Cluster Topology Research Articles

SOFCluster: Safety‐oriented, fuzzy logic‐based clustering scheme for vehicular ad hoc networks

AbstractVehicular ad hoc network (VANET) nodes are characterized by their high mobility and by exhibiting different mobility patterns. Therefore, VANET clustering schemes are required to account for the mobility parameters among neighboring nodes to produce relatively stable clustering schemes. In this article, we propose a novel cluster‐head (CH) selection scheme for VANETs. This scheme is based on a fuzzy logic‐powered, k‐hop distributed clustering algorithm. It deals efficiently with scalability and stability issues of VANETs and is able to achieve highly stable clustering topologies as compared with other schemes. Our proposed clustering scheme strives to maintain a safe intervehicle distance as a one prime metric for CH selection. Moreover, a major contribution of our work is the proposal of a novel strategy for constructing fuzzy logic‐based clustering algorithms useful for VANETs. This proposed solution is useful in an Internet of things‐based setting that involves controlled vehicle‐to‐vehicle communication. We first derive mathematically, a new average distance estimation formula that is used as a metric for selecting CHs, leading to safer clusters that avoid collisions with front and rear vehicles. Furthermore, the new proposed scheme creates stable clusters by reducing reclustering overhead and prolonging clusters' lifetimes.

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training.

FCO — Fuzzy constraints applied Cluster Optimization technique for Wireless AdHoc Networks

Efficient energy utilization in wireless adhoc networks is always an open issue. To achieve energy efficiency, clustering technique would be an optimal one. In clustering technique using advanced optimization methods will provide expected results. By considering this, in this research work fuzzy constraints based cluster optimization technique is proposed. This technique considers fuzzy parameters namely device energy, device position, device movement/speed and device hop-count for calculating the fitness value of the nodes. Based on the value cluster head selection happens. This technique is enhanced to handle uncertainty conditions like cluster head failure, topology changes and energy depletion of the cluster head. The proposed technique is compared with four existing systems and the comparison results illustrates that the proposed technique is more optimal than compared LEACH, MPO, EDC, TCACWCA.

Two-Layer Cooperative Control for Multiple Converter-Network Clusters

Cooperative control are mainly employed for multi-converter networks due to the distributed nature of renewable resources and communication network limitations. By dividing all converters within each converter-network (CN) cluster into the leader-converters and follower-converters according to their physical cluster topology structure, the leader and follower control layers are respectively formulated. Then a two-layer cooperative strategy is developed, under which the weighted average voltage of all converters can be regulated to their rated references, meanwhile, the accurate current sharing can be simultaneously realized not only within each CN cluster but also among multiple CN clusters. Compared with previous work, the proposed control strategy has the advantages of accurate current sharing, short time consumption and faster convergence. All controllers are fully distributed and can be applied in all sparse two-layer cyber networks, and control time constant condition is derived to ensure the system stability. Different cases in MATLAB/SimPowerSystems are investigated to verify the effectiveness of the results.

A Topological Cluster of Differentially Regulated Genes in Mice Lacking PER3.

Polymorphisms in the human circadian clock gene PERIOD3 (PER3) are associated with a wide variety of phenotypes such as diurnal preference, delayed sleep phase disorder, sleep homeostasis, cognitive performance, bipolar disorder, type 2 diabetes, cardiac regulation, cancer, light sensitivity, hormone and cytokine secretion, and addiction. However, the molecular mechanisms underlying these phenotypic associations remain unknown. Per3 knockout mice (Per3–/–) have phenotypes related to activity, sleep homeostasis, anhedonia, metabolism, and behavioral responses to light. Using a protocol that induces behavioral differences in response to light in wild type and Per3–/– mice, we compared genome-wide expression in the eye and hypothalamus in the two genotypes. Differentially expressed transcripts were related to inflammation, taste, olfactory and melatonin receptors, lipid metabolism, cell cycle, ubiquitination, and hormones, as well as receptors and channels related to sleep regulation. Differentially expressed transcripts in both tissues co-localized with Per3 on an ∼8Mbp region of distal chromosome 4. The most down-regulated transcript is Prdm16, which is involved in adipocyte differentiation and may mediate altered body mass accumulation in Per3–/– mice. eQTL analysis with BXD mouse strains showed that the expression of some of these transcripts and also others co-localized at distal chromosome 4, is correlated with brain tissue expression levels of Per3 with a highly significant linkage to genetic variation in that region. These data identify a cluster of transcripts on mouse distal chromosome 4 that are co-regulated with Per3 and whose expression levels correlate with those of Per3. This locus lies within a topologically associating domain island that contains many genes with functional links to several of the diverse non-circadian phenotypes associated with polymorphisms in human PER3.

