Clusters C1 Research Articles

Two multidrug-resistant Proteus mirabilis clones carrying extended spectrum beta-lactamases revealed in a single hospital department by whole genome sequencing

Proteus mirabilis bacteria is a component of normal intestinal microflora of humans and animals, but can also be found in hospital settings causing urinary tract infections and sepsis. The problem of treating such infections is complicated by multidrug-resistant isolates producing extended spectrum beta-lactamases (ESBL), and the number of ESBL-carrying P. mirabilis strains has significantly increased recently.This study presents a detailed analysis of 12 multidrug-resistant P. mirabilis isolates obtained from the wounds of different patients in one surgical department of a multidisciplinary hospital in Moscow, Russia, using the short- and long-read whole genome sequencing. The isolates under investigation divided into two clusters (clones) C1 and C2 based on their genomic profiles and carried antimicrobial resistance (AMR) genes corresponding well with phenotypic profiles, which was the first case of reporting two different P. mirabilis clones obtained simultaneously from the same specimens at one hospital, to the best of our knowledge. Some genes, including ESBL encoding ones, were specific for either C1 or C2 (aac(6′)-Ib10, ant(2″)-Ia, qnrA1, blaVEB-6 and fosA3, blaCTX -M-65, correspondingly). Additionally, the Salmonella genomic islands 1 were found that differed in composition of multiple antibiotic resistance regions between C1 and C2 groups. CRISPR-Cas system type I-E was revealed only in C2 isolates, while the same set of virulence factors was determined for both P. mirabilis clones.Diversity of all genetic factors found in case of simultaneous existence of two clones collected from the same source at one department indicates high pathogenic potential of P. mirabilis and poses a requirement of proper spreading monitoring. The data obtained will facilitate the understanding of AMR transfer and dynamics within clinical P. mirabilis isolates and contribute to epidemiological surveillance of this pathogen.

Skeletal phenotypes in postmenopausal women affected by primary hyperparathyroidism.

The current primary hyperparathyroidism (PHPT) presents as a mild disease. We explored skeletal phenotypes in postmenopausal women affected by PHPT, focusing on fracture prevalence. PHPT women were retrospectively evaluated at four Italian centers for osteoporosis management (two centers in Milan, n = 244; Cuneo, n = 128; Pisa, n = 131). Data collected from clinical records were analyzed by hierarchical clusterization. Considering the whole PHPT series [n = 503, aged 67.0 (61.0-74.0) years], 90% had low bone mineral density (BMD) and approximately 30% reported at least one fracture. Vertebral fractures were associated with older age and lower hypophosphatemia, while women with appendicular fractures were younger with less severe hypophosphatemia. Fractures were predicted by lumbar T-score. By using a clustering approach, we identified four different skeletal phenotypes (cluster, C): C1 (n = 53) and C2 (n = 172) included women with lumbar and femur neck osteopenia, with low prevalence of fractures (11.3%). Osteoporotic PHPT women were grouped into C3 (n = 142) and C4 (n = 136); all women in C4 experienced fractures, were older, and were more frequently affected with cardiovascular diseases. In contrast, women included in C3 never experienced fractures and had a lower body mass index (BMI), though they were characterized by severe reduction in BMD at both lumbar and femur sites. Ionized and total calcium, phosphate, 25hydroxyvitamin D levels, kidney function, and stone prevalence (range, 26.4%-29.0%) were similar among clusters C1, C2, and C4, while unfractured women in C3 showed slightly higher ionized hypercalcemia, lower hypophosphatemia, and higher hypercalciuria with a trend to more frequently develop kidney stones (38.7%) than women in the remaining clusters. Skeletal involvement in women with PHPT presented heterogeneous phenotypes with different prevalence of fractures. Fractures were not related to PHPT severity, suggesting that other factors besides PHPT, such as age, BMI, and lumbar and femur BMD, should be considered in the evaluation of bone involvement in postmenopausal women with PHPT.

