Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms.

The evolution of open-pit coal mining over several decades has encompassed two distinct stages: resource exploitation and ecological restoration. Each phase has led to significant changes in surface coverage and elevation, accompanied by distinct patterns of surface deformation. Reconstructing deformation characteristics across both mining and restoration periods, analyzing the underlying mechanisms, and validating results are crucial for understanding the surface evolution. This study examines post-restoration deformation characteristics using archived Synthetic Aperture Radar (SAR) data and Intermittent Small Baseline Subset-Interferometric Synthetic Aperture Radar (ISBAS-InSAR) techniques. Non-negative Matrix Factorization (NMF) is then applied to decompose deformation signals and analyze the two-stage deformation mechanisms. Lastly, validation is performed through comparisons with differential Digital Elevation Models (DEM) and optical imagery acquired before and during the open-pit mining phase. The Emma mining area in Spain was selected as the study site. Sentinel-1 SAR imagery from 2017 to 2024 reveals vertical deformation rates of up to 21.6 cm/year. The two components derived from NMF correspond to long-term subsidence due to historical mining and short-term deformation resulting from slope restoration. The first NMF component is validated using differential DEM (2009–2020), revealing a positive correlation between deformation magnitude and backfill thickness. The second component is supported by optical imagery from 2015 to 2016, indicating that large-scale slope modification during ecological restoration contributed to short-term deformation. This study presents a quantitative framework for analyzing mining-induced deformation and assessing the effects of ecological restoration on terrain stability.

to the editor: The electromyogram (EMG) as an indirect measure of motor unit activity has proved invaluable in motor control studies. Increasingly, the application of frequency domain techniques to EMG signals has provided important insight into the structure and function of the human motor system.

Mass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. In this paper, we introduce three novel approaches in MSI data processing to perform the tasks of data augmentation, feature ranking, and image registration. We use these approaches in conjunction with non-negative matrix factorization (NMF) to resolve two of the biggest challenges in MSI data analysis, namely: 1) the large file sizes and associated computational resource requirements and 2) the complexity of interpreting the very high dimensional raw spectral data. There are many dimensionality reduction techniques that address the first challenge but do not necessarily result in readily interpretable features, leaving the second challenge unaddressed. We demonstrate that NMF is an effective dimensionality reduction algorithm that reduces the size of MSI datasets by three orders of magnitude with limited loss of information, yielding spatial and spectral components with meaningful correlation to tissue structure that may be used directly for subsequent data analysis without the need for additional clustering steps. This analysis is demonstrated on an MSI dataset from female Sprague-Dawley rats for an animal model of comorbid visceral pain hypersensitivity (CPH). We find that high-dimensional MSI data (∼ 100,000 ions per pixel) can be reduced to 20 spectral NMF components with < 20% loss in reconstruction accuracy. The resulting spatial NMF components are reproducible and correlate well with H&E-stained tissue images. These components may also be used to generate images with enhanced specificity for different tissue types. Small patches of NMF data (i.e., 20 spatial NMF components over 20 × 20 pixels) provide an accuracy of ∼ 87% in classifying CPH vs naïve control subjects. This paper presents the novel data processing methodologies that were used to produce these results, encompassing novel data processing pipelines for data augmentation to support training for classification, ranking of features according to their contribution to classification, and image registration to enhance tissue-specific imaging.

Spectral analysis of surface electromyograms (sEMG) is often used to estimate central and peripheral characteristics of a motor unit (MU) population, such as average conduction velocity, proportion of muscle fiber types, and pattern of MU recruitment. This estimation is based on the assumption that the sEMG adequately reflects the frequency characteristics of the underlying MU action potentials (MUAP). However, sEMG has limitations in this respect, based on physiological and non-physiological factors that influence its frequency content. We present a method to examine characteristics of a MU population more reliably by assessing the distributions of frequency content and amplitude for a collection of individual MUAPs, identified using high-density sEMG decomposition. We demonstrate the use of this approach to examine how MU characteristics differ across muscles and in the post-stroke state by presenting preliminary data from deltoid (DELT), biceps (BIC), and finger flexor (FF) MU populations from 12 post-stroke individuals and 8 able-bodied controls. The results show differences in the magnitude and range of MUAP median frequencies across muscles in both groups. The group median values were higher in the stroke group for the DELT and FF and lower in the stroke group for the BIC. The range of frequencies was larger in the stroke group for all muscles. The distribution of MUAP RMS amplitude in both stroke and control groups had a substantially larger range in FF than in DELT and BIC. The group median values for the FF were twice as large in the stroke group. In addition, there were differences in the frequency and amplitude results between MUAP and global sEMG analyses. The implications of these findings and possible applications of the approach are discussed.

