Functional Analysis Research Articles

Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis.

To explore the diagnostic biomarkers for diagnosing endometriosis. Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis. To explore the diagnostic biomarkers for endometriosis diagnosis. Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking results. A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. In vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya. These results may facilitate the in-depth understanding of the development of endometriosis, and guide early diagnostic as well as clinical treatments for patients with endometriosis.

What are microsatellites and how to choose the best tool: a user-friendly review of SSR and 74 SSR mining tools

Microsatellites, also known as SSR or STR, are essential molecular markers in genomic research, playing crucial roles in genetic mapping, population genetics, and evolutionary studies. Their applications range from plant breeding to forensics, highlighting their diverse utility across disciplines. Despite their widespread use, traditional methods for SSR analysis are often laborious and time-consuming, requiring significant resources and expertise. To address these challenges, a variety of computational tools for SSR analysis have been developed, offering faster and more efficient alternatives to traditional methods. However, selecting the most appropriate tool can be daunting due to rapid technological advancements and the sheer number of options available. This study presents a comprehensive review and analysis of 74 SSR tools, aiming to provide researchers with a valuable resource for SSR analysis tool selection. The methodology employed includes thorough literature reviews, detailed tool comparisons, and in-depth analyses of tool functionality. By compiling and analyzing these tools, this study not only advances the field of genomic research but also contributes to the broader scientific community by facilitating informed decision-making in the selection of SSR analysis tools. Researchers seeking to understand SSRs and select the most appropriate tools for their projects will benefit from this comprehensive guide. Overall, this study enhances our understanding of SSR analysis tools, paving the way for more efficient and effective SSR research in various fields of study.

Computational Advancement towards the Identification of Natural Inhibitors for Dengue Virus: A Brief Review.

Viral infectious illnesses represent a severe hazard to human health due to their widespread incidence worldwide. Among these ailments, the dengue virus (DENV) infection stands out. World Health Organization (WHO) estimates that DENV infection affects ~400 million people each year, with potentially fatal symptoms showing up in 1% of the cases. In several instances, academic and pharmaceutical researchers have conducted several pilot and clinical studies on a variety of topics, including viral epidemiology, structure and function analyses, infection source and route, therapeutic targets, vaccinations, and therapeutic drugs. Amongst Takeda, TAK-003, Sanofi, Dengvaxia®, and Butantan/NIH/Merck, Dengvaxia® (CYD-TDV) is the only licensed vaccination yet; however, the potential inhibitors are under development. The biology and evolution of DENVs are briefly discussed in this review, which also compiles the most recent studies on prospective antiviral targets and antiviral candidates. In conclusion, the triumphs and failures have influenced the development of anti-DENV medications, and the findings in this review article will stimulate more investigation.

Development of a Spectral Flow Cytometry Analysis Pipeline for High-dimensional Immune Cell Characterization.

Flow cytometry is used for immune cell analysis for cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large Ab panels is challenging using traditional biaxial gating strategies. We present, to our knowledge, a novel analysis pipeline to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 mo old) and aged (18-19 mo old) female C57BL/6N mice and then stained these with a panel of 20 fluorescently labeled Abs. We performed spectral flow cytometry and then data processing and analysis using Python within a Jupyter Notebook environment to perform dimensionality reduction, batch correction, unsupervised clustering, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 35 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with clusters enriched in one age group over the other. Naive, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. We also demonstrate the utility of our pipeline in a human PBMC dataset that used a 50-fluorescent color panel. By leveraging high-dimensional analysis methods, we provide insights into the immune aging process. This approach offers a robust and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.

Adaptive Saturated Obstacle Avoidance Trajectory Tracking Control for Euler–Lagrange Systems With Velocity Constraints

ABSTRACTThis article pays attention to the obstacle avoidance trajectory tracking control problem for uncertain Euler–Lagrange (EL) systems subject to velocity constraints and input saturation. The existing obstacle avoidance results do not consider velocity constraints under input saturation, which means that an EL system may not be able to obtain sufficient control inputs to avoid a collision with an obstacle if it has a high speed when approaching the obstacle. Therefore, the velocity constraints in the obstacle avoidance tracking control are considered in this paper. A novel velocity constraint function that depends on the distance between the system and the obstacle is proposed. Integral‐multiplicative Lyapunov‐barrier functions (LBFs) are constructed and incorporated into the backstepping procedure to design an adaptive fuzzy obstacle avoidance tracking control scheme. Moreover, an auxiliary dynamic system is designed by constructing a bounded nonlinear vector related to an auxiliary variable to compensate for the effects of saturation. Through the Lyapunov method and boundedness analysis for the barrier function, it is shown that the protocol achieves obstacle avoidance for the EL system without violating the velocity constraints inside the obstacle detection region, while also guaranteeing the ultimate uniform boundedness of all the closed‐loop signals. Numerical simulations are presented to demonstrate the efficacy of the proposed control strategy.

