Positive Matrix Research Articles

Denoiseit: denoising gene expression data using rank based isolation trees

BackgroundSelecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers.MethodIn contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes.ResultsDenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt

Organophosphorus flame retardants in the Qiantang River of China: occurrence, source and ecological risk assessment.

In recent years, the prevalence and danger of organophosphorus flame retardants (OPFRs) have drawn attention from all around the world. This study examined twenty-five OPFRs observed in water and sediment samples from the Qiantang River in eastern China, as well as their occurrence, spatial distribution, possible origins, and ecological hazards. All the 25 OPFRs were detected in water and sediment samples. The levels of Σ25OPFRs in water and sediment were 35.5-192ng/L and 8.84-48.5ng/g dw, respectively. Chlorinated OPFRs were the main contributions in water, whereas alkyl-OPFRs were the most common congeners found in sediment. Spatial analysis revealed that sample locations in neighboring cities had somewhat higher water concentrations of OPFRs. Slowing down the river current and making the reservoir the main sink of OPFRs, the dam can prevent OPFRs from moving via the Qiantang River. Positive matrix factorization indicated that plasticizer in polyvinyl chloride, polyester resins, and polyurethane foam made the greatest contributions in water, whereas polyurethane foam and textile were the predominant source in sediment. Analysis of sediment-water exchange of OPFRs showed that twelve OPFRs in sediments can re-enter into the water body. The risk quotients showed the ecological risk was low to medium, but trixylyl phosphate exposures posed high ecological risk to aquatic organisms.

SVMBN: A new method for predicting metabolite-disease associations based on biological networks and machine learning

Identifying metabolite-disease associations is of paramount significance. With the advancement of research, computational methods have surpassed traditional experiments in efficiency. Nevertheless, current computational methods often overlook the integration of multiomics data, and the performance of the predictive models used is limited. To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately. Second, the metabolite and disease similarity networks are combined to obtain the original link features. Third, nonnegative Matrix Factorization (NMF) is applied to extract effective features from the original features, thereby reducing noise. Finally, Support Vector Machine (SVM) is utilized to predict potential associations between metabolites and diseases. Experimental results demonstrate that the SVMBN algorithm achieves an average AUC of 0.98 in 5-fold cross-validation, indicating its superiority over other methods. Furthermore, case studies prove that the SVMBN algorithm can accurately forecast the relationships between metabolites and diseases.

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (<3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

Effects of seasonal management programs on PM2.5 in Seoul and Beijing using DN-PMF: Collaborative efforts from the Korea-China joint research

South Korea and China have implemented increasingly stringent mitigation measures to reduce the health risks from PM2.5 exposure, jointly conducting a ground-based air quality observation study in Northeast Asia. Dispersion normalized positive matrix factorization (DN-PMF) was used to identify PM2.5 sources in Seoul and Beijing and assess the effectiveness of the seasonal management programs (SMPs) through a comparative study. Samples were collected during three periods: January-December 2019, September 2020-May 2021, and July 2021-March 2022. In Seoul, ten sources were resolved (Secondary nitrate: 8.67 μg/m3, 34 %, Secondary sulfate: 5.67 μg/m3, 22 %, Motor vehicle: 1.83 μg/m3, 7.2 %, Biomass burning: 2.30 μg/m3, 9.1 %, Residual oil combustion: 1.66 μg/m3, 6.5 %, Industry: 2.15 μg/m3, 8.5 %, Incinerator: 1.39 μg/m3, 5.5 %, Coal combustion: 0.363 μg/m3, 1.4 %, Road dust/soil: 0.941 μg/m3, 3.7 %, Aged sea salt: 0.356 μg/m3, 1.4 %). The SMP significantly decreased PM2.5 mass concentrations and source contributions of motor vehicle, residual oil combustion, industry, coal combustion, and biomass burning sources (p-value < 0.05). For Seoul, the reduction effects of the SMPs were evident even considering the influence of the natural meteorological variations and the responses to COVID-19. In Beijing, nine sources were resolved (Secondary nitrate: 12.6 μg/m3, 28 %, Sulfate: 8.27 μg/m3, 18 %, Motor vehicle: 3.77 μg/m3, 8.4 %, Biomass burning: 2.70 μg/m3, 6.0 %, Incinerator: 4.50 μg/m3, 10 %, Coal combustion: 3.52 μg/m3, 7.8 %, Industry: 5.01 μg/m3, 11 %, Road dust/soil: 2.92 μg/m3, 6.5 %, Aged sea salt: 1.63 μg/m3, 3.6 %). Significant reductions in PM2.5 mass concentrations and source contributions of industry, coal combustion, and incinerator (p-value < 0.05) were observed, attributed to the SMP and additional measures enforced before the 2022 Beijing Winter Olympics. Unlike comparing PM2.5 mass concentration variations using conventional methods, investigation of the source contribution variations of PM2.5 by using DN-PMF can provide a deeper understanding of the effectiveness of the air quality management policies.