Extending the Lifetime of Wireless Sensor Networks using an Improved Clustering Protocol

Wireless Sensor Network (WSN) is one of the underlying mechanics of IoT (Internet-of-Things) which has recently seen a worldwide interest by its use in several domains as military, automation, agriculture, environment, underwater and etc. Energy efficiency and reliability of transmitted messages are two of the major requirements for the surveillance and wireless detection applications in WSN. For this reason, a growing number of research studies are carried out. Several routing protocols have been developed to furnish better performance for optimizing the network's energy consumption in WSN; most of them are based in clustering and hierarchical topology. However, most of these routing protocols cannot consider all required and important metrics to increase the lifespan of the network. This work introduces an enhanced algorithm of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol called Balanced Current Energy-LEACH (BCE-LEACH) which aims to equity the power consumption of sensor nodes of the network due to extend the lifetime of the network. The proposed protocol is based on the residual energy of sensor nodes to pick out cluster heads (CHs) that means only nodes with sufficient current energy can participate in CH selection, and then it is focused on both current energy and the distance toward the base station (BS) to select a parent CH which has current energy greater than the mean energy of CHs and the distance to the BS is fewer than the average. This parent CH groups all data from other CHs. The root CH after doing data aggregation sends compressed information to the BS using the multi-hop process between CHs. The improved algorithm is simulated in MATLAB R2016b simulation tool. Simulation results in this paper indicate that the introduced protocol works better than LEACH through extending the lifetime of the WSN.

Topology influence on the radio communication range of sensor networks

The Internet of Things (IoT) applications use wireless technologies in order to establish Internet connection. Communication technologies such as Wi-Fi, cellular, ZigBee, Bluetooth, etc. are used to transfer data in IoT. The choice of the appropriate technology or combination of technologies depends on the type of application, its characteristics, user requirements, and other factors such as communication range, security requirements, battery life, etc. In our project, we will focus on Star, Cluster and Mesh wireless topologies, conduct comparative analysis between them, and testing different XBee devices with specialized software. The experiments are connected to the quality of the transferred data using different XBee devices and how this will affect the transmission range. The results of our research show that in terms of the range and quality of packet transmission, the cluster topology shows the best results.
 

Machine learning enabled autonomous microstructural characterization in 3D samples

We introduce an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional (3D) samples obtained from molecular dynamics simulations, particle tracking data, or experiments. Our technique combines topology classification, image processing, and clustering algorithms, and can handle a wide range of microstructure types including grains in polycrystalline materials, voids in porous systems, and structures from self/directed assembly in soft-matter complex solutions. Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects. We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples, characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids. To demonstrate the efficacy of our ML approach, we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals, polymers and complex fluids as well as experimentally published characterization data. Our technique is computationally efficient and provides a way to quickly identify, track, and quantify complex microstructural features that impact the observed material behavior.

A Statistical Comparative Study on Image Reconstruction and Clustering With Novel VAE Cost Function

Deep clustering achieves unprecedented levels of accuracy with unsupervised feature extraction on rich datasets where the joint statistics of the latent space is learned via highly nonlinear compression. This paper has two separate contributions to this field. First, we conduct an extensive and first-of-its-kind empirical study on the statistical relationship between the clustering accuracy and image reconstruction quality of a state-of-the-art deep clustering topology in the form of a convolutional variational autoencoder (VAE) with a K-means back end. We change the latent variable $z$ at the bottleneck of the network to create different latent dimensions and explore how clustering performance metrics and reconstruction metrics are statistically related. Secondly, based on our data-driven statistical findings, we also propose a novel cost function for the VAE which includes the structural similarity index measure to jointly optimize image quality and latent statistics for improved clustering. The preliminary results show significant increases in clustering accuracy of as much as 10.76% on two popular benchmark datasets. The TensorFlow implementation for the experimental framework can be found here: https://github.com/alla15747/IEEE-Comparitive-Study-VAE-Paper- (Python code will be available at the time of publication).

