Identification Of Clusters Research Articles

On the Use of a Cluster Identification Method and a Statistical Approach for Analyzing Atom Probe Tomography Data for GP Zones in Al-Zn-Mg(-Cu) Alloys.

Early-stage clustering in two Al-Mg-Zn(-Cu) alloys has been investigated using atom probe tomography and transmission electron microscopy. Cluster identification by the isoposition method and a statistical approach based on the pair correlation function have both been applied to estimate the cluster size, composition, and volume fraction from atom probe data sets. To assess the accuracy of the quantification of clusters of different mean sizes, synthesized virtual data sets were used, accounting for a simulated degraded spatial resolution. The quality of the predictions made by the two complementary methods is discussed, considering the experimental and simulated data sets.

Proximity Estimation and Quantification of Ionizing Radiation-induced DNA Lesions in Aqueous Media using Fluorescence Spectroscopy.

Clustered DNA damage (cluster) or a multiply damaged site, which is a region with two or more lesions within one or two helical turns, has a high mutagenic potential and causes cell death. We quantified fluorophore-labeled lesions and estimated their proximity through fluorescence anisotropy measurements depending on Förster resonance energy transfer (FRET) among the fluorophores close to each other. pUC19 plasmid DNA (2,686 base pairs) dissolved in water or 0.2 M Tris-HCl buffer at a concentration of 10 μg/μL was irradiated by several ionizing radiations with varying linear energy transfers (LET, 0.2-1890 keV/μm). Electrophilic carbonyls (aldehydes and ketones) at abasic sites (APs) produced in DNA were labeled with Alexa Fluor 488 fluorescent dyes with an O-amino functional group. Regardless of the presence or absence of the buffer, AP yields (the number of APs/base pair/Gy) tended to decrease with increasing LET, and the ratio of the AP yield (in 0.2 M Tris-HCl/in water) was less than 0.1 in the LET range of 0.2-200 keV/μm. However, in a higher LET range, the ratios were greater than 0.1. At a low dose, fluorescence anisotropy decreased with increasing LET in 0.2 M Tris-HCl, whereas, in water, this LET dependence was almost insignificant. These findings suggest that 1. the damage distribution on a DNA molecule formed by indirect effects (e.g., by hydroxyl radicals) does not depend on radiation quality and 2. greater LET radiation is more likely to produce a cluster and/or to produce a cluster with shorter distances between lesions by direct effects. This FRET-based proximity estimation of DNA lesions will contribute not only to the identification of clusters and their complexity in a whole genome, but also to the study of their repair mechanism by single-molecular level fluorescence microscopy.

Advances and Challenges in the Classification of Childhood Arthritis

The most appropriate classification of childhood arthritis remains controversial. Several efforts have been made over the years to devise classification systems that identify homogeneous subgroups within the disease spectrum. Although widely used, the International League of Associations for Rheumatology (ILAR) classification has shown major limitations as it was found to have failed its primary goal of identifying homogeneous disease categories. Furthermore, its use of the count of affected joints and of the presence of psoriatic features to define individual disease subsets has been criticized. A novel classification system has been proposed by the Pediatric Rheumatology International Trials Organization (PRINTO) through expert consensus. The preliminary scheme is currently being scrutinized by means a large-scale data collection aimed to formulate an evidence-based classification, whose results will likely be available in 2024. The development of a clinicobiologic classification has been tried in a proof-of-concept study by integrating meaningful biologic and clinical characteristics, including levels of proinflammatory cytokines and measures of disease activity, that defined indicators or composite variables capable of identifying homogeneous patient subgroups by cluster analysis. The current advance in biotechnology, especially genomics, proteomics and transcriptomics, may pave the way to the future identification of well-defined clusters of patients that will inform a biology-based and data-driven classification system. However, any attempt to defining biologic subtypes should be combined with precise clinical and prognostic data in order to devise a rational classification that facilitates the progress towards personalized management of children with JIA. Furthermore, the observed variability in the prevalence of disease subtypes across geographic areas and ethnic groups must be taken into account to develop a classification that is applicable on a global scale.

Phenotypic plasticity for improved light harvesting, in tandem with methylome repatterning in reef-building corals.

