Functional Analysis Approach Research Articles

Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach.

This study explores the mechanisms of glucocorticoid-induced osteoporosis (OP) and Rheumatoid arthritis (RA), focusing on apoptosis and its role in the progression from RA to OP. Using microarray data from the GEO database, differential gene expression analysis was conducted with the limma package, identifying significant genes in RA and OP. Weighted Gene Co-expression Network Analysis (WGCNA) further examined gene relationships with the disease status, identifying co-expression patterns. Key genes were pinpointed by intersecting differentially expressed genes from RA and OP datasets with WGCNA module genes. Functional enrichment analysis using the "clusterProfiler" package focused on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Machine learning methods, including Lasso and Random Forest, refined the selection of key genes related to apoptosis. Immune infiltration analysis using CIBERSORT assessed immune cell differences between disease and normal samples. The study highlighted two crucial genes: ATXN2L and MMP14. These genes were identified through various analyses and found to be significantly associated with the progression of RA and OP. Gene Set Enrichment Analysis of ATXN2L and MMP14 revealed their involvement in specific biological processes and pathways. Correlation analysis between these key genes and immune cell infiltration showed significant associations. The ROC analysis evaluated the diagnostic performance of ATXN2L and MMP14, with miRNA regulatory networks related to these genes also predicted. In summary, this research provides valuable insights into the molecular mechanisms of RA and OP, emphasizing the importance of apoptosis and immune processes.

Novel Approaches to Existence and Uniquenessin Nonlinear Higher-Order Differential Equations

The study of nonlinear higher-order differential equations presents significant challenges in terms of existence and uniqueness of solutions. This paper explores novel approaches to addressing these challenges, focusing on recent advancements and methodologies that offer new perspectives on these classical problems. We investigate advanced techniques including topological methods, functional analysis, and computational approaches to enhance our understanding of existence and uniqueness in nonlinear higher-order differential equations. By reviewing the latest literature and integrating new findings, this paper aims to provide a comprehensive overview of current research trends and future directions in this area.

Design of a Robust Flow Cytometric Approach for Phenotypical and Functional Analysis of Human Monocyte Subsets in Health and Disease.

Human monocytes can be subdivided into phenotypically and functionally different classical, intermediate and non-classical monocytes according to the cell surface expression of CD14 and CD16. A precise identification and characterisation of monocyte subsets is necessary to unravel their role in inflammatory diseases. Here, we compared three different flow cytometric strategies (A-C) and found that strategy C, which included staining against CD11b, HLA-DR, CD14 and CD16, followed by several gating steps, most reliably identified monocyte subtypes in blood samples from healthy volunteers and from patients with stable coronary heart disease (CHD) or ST-elevation myocardial infarction (STEMI). Additionally, we established a fixation and permeabilisation protocol to enable the analysis of intracellular markers. We investigated the phagocytosis of lipid nanoparticles, the uptake of 2-NBD-glucose and the intracellular levels of CD74 and HLA-DM. This revealed that classical and intermediate monocytes from patients with STEMI showed the highest uptake of 2-NBD-glucose, whereas classical and intermediate monocytes from patients with CHD took up the largest amounts of lipid nanoparticles. Interestingly, intermediate monocytes had the highest expression level of HLA-DM. Taken together, we present a robust flow cytometric approach for the identification and functional characterisation of monocyte subtypes in healthy humans and patients with diseases.

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.

Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data. We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination. Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach. When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results. This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Pelvic and Lower Limb Kinematics in Individuals With Chronic Low Back Pain During Sit-to-Stand Function: A Cross-Sectional Study