Effect of silver nanoparticles and REP-PCR typing of Staphylococcus aureus isolated from various sources

This is the primary study at Matrouh Governorate to unveil antibiotic resistance, biofilm formation, silver nanoparticles (Ag-NPs) effect using electron microscopy, and REP-PCR analysis of Staphylococcus aureus strains isolated from COVID-19 patients, contaminated food, and Morel’s diseased sheep and goats. A total of 15 S. aureus strains were isolated; five from each of the COVID-19 patients, Morel's diseased sheep and goats, and contaminated food. All strains were considered multidrug-resistant (MDR). All strains showed the presence of biofilm. Morphological changes in the cell surface of the bacterium were evidenced, and penetration with the rupture of some bacterial cells. Based on REP-PCR analysis, 4 clusters (C1-C4) with dissimilarity between clusters C1 and C2 8% and between C3 and C4 15%. Cluster I included 3 strains from contaminated food with a similarity of 97%, and Cluster II included 2 strains from contaminated food and 2 from COVID-19-infected patients with a similarity of 96% (confirming the zoonotic nature of this pathogen). Cluster III contained 4 strains isolated from Morel's diseased sheep & goats with a similarity ratio of 99% in comparison the 4th cluster contained 3 strains isolated from COVID-patients and one from Morel's diseased sheep & goats with a similarity ratio of 92%.

Antioxidant network-based signatures cluster glioblastoma into distinct redox-resistant phenotypes.

Aberrant reactive oxygen species (ROS) production is one of the hallmarks of cancer. During their growth and dissemination, cancer cells control redox signaling to support protumorigenic pathways. As a consequence, cancer cells become reliant on major antioxidant systems to maintain a balanced redox tone, while avoiding excessive oxidative stress and cell death. This concept appears especially relevant in the context of glioblastoma multiforme (GBM), the most aggressive form of brain tumor characterized by significant heterogeneity, which contributes to treatment resistance and tumor recurrence. From this viewpoint, this study aims to investigate whether gene regulatory networks can effectively capture the diverse redox states associated with the primary phenotypes of GBM. In this study, we utilized publicly available GBM datasets along with proprietary bulk sequencing data. Employing computational analysis and bioinformatics tools, we stratified GBM based on their antioxidant capacities and evaluated the distinctive functionalities and prognostic values of distinct transcriptional networks in silico. We established three distinct transcriptional co-expression networks and signatures (termed clusters C1, C2, and C3) with distinct antioxidant potential in GBM cancer cells. Functional analysis of each cluster revealed that C1 exhibits strong antioxidant properties, C2 is marked with a discrepant inflammatory trait and C3 was identified as the cluster with the weakest antioxidant capacity. Intriguingly, C2 exhibited a strong correlation with the highly aggressive mesenchymal subtype of GBM. Furthermore, this cluster holds substantial prognostic importance: patients with higher gene set variation analysis (GSVA) scores of the C2 signature exhibited adverse outcomes in overall and progression-free survival. In summary, we provide a set of transcriptional signatures that unveil the antioxidant potential of GBM, offering a promising prognostic application and a guide for therapeutic strategies in GBM therapy.

Hunting for sources of durable resistance in crop cultivar evaluation data: The case of wheat yellow rust in France

AbstractCultivar resistance is a major asset for the management of crop diseases and can play an important role in agroecological transition. However, the wide deployment of a reduced number of resistance genes can lead to a rapid adaptation of pathogen populations and to a loss of resistance efficiency. The objective of this study was to characterize and discuss different trajectories of adult plant ratings for resistance to yellow rust in French wheat cultivars between 1963 and 2018. Among 719 cultivars assessed for at least 2 years, 590 cultivars showed no variation in their resistance scores, despite a mean of 4.3 years and up to 33 years of assessment. A set of descriptive variables was computed in order to compare the evolution of resistance score of 129 cultivars that experienced resistance variation. We applied a principal component analysis and a hierarchical clustering on principal components to this subdataset to constitute clusters corresponding to different cultivar profiles. Clusters C1 and C2 had small resistance variations (1–2 points on a 1–9 scale); Cluster C3 had long assessment durations and several small drops in resistance score and could be associated with quantitative resistance erosion; Cluster C4 included major drops in resistance score (4–5 points), often associated with known breakdowns of major resistance genes. Cases of limited drops in resistance score as a known resistance gene was broken down suggest the presence of efficient adult plant resistance. We discuss the use of information extracted from this dataset and methods to further explore sources of resistance to yellow rust present in French cultivars.