Droop-Based Two-Layer Cooperation for Multiple DC Microgrid Clusters

Abstract Enabling energy exchange among multiple dc microgrids (MGs) and adjusting the current outputs of all converters proportional to their power capacities can significantly improve power supply reliability as well as effectively avoid overloaded or uncertainty. By dividing all converters within each dc MG cluster into the leader-converters and follower-converters according to their physical cluster topology structure, the leader and follower control layers are respectively formulated. Then a droop-based two-layer cooperative strategy is developed, under which the weighted average voltage of all converters can be regulated to their rated references, meanwhile, the accurate current sharing can be simultaneously realized not only within each dc MG but also among multiple dc MG clusters. All controllers are fully distributed and can be applied in all sparse two-layer cyber networks, control time constant related sufficient conditions are also derived to ensure the whole system stability. The effectiveness of the results are verified through different cases in MATLAB/SimPowerSystems.

Comparison of Phylogenetic Tree Topologies for Nitrogen Associated Genes Partially Reconstruct the Evolutionary History of Saccharomyces cerevisiae.

Massive sequencing projects executed in Saccharomyces cerevisiae have revealed in detail its population structure. The recent “1002 yeast genomes project” has become the most complete catalogue of yeast genetic diversity and a powerful resource to analyse the evolutionary history of genes affecting specific phenotypes. In this work, we selected 22 nitrogen associated genes and analysed the sequence information from the 1011 strains of the “1002 yeast genomes project”. We constructed a total evidence (TE) phylogenetic tree using concatenated information, which showed a 27% topology similarity with the reference (REF) tree of the “1002 yeast genomes project”. We also generated individual phylogenetic trees for each gene and compared their topologies, identifying genes with similar topologies (suggesting a shared evolutionary history). Furthermore, we pruned the constructed phylogenetic trees to compare the REF tree topology versus the TE tree and the individual genes trees, considering each phylogenetic cluster/subcluster within the population, observing genes with cluster/subcluster topologies of high similarity to the REF tree. Finally, we used the pruned versions of the phylogenetic trees to compare four strains considered as representatives of S. cerevisiae clean lineages, observing for 15 genes that its cluster topologies match 100% the REF tree, supporting that these strains represent main lineages of yeast population. Altogether, our results showed the potential of tree topologies comparison for exploring the evolutionary history of a specific group of genes.

Algorithm for predicting weighted protein complexes by using modularity function

Due to the difficulties in obtaining the identification results of protein complexes with higher accuracy, as well as the running efficiency of the protein complexes identification algorithm based on weighted modularity function clustering is low, a novel protein complex recognition algorithm named IWPC-MF (Algorithm for identifying weighted protein complexes based on modularity function) was proposed. Firstly, we build the TCEWS (topological clustering for weighted edges) algorithm, which is founded on improved edge clustering coefficient and the combination of Pearson correlation coefficient with edge point clustering coefficient. Secondly, seed nodes were selected according to the weight of nodes, then the IMS (initial module selection strategy) program has been proposed. The similarity measurement and the protein attachment degree between nodes were selected to obtain the initial clustering module by traversing neighbors of seeds. Finally, based on the tightness, we take the IMC (initial module combination strategy) module with modularity function was added to initial module and finally complete the protein complex detection, which can overcome the defect that the traditional modularity function cannot identify the overlapped and small complex. IWPC-MF algorithm was used to identify protein complexes on DIP (the Database of Interacting Proteins) data, in order to reduce effectively the computational complexity and randomness, speed up the clustering speed. The experimental results show that IWPC-MF algorithm has better performance on accuracy and recall, which is more reasonable to identify protein complexes.