ObjectivesThe aim of this study was to investigate the 3-dimensional movement kinematics of pelvic and lower limb joints in CLBP subjects, compared to healthy individuals, utilizing a functional data analysis (FDA) approach. MethodsIn this study, a 7-camera Qualisys motion capture system was employed to record the kinematics of the pelvic, hip, knee, and ankle joints in 20 patients with low back pain (LBP) and 20 control subjects on both sides during the sit-to-stand motion. An FDA statistical approach was utilized to compare the data collected over time between the 2 groups. The STS task was divided into two distinct phases: the prelift off phase (pre-Lo) and the postlift off phase (post-Lo). ResultsDuring the “pre-Lo phase” of the STS motion, our statistical analysis revealed that the only significant difference between the two groups was a decreased medial rotation (P = .00) in the hip joint position of LBP patients on their dominant side, in comparison to healthy subjects. As for the “post-Lo phase,” the LBP group exhibited a more anterior pelvic tilt position (P = .00) and increased pelvic counterclockwise rotation (P = .03) in the sagittal and transverse planes when compared to the control group. Regarding hip joints, the LBP group demonstrated a more flexed position (indicative of reduced extension) on both the dominant (P = .00) and nondominant (P = .00) sides, coupled with diminished abduction (P = .04) and internal rotation (P = .03) of the hip on the dominant side. In relation to the knee and ankle joints, the results indicated a more adducted knee position (P = .00) and decreased ankle joint flexion (P = .02) on the dominant side in CLBP subjects when compared to the healthy group during the STS motion. ConclusionOur findings indicate that patients with CLBP exhibited distinct kinematic patterns in the pelvic and lower limb joints during the STS motion. These kinematic alterations primarily manifest in the joints of the dominant limb, with most of the differences observed during the second phase (postlift off phase) of the motion. It appears that these changes in pelvic and lower limb joint kinematics should be taken into account when planning future rehabilitation programs for CLBP patients engaged in this task.

Optoelectronic Properties of Triply Twisted Möbius Carbon Nanobelt and the Design of Its Isomeric Nanomaterials

The triply twisted Möbius carbon nanobelt (TMCNB) possesses an extremely distinctive geometric and electronic structure and therefore is anticipated to demonstrate outstanding optical properties. In this paper, through integrating quantum chemical calculations and wave function analysis approaches, in-depth studies are conducted on the one-photon absorption (OPA) and two-photon absorption (TPA) characteristics, aromaticity, and circular dichroism of the TMCNB. Inspired by this structure, we further deform it to construct a novel structure, MCNB2, and verify the stability of this structure, thereby confirming its rationality. Since variations in structure will result in dissimilar optical properties, we also undertake theoretical analyses on the absorption properties and circular dichroism of MCNB2. The outcomes of this study offer a significant theoretical foundation for the design and construction of chiral optoelectronic materials.

Decentralized Adaptive Secure Control of Uncertain Nonlinear Time-Varying Interconnected Systems Against Sensor and Actuator Attacks.

In this article, the decentralized adaptive secure control problem for cyber-physical systems (CPSs) against deception attacks is investigated. The CPSs are formed as a type of nonlinear interconnected strict-feedback systems with uncertain time-varying parameters. The attack affects the information transmission between sensor and actuator in a multiplicative manner. A novel decentralized adaptive backstepping secure control strategy is established by exploiting a particular kind of Nussbaum functions and a flat-zone Lyapunov function analysis approach. It is shown that all of closed-loop signals remain globally bounded, and each output signal eventually converges into a small neighborhood of the origin. Simulation results on an illustrative example are provided to display the effectiveness of the proposed control scheme.

Bioinformatics advances in eccDNA identification and analysis.

Extrachromosomal circular DNAs (eccDNAs) are a unique class of chromosome-originating circular DNA molecules, which are closely linked to oncogene amplification. Due to recent technological advances, particularly in high-throughput sequencing technology, bioinformatics methods based on sequencing data have become primary approaches for eccDNA identification and functional analysis. Currently, eccDNA-relevant databases incorporate previously identified eccDNA and provide thorough functional annotations and predictions, thereby serving as a valuable resource for eccDNA research. In this review, we collected around 20 available eccDNA-associated bioinformatics tools, including identification tools and annotation databases, and summarized their properties and capabilities. We evaluated some of the eccDNA detection methods in simulated data to offer recommendations for future eccDNA detection. We also discussed the current limitations and prospects of bioinformatics methodologies in eccDNA research.

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.