Approximation Scheme for Weighted Metric Clustering via Sherali-Adams

Motivated by applications to classification problems on metric data, we study Weighted Metric Clustering problem: given a metric d over n points and a k x k symmetric matrix A with non-negative entries, the goal is to find a k-partition of these points into clusters C1,...,Ck, while minimizing the sum of A[i,j] * d(u,v) over all pairs of clusters Ci and Cj and all pairs of points u from Ci and v from Cj. Specific choices of A lead to Weighted Metric Clustering capturing well-studied graph partitioning problems in metric spaces, such as Min-Uncut, Min-k-Sum, Min-k-Cut, and more. Our main result is that Weighted Metric Clustering admits a polynomial-time approximation scheme (PTAS). Our algorithm handles all the above problems using the Sherali-Adams linear programming relaxation. This subsumes several prior works, unifies many of the techniques for various metric clustering objectives, and yields a PTAS for several new problems, including metric clustering on manifolds and a new family of hierarchical clustering objectives. Our experiments on the hierarchical clustering objective show that it better captures the ground-truth structural information compared to the popular Dasgupta's objective.

Abstract 861: Integrative multi-omics analysis enables a comprehensive characterization of prostate cancer and unveils metastasis-associated candidate biomarkers

Abstract Prostate cancer (PCa), a common malignancy in men, presents a complex and variable disease landscape, requiring precise evaluation for personalized treatment decisions. Urologists employ a range of assessments, including clinical staging, PSA levels, imaging, Gleason scores, and emerging biomarkers. Nevertheless, the need for additional biomarkers to enhance precision medicine continues. This study utilizes extensive omics datasets to thoroughly profile PCa. We conducted an in-depth analysis of primary PCa data from the multi-omics dataset from The Cancer Genome Atlas studies to identify molecular subtypes showing significant prognostic differences. We conducted integrative clustering analysis on a dataset comprising 341 primary prostate adenocarcinomas with comprehensive multi-omics data, including substitutions, small insertions/deletions, copy number alterations, DNA methylation, gene expression profiles, micro-RNA expression, reverse-phase protein arrays, and microbiome data. The analysis revealed seven distinct clusters (C1 to C7) based on multi-omics features. At the genomic level, recurrent copy number losses and DNA methylation/demethylation were the most prominent alterations across clusters. When looking at survival curves, the 7 clusters could be clearly partitioned into two groups. Therefore, to further investigate multi-omics features specifically linked to prognosis, we grouped clusters with similar prognoses into two macro-clusters. Clusters C1 to C4 formed MC1 (favorable prognosis), while C5 to C7 constituted MC2 (poor prognosis). A comprehensive comparison between MC1 and MC2 revealed molecular differences associated with survival. We performed computational validation of candidate prognostic biomarkers using two external datasets. By utilizing these selected features, we could stratify patients in both the validation datasets into groups with significant differences in survival. The analysis identified six features consistently associated with prognosis across both datasets, including CCNB1, PCNA, RAD51, FOXM1, miR-503-5p (positively associated with risk), and miR-7704 (negatively associated with risk). The practical applicability of these biomarkers was explored through immunohistochemical analysis on biopsy samples obtained at diagnosis and from metastatic sites. Based on their potential relevance according to the literature, we investigated three candidate biomarkers derived from the multi-omics analysis: FOXM1, CCNB1 and RAD51. The expression of these proteins was found to correlate with PCa progression and mark the metastatic stage. In summary, this study highlights the potential of translating comprehensive multi-omics bioinformatic analyses into practical approaches for understanding the molecular mechanisms of tumors and identifying prognostic markers. Citation Format: Matteo Villa, Giorgio Cazzaniga, Maddalena Bolognesi, Valentina Crippa, Federica Malighetti, Andrea Aroldi, Giorgio Bozzini, Fabio Pagni, Rocco Piazza, Luca Mologni, Daniele Ramazzotti. Integrative multi-omics analysis enables a comprehensive characterization of prostate cancer and unveils metastasis-associated candidate biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 861.