Acclimatization through phenotypic plasticity represents a more rapid response to environmental change than adaptation and is vital to optimize organisms' performance in different conditions. Generally, animals are less phenotypically plastic than plants, but reef-building corals exhibit plant-like properties. They are light dependent with a sessile and modular construction that facilitates rapid morphological changes within their lifetime. We induced phenotypic changes by altering light exposure in a reciprocal transplant experiment and found that coral plasticity is a colony trait emerging from comprehensive morphological and physiological changes within the colony. Plasticity in skeletal features optimized coral light harvesting and utilization and paralleled significant methylome and transcriptome modifications. Network-associated responses resulted in the identification of hub genes and clusters associated to the change in phenotype: inter-partner recognition and phagocytosis, soft tissue growth and biomineralization. Furthermore, we identified hub genes putatively involved in animal photoreception-phototransduction. These findings fundamentally advance our understanding of how reef-building corals repattern the methylome and adjust a phenotype, revealing an important role of light sensing by the coral animal to optimize photosynthetic performance of the symbionts.

Lipidomic Profile of Human Sperm Membrane Identifies a Clustering of Lipids Associated with Semen Quality and Function.

Reduced sperm motility and/or count are among the major causes of reduced fertility in men, and sperm membranes play an important role in the spermatogenesis and fertilization processes. However, the impact of sperm lipid composition on male fertility remains under-investigated. The aim of the present study was to perform a lipidomic analysis of human sperm membranes: we performed an untargeted analysis of membrane lipid composition in fertile (N = 33) and infertile subjects (N = 29). In parallel, we evaluated their serum lipid levels. Twenty-one lipids were identified by their mass/charge ratio and post-source decay spectra. Sulfogalactosylglycerolipid (SGG, seminolipid) was the most abundant lipid component in the membranes. In addition, we observed a significant proportion of PUFAs. Important differences have emerged between the fertile and infertile groups, leading to the identification of a lipid cluster that was associated with semen parameters. Among these, cholesterol sulfate, SGG, and PUFAs represented the most important predictors of semen quality. No association was found between the serum and sperm lipids. Dietary PUFAs and SGG have acknowledged antioxidant functions and could, therefore, represent sensitive markers of sperm quality and testicular function. Altogether, these results underline the important role of sperm membrane lipids, which act independently of serum lipids levels and may rather represent an independent marker of reproductive function.

Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension

BackgroundPulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.MethodsSingle-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.ResultsNineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.ConclusionWDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples.

Direct observation of the effects of chemical fixation in MNT-1 cells: A SE-ADM and Raman study.

Aldehydes fixation was accidentally discovered in the early 20th century and soon became a widely adopted practice in the histological field, due to an excellent staining enhancement in tissues imaging. However, the fixation process itself entails cell proteins denaturation and crosslinking. The possible presence of artifacts, that depends on the specific system under observation, must therefore be considered to avoid data misinterpretation. This contribution takes advantage of scanning electron assisted-dielectric microscopy (SE-ADM) and Raman 2D imaging to reveal the possible presence and the nature of artifacts in unstained, and paraformldehyde, PFA, fixed MNT-1 cells. The high resolution of the innovative SE-ADM technique allowed the identification of globular protein clusters in the cell cytoplasm, formed after protein denaturation and crosslinking. Concurrently, SE-ADM images showed a preferential melanosome adsorption on the cluster's outer surface. The micron-sized aggregates were discernible in Raman 2D images, as the melanosomes signal, extracted through 2D principal component analysis, unequivocally mapped their location and distribution within the cells, appearing randomly distributed in the cytoplasm. Protein clusters were not observed in living MNT-1 cells. In this case, mature melanosomes accumulate preferentially at the cell periphery and are more closely packed than in fixed cells. Our results show that, although PFA does not affect the melanin structure, it disrupts melanosome distribution within the cells. Proteins secondary structure, conversely, is partially lost, as shown by the Raman signals related to α-helix, β-sheets, and specific amino acids that significantly decrease after the PFA treatment.