Hepatocellular carcinoma (HCC) is one of the most common types of cancer globally. However, HCC features poor prognosis due to complex pathogenesis and limitations of therapeutic approaches. To improve the prognosis of HCC patients, we analyzed single-cell transcriptome and spatial transcriptome on liver tissues from HCC patients. We applied Non-negative Matrix Factorization (NMF) method on spatial transcriptome and found a critical pathway associated with HCC through GO over-representation analysis. According to the pathway activity score, the cells in single-cell transcriptome were divided into three groups. Then we conducted hdWGCNA and selected HCC-related co-expressed gene modules which showed significant intergroup differences. Integrative machine learning algorithms on bulk transcriptome were employed for core genes selection. We respectively evaluated their AUC values as independent diagnostic markers and retained a core gene. We conducted a survival analysis using this gene. Next, we performed immune infiltration analysis, tumor microenvironmental analysis and drug sensitivity analysis. Finally, pseudotime analysis, differentiation potential analysis and cell-cell communication analysis were applied to decipher the mechanisms underlying tumor initiation and progression. Single-cell transcriptome analysis revealed the high heterogeneity of cells in patients with HCC. The GO over-representation analysis based on NMF indicated that cytoplasmic translation path was enriched in most NMF components. The SSR2 gene passed the integrative machine learning algorithms and possessed excellent diagnosis (AUC > 0.8) and prognostic abilities (K-M curve, P = 0.007). Further analysis identified SSR2 as the top-ranked gene by average expression level in NK cells compared to all other immune cell clusters. Its expression showed a consistent downward trend during NK cell maturation.NK cells with high expression of SSR2 exhibited stronger cell-cell communication than those with low expression of SSR2. NK cells mainly communicated with other cells through MIF-mediated signaling pathway. In hepatocellular carcinoma (HCC), the high expression of the SSR2 gene in NK cells is accompanied by enhanced cytoplasmic translation pathway, which promotes MIF secretion and activates its downstream signaling pathways, thereby driving the disruption of the tumor immune microenvironment and the progression of the disease.

As more and more intramuscular electromyogram (EMG) decomposition programs are being developed, there is a growing need for evaluating and comparing their performances. One way to achieve this goal is to generate synthetic EMG signals having known features. Features of interest are: the number of channels acquired (number of detection surfaces), the number of detected motor unit action potential (MUAP) trains, their time-varying firing rates, the degree of shape similarity among MUAPs belonging to the same motor unit (MU) or to different MUs, the degree of MUAP superposition, the MU activation intervals, the amount and type of additive noise. A model is proposed to generate one or more channels of intramuscular EMG starting from a library of real MUAPs represented in a 16-dimensional space using their Associated Hermite expansion. The MUAP shapes, regularity of repetition rate, degree of superposition, activation intervals, etc. may be time variable and are described quantitatively by a number of parameters which define a stochastic process (the model) with known statistical features. The desired amount of noise may be added to the synthetic signal which may then be processed by the decomposition algorithm under test to evaluate its capability of recovering the signal features.

In this paper, the problem of single microphone source separation via Nonnegative Matrix Factorization (NMF) by exploiting video information is addressed. Respective audio and video modalities coming from a single human speech usually have similar time changes. It means that changes in one of them usually corresponds to changes in the other one. So it is expected that activation coefficient matrices of their NMF decomposition are similar. Based on this similarity, in this paper the activation coefficient matrix of the video modality is used as an initialization for audio source separation via NMF. In addition, the mentioned similarity is used for post-processing and for clustering the rows of the activation coefficient matrix which were resulted from randomly initialized NMF. Simulation results confirm the effectiveness of the proposed multimodal approaches in single microphone source separation.

We analyzed the problem of motor unit (MU) crosstalk in the spike trains that are identified from high-density surface electromyograms (HDsEMG). We used advanced volume conductor model to simulate five muscles with 500 MUs distributed in the 60 × 30 mm elliptical muscle cross-section. HDsEMG signals were generated during 20 s long isometric muscle contractions at 10%, 30%, 50% and 70% of the maximum excitation. Noiseless and noisy HDsEMG signals were recorded by an array of 10 × 9 electrodes and decomposed by the Convolution Kernel Compensation (CKC) method to yield estimated MU spike trains (MUSTs). Results show that 70.4 ± 26.1% of MUSTs exhibit crosstalk from other MUs, but with relatively low spike amplitudes. Across all the simulated contraction and noise levels, the crosstalk in the identified MUSTs came mostly from nearby MUs (average distance from the dominant MU with the largest spikes in MUST <3.5 mm) and relatively small MUs (average number of MU fibers <150).