Matrix analysis of discrete functionals in compact difference method for nonlinear problems with higher derivatives and program code (I: 1D problem)

Matrix analysis of discrete functionals in compact difference method for nonlinear problems with higher derivatives and program code (I: 1D problem)

Comparative study of hemostatic profile between conventional methods and “Sonoclot Coagulation and Platelet Function Analyzer” in patients with septicemia

Abstract: BACKGROUND: Sonoclot Coagulation and Platelet Functional Analyzer (Sonoclot) operating on the principle of micro-viscometry is known to provide comprehensive information on the entire hemostatic process. We hypothesized that performing global hemostasis assessment in patients with sepsis would identify many more patients with altered hemostasis than the conventional tests. OBJECTIVES: We compared the hemostatic status determined by Sonoclot and that by conventional hemostatic tests. METHODS: It was a prospective analytical study conducted for 18 months in a tertiary care hospital. Sixty-one cases of septicemia admitted in the intensive care units were included in the study. RESULTS: Conventional hemostatic tests (platelet count, prothrombin time [PT], international normalized ratio, and activated partial thromboplastin time [APTT]) were compared with the Sonoclot parameters. Conventional hemostatic parameter PT was prolonged in 60.7% and APTT in 31.1%. Overall, 65.6% of the patients had prolonged coagulation on assessment by conventional hemostatic parameters. Sonoclot-derived parameter-activated clotting time was prolonged in 31.1%, indicating 31.1% of cases to have a hypocoagulable state. The clot rate was increased in 93.4% of subjects, indicating 93.4% to have a hypercoagulable state. Thrombocytopenia was seen in 34.4% and thrombocytosis in 10%. Defective platelet function was observed in 92% of the cases in Sonoclot, and all of them had normal platelet counts. This finding was statistically significant. CONCLUSION: Information about hemostatic status identified by Sonoclot is significantly higher and more complete than that identified by conventional coagulation tests in septicemic patients.

Longitudinal transcriptional immune profiles and persistent wheezing in moderate-to-late preterm infants.

Prematurity is associated with an increased risk of persistent wheezing but the underlying mechanisms are not well defined. The aim of this study was to identify blood transcriptional profiles associated with the development of wheezing in a cohort of moderate to late preterm infants and to define immune gene expression changes associated with wheezing. A convenience sample of a multicenter birth cohort (SAREPREM) of moderate-late preterm children followed during the first 3 years of life was analyzed. Children were enrolled in the first 2 weeks of life (Y0) and longitudinally evaluated at 1 (Y1), 2 (Y2), and 3 years (Y3) of age, for the presence of wheezing and to obtain samples for transcriptional profile analysis. Samples were processed on Illumina HT12 chips and genomic expression analyses performed with R programming, modular analysis for biological function, and QuSAGE for quantitative gene expression. Seventy-six children were included in the study; 33 were classified as non-wheezing and 43 (56.6%) in the wheezing group. At Y0, children who developed wheezing had decreased expression of interferon genes and increased expression of B cell genes compared with the non-wheezing group. These changes in IFN and B cell gene expression were especially significant in children with late/persistent wheezing compared with transient wheezers. Changes in IFN and B lymphocyte gene expression identified in early life suggest the existence of specific immunological mechanisms that play an important role in the development of wheezing in late-preterm infants.