Single Cell Signaling Responses Stratify Immune and Leukemia Cell Populations in Acute Myeloid Leukemia

In healthy hematopoiesis, cell identity and signaling response are tightly linked, with predictable cell type-specific responses to cytokines and growth factors. However, this correlation is often disrupted in myeloid malignancies, including acute myeloid leukemia (AML), wherein signaling responses may be driven directly by kinase mutational activation or cell state changes due to epigenetic alterations, for instance. In order to resolve ligand-driven signaling pathways in bone marrow, tools that allow simultaneous phenotypic characterization and functional cellular responses at single cell resolution are needed. Here, we present the use of a high content mass cytometry panel combined with mass-tag cell barcoding in order to characterize cell identity and signaling responses in bone marrow hematopoietic cells from healthy donors and leukemic patients. We created a phospho-specific mass cytometry panel comprising 24 surface phenotyping markers to resolve the predominant cellular subsets within bone marrow and blood. We perturbed cellular signaling with nine growth factors, cytokines, and chemicals and measured immediate (15 minute) responses at 10 intracellular signaling markers (pSTAT1, pSTAT3, pSTAT5, pSYK, pp38, pERK1/2, pS6, pNFkB, IkBa, & pAKT). To improve robustness of the signaling response analysis, we used mass-tag cell barcoding with palladium prior to surface and intracellular antibody staining, followed by computational debarcoding. Downstream analysis was performed with Cytobank and R. The data set included thirty-five AML patient samples and seven healthy controls with greater than 300,000 cells collected over the 10 barcoded conditions (unstimulated and 9 stimulation conditions). Dimensionality reduction with uniform manifold approximation and projection (UMAP) combined with topological clustering (HDBSCAN) enabled initial data analysis and was followed by expert identification of resultant clusters via surface marker expression. Density-based clustering of the common UMAP embedding of all samples identified known subsets of hematopoietic cells (B cells, CD4 (CD25+ and CD25-) and CD8 T cells, double negative (DN) T cells, NK cells (three subsets), erythroblasts, several subsets of myeloid and leukemia cells, and hematopoietic stem cells (HSCs)). Mass-tag cell barcoding provided stable UMAP embeddings for each sample over the 10 stimulation conditions. High dimensional signaling response was calculated per cell and per each major cell subset for the 90 nodes (9 conditions by 10 markers) and hierarchical clustering stratified samples based upon signaling signatures. Signaling responses varied across non-leukemia and leukemia cell populations in AML samples, whereas cellular phenotypes were more well correlated with signaling phenotypes in healthy samples. Heterogeneity in signaling response was driven by variability seen in several "stimulation:response" pairs. The most impactful pairs to clustering of AML blasts were IFNγ:pSTAT1, GM-CSF:pSTAT5, IL-3:pSTAT5, PMA:pS6, and IL-6:pSTAT3. Favorable risk samples (by European LeukemiaNet risk stratification) were found to have significantly larger pSTAT5 increases to IL-3 and GM-CSF than both intermediate and adverse risk subgroups. In CD8 T cells, responsiveness to PMA and IL-10 drove clustering, and, in particular, samples with ELN adverse risk showed reduced PMA:pS6 and PMA:pERK responses. We present a robust evaluation of intracellular signaling responses in the bone marrow cellular environment of AML. These data provide rationale for ongoing investigation aimed at targeting both leukemia and non-leukemia cell signaling pathways in the treatment of AML. Disclosures Ferrell: Incyte: Research Funding; Forma Therapeutics: Research Funding; Agios: Consultancy; Astex Pharmaceuticals: Research Funding.