Which pollutants and sources should be prioritized for control in multi-pollutants complex contaminated areas?

Risk assessment and source identification of multi-pollutants are essential for accurate control of soil contamination. However, complexity in pollutant properties and diversity in source types raise challenges to the target. Therefore, this study constructed a hierarchical ecological risk quantification method combined with risk ranking, risk of single pollutant using potential affected fraction (PAF), and joint risk of multi-pollutants employing msPAF. Taking regional contamination in South China as a case, the risk ranking was determined, while single and joint effects showed msPAF reaching 79.4 %, with risk as heavy metals (HMs) > per- and polyfluoroalkyl substances (PFASs) > polycyclic aromatic hydrocarbons (PAHs). Meanwhile, an integrated source apportionment method was established from three layers by principal component analysis to classify source types, multiple linear regression of distance to identify key sources, and positive matrix factorization to track omitted sources. Consequently, key sources were captured, with 80.8 %−93.2 % contribution of farmland and electroplating to three main HMs, 52.2 %−69.4 % contribution of roads to three main PAHs, and 71.1 %−73.2 % contribution of electroplating to two main PFASs. Further, omitted sources were tracked with contribution of 31.2 %−84.1 % to eight pollutants. The established methods can identify control targets, including high-risk pollutants and their key sources.

Neural network-based distributed consensus tracking control for uncertain Euler–Lagrange systems over directed topologies

This paper proposes a neural network-based robust control approach for driving disturbed Euler–Lagrange systems (e.g., robot manipulator) in a directed network to perform consensus tracking of a heterogeneous leader’s output signal. The parameter matrices and external disturbances in each follower’s Euler–Lagrange dynamics are unmeasurable, thus we unify them into one lumped uncertainty term. Neural networks are then employed to online estimate these uncertainties, with adaptive update laws ensuring the boundedness of the estimation errors. Subsequently, a set of distributed observers are developed to adaptively estimate the leader’s states as well as the unidentified parameter vector and output matrix in the leader’s model. With their help, the robust cooperative controller is designed. Its efficacy on directed networks is theoretically justified by designing a Lyapunov function with a special structure. This function produces symmetric positive definite matrices when its first derivative terms interact with the specified parameters in the observer and Laplacian matrices for directed graphs. Finally, numerical simulations based on two-link robot manipulators are carried out to verify that the tracking errors converge to zero when applying the neural network-based robust controller.

Efficient method for symmetric nonnegative matrix factorization with an approximate augmented Lagrangian scheme

In this paper, we propose an efficient method for solving symmetric nonnegative matrix factorization following an approximate augmented Lagrangian scheme. The augmented Lagrangian subproblem was solved column by column under the block coordinate descent (BCD) framework. In particular, we extend the recursive formula for rank-k nonnegative least squares problems by Chu et al. (2021) to subproblems generated by BCD framework in our method. Thereafter we derive the closed-form solution for k=4. Experiments for clustering show that the proposed method is noticeably efficient and achieves competitive performance compared with state-of-the-art methods.