Using Exact Tests from Algebraic Statistics in Sparse Multi-Way Analyses: An Application to Analyzing Differential Item Functioning

Asymptotic goodness-of-fit methods in contingency table analysis can struggle with sparse data, especially in multi-way tables where it can be infeasible to meet sample size requirements for a robust application of distributional assumptions. However, algebraic statistics provides exact alternatives to these classical asymptotic methods that remain viable even with sparse data. We apply these methods to a context in psychometrics and education research that leads naturally to multi-way contingency tables: the analysis of differential item functioning (DIF). We explain concretely how to apply the exact methods of algebraic statistics to DIF analysis using the R package algstat, and we compare their performance to that of classical asymptotic methods.

Coherence resonance in a memristive map neuron and adaptive energy regulation

Involvement of memristive term and additive physical variables including magnetic flux and charge can enhance the physical description of the biophysical neurons. Neural circuits coupled with memristors can be built and tamed to mimic the intrinsic biophysical characteristics and dynamical properties of biological neurons, and these memristive oscillator models are effective in predicting the mode transition in neural activities and self-organization in collective electric behaviors of neural networks. Any proposal of memristive map neurons requires reliable physical description. For example, the energy definition and self-adaptive working mechanism are crucial to verify the reliability of memristive maps. A capacitive variable is useful to describe the membrane potential, while the complexity of ion channels requires careful evaluation and description by using inductive variables relative to the electromagnetic field. In this work, a charge-controlled memristor is connected to an inductor in series for building a hybrid ion channel, and then a capacitor and a nonlinear resistor are combined to couple the ion channel. As a result, a simple memristive neural circuit is designed to discern the inner effect of electric field and magnetic field synchronously. The energy function is defined and verified with theoretical proof. Furthermore, a linear transformation is applied to convert this memristive neuron into a memristive map with an exact energy description, in which its dynamics and mode transition will be controlled by an adaptive law when its energy is beyond the threshold. Additive noise is imposed to induce coherence resonance, which can be detected by using the statistical analysis and average value for Hamilton energy function during changes in noise intensity. This scheme provides guidance for energy definition in memristive maps and the intrinsic energy regulation mechanism in neural activities is explained from physical and dynamical aspects.

A Method for Assessing the Reliability of the Pepper Robot in Handling Office Documents: A Case Study.

Humanoid robots are increasingly being utilized in various activities involving humans, as they can facilitate certain tasks and provide benefits to users. Humanoid service robots possess capabilities akin to human performance, often proving advantageous due to their operational speed and immunity to fatigue. Within the scope of this study, Pepper, a humanoid robot renowned for its fidelity in mimicking human gestures and behavior, serves as the focal point. Tasked with aiding office occupants in object manipulation and relocation, Pepper underwent a targeted reliability assessment. This assessment encompassed the development of a reliability block diagram (RBD), alongside meticulous analyses of individual components and system functionality across diverse operational scenarios.

B - 70 Profile Analysis of Executive Functioning in Adults with Developmental Disorders on the Behavior Rating Inventory of Executive Function, Second Edition—Adult Version (BRIEF2A)

Abstract Objective Children with developmental disorders including attention deficit/hyperactivity disorder (ADHD), autism (ASD), and learning disabilities (LD) often exhibit different profiles of everyday executive function (EF) strengths and weakness. This has not been tested in adults. We examined profiles of self- and informant-reported EF in adults with these developmental disorders on the BRIEF2A. Method Ratings on the BRIEF2A scales (Inhibit, Self-Monitor, Shift, Emotional Control, Initiate, Working Memory, Plan/Organize, Task-Monitor, Organization of Materials) were within-subjects dependent variables in a profile analysis with diagnostic group (ADHD, ASD, LD) as the between-subjects factor in two MANOVAs (self and informant). 79 adults diagnosed with ADHD, 35 with ASD, and 24 with LD along with 165 demographically-matched healthy adults completed Self-Report forms, and 57 with ADHD, 36 with ASD, 24 with LD, and 132 healthy adults completed Informant Report forms. Results All effects were significant (p.40) for both self- and informant reports. Profiles of scale elevations differed between diagnostic groups for self- (ŋ2 > 0.10) and informant (ŋ2 > 0.13) reports. Adults with ADHD had highest elevations on the Working Memory and Plan/Organize scales, with the LD group showing a similar profile but to a lesser degree. Adults with ASD were characterized by highest elevations on the Shift scale. Conclusions Individuals with common developmental disorders report distinguishable patterns of everyday EF. Those with ADHD report substantial difficulty sustaining working memory, planning, and organizing, while those with ASD report greatest difficulty adapting to change. Individuals with LD show similar profiles to ADHD but to a lesser degree.