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Functional data analysis using deep neural networks

AbstractFunctional data analysis is an evolving field focused on analyzing data that reveals insights into curves, surfaces, or entities within a continuous domain. This type of data is typically distinguished by the inherent dependence and smoothness observed within each data curve. Traditional functional data analysis approaches have predominantly relied on linear models, which, while foundational, often fall short in capturing the intricate, nonlinear relationships within the data. This paper seeks to bridge this gap by reviewing the integration of deep neural networks into functional data analysis. Deep neural networks present a transformative approach to navigating these complexities, excelling particularly in high‐dimensional spaces and demonstrating unparalleled flexibility in managing diverse data constructs. This review aims to advance functional data regression, classification, and representation by integrating deep neural networks with functional data analysis, fostering a harmonious and synergistic union between these two fields. The remarkable ability of deep neural networks to adeptly navigate the intricate functional data highlights a wealth of opportunities for ongoing exploration and research across various interdisciplinary areas.This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

Higher-ordered hybrid fractional differential equations with fractional boundary conditions: Stability analysis and existence theory

In the present article, the p-Laplacian operator is applied for examining hybrid fractional differential equations (HFDEs). Establishing the existence and uniqueness (EU) results and analyzing the Hyers–Ulam (HU) stability for HFDEs incorporating fractional derivatives of different orders with the p-Laplacian operator are the primary objectives of this research. With an understanding of the Green function, we will transform the provided HFDE into a corresponding integral form of hybrid FDEs for EU results. A fixed point theorem is employed to examine the existence of solution (ES), and the Banach contraction mapping principle technique is employed to determine the uniqueness of solution. The result of the HU stability study is examined using dynamical systems and functional analysis approaches. Additionally, an application is presented to demonstrate the findings.

STRATEGIC MANAGEMENT AT SEVERAL OF THE BEST UNIVERSITIES IN INDONESIA

The strategic management process of each category of organization will undoubtedly be distinctive. This study aims to ascertain the strategic management process in higher education institutions in Indonesia. This research is a qualitative case study that examines the University of Indonesia (UI), Gajah Mada University (UGM), Bogor Agricultural Institute (IPB), Telkom University (TELU), Indonesian Islamic University (UII), and Muhammadiyah University of Surakarta (UMS). The strategic management process at Indonesian HEI is described or concluded by analyzing secondary data in the form of strategic plan documents compiled with analysis of primary data in the form of interviews with experts in the field of higher education. The research findings indicate that the strategic management process in Indonesian higher education institutions is comprised of four stages: (1) environmental analysis, (2) strategy formulation, (3) strategy implementation, and (4) evaluation control. An example of the originality or uniqueness of the strategic management process in Indonesian higher education institutions is internal environmental analysis, which employs its criteria rather than the commonly used value chain analysis or functional analysis approach criteria.

Optimizing the SPIF parameters for enhancing microhardness and surface quality in Inconel 625 superalloy components

Inconel 625, a high-strength superalloy with excellent corrosion resistance, discovers wide applications in critical sectors such as aerospace, nuclear, marine, and petrochemical. Single-Point Incremental Forming (SPIF) has appeared as an adequate fabricating process for shaping superalloy components. This work used a systematic approach, implementing Response Surface Methodology (RSM) to explore the influence of SPIF process variables, including tool nose diameter, step size, tool spindle speed, and wall angle, on microhardness (MH) and surface roughness (SR). The experimental results have been analyzed to determine the influencing process variables and their optimal settings, which simultaneously enhance MH and reduce SR of Inconel 625 superalloy truncated cones using analysis of variance and the desirability function analysis approach. The most significant parameters that influence MH and SR were TND and SS, respectively. The development of quadratic models for MH and SR has been completed successfully. The maximum MH of 486.8 HV and minimum SR of 0.432 µm have been obtained at the optimal parametric setting of TND = 15 mm, SS = 0.4 mm, TSS = 900 rpm, and WA = 57.5° with the desirability values for MH, SR and combined are 0.832, 0.933, 0.881 respectively. The F-value of the MH model = 109.18 and SR Model = 78.24 implies that both models are significant and both models have excellent prediction strength. The percentage error between predicted and actual values of MH and SR in confirmation run results is less than 5 %.