Multi-functional scar tissue discrimination platform construction and exploration of molecular mechanism for scar formation

Scars that form after skin injury can cause structural and functional skin damage. Currently, scar tissue determination relies mainly on doctors’ subjective observations and judgments and lacks objectivity. However, current deep learning models can only achieve specific discrimination using unimodal data, which limits the comprehensive understanding of scar tissue and may reduce accuracy and stability. To solve these problems, in this study, a skin scar recognition platform based on advanced deep learning and a weighted aggregation network fusion method is proposed. It is implemented using a residual network-based CNN model and a logistic regression model with L1 regularization and is suitable for both unimodal and multimodal data. The experimental results showed that the proposed platform achieved a satisfactory accuracy of 98.26% for image discrimination. In the gene discrimination model test performed on a test dataset containing 17 gene expression samples, all samples were accurately discriminated. In addition, the proposed multimodal discrimination model achieved a discrimination accuracy of 98.23%. These results validate the effectiveness of deep feature extraction and multimodal feature fusion techniques for image discrimination tasks. On this basis, to deeply explore the pathogenesis of scar formation, a method with the ability to integrate regularization, sparsity, and orthogonality constraints, multiconstraint joint non-negative matrix factorization (MCJNMF), was used to explore the genetic correlation between collagen micrographic image features and gene expression data. In this study, we confirmed the association between the calcium signaling pathway, MAPK signaling pathway, and collagen fiber repair, and successfully identified 11 potential therapeutic targets, including TRIM59 and TBC1D9, which provide important clues for future scar treatment and prevention strategies.

Machine Learning Recognizes Frequency-Following Responses in American Adults: Effects of Reference Spectrogram and Stimulus Token.

Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophysiologic measure. While the FFR has been studied extensively, there are limitations in obtaining and analyzing FFR recordings that recent machine learning algorithms may address. In this study, we aimed to investigate whether FFRs can be enhanced using an "improved" source-separation machine learning algorithm. For this study, we recruited 28 native speakers of American English with normal hearing. We obtained two separate FFRs from each participant while they listened to two stimulus tokens /i/ and /da/. Electroencephalographic signals were pre-processed and analyzed using a source-separation non-negative matrix factorization (SSNMF) machine learning algorithm. The algorithm was trained using individual, grand-averaged, or stimulus token spectrograms as a reference. A repeated measures analysis of variance revealed that FFRs were significantly enhanced (p < .001) when the "improved" SSNMF algorithm was trained using both individual and grand-averaged spectrograms, but not when utilizing the stimulus token spectrogram. Similar results were observed when extracting FFRs elicited by using either stimulus token, /i/ or /da/. This demonstration shows how the SSNMF machine learning algorithm, using individual and grand-averaged spectrograms as references in training the algorithm, significantly enhanced FFRs. This improvement has important implications for the obtainment and analytical processes of FFR, which may lead to advancements in clinical applications of FFR testing.

Source Identification of Potentially Toxic Metals in Plants of Alpine Ecosystems of Mt. Madra by Positive Matrix Factorization.

In this study, the concentrations of cadmium (Cd), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in plants sampled from Mt. Madra were investigated. Furthermore, the distribution characteristics and source identification of potentially toxic metals were investigated with the application of Positive Matrix Factorization (PMF) modelling. Samples of 26 different plant species were taken from Mt. Madra at elevations ranging from 177 to 1347m using the multi-point sampling approach. The metal quantities measured by ICP-OES are the following sequences (mean ± SD) (mg/kg): Fe (974.96 ± 29.6) > Mn (111.81 ± 2.6) > Zn (27.28 ± 0.2) > Ni (2.17 ± 0.03) > Pb (0.77 ± 0.01) > Cd (0.12 ± 0.01). According to the plant samples in which the highest values were determined, the metals are as follows: Cd (Lathyrus laxiflorus, 0.401mg/kg), Fe (Ajuga orientalis, 7621.207mg/kg), Mn (Castanea sativa, 724.927mg/kg), Ni (Prunella laciniata, 6.947mg/kg), Pb (Crataegus stevenii, 3.955mg/kg) and Zn (Prunella laciniata, 50.802mg/kg). The results of the PMF model showed that Cd had an atmospheric transport factor originated and transported from industrial activites, Ni had a substrate factor, Fe, Mn, Pb and Zn were influenced by different anthropogenic factors.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM

BackgroundAnoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored.MethodsUtilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays.ResultsNMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion.ConclusionThis pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

A multi-layered integrative analysis reveals a cholesterol metabolic program in outer radial glia with implications for human brain evolution.