Rasch Measurement Theory (RMT) Analyses of the Huntington's Disease Everyday Functioning (Hi-DEF) to Evaluate Item Fit and Performance.

The Huntington's Disease (HD) Everyday Functioning (Hi-DEF) is a new patient-reported outcome (PRO) instrument designed to measure the impact of cognitive impairment on daily functioning in the early stages of HD. To assess the measurement properties and finalize item content of the Hi-DEF. A cross-sectional, observational psychometric validation study was conducted among individuals with early stages of HD at 9 US centers of excellence. Rasch Measurement Theory (RMT) analysis of the initial draft version of the Hi-DEF (47 items) and subscales (i.e., 'Home', 'At work', 'Driving', and 'Communication') was conducted to examine measurement properties including sample-to-scale targeting, suitability of response scale (ordering of response thresholds), scale cohesiveness (item fit), local independence, and person fit. 151 participants (mean age 47 years (SD 12), 59% female) were included. Seven items were removed based on dependency and item fit. The remaining 40-item version of the Hi-DEF demonstrated good measurement properties. Across the four subscales, targeting ranged from 49-70% (72% full scale), reliability ascertained by person separation index ranged from 0.53-0.87 (0.92 full scale), response scales were ordered for 25-100% of items (75% full scale), 0-12% items displayed misfit (2% full scale), and 0-1% (2% full scale) item pairs displayed dependency. Our study supports the psychometric integrity of the Hi-DEF as a reliable and valid new PRO instrument designed to assess the impact of cognitive impairment on daily functioning in the early stages of HD. Future work will evaluate the external validity and utility in clinical trial applications.

Age differences in functional connectivity track dedifferentiation of category representations.

With advancing age, the distinctiveness of neural representations of information declines. While the finding of this so-called 'age-related neural dedifferentiation' in category-selective neural regions is well-described, the contribution of age-related changes in network organization to dedifferentiation is unknown. Here, we asked whether age differences in a) whole-brain network segregation (i.e., network dedifferentiation) and b) functional connectivity to category-selective neural regions are related to regional dedifferentiation of categorical representations. Younger and older adults viewed blocks of face and house stimuli in the fMRI scanner. We found an age-related decline in neural distinctiveness for faces in the fusiform gyrus (FG) and for houses in the parahippocampal gyrus (PHG). Functional connectivity analyses revealed age-related dedifferentiation of global network structure as well as age differences in connectivity between the FG and early visual cortices. Interindividual correlations demonstrated that regional distinctiveness was related to network segregation. Together, our findings suggest that dedifferentiation of categorical representations may be linked to age-related reorganization of functional networks.

Untargeted Metabolomic Analyses of Body Fluids to Differentiate TBI DOC and NTBI DOC.

To investigate the metabolomic differences between Traumatic brain injury (TBI) disorder of consciousness (DOC) patients and non-traumatic brain injury (NTBI) DOC patients by using cerebrospinal fluid (CSF), serum and urine samples beneficial to understand the pathological mechanism differences between the two etiologies, provide potential clues for the subsequent treatment and prognosis, and investigate the metabolome differences and similarities between TBI and NTBI among three different body fluids. In total, 24 TBI DOC subjects and 29 NTBI DOC subjects were enrolled. CSF, serum and urine samples from TBI DOC and NTBI DOC patients were collected and analyzed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarkers and altered metabolic functions. When comparing TBI DOC and NTBI DOC, 36, 31 and 52 differential metabolites were obtained in CSF, serum and urine, respectively. The functional analysis of differential metabolites obtained in CSF, serum and urine were all related to amino acid metabolism. Except for amino acid metabolism, metabolic biomarkers in CSF, serum and urine mainly focus on central function, cognitive function, necrosis and apoptosis and neurological function, respectively. In CSF, the highest AUC was 0.864 (Isoproturon) and 0.816 (Proline betaine). Then, the AUC of NFurfurylformamide in serum was 0.941, while the AUC of Dihydronepetalactone and Doxepin N-oxide glucuronide were 1.0 in urine. CSF, serum and urine metabolomic analyses could differentiate TBI DOC from NTBI DOC and functional analyses showed a metabolic change difference between TBI DOC and NTBI DOC.