Spatial clustering based gene selection for gene expression analysis in microarray data classification

A typical application of categorization in data mining is to uncover interesting distributions and significant patterns in the information that underlies it using density-based spatial clustering for workloads with noise. In these conditions, it is anticipated that the classification of the microarray gene expression database will have the necessary clustering property that may be utilized to emphasize the effects of the alterations. The proposed method typically guarantees that the subsequent identification of gene clusters’ best global arrangement of genes. It provides an iterative method for figuring out the precise number of clusters needed for each data collection. The technique is based on practices frequently used in statistical tests. The key idea is to coordinate gene redistribution optimization across clusters with the search for the optimal number of groups. An experiment that finds the most effective number of genes over time was used to evaluate the effectiveness of the suggested strategy. It used this stringent statistical test to show that our technique accurately clusters more than 95% of the genes. Finally, since the basic principles of gene development and gene cluster assignment have been well characterized by earlier studies and the technique was verified using real gene expression information.

PiracyAnalyzer: Spatial temporal patterns analysis of global piracy incidents

Maritime piracy incidents present significant threats to maritime security, resulting in material damages and jeopardizing the safety of crews. Despite the scope of the issue, existing research has not adequately explored the diverse risks and theoretical implications involved. To fill that gap, this paper aims to develop a comprehensive framework for analyzing global piracy incidents. The framework assesses risk levels and identifies patterns from spatial, temporal, and spatio-temporal dimensions, which facilitates the development of informed anti-piracy policy decisions. Firstly, the paper introduces a novel risk assessment mechanism for piracy incidents and constructs a dataset encompassing 3,716 recorded incidents from 2010 to 2021. Secondly, this study has developed a visualization and analysis framework capable of examining piracy incidents through the identification of clusters, outliers, and hot spots. Thirdly, a number of experiments are conducted on the constructed dataset to scrutinize current spatial-temporal patterns of piracy accidents. In experiments, we analyze the current trends in piracy incidents on temporal, spatial, and spatio-temporal dimensions to provide a detailed examination of piracy incidents. The paper contributes new understandings of piracy distribution and patterns, thereby enhancing the effectiveness of anti-piracy measures.

A 19-color single-tube full spectrum flow cytometry assay for the detection of measurable residual disease in acute myeloid leukemia.

Multiparameter flow cytometry (MFC) has emerged as a standard method for quantifying measurable residual disease (MRD) in acute myeloid leukemia. However, the limited number of available channels on conventional flow cytometers requires the division of a diagnostic sample into several tubes, restricting the number of cells and the complexity of immunophenotypes that can be analyzed. Full spectrum flow cytometers overcome this limitation by enabling the simultaneous use of up to 40 fluorescent markers. Here, we used this approach to develop a good laboratory practice-conform single-tube 19-color MRD detection assay that complies with recommendations of the European LeukemiaNet Flow-MRD Working Party. We based our assay on clinically-validated antibody clones and evaluated its performance on an IVD-certified full spectrum flow cytometer. We measured MRD and normal bone marrow samples and compared the MRD data to a widely used reference MRD-MFC panel generating highly concordant results. Using our newly developed single-tube panel, we established reference values in healthy bone marrow for 28 consensus leukemia-associated immunophenotypes and introduced a semi-automated dimensionality-reduction, clustering and cell type identification approach that aids the unbiased detection of aberrant cells. In summary, we provide a comprehensive full spectrum MRD-MFC workflow with the potential for rapid implementation for routine diagnostics due to reduced cell requirements and ease of data analysis with increased reproducibility in comparison to conventional FlowMRD routines.

Peduncle collision-free grasping based on deep reinforcement learning for tomato harvesting robot

Collision-free grasping of the thin, brief peduncles connecting cherry tomato clusters to the main stem was crucial for tomato harvesting robots. Recognizing that the optimal operating posture for each individual peduncle was various, this study proposed a novel peduncle grasping posture decision model using deep reinforcement learning (DRL) for tomato harvesting manipulators, to overcome the collision issue caused by fixed-posture grasping. This model could dynamically generated action sequences for the harvesting manipulator, ensuring that the end-effector approach to the peduncle along the collision-free path with the optimal grasping posture. Building upon prior research into the multi-task identification of tomato clusters, peduncles, and the main stem, a keypoint-based spatial pose description model for tomato bunches was devised. Through this, the optimal operating posture for the end-effector on the peduncle was established. An improved HER-SAC (Soft Actor Critic with Hindsight Experience Replay) algorithm was subsequently established to guide the end-effector in collision-free grasping motions. The reward function of this algorithm incorporated end-effector posture constraints obtained from the optimal posture plane. In the training phase, a heuristic strategy model, providing prior knowledge, was merged with a dynamic gain module to sidestep local optimal policies, collectively enhancing the learning efficiency. In the simulation, our method improved the success rate of the peduncle grasping by at least 14 %, compared with SAC, HER-DDPG and HER-TD3. For the identical scenarios, improved HER-SAC reached the desired posture with a minimum of 15.5 % fewer steps compared to other algorithms. In field experiments conducted in tomato greenhouses, the robot achieved a harvesting success rate of 85.5 %, which was an increase of 57.3 % and 43.0 % compared to traditional methods with fixed horizontal and parallel-to-main-stem postures, respectively. The average operation time, from identification to successful harvesting, was 11.42 s. Our findings offer a promising solution to enhancing the efficiency of tomato-harvesting robots.