Advanced hemodynamic and cluster analysis for identifying novel RV function subphenotypes in patients with pulmonary hypertension

BackgroundQuantifying right ventricular (RV) function is important to describe the pathophysiology of in pulmonary hypertension (PH). Current phenotyping strategies in PH rely on few invasive hemodynamic parameters to quantify RV dysfunction severity. The aim of this study was to identify novel RV phenotypes using unsupervised clustering methods on advanced hemodynamic features of RV function. MethodsParticipants were identified from the University of Arizona Pulmonary Hypertension Registry (n=190). RV-pulmonary artery coupling (Ees/Ea), RV systolic (Ees) and diastolic function (Eed) was quantified from stored RV pressure waveforms. Consensus clustering analysis with bootstrapping was used to identify the optimal clustering method. Pearson correlation analysis was used to reduce collinearity between variables. RV cluster subphenotypes were characterized using clinical data and compared to pulmonary vascular resistance (PVR) quintiles. ResultsFive distinct RV clusters (C1-C5) with distinct RV subphenotypes were identified using k-medoids with a Pearson distance matrix. Clusters 1 and 2 both have low diastolic stiffness (Eed) and afterload (Ea) but RV-PA coupling (Ees/Ea) is decreased in C2. Intermediate cluster (C3) has a similar Ees/Ea as C2 but with higher PA pressure and afterload. Clusters C4 and C5 have increased Eed and Ea but C5 has a significant decrease in Ees/Ea. Cardiac output was high in C3 distinct from the other clusters. In the PVR quintiles, contractility increased and stroke volume decreased as a function of increased afterload. World Symposium PH classifications were distributed across clusters and PVR quintiles. ConclusionsRV-centric phenotyping offers an opportunity for a more precise-medicine based management approach.

Integration of single-nucleus RNA sequencing and network disturbance to elucidate crosstalk between multicomponent drugs and trigeminal ganglia cells in migraine

Ethnopharmacological relevanceMigraine is caused by hyperactivity of the trigeminovascular system, where trigeminal ganglia (TG) plays an important role. TG is composed of multiple neuronal and non-neuronal cell types, which is related to “neuro-inflammation-vascular” disorder in migraine. Tou Tong Ning capsule (TTNC), a CFDA-approved traditional Chinese medicine for treating migraine, has the characteristics of “multicomponents, multitargets, multipathways". Aim of the studyTo clarify the mechanism of TTNC and elucidate crosstalk between multicomponent drugs and neuronal and non-neuronal functions and cells in migraine. Materials and methodsWe integrated single-nucleus RNA sequencing and a quantitative evaluation algorithm of the disturbance of multitarget drugs on the disease network and explored the specific pathology of migraine and corresponding compounds. A cerebrovascular smooth muscle spasmolytic activity experiment was carried out to verify the results of the bioinformatics analysis. ResultsTTNC exhibited its regulation activities in neuronal and non-neuronal aspects based on drugs attack to four subnetworks and cell specific networks, which explored the MoA of TTNC in comprehensive and refined perspectives. Compared to neuronal regulation, TTNC showed more significant attack score on non-neuronal biological function (smooth muscle and vessel). And TTNC compound clusters C1, C6 and C7, targeting non-neuronal function and cells, had larger group area than C10, C4 and C6 for neuronal function and cell, which implied that TTNC may mainly regulate the non-neuronal function, e.g., vessel smooth muscle contraction. Contraction of cerebrovascular smooth muscle of mice ex vivo confirmed the vasodilation activity of TTNC and active compounds from C1, C6, C9 (Emodin, Luteolin and Levistilide A). Literature mining confirmed the vasospasmodolytic activity and neuroprotective effect of TTNC. ConclusionsThe study found that TTNC may primarily alleviate non-neuronal functional disorders in migraine by relaxing cerebral vascular smooth muscle cell spasm to alleviate migraine. Integrating single-nucleus RNA sequencing data and network disturbance tools provides a new strategy for the pharmacological mechanism of multicomponent drugs through cell subtyping.