The definition of molecular and cellular mechanisms contributing to brain ontogenetic trajectories is essential to investigate the evolution of our species. Yet their functional dissection at an appropriate level of granularity remains challenging. Capitalizing on recent efforts that have extensively profiled neural stem cells from the developing human cortex, we develop an integrative computational framework to perform trajectory inference and gene regulatory network reconstruction, (pseudo)time-informed non-negative matrix factorization for learning the dynamics of gene expression programs, and paleogenomic analysis for a higher-resolution mapping of derived regulatory variants in our species in comparison with our closest relatives. We provide evidence for cell type-specific regulation of gene expression programs during indirect neurogenesis. In particular, our analysis uncovers a key role for a cholesterol program in outer radial glia, regulated by zinc-finger transcription factor KLF6. A cartography of the regulatory landscape impacted by Homo sapiens-derived variants reveals signals of selection clustering around regulatory regions associated with GLI3, a well-known regulator of radial glial cell cycle, and impacting KLF6 regulation. Our study contributes to the evidence of significant changes in metabolic pathways in recent human brain evolution.

A unified framework of semi-supervised community detection integrating network topology and node content

Detecting the community structure within networks is important because it aids in the analysis of complex networks. However, the existence of diverse complex structures in the network topology can limit the topological information’s ability to represent communities. For instance, the accuracy of many traditional methods based on topology alone decreases sharply when a link is missing or noisy. To solve the above-mentioned issues simultaneously, we propose a unified framework of semi-supervised community detection integrating content information. Three models are proposed, instead of a special model-handling problem. The framework used performs the same interpretation for a target problem. The inputs contain the adjacent and node content matrix. The proposed models describe the network topology using a non-negative matrix factorization (NMF) algorithm–based generative model or modularity maximization algorithm. Additionally, we use the equivalence theory of the pLSI, and NMF models the content information based on NMF. Community membership is also used as the relationship between communities and node contents. The models uniformly integrate content information and network topology. Further, a constraint matrix that depicts must-links is built by calculating the similarity of neighbors between any two nodes. Must-link pairs can represent the network topology’s prior information. The models’ community memberships are adjusted by using a strong constraint based on the connected component of this matrix. The models also introduce prior information into the topological and content information in the mentioned models. We compared the proposed models with the state-of-the-art community detection models on artificial networks and five real networks. The experimental results show that the proposed models outperformed the models combining topology and content and semi-supervised models. Fusing the topology with the content and the prior information in the models was also effective for community detection. Thus, the models under the framework are more advantageous than other semi-supervised models when incorporating the same amount of prior information into the models. The semi-supervised framework used also has a certain level of competitiveness in detecting communities.

Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model

Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model

Stability analysis of polynomials with an approach of differential topology

SummaryIn this paper, we study the Hurwitz stability of polynomials. By considering that a degree Hurwitz polynomial has its corresponding Markov parameters, we define the set in Section 3, we also define . Based on properties of the Hankel matrices and the stability test, as well as by using ideas of differential topology, we show that is a fiber bundle with a base. This result allows us to obtain an interesting application: Given a Hurwitz polynomial, we can generate two families of positive definite Hankel matrices.

Variations in Cloud Concentration Nuclei Related to Continental Air Pollution Control and Maritime Fuel Regulation over the Northwest Pacific Ocean

Here, we compared the concentrations of cloud condensation nuclei (CCN) and particle number size distributions (PNSDs) measured during the transient period from the winter to the summer East Asian monsoon in 2021 with those in 2014 to explore possible responses to how CCN responds to upwind continental air pollutant mitigation and marine traffic fuel sulfur content (FSC) regulation over the northwest Pacific Ocean (NWPO). We also employed the Positive Matrix Factorization (PMF) analysis to apportion concentrations of CCN (Nccn) to different sources in order to quantify its source-specified responses to mitigation of air pollution during the transient period. Our results showed that (1) upwind continental mitigation likely reduced Nccn by approximately 200 cm−3 and 400 cm−3 at 0.2% and 0.4% supersaturation (SS), respectively, in the marine background atmosphere over the NWPO; (2) FSC regulation resulted in a decrease in Nccn at 0.4% SS by about 50 cm−3 and was nearly negligible at 0.2% SS over the NWPO. Additionally, a PMF-resolved factor, characterized by a dominant nucleation mode, was present only in 2014 and disappeared in 2021, likely due to the reduction. This estimation, however, suffered from uncertainties since seasonal changes were hard to be deducted accurately. PMF-resolved factors accurately represented Nccn in 80–90% of cases, but this accuracy was not observed in the remaining cases. Finally, an integrated analysis of satellite-derived cloud parameters and ship-based measurements indicated that the reduced Nccn over the NWPO might be co-limited with meteorological factors in forming cloud droplets during the transient period.