Introducing a weighted non-negative matrix factorization for image classification

We analyze the performance of recently introduced technique for decomposition of compound muscle action potentials (CMAPs), recorded by high-density surface electromyograms (HDEMG). This technique utilizes Convolution Kernel Compensation (CKC) method to estimate motor unit (MU) filters from HDEMG recordings of voluntary isometric muscle contractions. Afterwards, it applies these filters to the HDEMG recordings of elicited contractions and identifies MU spike trains. We then propose a novel method for segmentation of MU firings out of the identified spike trains and demonstrate on a synthetic HDEMG signals that at high MU synchronization levels this novel segmentation method significantly outperforms the spike segmentation introduced by CKC method. Namely, at 80% MU firing synchronization the area under receiver operating characteristic curve (AUC) increases from 0.96 ± 0.03 to 0.99 ± 0.01 when newly proposed segmentation is used instead of the previously introduced CKC-based segmentation. Thus, newly proposed segmentation supports significantly more accurate discrimination of true positive and false positive MU firings. At low MU synchronization levels (<60%) the newly proposed MU firing segmentation yields results that are comparable with the previously introduced CKC-based segmentation.KeywordsCompound muscle action potentialMotor unitDecompositionHigh-density surface electromyogramsSpike segmentation

Background: Recent technological advances have drastically reduced the cost and increased the speed of DNA sequencing, developments that have been heralded as the beginning of a new era of personalized medicine. However, there still remains a pressing need to translate improved knowledge of cancer genomics into better clinical outcomes. Like many solid tumors, high grade serous ovarian carcinoma (HGS-OvCa) is a heterogeneous entity with a widely variable clinical course even among patients of the same stage and histological subtype. Most prior attempts to stratify tumors into clinically meaningful subtypes have utilized gene expression profiles; however as a whole such methods have not reliably discovered subtypes with significant differences in survival or response to chemotherapy. There has been much effort to stratify tumors based on their somatic mutations; however this has proved difficult due to the great heterogeneity and sparseness of somatic mutations in most solid tumors. For example, in the TCGA HGS-OvCa cohort the average tumor had 61 non-silent mutations spread out over the ~20,000 genes in the human genome; most genes were mutated in one tumor only. Standard consensus clustering approaches applied to this sparse mutational data have repeatedly failed to produce a clinically valuable stratification of patients. Methods: The technique of Network-based Stratification (NBS) combines genome-scale somatic mutation profiles with a genetic interaction network to produce a subdivision of patients into subtypes. Briefly, somatic mutations for each patient are represented as a profile of binary (1,0) states on genes, in which a ‘1’ indicates a mutated gene. For each patient independently we project the mutation profiles onto a human gene interaction network obtained from public databases. Next the technique of network propagation is applied to spread the influence of each subsampled mutation profile over its network neighborhood, resulting in a ‘network-smoothed’ profile. Following this ‘network-smoothing’, profiles are clustered into a predefined number of subtypes using the unsupervised technique of non-negative matrix factorization (NMF). This procedure is repeated for 1000 different subsamples using the technique of consensus clustering to promote robust cluster assignments. This method was repeated with three difference sources of network data: STRING, HumanNet, and PathwayCommons. Results: Using the NBS technique four distinct subtypes were identified from the TCGA HGS-OvCa cohort of 330 exome sequenced and clinically-annotated tumors. The median overall survival (OS) in months for these four subtypes was 36, 48, 56, and not-yet-reached respectively, which was highly significant (Logrank p = 1.59 x 10-5). Subtypes were also predictive of platinum free interval (PFI), (Logrank p = 0.046). No significant differences between subtypes were found in age, tumor stage at presentation, or percentage of patients with an optimal surgical resection, and a cox proportional hazards model was significant even after taking those factors into account (Likelihood ratio test p = 3.3x10-4). Comparing each subtype against the other three, the network of mutated genes for subtype 1, which had the worst overall survival and shortest platinum free interval, was enriched for over 20 genes in the fibroblast growth pathway. The network for subtype 2 was enriched in DNA damage response genes including ATM, ATR, BRCA1/2, RAD51 and Chek2, and also contained the majority of patients with BRCA1/2 germline mutations (15/20 and 5/6, respectively). Conclusions: Network-based Stratification (NBS) combines genome-scale somatic mutation profiles with a genetic interaction network to stratify a cohort of HGS-OvCa tumors into subtypes with significant differences is OS and PFI. Citation Format: Matan Hofree, John Paul Shen, Hannah Carter, Andrew Gross, Trey Ideker. Network-based stratification: Combining genome-scale somatic mutation profiles with genetic interaction networks to identify clinically relevant subtypes in high grade serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A14.

