Positive Matrix Research Articles

TMIC-70. CORRELATION OF LLT-1 AND NLRC4 INFLAMMASOME AND ITS EFFECT ON GLIOBLASTOMA PROGNOSIS

Abstract PURPOSE LLT-1 is a well-known ligand for the natural killer (NK) cell inhibitory receptor NKRP1A. Here, we examined NLRC4 inflammasome components and LLT-1 expression in glioblastoma (GBM) tissues to elucidate potential associations and interactions between these factors. METHODS Nine GBM tissues were collected for experiments and RNA sequencing, while fifty-two tumor core tissues were collected for RNA sequencing. Expression of LLT-1 and other proteins was assessed by immunofluorescence. Computational analyses utilized RNA-seq data from 296 and 52 patients from the Chinese Glioma Genome Atlas and CHA medical records, respectively, using survival, non-negative matrix factorization clustering, Gene Ontology enrichment, and protein-protein interaction analyses. Receptor-ligand interactions between tumor and immune cells were confirmed by single cell RNA sequencing analysis. RESULTS In GBM tissues, LLT-1 was predominantly colocalized with Glial fibrillary acidic protein (GFAP) -expressing astrocytes, but not with microglial markers like Iba-1. Additionally, LLT-1 and activated NLRC4 inflammasomes were co-expressed mainly in intratumoral astrocytes, suggesting an association between LLT-1, NLRC4, and glioma malignancy. High LLT-1 expression correlates with poor prognosis, particularly in the mesenchymal subtype, and is associated with TNF and NOD-like receptor signaling pathway enrichment, indicating a potential role in tumor inflammation and progression. At the single-cell level, mesenchymal-like malignant cells were highly enriched in the TNF, NLR, and IL-1 signaling pathways compared to other malignant cell types. CONCLUSION We revealed an association between NLRC4 inflammasome activity and LLT-1 expression, suggesting a novel regulatory pathway involving TNF, inflammasomes, and IL-1, potentially offering new anti-glioma approaches by enhancing NK cell-based treatments.

Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

High-Pressure Monosulfide Solid Solution FexNi1−xS Phases: X-Ray Diffraction Analysis and Raman Spectroscopy

High-pressure high-temperature (HPHT) crystalline monosulfide solid solution (Mss) phases (FexNi1−xS, x = 0.90, 0.75, 0.50, 0.25), troilite (FeS I), and α-NiS of the Fe-Ni-S system were synthesized at 7 GPa and 900–1550 °C. The structural parameters of the obtained phases were refined by XRD using the Rietveld method. Factor group analysis revealed the number of active Raman modes for FeS I and α-NiS. Raman spectra of troilite, α-NiS, and Mss phases were obtained. It was shown that the Raman spectra of Mss phases and α-NiS have a similar topology. The Raman spectra of the experimental phases in the Fe-Ni-S system were analyzed with non-negative matrix factorization, which provided a meaningful concentration dependence of the spectral patterns. The spectral components were assigned to the FeS I and α-NiS structures. The structural and spectroscopic studies show linear dependencies of unit cell parameters and spectral components on composition and confirm the existence of a series of monosulfide solid solution FexNi1−xS.

Strong convergence of the empirical spectral distribution of a unified matrix model

Let [Formula: see text] is a positive definite matrix and [Formula: see text] with [Formula: see text] being independent and identically distributed (i.i.d.) centered real random variables. Consider the matrix [Formula: see text] where [Formula: see text] and [Formula: see text] are two projection matrices (deterministic or random) satisfying [Formula: see text] and [Formula: see text]. Additionally, if [Formula: see text] and [Formula: see text] are random matrices, they are independent of [Formula: see text]. In this paper, we demonstrate that the empirical spectral distribution of [Formula: see text] converges almost surely to a non-random distribution when [Formula: see text] and [Formula: see text], assuming [Formula: see text] has finite second moment.