Molecular subtypes of epilepsy associated with post-surgical seizure recurrence.

Approximately 50% of individuals who undergo resective epilepsy surgery experience seizure recurrence. The heterogenous post-operative outcomes are not fully explained by clinical, imaging and electrophysiological variables. We hypothesized that molecular features may be useful in understanding surgical response, and that individuals with epilepsy can be classified into molecular subtypes that are associated with seizure freedom or recurrence after surgical resection. Pre-operative blood samples, brain tissue and post-operative seizure outcomes were collected from a cohort of 40 individuals with temporal lobe epilepsy, 23 of whom experienced post-operative seizure recurrence. Messenger RNA and microRNA extracted from the blood and tissue samples were sequenced. The messenger RNA and microRNA expression levels from the blood and brain were each subjected to a novel clustering approach combined with multiple logistic regression to separate individuals into genetic clusters that identify novel subtypes associated with post-operative seizure outcomes. We then compared the microRNAs and messenger RNAs from patient blood and brain tissue that were significantly associated with each subtype to identify signatures that are similarly over- or under-represented for an outcome and more likely to represent endophenotypes with common molecular aetiology. These target microRNAs and messenger RNAs were further characterized by pathway analysis to assess their functional role in epilepsy. Using blood-derived microRNA and messenger RNA expression levels, we identified two subtypes of epilepsy that were significantly associated with seizure recurrence (clusters A1 and B4) (adjusted P < 0.20). A total of 551 microRNAs and 2486 messenger RNAs were associated with clusters A1 and B4, respectively (adjusted P < 0.05). Clustering of brain-tissue messenger RNA expression levels revealed an additional subtype (C2) associated with seizure recurrence that had high overlap of dysregulated messenger RNA transcripts with cluster B4. Clusters A1, B4 and C2 also shared significant overlap of subjects, which altogether suggests a coordinated mechanism by which microRNA and messenger RNA transcripts may be related to seizure recurrence. Epileptic subtypes A1, B4 and C2 reveal both known and novel microRNA and messenger RNA targets in seizure recurrence. Furthermore, targets identified in A1 and B4 are quantifiable in pre-operative blood samples and could potentially serve as biomarkers for surgical resection outcomes.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis.

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively. LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay. A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues. Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

Molecular subtypes based on DNA sensors predict prognosis and tumor immunophenotype in hepatocellular carcinoma.

DNA sensors play crucial roles in inflammation and have been indicated to be involved in antitumor or tumorigenesis, while it is still unclear whether DNA sensors have potential roles in the prognosis and immunotherapy of hepatocellular carcinoma (HCC). Herein, The Cancer Genome Atlas and Gene Expression Omnibus databases were used to analyze RNA sequencing data and clinical information. A total of 14 DNA sensors were collected and performed consensus clustering to determine their molecular mechanisms in HCC. Two distinct molecular subtypes (Clusters C1 and C2) were identified and were associated with different overall survival (OS). Immune subtype analysis revealed that C1 was mainly characterized by inflammation, while C2 was characterized by lymphocyte depletion. Immune scoring and immunomodulatory function analysis confirmed the different immune microenvironment of C1 and C2. Notably, significant differences in "Hot Tumor" Immunophenotype were observed between the two subtypes. Moreover, the prognostic model based on DNA sensors is capable of effectively predicting the OS of HCC patients. Besides, the chemotherapeutic drug analysis showed the different sensitivity of two subtypes. Taken together, our study shows that the proposed DNA sensors were a reliable signature to predict the prognosis and immunotherapy response with potential application in the clinical decision and treatment of HCC.