Theoretical and experimental considerations regarding the airbag system operation

Abstract The concept of Passive Safety aims to reduce the harmful effects on vehicle occupants and pedestrians in the event of an accident. Intelligent car bodies and restraint systems have been designed. Thus, side, front, knee and head airbag systems have been introduced, as well as pre-strained seat belts and force limiters. The objectives of car manufacturers are not only to reduce injuries that can cause death, but also a significant reduction of those that can result in permanent damage such as a whiplash. Advanced airbag systems are currently introduced, which offer protection to the occupants at the knees, torso, hips and abdomen. The topic proposed in the paper concerns both theoretical research on the operation of passive security systems, respectively the airbag system, and experimental research. Regarding the theoretical research, this paper proposes a virtual prototyping method by means of the LS Dyna software package of the airbag system functioning in order to protect the driver in the event of a frontal collision. For the analysis of the airbag system functioning, a complex assembly which contains of a dummy, seat, steering wheel, passive safety systems was used. For the experimental research a stand was designed for the analysis and testing of the airbag system, which has the role to protect the driver in the event of a frontal collision of the vehicle. In order to carry out the experimental tests, it was proposed to create a sledge type stand, which simulates frontal collisions.

Estimation of sex from femoral bone using radiological imaging methods in Turkish population.

Sex estimation is leading to determine the biological profile in forensic medicine. The aim of this study is to research the effectiveness of logistic regression (LogR) and discriminant function analysis (DFA) to create sex estimation models on femur images obtained with Computed Tomography (CT) angiography and to address the differences of femur, which show sexual dimorphism, among populations. All parameters were measured on three planes by adjusting the 300 CT angiography images from 150 women and 150 men that focused on the left femur to the orthogonal plane with standard magnification. The subgroup, which included 30 images randomly generated from these images, was measured twice with an interval of 3 weeks by the first radiologist and once by the second radiologist. According to the Fisher's Linear Discriminant analysis, which was evaluated with ten parameters in the study, it was concluded that the power of discriminating women was 96.7%, the power of discriminating men was 98.7%, and the total discrimination power was 97.7%; these results were 98%, 99.3%, and 98.7%, respectively according to LogR. In this study, DFA and LogR analysis showed that femur provided a very good rate of sexual dimorphism. A database belonging to the Turkish population was created for the femur, allowing for comparison between populations.

Progesterone boosts abiraterone-driven target and NK cell therapies against glioblastoma

IntroductionGlioblastoma (GBM) poses a significant challenge in oncology, with median survival times barely extending beyond a year due to resistance to standard therapies like temozolomide (TMZ). This study introduces a novel therapeutic strategy combining progesterone (Prog) and abiraterone (Abi) aimed at enhancing GBM treatment efficacy by modulating the tumor microenvironment and augmenting NK cell-mediated immunity.MethodsWe employed in vitro and in vivo GBM models to assess the effects of Prog and Abi on cell viability, proliferation, apoptosis, and the immune microenvironment. Techniques included cell viability assays, Glo-caspase 3/7 apoptosis assays, RNA-seq and qPCR for gene expression, Seahorse analysis for mitochondrial function, HPLC-MS for metabolomics analysis, and immune analysis by flow cytometry to quantify NK cell infiltration.ResultsProg significantly reduced the IC50 of Abi in TMZ-resistant GBM cell, suggesting the enhanced cytotoxicity. Treatment induced greater apoptosis than either agent alone, suppressed tumor growth, and prolonged survival in mouse models. Notably, there was an increase in CD3−/CD19−/CD56+/NK1.1+ NK cell infiltration in treated tumors, indicating a shift towards an anti-tumor immune microenvironment. The combination therapy also resulted in a reduction of MGMT expression and a suppression of mitochondrial respiration and glycolysis in GBM cells.ConclusionThe combination of Prog and Abi represents a promising therapeutic approach for GBM, showing potential in suppressing tumor growth, extending survival, and modulating the immune microenvironment. These findings warrant further exploration into the clinical applicability of this strategy to improve outcomes for GBM patients.

Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis.

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor.

Management systems of food enterprises on the example of the dairy processing industry: analysis and features of functioning

Management systems of food enterprises on the example of the dairy processing industry: analysis and features of functioning