Artificial Identification: A Novel Privacy Framework for Federated Learning Based on Blockchain

To provide off-chain federations with complete privacy services to realize on-chain federated learning (FL), this article proposes a novel privacy framework for FL based on blockchain and smart contracts, named Artificial Identification. It consists of two modules: private peer-to-peer identification and private FL, using two scalable smart contracts to manage the identification and learning process, respectively. Based on Ethereum and interplenary file systems (IPFS), we implement our framework and comprehensively analyze its performance. Experiments show that the proposed framework has acceptable collaboration costs and offers advantages in terms of privacy, security, and decentralization. Furthermore, combined with radio frequency identification (RFID) technology, the framework has the potential to realize automatic on-chain identification and autonomous FL of machine clusters composed of Internet of Things (IoT) devices or distributed participants.

Classification of Sugarcane Area Using Landsat 8 and Random Forest based on Phenology Knowledge

Indonesia is one of the largest countries globally with an area for planting sugarcane. The current problem is that determining the planting area of sugarcane is still done conventionally; this is very limited and wastes time. Thus, knowing the sugarcane planting area becomes essential for policymaking through Remote Sensing technology. However, the challenge of Remote Sensing is the limited data due to weather and the spectral variability of other plants. So, it is necessary to classify based on phenological knowledge. The study aims to classify sugarcane areas based on phenological knowledge using Remote Sensing and Machine Learning. This application finished on the cloud platform Google Earth Engine (GEE) through Landsat 8 satellite imagery data. The knowledge of sugarcane phenology was built based on the Normalized Difference Vegetation Index (NDVI) spectral value and built with the harmonic model. In addition, classification is accomplished by object-oriented (OO) methods for segmentation classification. Object-oriented is solved by the Simple Non-Iterative Clustering (SNIC) algorithm for spatial cluster identification, the Gray-Level Co-occurrence Matrix (GLCM) for texture extraction, and the Random Forest algorithm for Land Use-Land Cover (LULC) classification. The results of the accuracy analysis using the confusion matrix and the classification of sugar cane areas based on phenological knowledge obtained the best results with an overall accuracy of 95.9% with a Kappa coefficient of 0.92. It can be concluded that a classification approach with knowledge of plant phenology can help better classify the availability of land for plantations in the future.

Phenotypic and Molecular Landscape of Bone Marrow CD8+ T Cells in Myeloid Neoplasms Predicts Response to Treatment with Hypomethylating Agents