Whole Genome Bisulfite Sequencing of 63 Primary AML Samples Identifies a Unique DNA Hypermethylation Signature for Mutant IDH1/2 Cases That Is Different from That of TET2 Mutant AML

Mutations in IDH1, IDH2, and TET2 occur in more than 30% of AML patients, and are thought to contribute to leukemogenesis by disrupting TET2-mediated DNA demethylation, either directly or indirectly. While IDH1/2 and TET2 mutations are both predicted to result in hypermethylation, the specific methylation changes associated with these mutations are poorly understood. Additionally, we recently showed that CpG island (CGI) hypermethylation is common across virtually all AML subtypes, and it has been yet unclear whether IDH or TET2 hypermethylation is distinct from these 'generic', AML-associated changes. To investigate genome-wide methylation in IDH and TET2 mutated AML samples, we performed whole genome bisulfite sequencing (WBGS) on 15 primary AML samples with canonical IDH mutations (IDH1R132C, N=6; IDH1R132G, N=1; IDH2R140Q, N=7; and IDH2R172K, N=1) and 9 samples with biallelic loss-of-function mutations in TET2. These data were analyzed with WGBS data from primary CD34+ hematopoietic stem/progenitor cells (HSPCs) from 6 normal adult bone marrow donors, and 39 additional primary AML samples (representing 9 AML mutational categories) to define the methylation phenotypes specifically associated with IDH and/or TET2 mutations. Differential methylation analysis of each AML subtype vs. HSPCs identified a mean of 8,622 (53,73-13,773) AML-associated Differentially Methylated Regions (DMRs) in each mutational category (mean size: 986 bp), of which 6% to 97% were hypermethylated in the AML samples vs. HSPCs. IDH-mutant AML samples were the most hypermethylated subtype, with 97% of the 6,392 identified DMRs having increased methylation compared to HSPCs. The TET2-mutant samples were hypermethylated at fewer loci, representing 71% of the 5,892 identified DMRs vs HSPCs. Analysis of all AML subtypes demonstrated that 55% (16,275/29,534) of identified DMRs were shared across multiple subtypes, with more pronounced overlap among hypermethylated DMRs (>70%). To distinguish mutation-specific changes in IDH and TET2 mutated AMLs from commonly hypermethylated loci across AML subtypes, we conducted a joint analysis of either IDH or TET2 mutant AMLs with data from all other AML samples and normal HSPCs. We performed hierarchical clustering of the methylation values at each DMR, and used the clustering topology to identify DMRs where the single outlier branch represented either the IDH or TET2 AML samples. We identified 2,024 DMRs (mean size: 998 bp) specific to IDH mutant AML samples, of which 100% were hypermethylated with respect to normal HSPCs (mean DMR b value of 0.60 vs 0.35 in HSPCs). IDH outlier DMRs were enriched for functional genomic elements relative to the set of commonly hypermethylated loci, including CGIs (65% of DMRs; 2.4-fold enrichment) and potential enhancers marked by H3K27ac in HSPCs (82% of DMRs; 1.4-fold enrichment); overlap with promoter regions was reduced (20% of DMRs; 0.77-fold decrease). Outlier analysis of TET2 mutated samples demonstrated that most changes were also present in other AML types, with only 93 unique DMRs (mean size 552 bp), and a subtler hypermethylation phenotype (83% of DMRs were hypermethylated, with a mean b value of 0.51 vs. 0.32 in HSPCs). TET2 DMRs were also enriched for overlap with CGIs with respect to background, but to a lesser extent than IDH outliers (52% of DMRs; 1.9-fold). Intersection of IDH and TET2-associated DMRs showed that TET2-specific DMRs represented a subset of IDH hypermethylated regions, with 60% of TET2 DMRs overlapping a DMR independently identified in the IDH samples. Conversely, the vast majority of IDH outliers (97%) were identified only in IDH mutated AMLs. This comprehensive, genome-wide analysis demonstrates that IDH mutations result in a clear hypermethylation phenotype in primary AML samples that is far more expansive than the observed TET2-associated methylation changes. IDH-specific methylation changes were enriched for CGIs located outside of promoters and were distinct from the AML-associated CGI hypermethylation that occurs in other AML subtypes. TET2 mutations were associated with far fewer hypermethylated DMRs, and only a small fraction of mutation-specific changes occurred in bothIDH and TET2 samples. Our results demonstrate that the methylation phenotypes of IDH and TET2 mutant AML samples do not phenocopy, and suggest that IDH-associated hypermethylation must have a TET2-independent component. Disclosures No relevant conflicts of interest to declare.