Microelements in Dust in the Snow Cover: a Case Study of the Cities of Tyumen and Tobolsk

Received April 5, 2023; revised May 10, 2023; accepted June 27, 2023Inhomogeneities of trace elements content in dust of snow cover were studied in two industrial Siberian cities Tobolsk and Tyumen. The clustering method was used, for which standardized values of the content of trace elements in the snow dust of both cities were used. Eight clusters have been identified, which were divided into two classes by location: Tyumen and Tobolsk. The classes were divided into groups: non-specific and specific ones, of which the two subgroups were distinguished: technogenic and natural. The average values of trace elements in nominal terms were calculated for each cluster. Clusters C1, C2, C4, C7, C8 are characterized by a high content of heavy metals V, Cr, Ni, Cu, Co, Zn, Cd, W, Pb. Background clusters C5 and C6 have a low content of trace elements. Specific technogenic C4 contains more copper than other clusters, and C2 contains more lead. Sources of the formation of technogenic clusters are emissions into the atmosphere of enterprises of the fuel and energy complex, foundry and machine-building industries, and transport. The heterogeneity of the content of microelements in the snow dust under background conditions is demonstrated. The microelements are divided into natural background (cluster C5) and the background with anthropogenic pollution with higher content of Ag and Sn (cluster C6). In urban conditions, a solid phase of snow with a low and minimal content of trace elements in dust (cluster C3) is formed. Increased content of Ni and a reduced content of Pb and Sc is noted in the C3 cluster relative to C5 щту. Using the Mann-Whitne test, it was revealed that the content of trace elements in snow dust within the cities Tyumen and Tobolsk are different for the following elements: Li, V, Cr, Cu, Zn, Ga, As, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, Sb, Cs, Ba, Pb. Mean values in are higher than similar ones in Tyumen. The content of Zn, As, Rb, Sc elements in urban clusters differs by more than two times relative to the background ones. In the case of elements Sn, Cs, W, the content of them in the snow dust of Tyumen is higher than that of Tobolsk. The method of clustering makes possible to identify natural background values (C5) and to calculate more precise values of the coefficient Kc as well as to determine the index of the integral pollution. In Tobolsk, the index is indicative of high and dangerous level of pollution, especially in the zone of technogenic impact. The average level is typical for the city of Tyumen.

Unsupervised learning for identifying silicified and dolomitized zones in pre-salt using portable XRF data

The discovery of significant oil reserves in the pre-salt region along the eastern coast of Brazil marks a significant milestone in hydrocarbon exploration and production, triggering extensive discussions on the origin and composition of non-marine carbonate rocks. Dolomitization and silicification have been identified as major processes responsible for substantial syn/post-depositional alterations in pre-salt reservoir rocks. In this study, we assess the feasibility of employing machine learning techniques to identify highly dolomitized and/or silicified intervals. A 25-m-thick rock succession from a drill core in the Tupi oil field, Santos Basin, was selected for analysis. Chemical element measurements were collected at approximately 10 cm intervals using an Olympus Vanta M Series model portable XRF analyzer. Subsequently, the acquired dataset underwent preprocessing and processing steps, including: (i) standardization of the database, (ii) data cleaning and imputation, (iii) basic statistical analysis, (iv) data transformation, and (v) outlier removal. Due to the considerable number of variables, the dataset was reduced to three principal components (PCs) using principal component analysis (PCA). Unsupervised learning was performed using the k-Means clustering algorithm, which employs Euclidean distance to determine the similarity between objects. Pearson's correlation analysis was also conducted to support the chemical interpretation of the clusters by comparing them with macroscopic and microscopic geological information. To ensure the optimal representativeness of the data, five clusters (C1, C2, C3, C4, and C5) were defined. Clusters C1, C2, and C3 exhibited increasing values on PC1, while C4 and C5 were characterized by higher values on PC2 and PC3, respectively. Based on significant correlations identified by Pearson's analysis, we can conclude that there is a progressive increase in Si content represented by C1, C2, and C3, accompanied by a relative decrease in all other chemical elements measured by the portable XRF. C1 not only emerged as the most frequently occurring cluster but also displayed the most common chemical composition in the studied interval, with Ca > Si. C2 and C3 corresponded to more intensely silicified levels. C4 exhibited a stronger affinity to Ca and Mg, suggesting intensified dolomitization. C5 stood out due to its relationship with Ba, an element associated with barite, a mineral that may result from the percolation of hydrothermal fluids depending on the associated paragenesis.