Background and aims Myeloid malignancies encompass a diverse group of conditions, including myelodysplastic neoplasms (MDS) and chronic myelomonocytic leukemia (CMML). There is evidence suggesting the role of bone marrow (BM) environment and immune cells in the development and progression of MDS and CMML. Azacytidine (AZA) remains the backbone therapy for higher-risk MDS and non-proliferative CMML. To date, molecular predictors of response to AZA are not yet well-defined. Herein, we sought to investigate the immune landscape of BM samples from patients with lower-risk MDS (LR-MDS; i.e., IPSS-R <3.5), CMML, higher-risk MDS (HR-MDS; i.e., IPSS-R ≥3.5), and acute myeloid leukemia (AML). Methods Samples were collected before treatment initiation. Mass cytometry (CyTOF) was performed on isolated BM mononuclear cells from 48 patients, using the Maxpar® Direct™ Immune Profiling Assay. Flow cytometry was performed on a secondary cohort of 56 patients. scRNA-sequencing was performed on sorted BM CD8+ T cells from patients with HR-MDS (n=4) and AML (n=5), using a 10x Genomics pipeline. Mann-Whitney U test and the Kruskal-Wallis test were used as appropriate. Kaplan-Meier analysis was utilized for survival analysis, and the log-rank test was employed to assess the significance. The level of significance was established at P < 0.05. Results The immunophenotypic analysis using CyTOF demonstrated increased expression of CXCR3 on cytotoxic CD8+ T-cells in CMML and AML samples compared to samples from patients with MDS. Increased frequency of the CD57 +CXCR3 +CD8 + T-cell subset was further observed in patients with HR-MDS and AML that were non-responders to AZA, compared to responders, as observed by both CyTOF and flow cytometry. After determining the optimal cut-off for the frequency of CD8 +CD57 +CXCR3 + T cells at 29%, we observed that HR-MDS and AML patients (Figure 1A) with a frequency of CD57 +CXCR3 +CD8 + of <29% had better overall survival (OS).Multivariate analysisshowed that a frequency of CD57 +CXCR3 +CD8 + of >29% was independently associated with decreased survival in patients with TET2 mutations (HR=2.603; 1.275-5.317). scRNA-seq analysis of isolated BM CD8 + T cells was further performed to characterize the molecular signature that favors response to AZA, which resulted in the identification of several cell clusters based on gene expression. The cluster characterized by the expression of genes associated with cytotoxicity showed significant differences between responders and non-responders. Specifically, within the cytotoxic cluster, genes associated with IFN-related pathways were upregulated in cells from responders to AZA. In contrast, genes upregulated in non-responders displayed an enrichment for the TGF-β signaling pathway, which is known to be associated with the suppression of the killing capacity of T cells (Figure 1B). The aforementioned molecular signatures were further associated with a higher cytotoxic molecular signature in the cytotoxic cell cluster from the group of responders. Conclusion In this study, we focused on BM CD8 + T cells in samples from patients with myeloid neoplasias and performed deep phenotypic analysis using CyTOF coupled with scRNA, in order to characterize the BM immune environment. We demonstrate for the first time that BM CD8 + T cells show different phenotypic and transcriptomic characteristics in responders to AZA. Specifically, we show that the frequency of the effector CD57 +CXCR3 +CD8 + subset is decreased in responders before treatment initiation, and this is associated with better survival. Despite the decreased proportion of this cytotoxic cell population in responders, there are specific signatures associated with effector T cell functionality that can explain the improved outcomes in these patients. These findings underscore the significance of the crosstalk between leukemic and immune cells in the progression of the disease and the effect of treatment. Figure legend A. Increased (%) CD57 +CXCR3 + correlates significantly with worse survival of HR-MDS and AML patients. Kaplan Meier curves for Overall Survival in HR-MDS and AML patients respectively. B. Gene signature derived from the scRNAseq analysis. Interferon-stimulated gene (ISG) signature is increased in the cytotoxic cell cluster in responders and and TGF-β signature in non-responders.

Genetic Subtyping Using Whole Exome Sequencing across Large B Cell Lymphoma Entities in a Series of 109 DLBCL and Plasmablastic Lymphoma Cases