Risk assessment and sources associated with potentially toxic elements in suspended particulate matter: A karst river perspective in active mining area

Study regionDaiyang River Basin, a typical karst river basin impacted by mining activities in Guizhou, Southwest China. Study focusRivers are fundamental components of regional water security, but they are facing potentially toxic elements (PTEs) pollution caused by mining activities. This study investigated the concentration, behavior, risks and sources of nine PTEs in suspended particulate matter (SPM) from mining-impacted karst rivers, which are essential for the safety and management of karst water environments. New hydrological insights for the regionThe contents of Cr, Ni, Cu, Zn, As, Cd, Sb and Hg exceeded the corresponding local soil background values, with Zn and Cu being the most important pollutants. These elements caused a very high potential toxicity risk to the basin and unacceptable carcinogenic and non-carcinogenic risks to the local residents. Correlation analysis and the positive matrix factorization (PMF) model indicated that Ni, Cd, Zn and Cu were mainly derived from mixed sources of geological background and anthropogenic activities (30.95 %), Hg, Sb, and As were related to coal mining and combustion sources (28.91 %), while Pb, As, Cr and Sb were mainly contributed by natural sources (40.15 %). Furthermore, mixed sources, mining-related sources and As were identified as priority control factors in the study area. These insights can provide powerful support for decision-makers to develop control policies and reduce PTEs pollution in karst areas.

Characterization and source apportionment of heavy metal pollution in soil around red mud disposal sites using absolute principal component scores-multiple linear regression and positive matrix factorization models

Characterization and source apportionment of heavy metal pollution in soil around red mud disposal sites using absolute principal component scores-multiple linear regression and positive matrix factorization models

Novel Steganographic Method Based on Hermitian Positive Definite Matrix and Weighted Moore–Penrose Inverses

In this paper, we describe the concept of a new data-hiding technique for steganography in RGB images where a secret message is embedded in the blue layer of specific bytes. For increasing security, bytes are chosen randomly using a random square Hermitian positive definite matrix, which is a stego-key. The proposed solution represents a very strong key since the number of variants of positive definite matrices of order 8 is huge. Implementing the proposed steganographic method consists of splitting a color image into its R, G, and B channels and implementing two segments, which take place in several phases. The first segment refers to embedding a secret message in the carrier (image or text) based on the unique absolute elements values of the Hermitian positive definite matrix. The second segment refers to extracting a hidden message based on a stego-key generated based on the Hermitian positive definite matrix elements. The objective of the data-hiding technique using a Hermitian positive definite matrix is to embed confidential or sensitive data within cover media (such as images, audio, or video) securely and imperceptibly; by doing so, the hidden data remain confidential and tamper-resistant while the cover media’s visual or auditory quality is maintained.

Observing super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate at an agro-forest site in Thailand.

Coarse aerosol particles containing chloride in tropical forests are significant for understanding biogeochemical cycles and atmospheric processes, with implications for environmental health and climate change mitigation. This study explored the sources of super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate. Aerosol samples were collected from an agro-forest site in Thailand during the dry season and analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy. By examining the morphology and elemental compositions of individual aerosol particles, along with employing Positive Matrix Factorization (PMF) and backward trajectory analysis, potential sources were identified. The findings revealed two primary sources for the super-coarse aerosol particles: a mixture of biomass burning smoke and inorganic salts (likely from saline soil and sea salt), as well as halophilic fungal spores. FTIR analysis indicated the presence of compounds linked to biomass burning and clay minerals, influenced by prevailing northeast and southeast winds. Recommendations for future research include continued monitoring, correlation with meteorological parameters, and the application of transmission electron microscopy (TEM) for more detailed visualization and confirmation of aerosol sources.

Characteristic of PM2.5 concentration and source apportionment during winter in Seosan, Korea

Seosan is a concentrated industrial complex in the midwestern region of Korea. A study was conducted from December 2020 to January 2021, measuring PM2.5 and chemical components in Seosan using online instruments every hour. The concentration of PM2.5 during the winter season was 31.4±17.8 μg/m3, exceeding the national ambient air quality standard of Korea. The mass fraction of organic matter, elemental carbon, three major ions, five minor ions, crustal elements, and trace elements in PM2.5 accounted for 24.5%, 4.36%, 32.0%, 2.82%, 4.11%, and 5.17% of the total PM2.5 mass concentration, respectively. Source identification was conducted using positive matrix factorization modeling, revealing eight sources of PM2.5: Secondary inorganic aerosol (SIA), vehicle exhaust, industry, coal combustion, biomass burning/incinerator, oil combustion, soil, and aged sea salt. Source contributions varied during high pollution episodes (HPE), with SIA dominating in HPE1 and soil and aged sea salt in HPE2. The potential source contribution function and conditional probability function were utilized to estimate the potential local and regional emission areas for the identified sources. In Seosan, vehicle exhaust and biomass burning/incinerator were primarily influenced by local sources. SIA, industry, and oil combustion sources were significantly affected by short-range transport from eastern China. Soil and aged sea salt, which exhibited high contributions during HPE2, were associated with long-range transport from Inner Mongolia. Coal combustion was attributed to both local sources, particularly large industrial complexes near Seosan, and long-range transport from Northeast China and Inner Mongolia.Graphical