The functional distance-based approach: An application on long-term Metropolitan Development

Metropolitan development is a complex phenomenon depending on the joint action of multiple socioeconomic and territorial factors, which are generally quantified through indicators interacting across spatial and temporal scales. A comprehensive understanding of metropolitan development requires a detailed examination of the dependence structure within the development trajectories of both urban and suburban areas. Multidimensionality, non-linearity, and evolutionary components at the base of metropolitan development should be jointly considered when investigating the socioeconomic mechanisms underlying the long-term expansion of cities and their surrounding regions. In this multidimensional perspective, the Functional Data Analysis approach enables the simultaneous investigation of the level and evolution over time of multiple socioeconomic processes through summary functional measures, such as the modified hypograph index and the weighted integrated first derivative. Starting from these tools, a functional distance-based approach is proposed to study the relationship between urban and suburban areas from a twofold perspective: focusing on the entire set of metropolitan development indicators, and considering each dimension individually. This approach contributes to overpass the limitations of standard econometric approaches offering a multivariate measure of change in local systems experiencing complex development trajectories. The proposal is illustrated via a simulation study and a real data set.

Functional Analysis Approach to the Collatz Conjecture

Functional Analysis Approach to the Collatz Conjecture

Predicting Driver's mental workload using physiological signals: A functional data analysis approach

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers’ mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

Functional Systemic Role Analysis (FSRA) on Early Childhood in the Development of Vocabulary: Insights from Indonesia

This study analyzes the role of functional systemics in vocabulary development in early childhood. Vocabulary development in early childhood plays an important role in the formation of cognitive foundations and more complex language abilities in the future. The study adopts a functional systemic role analysis approach to investigate the relationship between linguistic, cognitive and contextual aspects in vocabulary development. It utilized observations as a research methodology. Data were collected through direct observation of a group of children aged 2-5 years old in their daily environment. Children's interactions with objects, people and their surroundings were noted for period of August until December 2022 at Methodist Kindergarten in Pematang Siantar. Findings show that there are various functional systemic roles in vocabulary development in early childhood. Some of the roles identified include expressive functions, representative functions and interactional functions. The expressive function involves the use of vocabulary to convey feelings, wishes or opinions. The representational function involves the use of vocabulary to describe objects or events around them. The interactional function involves the use of vocabulary in social interactions with adults or peers.

Phylometagenomics of cycad coralloid roots reveals shared symbiotic signals.

Cycads are known to host symbiotic cyanobacteria, including Nostocales species, as well as other sympatric bacterial taxa within their specialized coralloid roots. Yet, it is unknown if these bacteria share a phylogenetic origin and/or common genomic functions that allow them to engage in facultative symbiosis with cycad roots. To address this, we obtained metagenomic sequences from 39 coralloid roots sampled from diverse cycad species and origins in Australia and Mexico. Culture-independent shotgun metagenomic sequencing was used to validate sub-community co-cultures as an efficient approach for functional and taxonomic analysis. Our metanalysis shows a host-independent microbiome core consisting of seven bacterial orders with high species diversity within the identified taxa. Moreover, we recovered 43 cyanobacterial metagenome-assembled genomes, and in addition to Nostoc spp., symbiotic cyanobacteria of the genus Aulosira were identified for the first time. Using this robust dataset, we used phylometagenomic analysis to reveal three monophyletic cyanobiont clades, two host-generalist and one cycad-specific that includes Aulosira spp. Although the symbiotic clades have independently arisen, they are enriched in certain functional genes, such as those related to secondary metabolism. Furthermore, the taxonomic composition of associated sympatric bacterial taxa remained constant. Our research quadruples the number of cycad cyanobiont genomes and provides a robust framework to decipher cyanobacterial symbioses, with the potential of improving our understanding of symbiotic communities. This study lays a solid foundation to harness cyanobionts for agriculture and bioprospection, and assist in conservation of critically endangered cycads.