Background: Diffuse Large B Cell Lymphomas are still a significantly heterogeneous disease with few molecularly defined entities. Recently genomic based algorithms have been proposed for DBCL NOS disease classification, including the Lymphgen tool 1. Other large B cell lymphoma entities, including High Grade BCL Dual Hit or plasmablastic lymphoma remain poorly characterized according to this molecular based clustering methods. Here we sought to apply this tool for disease classification in a series of 109 large B cell lymphoma cases derived from GELTAMO clinical trials (NCT01848132 and NCT2015-005390-21) and retrospective multicentric series to confirm its application across different large B cell lymphoma entities. Methods: We retrieved FFPE and liquid biopsy diagnostic samples from 109 cases and performed histopathological confirmation, FISH testing and whole exome sequencing (Agilent Sureselect v6) by NGS. Sequencing data and available fusion data for BCL2 and BCL6 were used to classify the cases according to Lymphgen (https://llmpp.nih.gov/lymphgen). Results: 109 cases were classified as different large B cell lymphoma entities, including 31 DLBCL NOS GCB type, 8 HGBCL/DLBCL DH/NOS, 41 DLBCL NOS non-GCB type, 22 plasmablastic lymphoma, 3 THRLBCL, 1 PMBL, 2 DLBCL NOS and 1 3B Follicular Lymphoma. After WES median Tumor Mutation Burden was 972. Overall genetic classification by Lymphgen was obtained in 53 cases (49%) with the remaining 56 cases considered unclassified (molecular subtype other/NOS). Of the genetically identified cases, 18 were classified as ST2 (33%), 15 were considered EZB (28%), 5 BN2 (10%) and 3 MCD (5%). Interestingly 12 (22%, overall, 11%) were genetically composite, including 8, ST2/EZB; 1, ST2/BN2; 1, ST2/MCD and 2 ST2/EZB/MCD. According to histopathological subtype 17 GCB type DLBCL were classified as EZB (8, 47%), ST2 (5, 30%) and BN2 (1, 6%). 3 cases (17%) were genetically composite. 6 out of 8 cases of HGBCL/DLBCL DH/NOS were classified as either EZB (2), ST2 (2) or composite EZB/ST2 (2). 19 non-GCB type DLBCL were classified as ST2 (5, 26%), BN2 (4, 21 %), EZB (3, 16 %) and MCD (3, 16%). 4 cases were genetically composite (21%). Interestingly, plasmablastic lymphomas showed ST2 genetic features, found in 5 out of 22 (23%) and composite ST2/EZB in 2 cases. Genes overrepresented in the ST2 cluster include TET2, PRRC2C, STAT3,DOCK8 and CLTC. Plasmablastic Lymphomas classified as other showed recurrent mutations in PRRC2C, PABPC1, TRRAP, PRKCB, EP300 and TP53. Conclusions: ST2 and EZB genetic groups stand as the most frequent genetic subtypes of DLBCL NOS. While ST2 is found in either GCB and non-GCB subtypes, EZB is found preferentially in cases with the GCB phenotype, including GCB type DLBCL and HGBCL/DLBCL DH. In comparison with already published series we found a relatively higher prevalence of the ST2 genetic subtype among DLBCL NOS. MCD and BN2 genetic subtypes are restricted to the non-GCB subtype, but its prevalence is relatively low. Importantly, a significant fraction of cases (~50%) is still genetically unclassified (molecular subtype NOS) using WES and FISH-derived fusion data. Proper identification of the A53 cluster and relevant dysregulated pathways in the molecular NOS group require more research. Specific large B cell lymphoma entities such as plasmablastic lymphoma show features of ST2 genetic subtype in a significant fraction of tumors. These features include JAK/STAT3 pathway activation related genes and fit well with already published data 2,3. In addition, recurrent mutations in TET2, involved in epigenetic regulation of gene expression and TRRAP, target of C-MYC transcriptional activation, were enriched in this molecular subtype of plasmablastic lymphoma. Importantly ~70% remained unclassified. Mutations in this group are recurrently found in PRRC2C as well as PABPC1, both involved in VEGFA-VEGFR2 signaling. In summary, DLBCL NOS molecular subtyping supplies exact molecular classification in ~50% of our cohort. ST2 and EZB subtypes constitute ~60% of the cases with a fraction of ~15% of composite ST2/EZB. Application of the molecular classifier to plasmablastic lymphomas shows intraspecific genetic diversity and points toward dysregulation of JAK/STAT, MAPK/Erk and VEGFA-VEGFR2 signaling as well as MYC transcriptional activation. REFERENCES 1. PMID: 32289277 2. PMID: 33225311 3. PMID: 32273478