Accelerated-gradient-based generalized Levenberg–Marquardt method with oracle complexity bound and local quadratic convergence

AbstractMinimizing the sum of a convex function and a composite function appears in various fields. The generalized Levenberg–Marquardt (LM) method, also known as the prox-linear method, has been developed for such optimization problems. The method iteratively solves strongly convex subproblems with a damping term. This study proposes a new generalized LM method for solving the problem with a smooth composite function. The method enjoys three theoretical guarantees: iteration complexity bound, oracle complexity bound, and local convergence under a Hölderian growth condition. The local convergence results include local quadratic convergence under the quadratic growth condition; this is the first to extend the classical result for least-squares problems to a general smooth composite function. In addition, this is the first LM method with both an oracle complexity bound and local quadratic convergence under standard assumptions. These results are achieved by carefully controlling the damping parameter and solving the subproblems by the accelerated proximal gradient method equipped with a particular termination condition. Experimental results show that the proposed method performs well in practice for several instances, including classification with a neural network and nonnegative matrix factorization.

Adaptive Weighted Multiview Kernel Matrix Factorization and Its Application in Alzheimer’s Disease Analysis

Recent technology and equipment advancements have provided us with opportunities to better analyze Alzheimer’s disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper, we propose a novel model to leverage data from all different modalities/views, which can learn the weights of each view adaptively. Different from previous vanilla Non-negative matrix factorization which assumes data is linearly separable, we propose a simple yet efficient method based on kernel matrix factorization, which is not only able to deal with non-linear data structure but also can achieve better prediction accuracy. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method, which indicates promising prospects for kernel application in AD analysis.

Anomaly-aware symmetric non-negative matrix factorization for short text clustering

Anomaly-aware symmetric non-negative matrix factorization for short text clustering

Spatial distribution, source apportionment, and health risks assessment of trace elements in pre- and post-monsoon soils in the coal-mining region of North Karanpura basin, India

Coal mining activities in the North Karanpura basin have significantly increased the trace element (TE) concentrations in the soil, resulting in soil pollution and potential health risks. To assess this, 113 soil samples, along with coal, shale, and overburden rocks, were collected from open-cast mining areas during pre-monsoon (Pre-M) and post-monsoon (Post-M) seasons. Seasonal analysis revealed higher TE concentrations in the Post-M period, especially in the SE direction, followed by NE and NW, likely due to surface runoff and deposition, demonstrating temporal variability in TE distribution which corroborated from the spatial distribution maps. Positive matrix factorization (PMF) model identified four factors: mixed sources (F1Pre-M: 37.6 %; F4Post-M: 28.9 %), coal-fired emissions (F2Pre-M: 20.5 %; F3Post-M: 26.0 %), overburden rocks (F3Pre-M: 25.5 %; F2Post-M: 16.7 %), and agricultural and lithogenic origin (F4Pre-M: 16.4 %) during the Pre-M period, attributed to coal mining. Post-M sources were similar, but agricultural and lithogenic origins were replaced by atmospheric deposition (F1Post-M: 28.4 %), enhanced by monsoon effects. Carcinogenic risk assessment revealed that As, Cr, and Ni exceeded acceptable levels for children via ingestion, though adults remained within safe limits. Inhalation and dermal contact were also considered, but ingestion posed the highest risk. The hazard index (HI) via ingestion showed that children had an HI of 1.6 in Pre-M, increasing to 2.66 in Post-M, highlighting their potential vulnerability to non-carcinogenic risks, while adults stayed within safe limits. The expansion of mining areas in the study region led to decrease in vegetative areas which could affect agriculture and local communities, raising a comprehensive environmental and public health issues. These results underline the need for implementing effective biannual soil monitoring and mitigation strategies, such as phytoremediation, bioremediation, rock dust remediation, chemical amendments and improved waste management, to reduce TE contamination.