For the quantitation of cerebral blood flow (CBF) using H/sub 2//sup 15/O PET, the measurement of arterial input function (AIF) is essential. In this study, for the simplified quantitation, we present a method for the blind and noninvasive extraction of carotid input function (CIF) from dynamic PET images. On 8 healthy volunteers, the PET scans of C/sup 15/O and H/sub 2//sup 15/O were sequentially performed with arterial blood sampling using detector. With the inhalation of C/sup 15/O gas, dynamic PET data was acquired. And then after the injection of H/sub 2//sup 15/O, dynamic PET scans of H/sub 2//sup 15/O was perform. For 4 subjects, PET data for both rest and Acetazolamide (ACZ)-induced stress states were acquired, respectively. In the transverse dynamic images of C/sup 15/O and H/sub 2//sup 15/O, the regions of dynamic images including carotid artery were selected by masking. Non-negative matrix factorization (NMF) algorithm was used to extract the CIF from the selected dynamic images. The partial volume correction estimated from dynamic C/sup 15/O image, was applied to the extracted CIF. Whole brain CBF was estimated by kinetic analysis based on single compartment model. The error was analyzed using the area under the curve (AUC) and estimated CBF. NMF provided the good separation of the component of CIF from others in both images of C/sup 15/O and H/sub 2//sup 15/O. The shape of estimated CIF by NMF was similar with AIF measured by blood sampling. The AUC between the measured AIF and the estimated CIF was not so different at both rest and ACZ states. The difference of CBF before and after the injection of ACZ was also same between the measured AIF and the estimated CIF. These results show that NMF technique may be a tool for the noninvasive extraction of carotid input function, and this carotid input function might be used in the noninvasive quantitation of CBF using H/sub 2//sup 15/O PET.

BackgroundIn the last few years, the Non-negative Matrix Factorization(NMF) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster.In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units(GPUs). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU.ResultsNMF-mGPU is based on CUDA(Compute Unified Device Architecture), the NVIDIA’s framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system’s main memory to the GPU’s memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI(Message Passing Interface). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup).ConclusionsApplications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used “out of the box” by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu.

The global utilization of electric vehicles (EVs) is exponentially increasing due to the increased availability of cost-efficient EVs and infrastructure managements for the EVs. In spite of the increasing usage of EVs, the problem of EV usage patterns’ analysis and implementing sustainable infrastructure for the EV transportation is still under development. In addition to this, there is a challenging problem of long waiting hours in traffic signals. This study deals with these problems by proposing an architecture that includes EV usage pattern analysis using nonnegative matrix factorization (NMF) technique and renewable solar-powered wireless smart charging grid to effectively utilize or mitigate the long traffic signal waiting hours. The insights from the EV usage patterns are analyzed and presented showing the importance of usage pattern analysis alongside to the presented architecture of renewable solar-powered wireless EV-smart charging grid. These implementations improvise the usage of the EVs and enhancing the transportation experience, which in turn leads to the development of sustainable smart transportation.

Non-negative matrix factorization (NNMF) technique has been widely applicable for dimension reduction. NNMF has shown its usability to solve numerous challenges in areas such as signal processing, image classification, and text mining. Existing matrix factorization methods need a predefined value of rank of a base matrix. Our primary intention in this article is to automatically identify the optimum value of the rank to factorize the matrix. Here we propose an Incremental Cauchy NonNegative Matrix factorization (ICNNMF) which automatically finds the optimum rank and separates the overlapping sounds received from a single channel of a source. Classification is performed using an ensemble of one class fuzzy rule-based (FRB) classifiers. We evaluated the proposed method over the real data set, and the proposed model has shown better accuracy as compared to the traditional classifier. Moreover, the proposed matrix factorization method automatically determines the optimal value of the rank and adapt to new data.