Defining the Drivers of Idelalisib-Related Early Autoimmune Toxicity in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) patients with disease resistant to BTK and BCL-2 inhibitors can potentially be treated with PI3K inhibitors to overcome resistance and improve outcomes. PI3K inhibitors such as idelalisib and duvelisib have shown promising efficacy in CLL patients, but severe toxicities remain a significant hurdle to their implementation in the clinic (PMID: 24615777). By investigating the molecular mechanisms that lead to severe early autoimmune toxicity with idelalisib, we hope to predict or prevent toxicity, thus expanding the use of this efficacious PI3K inhibitor class of drugs. To this end, the Brown lab showed that idelalisib-induced hepatitis is autoimmune. CyTOF analysis in CLL patients with early severe toxicity in a front-line idelalisib clinical trial cohort showed a significant decrease in CD4+ T regulatory cells (Treg) with a relative shift in favor of T effectors in peripheral blood. In addition, the Brown lab showed that the Th17 pathway is more active at baseline, with a further increase in patients with toxicity (PMID: 27247136, 36732326). Since the CYTOF data is restricted to fewer mostly T-cell markers in our studies, an unbiased single cell RNA sequencing (scRNA-seq) approach is warranted to explore the full effects of idelalisib on immune cells and CLL cells in patients with and without autoimmune toxicity. The question remains what molecular mechanisms drive the idelalisib-induced decrease in the Treg population, its effect on natural killer (NK) and CD8 T-cell function, and the activation of the Th17 pathway in CLL patients with early toxicity. We therefore carried out the first systematic effort to understand the mechanisms of idelalisib toxicity in molecular detail using combined scRNA and scTCR-seq. In this study, we profile T-cell enriched PBMCs of matched pre-treatment and on-idelalisib treatment samples from CLL patients with early severe toxicity and without any toxicity (n=4 CLL patients from each group, a total of 16 samples) using 10X single cell transcriptomic and TCR profiling. Raw sequencing data files were processed using the Cell Ranger suite (version 7.1.0) from 10x Genomics. Sample identification based on SNP information was done using the Popscle suite. Seurat package (version 4.9.9.9) was used for the single cell analysis. After filtering cells with fewer than 600 features, less than 1000 UMIs, mitochondrial content higher than 15%, 51,487 cells were left for the downstream analysis. To explore transcriptional heterogeneity and perform initial cell clustering, principal component analysis and nonlinear dimensional reduction using UMAP were applied using 20 dimensions. Leiden algorithm was used for cluster identification. Cell types were identified using Azimuth with the PBMC reference dataset and confirmed with expression plots. The initial observations of the cell proportions show that baseline samples from patients with early idelalisib toxicity have fewer Tregs, NK, and CD4+ cytotoxic T (CTL) cells compared to patients without toxicity. In addition, we observed a decline in Tregs, gamma-delta (γδ) T, and CD4+ CTL cells and a concurrent increase in proliferative CD4+ and CD8+ T-cells and proliferative NK on idelalisib treatment in the early toxicity group. On the contrary, CD4+ CTL and CD4+ T effector memory (TEM) cells are enriched with idelalisib therapy in patients without toxicities. At the same time, more CD4+ T-Cell Memory (TCM), γδ T, CD8+ naive T, CD8+ TCM, and Mucosal-associated invariant T (MAIT) cells are present in pretreatment samples from patients with toxicity compared to patients without toxicity. Furthermore, proliferative T-cells are depleted with idelalisib therapy in patients without toxicity whereas they are enriched in patients with early toxicity. To further characterize idelalisib-induced depletion of Tregs, we reclustered them to identify distinct subgroups and found particular clusters of Treg were depleted in patients with early toxicity. In the non-toxicity group, more diversity of Tregs was identified with depletion of some clusters but also an increase in a cluster. At present, we are integrating the scRNA & scTCR-seq data to characterize the cellular diversity and heterogeneous phenotypic states of the T-cell populations and updated results unraveling molecular mechanisms that drive early severe toxicity in CLL patients with idelalisib treatment will be presented at the meeting.

1426. Integrating artificial intelligence and dynamic programming to identify microbial clusters through antimicrobial susceptibility sequence analysis