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Tourism activities significantly contributed toatmospheric pollutants in a remote town of Tianshan revealed by the continuous observation of two years

The large reduction in tourism activities in 2020 provided a unique opportunity to investigate their contribution on atmospheric pollutants in Bayanbulak, a remote town in Tianshan, China. In this scenic and sensitive region, we measured total suspended particulates (TSP) along with concentrations of polycyclic aromatic hydrocarbons (PAHs), organic carbon (OC), elemental carbon (EC) and water-soluble inorganic ions (WSIIs) during the lockdown in 2020 and compared these to levels in the pre-pandemic period of 2019. The study revealed significant reductions in PAHs, OC, EC, and some WSIIs concentrations during the tourist season (May–October) of 2020 compared with 2019, whereas variations between non-tourist seasons were less pronounced. Furthermore, by comparing pollutant characteristics in tourist seasons of these two years, we found that 5- and 6-ring groups were main PAH constituents in 2019, while 5- and 3-ring groups contributed more to total PAHs in 2020. OC/EC ratio increased from 2019 to 2020, indicating an increase of biomass burning contribution after the lockdown. Using positive matrix factorization, we determined that emissions from tourism-related transportation and combustion of natural gas and coal (mainly for cooking) contributed 21.9% and 46.6%, respectively, to certain atmospheric pollutants in 2019, whereas they decreased significantly to 3% and 19.4% in 2020 with dust and biomass contributions increased substantially. The levels of PAHs, OC, and EC from tourism-related sources were as high as 9.31 ng m−3, 1.28 μg m−3 and 0.11 μg m−3 and decreased by 54.37%, 25.16%, and 9.54%, respectively, compared to 2019. Hence, the impact of tourism must be considered during the assessment of local air pollutant emissions, particularly in remote areas.

Evaluation of the potentially toxic elements and radionuclides in the soil sample of Novaya Zemlya in the Arctic Circle

The study presented here elucidate the concentrations of radionuclides and potentially toxic elements in the soil samples around the Novaya Zemlya in the Russian Arctic zone, determined using HPGe gamma spectrometry, inductively coupled plasma optical emission spectrometry and direct mercury analyzer. The average detected concentrations for 226Ra, 232Th, 40K, 235U and 137Cs were 36.40, 46.06, 768, 2.06 and 4.71 Bq/kg, respectively. At many sampling sites, the concentrations of potentially toxic elements (Zn, Cu, Pb, Cd, Ni, and Cr) were higher than the natural levels. Positive Matrix Factorization analysis revealed the contribution of oil dumps (32%), natural sources (16%), bird colonies (32%) and atmospheric deposition (20%) for elevated elements content. In the case of radionuclides, the natural occurring contamination (38%) was primary source followed by dumped material (32%) and bird colonies (30%). The radiological risk from radionuclides was relatively high, yet still under permissible levels. For potentially toxic elements, Fe was predominant non-carcinogenic pollutant and Ni emerged as major carcinogenic contaminant. Keeping in view the high content of some elements, future studies are required to keep the human and ecological risk low, and to establish scientific grounds for the contribution of settled bird species. The findings of the study advance the present knowledge about the contamination of the study area and lays the path for further effort.

Sources and Fates of Sedimentary Polycyclic Aromatic Hydrocarbons in the East Siberian Arctic Shelf: Implications for Input Pathways and Black Carbon Constraint

AbstractThe Arctic region is experiencing more rapid climate changes than the other parts of the world and serves as a sink for semi‐volatile persistent organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), which can be utilized as molecular markers for pyrogenic carbon, such as black carbon (BC). As the sea ice retreats and increased terrestrial inputs with widespread wildfires, the PAH concentrations in the Arctic Ocean are rising. In this study, the sources and fates of PAHs together with BC in surface sediments from the East Siberian Arctic Shelf (ESAS) were analyzed. Positive matrix factorization (PMF) elucidated a mixed petrogenic and pyrogenic sources and distinct PAH fates associated with diverse input pathways including coastal permafrost erosion contribution (∼30%), petrogenic‐related emission (∼34%), fossil fuel combustion (∼26%), and biomass burning (∼10%). Correlation analysis indicated that BC plays a key role in affecting the behavior and fates of PAHs. In the Chukchi Sea, PAHs are closely associated with soot‐BC, whereas in the Laptev Sea (LS) and west East Siberian Sea (W‐ESS), they exhibit a coupling process with char‐BC. The presence of these carbonaceous materials in the sediments of CS is likely influenced by atmospheric deposition and biological activity, whereas the LS and W‐ESS regions are mainly affected by long‐distance river transport and direct deposition from coastal permafrost. As global warming continues, permafrost thawing induces the remobilization and retranslocation of PAHs, thereby becoming a significant PAH contributor and input pathway in the rapidly changing Arctic coastal margin.