QTL Mapping of Agronomic and Physiological Traits at the Seedling and Maturity Stages under Different Nitrogen Treatments in Barley.

Nitrogen (N) stress seriously constrains barley (Hordeum vulgare L.) production globally by influencing its growth and development. In this study, we used a recombinant inbred line (RIL) population of 121 crosses between the variety Baudin and the wild barley accession CN4027 to detect QTL for 27 traits at the seedling stage in hydroponic culture trials and 12 traits at the maturity stage in field trials both under two N treatments, aiming to uncover favorable alleles for N tolerance in wild barley. In total, eight stable QTL and seven QTL clusters were detected. Among them, the stable QTL Qtgw.sau-2H located in a 0.46 cM interval on the chromosome arm 2HL was a novel QTL specific for low N. Notably, Clusters C4 and C7 contained QTL for traits at both the seedling and maturity stages. In addition, four stable QTLs in Cluster C4 were identified. Furthermore, a gene (HORVU2Hr1G080990.1) related to grain protein in the interval of Qtgw.sau-2H was predicted. Correlation analysis and QTL mapping showed that different N treatments significantly affected agronomic and physiological traits at the seedling and maturity stages. These results provide valuable information for understanding N tolerance as well as breeding and utilizing the loci of interest in barley.

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly's ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index (fsea) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI (fsea) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index (fsea), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation.

Epithelial-mesenchymal transition-related gene prognostic index and phenotyping clusters for hepatocellular carcinoma patients

Epithelial-mesenchymal transition (EMT) contributes to high tumor heterogeneity and the immunosuppressive environment of the HCC tumor microenvironment (TME). Here, we developed EMT-related genes phenotyping clusters and systematically evaluated their impact on HCC prognosis, the TME, and drug efficacy prediction. We identified HCC specific EMT-related genes using weighted gene co-expression network analysis (WGCNA). An EMT-related genes prognostic index (EMT-RGPI) capable of effectively predicting HCC prognosis was then constructed. Consensus clustering of 12 HCC specific EMT-related hub genes uncovered two molecular clusters C1 and C2. Cluster C2 preferentially associated with unfavorable prognosis, higher stemness index (mRNAsi) value, elevated immune checkpoint expression, and immune cell infiltration. The TGF-β signaling, EMT, glycolysis, Wnt β-catenin signaling, and angiogenesis were markedly enriched in cluster C2. Moreover, cluster C2 exhibited higher TP53 and RB1 mutation rates. The TME subtypes and tumor immune dysfunction and exclusion (TIDE) score showed that cluster C1 patients responded well to immune checkpoint inhibitors (ICIs). Half-maximal inhibitory concentration (IC50) revealed that cluster C2 patients were more sensitive to chemotherapeutic and antiangiogenic agents. These findings may guide risk stratification and precision therapy for HCC patients.