Abstract Background The spread of infectious diseases and antimicrobial resistance poses a significant threat, especially in developing countries where traditional DNA fingerprinting techniques are often not available. We propose a novel approach using dynamic programming algorithms to compare antimicrobial susceptibility sequences and identify clusters of related microbes. Methods We selected pathogens from specific infections and tested them with a range of antimicrobials, recording the test results as either sensitive (S), resistant (R), or unknown (U) to each drug. These results were combined into a sequence, or ATB string, for each pathogen (Fig. 1). To compare the sequences, we used dynamic programming (Fig. 2) to calculate the minimum edit distance between each pair of sequences. The edit distance reflects the number of atomic changes needed to transform one sequence into the other. In this case, atomic changes include insertion, deletion, substitution, and matching of individual test results (Fig. 3). We created a matrix of pairwise similarities by comparing all ATB strings. A cluster of similar strains was identified based on a cutoff in the edit distance matrix, such as a distance of less than 1. The method, which is available in the SACIH+ system (https://plus.sacihweb.com), was used to analyze the same strain of specific healthcare infections from a public hospital in Belo Horizonte, Brazil, diagnosed between Jan 2022 and Mar 2023.Figure 1.Antimicrobial testing and ATB string generation for selected pathogens. Antimicrobial testing and ATB string generation for selected pathogens.Figure 2.Sequence comparison using dynamic programming. Sequence comparison using dynamic programming. Results We analyzed S. aureus strains from surgical site infection - SSI (23 cases), pneumonia - PNEU (29 cases), and bloodstream infection - BSI (13 cases). Identified 4 SSI, 10 PNEU clusters (Fig. 4 and 5), and 4 BSI clusters using edit distance matrix. For K. pneumoniae strains from 21 BSI, 22 PNEU, and 34 urinary tract infection - UTI cases, found 5 clusters for PNEU and UTI, and 7 clusters for BSI (Fig. 6).Figure 3.Minimum edit distance calculation for sequence comparison. Minimum edit distance calculation for sequence comparison.Figure 4.Clustering of Staphylococcus aureus strains from 29 nosocomial pneumonia cases. Clustering of Staphylococcus aureus strains from 29 nosocomial pneumonia cases.Figure 5.Identification of a large cluster with 16 identical Staphylococcus aureus strains from 29 nosocomial pneumonia cases.Identification of a large cluster with 16 identical Staphylococcus aureus strains from 29 nosocomial pneumonia cases. Conclusion Our novel approach using dynamic programming to compare antimicrobial susceptibility sequences has shown promise in identifying clusters of related microorganisms, even without traditional DNA fingerprinting methods. The method represents a valuable tool for tracking the spread of infectious diseases, especially in settings where traditional methods are not available.Figure 6.Clustering of Klebsiella pneumoniae strains from 21 bloodstream infection cases.Clustering of Klebsiella pneumoniae strains from 21 bloodstream infection cases. Disclosures All Authors: No reported disclosures

Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression

This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field.

Identification of immunological characteristics and cuproptosis-related molecular clusters in primary Sjögren's syndrome

BackgroundPrimary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by lymphocyte infiltration of the exocrine glands. The typical clinical symptoms of pSS include dryness of the mouth (xerostomia) and eyes (xerophthalmia), fatigue, and joint pain. Cuproptosis is a recently identified mode of programmed cell death that leads to the progression of multiple diseases, and the precise etiology and pathophysiology of pSS remain unknown. Consequently, the aim of our study was to explore cuproptosis-related molecular clusters and identify key genes in pSS. MethodGene expression profiles of the peripheral blood in the GSE84844 dataset were downloaded to identify the expression characteristics of cuproptosis regulators and immune cell infiltration. Subsequently, further exploration was conducted on the clusters involving cuproptosis-related genes (CRGs) and the corresponding immune cell infiltration, and the WGCNA algorithm was applied to explore the cluster-specific differentially expressed genes. Finally, the best machine prediction model was selected for candidate hub cuproptosis-associated genes and the accuracy of predictive efficiency was verified by the salivary gland in an external dataset (GSE143153) and enzyme-linked immunosorbent assay. ResultThrough a comparison of patients with pSS and controls, 7 CRGs and 4 types of immune cells were identified. Immune cell infiltration revealed significant immune heterogeneity in three cuproptosis-related molecular clusters in pSS. The random forest machine model showed the best discriminatory performance (area under the receiver operating characteristic curve (AUC) = 1.000) and built a predictive model based on 5 genes, which demonstrated satisfactory performance (AUC = 0.70) in the GSE143153 dataset. Based on serum samples, EED (AUC = 0.557), CBL (AUC = 0.635), and NFU1 (AUC = 0.655) showed lower expression levels in patients with pSS (p = 0.037, p = 0.000, p = 0.000, respectively). ConclusionIn this study, we systematically analyzed the association between pSS and cuproptosis, established a predictive model that screened for high-risk genes linked to the advancement of pSS, and explored the pathogenic mechanisms and novel therapeutic strategies for pSS, targeting EED, CBL and NFU1.