Mild hypercapnia before reperfusion reduces ischemia-reperfusion injury in hyperacute ischemic stroke rat model.

Endovascular thrombectomy has a recanalization rate over 80%; however, approximately 50% of ischemic stroke patients still experience dependency or mortality. Recently, clinical trials demonstrated the benefits of administering neuroprotective agents prior to endovascular thrombectomy. Additionally, recent studies showed neuroprotective effects of mild hypercapnia in patients resuscitated after cardiac arrest. However, its efficacy in ischemic stroke remains unclear. We aimed to investigate whether carbon dioxide (CO2) per-conditioning has neuroprotective effects in rat models with middle cerebral artery occlusion (MCAO). Rat models received intermittent inhalation of mixed gas during the MCAO period. After surgery, behavioral assessments, infarct size measurement, immunohistochemistry, and western blot analysis were performed. We found CO2 per-conditioning reduced infarct size and neurological deficit. The number of 8-hydroxy-2-deoxyguanosine (8-OHdG) positive cells and matrix metalloproteinase 9 (MMP-9)/platelet derived growth factor receptor beta (PDGFRβ) double positive cells were significantly decreased after CO2 per-conditioning. The expressions of tight junction protein and pericytes survival were preserved. This study underscores mild hypercapnia before reperfusion not only reduces neurologic deficit and infarct size, but also maintains the integrity of the blood-brain barrier and neurovascular unit, alongside mitigating oxidative stress in hyperacute stroke rat models. Therapeutic mild hypercapnia before reperfusion is promising and requires further clinical application.

RNA methylation patterns of tumor microenvironment cells regulate prognosis and immunotherapeutic responsiveness in patients with triple-negative breast cancer

Immunotherapy research focuses on reshaping the tumor microenvironment (TME) to enhance its antitumor immune responses, with an emphasis on understanding the impact of RNA methylation in triple-negative breast cancer (TNBC) TME regulation. This study explored the influence of various RNA methyltransferases on TME cells in TNBC and their correlation with prognosis and immunotherapy response. Using non-negative matrix factorization on single-cell RNA-sequencing data, distinct TME cell clusters were identified based on the expression of 30 RNA methyltransferases. Various analyses, including pseudotime, cell communication, transcription factor regulatory network, and gene enrichment, were conducted on these clusters. The roles of RNA methyltransferase-mediated TME clusters in prognosis and immunotherapy response were determined using TNBC bulk RNA-Seq data, and the findings were validated through immunofluorescence analysis of a tissue microarray comprising 87 samples. Spatial transcriptomic analysis further revealed the distribution of TME cell clusters. Different methyltransferase-mediated cell clusters exhibited unique metabolic, immune, transcriptional, and intercellular communication patterns. Survival analysis indicated prognostic significance in specific TME cell clusters, and immunofluorescence analysis confirmed the prognostic value of m6A_WTAP + CD8T + cells. In conclusion, our study illustrated the involvement of these cell subgroups in tumor growth and antitumor immunity modulation, providing insights into the enhancement of TNBC immunotherapy.

Coherent Set Identification Via Direct Low Rank Maximum Likelihood Estimation

We analyze connections between two low rank modeling approaches from the last decade for treating dynamical data. The first one is the coherence problem (or coherent set approach), where groups of states are sought that evolve under the action of a stochastic transition matrix in a way maximally distinguishable from other groups. The second one is a low rank factorization approach for stochastic matrices, called direct Bayesian model reduction (DBMR), which estimates the low rank factors directly from observed data. We show that DBMR results in a low rank model that is a projection of the full model, and exploit this insight to infer bounds on a quantitative measure of coherence within the reduced model. Both approaches can be formulated as optimization problems, and we also prove a bound between their respective objectives. On a broader scope, this work relates the two classical loss functions of nonnegative matrix factorization, namely the Frobenius norm and the generalized Kullback–Leibler divergence, and suggests new links between likelihood-based and projection-based estimation of probabilistic models.