Temporal shifts in sources and composition of particulate metals in an environmental justice community: Impacts of continued emission reductions, COVID-19, and wildfires

In this study, we evaluated trends in the composition and source contributions of ambient particulate metals, many of which are toxic air contaminants, in Paramount, California. Paramount is an environmental justice community disproportionately impacted by air pollution from multiple emission sources near sensitive receptors. Concentrations of ambient metals bound to total suspended particulates (TSP) were measured hourly using a Xact 625 ambient metal monitor between 2017 and 2020. The Positive Matrix Factorization (PMF) model identified traffic, industry, and soil dust as major contributors. Results showed that contributions of traffic and soil dust noticeably declined during the COVID-19 lockdown period (March–April 2020) due to reduced human activities but reverted to normal levels within a few months. Industry-related factors, however, dropped significantly during this period and remained low throughout the year. Contributions from industry-related sources in Paramount began declining even before 2020, despite no similar trend in regional levels, implying local emission reductions. These reductions resulted from tariffs, decreased demand, regulatory actions by the South Coast Air Quality Management District, and voluntary controls by local facilities. Consequently, Paramount is experiencing drastically lower levels of metals (up to 80%, depending on the metal and source) from metal-processing facilities and related sources. Moreover, we observed markedly elevated concentrations of several metals (e.g., Ba, Ca, Mn, K, etc.) during the unprecedented September 2020 wildfires that engulfed much of the western US, adding to the limited body of studies on this phenomenon and emphasizing the considerable impact of massive wildfires on ambient levels of particulate metals.

Robust fatigue markers obtained from muscle synergy analysis.

This study aimed to utilize the nonnegative matrix factorization (NNMF) algorithm for muscle synergy analysis, extracting synergy structures and muscle weightings and mining biomarkers reflecting changes in muscle fatigue from these synergy structures. A leg press exercise to induce fatigue was performed by 11 participants. Surface electromyography (sEMG) data from seven muscles, electrocardiography (ECG) data, Borg CR-10 scale scores, and the z-axis acceleration of the weight block were simultaneously collected. Three indices were derived from the synergy structures: activation phase difference, coactivation area, and coactivation time. The indicators were further validated for single-leg landing. Differences in heart rate (HR) and heart rate variability (HRV) were observed across different fatigue levels, with varying degrees of disparity. The median frequency (MDF) exhibited a consistent decline in the primary working muscle groups. Significant differences were noted in activation phase difference, coactivation area, and coactivation time before and after fatigue onset. Moreover, a significant correlation was found between the activation phase difference and the coactivation area with fatigue intensity. The further application of single-leg landing demonstrated the effectiveness of the coactivation area. These indices can serve as biomarkers reflecting simultaneous alterations in the central nervous system and muscle activity post-exertion.

Adaptive Adjustments in Lower Limb Muscle Coordination during Single-Leg Landing Tasks in Latin Dancers.

Previous research has primarily focused on evaluating the activity of individual muscles in dancers, often neglecting their synergistic interactions. Investigating the differences in lower limb muscle synergy during landing between dancers and healthy controls will contribute to a comprehensive understanding of their neuromuscular control patterns. This study enrolled 22 Latin dancers and 22 healthy participants, who performed a task involving landing from a 30 cm high platform. The data were collected using Vicon systems, force plates, and electromyography (EMG). The processed EMG data were subjected to non-negative matrix factorization (NNMF) for decomposition, followed by classification using K-means clustering algorithm and Pearson correlation coefficients. Three synergies were extracted for both Latin dancers and healthy participants. Synergy 1 showed increased contributions from the tibialis anterior (p < 0.001) and medial gastrocnemius (p = 0.024) in Latin dancers compared to healthy participants. Synergy 3 highlighted significantly greater contributions from the vastus lateralis in healthy participants compared to Latin dancers (p = 0.039). This study demonstrates that Latin dancers exhibit muscle synergies similar to those observed in healthy controls, revealing specific adjustments in the tibialis anterior and medial gastrocnemius muscles among dancers. This research illustrates how dancers optimize control strategies during landing tasks, offering a novel perspective for comprehensively understanding dancers' neuromuscular control patterns.