction: Given the traffic safety and occupational injury prevention implications associated with cannabis impairment, there is a need for objective and validated measures of recent cannabis use. Pupillary light response may offer an approach for detection. Method: Eighty-four participants (mean age: 32, 42% female) with daily, occasional, and no-use cannabis use histories participated in pupillary light response tests before and after smoking cannabis ad libitum or relaxing for 15 min (no use). The impact of recent cannabis consumption on trajectories of the pupillary light response was modeled using functional data analysis tools. Logistic regression models for detecting recent cannabis use were compared, and average pupil trajectories across cannabis use groups and times since light test administration were estimated. Results: Models revealed small, significant differences in pupil response to light after cannabis use comparing the occasional use group to the no-use control group, and similar statistically significant differences in pupil response patterns comparing the daily use group to the no-use comparison group. Trajectories of pupillary light response estimated using functional data analysis found that acute cannabis smoking was associated with less initial and sustained pupil constriction compared to no cannabis smoking. Conclusion: These analyses show the promise of pairing pupillary light response and functional data analysis methods to assess recent cannabis use.

Genome-wide characterization and expression of Oryza sativa AP2 transcription factor genes associated with the metabolism of mesotrione

BackgroundThe APETALA2 (AP2) transcription factor (TF) superfamily, one of the largest gene families in plants, plays an essential role in regulating plant growth and their stress responses. However, the role of AP2 in rice under pesticide stress remains unclear. To investigate the characteristics and functions of the rice AP2 gene family under pesticide stress, the expression of 105 AP2-coding genes and 26 AP2 differentially expressed genes (DEGs) were identified in mesotrione (MTR)-treated rice transcriptome datasets.ResultsThree subfamilies of the AP2 gene family (AP2/ERF, RAV, and soloists) were identified using sequence alignment and phylogenetic analysis. Chromosome location analysis revealed that the 26 rice AP2 DEGs were unevenly distributed on 10 of the 12 rice chromosomes, and segmental duplication contributed to the expansion of Oryza sativa AP2 (OsAP2) gene family. Collinearity analyses demonstrated that rice AP2 genes displayed 16 orthologous gene pairs, and 12 and 26 orthologous gene pairs were shared of Arabidopsis and soybean, respectively. In addition, rice AP2 genes featured various gene structures, cis-elements, motif compositions, and conserved domains that allowed them to encode genes that elicit biotic and abiotic stress responses. An analysis of docking between MTR and six AP2 DEGs revealed amino acid residues involved in MTR binding. Quantitative reverse transcription–polymerase chain reaction verified that several AP2 genes were preferentially expressed during MTR-induced stress. The roles of OsAP2 proteins in MTR metabolism were further supported by protein–protein interaction network analysis, which illustrated how these proteins interact with target proteins.ConclusionThe initial findings of this study define the features of the OsAP2 superfamily and offer important tools for functional analyses of OsAP2 genes implicated in the metabolism of MTR.Graphical

On the Solvability of a Singular Time Fractional Parabolic Equation with Non Classical Boundary Conditions

This paper deals with a singular two dimensional initial boundary value problem for a Caputo time fractional parabolic equation supplemented by Neumann and non-local boundary conditions. The well posedness of the posed problem is demonstrated in a fractional weighted Sobolev space. The used method based on some functional analysis tools has been successfully showed its efficiency in proving the existence, uniqueness and continuous dependence of the solution upon the given data of the considered problem. More precisely, for proving the uniqueness of the solution of the posed problem, we established an energy inequality for the solution from which we deduce the uniqueness. For the existence, we proved that the range of the operator generated by the considered problem is dense.

Overexpression of HbGRF4 or HbGRF4-HbGIF1 Chimera Improves the Efficiency of Somatic Embryogenesis in Hevea brasiliensis.

Transgenic technology is a crucial tool for gene functional analysis and targeted genetic modification in the para rubber tree (Hevea brasiliensis). However, low efficiency of plant regeneration via somatic embryogenesis remains a bottleneck of successful genetic transformation in H. brasiliensis. Enhancing expression of GROWTH-REGULATING FACTOR 4 (GRF4)-GRF-INTERACTING FACTOR 1 (GIF1) has been reported to significantly improve shoot and embryo regeneration in multiple crops. Here, we identified endogenous HbGRF4 and HbGIF1 from the rubber clone Reyan7-33-97, the expressions of which dramatically increased along with somatic embryo (SE) production. Intriguingly, overexpression of HbGRF4 or HbGRF4-HbGIF1 markedly enhanced the efficiency of embryogenesis in two H. brasiliensis callus lines with contrasting rates of SE production. Transcriptional profiling revealed that the genes involved in jasmonic acid response were up-regulated, whereas those in ethylene biosynthesis and response as well as the S-adenosylmethionine-dependent methyltransferase activity were down-regulated in HbGRF4- and HbGRF4-HbGIF1-overexpressing H. brasiliensis embryos. These findings open up a new avenue for improving SE production in rubber tree, and help to unravel the underlying mechanisms of HbGRF4-enhanced somatic embryogenesis.

Single-Cell Transcriptomic Profiling Identifies Molecular Phenotypes of Newborn Human Lung Cells.

While animal model studies have extensively defined the mechanisms controlling cell diversity in the developing mammalian lung, there exists a significant knowledge gap with regards to late-stage human lung development. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. Transcriptomic profiling at the single-cell level created a cellular atlas of newborn human lungs. Frozen single-cell isolates obtained from two newborn human lungs from the LungMAP Human Tissue Core Biorepository, were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Transcriptional interrogation of 5500 newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, immune cells and their gene signatures. Computational integration of data from newborn human cells and with 32,000 cells from postnatal days 1 through 10 mouse lungs generated by the LungMAP Cincinnati Research Center facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the newborn human and mouse cellular transcriptomes also demonstrated cell type-specific differences in maturation states of newborn human lung cells. Specifically, newborn human lung matrix fibroblasts could be separated into those representative of younger cells (n = 393), or older cells (n = 158). Cells with each molecular profile were spatially resolved within newborn human lung tissue. This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of maturity.