Prognostication of DNA Damage Response Protein Expression Patterns in Chronic Lymphocytic Leukemia

Proteomic DNA Damage Repair (DDR) expression patterns in Chronic Lymphocytic Leukemia were characterized by quantifying and clustering 24 total and phosphorylated DDR proteins. Overall, three protein expression patterns (C1-C3) were identified and were associated as an independent predictor of distinct patient overall survival outcomes. Patients within clusters C1 and C2 had poorer survival outcomes and responses to fludarabine, cyclophosphamide, and rituxan chemotherapy compared to patients within cluster C3. However, DDR protein expression patterns were not prognostic in more modern therapies with BCL2 inhibitors or a BTK/PI3K inhibitor. Individually, nine of the DDR proteins were prognostic for predicting overall survival and/or time to first treatment. When looking for other proteins that may be associated with or influenced by DDR expression patterns, our differential expression analysis found that cell cycle and adhesion proteins were lower in clusters compared to normal CD19 controls. In addition, cluster C3 had a lower expression of MAPK proteins compared to the poor prognostic patient clusters thus implying a potential regulatory connection between adhesion, cell cycle, MAPK, and DDR signaling in CLL. Thus, assessing the proteomic expression of DNA damage proteins in CLL provided novel insights for deciphering influences on patient outcomes and expanded our understanding of the potential complexities and effects of DDR cell signaling.

Non-calcified active atherosclerosis plaque detection with 18F-NaF and 18F-FDG PET/CT dynamic imaging.

Arterial inflammation is an indicator of atheromatous plaque vulnerability to detach and to obstruct blood vessels in the heart or in the brain thus causing heart attack or stroke. To date, it is difficult to predict the plaque vulnerability. This study was aimed to assess the behavior of 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG) uptake in the aorta and iliac arteries as a function of plaque density on CT images. We report metabolically active artery plaques associated to inflammation in the absence of calcification. 18 elderly volunteers were recruited and imaged with computed tomography (CT) and positron emission tomography (PET) with 18F-NaF and 18F-FDG. A total of 1338 arterial segments were analyzed, 766 were non-calcified and 572 had calcifications. For both 18F-NaF and 18F-FDG, the mean SUV values were found statistically significantly different between non-calcified and calcified artery segments. Clustering CT non-calcified segments, excluding blood, resulted in two clusters C1 and C2 with a mean density of 30.63 ± 5.06 HU in C1 and 43.06 ± 4.71 HU in C2 (P < 0.05), and their respective SUV were found statistically different in 18F-NaF and 18F-FDG. The 18F-NaF images showed plaques not detected on CT images, where the 18F-FDG SUV values were high in comparison to artery walls without plaques. The density on CT images alone corresponding to these plaques could be further investigated to see whether it can be an indicator of the active plaques.

K-Means Cluster Algorithm for Grouping Inequality in Regional Development

Unsupervised learning is a subset of machine learning. Many unsupervised learning algorithms are used to solve various problems, especially the extraction of hidden data patterns. One of the problems that concerns unsupervised tasks is clustering. Clustering techniques are widely used for data grouping needs, one of which is development inequality clustering. The classification of development inequality is an important consideration in a country's regional development strategy. However, development groupings often do not pay attention to the hidden information aspects of the data, so they do not get the appropriate results. This research was conducted to provide an additional alternative in the realm of development inequality clustering, namely by classifying development data using the k-means algorithm. The data used is GRDP data for 41 regions in the western part of Java Island for the 2010–2021 range. The results show that the forty-one regions are grouped into four clusters. The first cluster (C1) contains 35 regions, the second cluster (C2) contains three regions, the third cluster (C3) contains four regions, and the fourth cluster (C4) contains three regions. Based on the cluster results, it can be seen that all members of cluster C4 are areas with the best level of development, while cluster C1 is the area with the lowest level of development. As for clusters C2 and C3, these are areas with development levels between clusters C1 and C4. The grouping results can be used by policymakers or local governments to determine the direction of future development priorities based on the cluster with the lowest level of development.&#x0D;