Long-Lifetime and Low Latency Data Aggregation Scheduling for Wireless Sensor Network

Abstract Wireless Sensor Networks contain an incredible number of hubs with limited registering, detecting, and wireless communication capacities. These systems have been utilized as a part of a wide zone of utilizations, such as human services, contamination checking, and target-following frameworks. The dynamic clustering of sensors into bunches is a prevalent procedure to expand the system lifetime and increment adaptability. To accomplish this, in this article the sensor hubs are adjusted to ensure a long lifetime and the activities are arranged into rounds that use fixed time intervals. In the first phase, a clustering topology is found, and a group head is picked in view of the outstanding energy level. Then the cluster head screens the network energy threshold value to identify the energy drain rate of all its cluster members. In the second stage, the Long-Lifetime and Low-Latency Data Aggregation Scheduling method is used. This scheduling method assigns schedule openings to group part information parcels. Here, congestion occurrence is completely kept away from the scenario. In the third stage, an Energy-Efficient Distributed Schedule–based convention is used to keep up greatest residual energy level over the network. The experimental outcome shows the steps proposed in this article ensure an increase in the network lifetime and decrease in the energy utilization.

A Framework of Topological Interference Management and Clustering for D2D Networks

In this paper, we develop a joint clustering and topological interference management (TIM) framework for a device-to-device (D2D) network. This scheme divides the whole network into multiple groups, each served on a different frequency, and the interference within each group is managed by TIM, based only on the connectivity pattern and not on the instantaneous channel state information (CSI). To this end, we model TIM as a low-rank-matrix-completion problem (LRMC) problem and solve it using a novel and low-complex scheme based on semidefinite programming (SDP). As for the clustering part, we develop a clustering algorithm that is suited for the LRMC approach to solve TIM while building on the SDP relaxation of the maximum- $k$ -cut algorithm, and extending it to account for each cluster’s capacity. This clustering problem turns out to be a capacitated maximum- $k$ -cut problem, for which we derive a relatively tight upper bound, that helps in determining the performance guarantee of many clustering algorithms. Simulation results show that the joint clustering-TIM can help, in some cases, improve the system degrees-of-freedom (DoF), especially in large D2D networks. Our proposed scheme also reduces the computation time of the LRMC-based TIM approach.

The role of inertia on a locally favoured structure in equimolar binary mass Hard Spheres

Molecular dynamics simulations have been carried out to investigate the effect of inertia on both dynamics and locally favoured structures (LFS) of a supercooled liquid toward glass transition. In this work, the equimolar supercooled liquid binary mixtures of Hard Spheres with the same diameter but different masses were studied. The mass ratio of the binary system was 2:1. The simulation was performed and sampled by using the microcanonical ensemble. Both dynamics and structure changing were analysed. A cluster structure analysis was detected by topological cluster classification (TCC) based on the results of local free energy minimisation. The slowing down dynamics were found to be related to cluster structure changing in both the binary mass Hard Sphere (BMHS) and Hard Sphere (HS). The inertia affected an increasing dynamical heterogeneity and had some effects on favouring some local structures before they get crystallisation.

Bladder Cancer Exhibiting High Immune Infiltration Shows the Lowest Response Rate to Immune Checkpoint Inhibitors.

Background: Bladder urothelial cancer (BLCA) treatment using immune checkpoint inhibitors (IMCIs) can result in long-lasting clinical benefits. However, only a fraction of patients respond to such treatment. In this study, we aimed to identify the relationships between immune cell infiltration levels (ICILs) and IMCIs and identify markers for ICILs.Methods: ICILs were estimated based on single-sample gene set enrichment analysis. The response rates of different ICILs to IMCIs were calculated by combining the ICILs of molecular subtypes in BLCA with the response rates of different molecular subtypes of IMvigor 210 trials to a programmed cell death ligand-1 inhibitor. Weighted gene co-expression network analysis was used to identify modules of interest with ICILs. Functional enrichment analysis was performed to functionally annotate the modules. Screening of key genes and unsupervised clustering were used to identify candidate biomarkers. Tumor IMmune Estimation Resource was used to validate the relationships between the biomarkers and ICILs. Finally, we verified the expression of key genes in molecular subtypes of different response rates for IMCIs.Findings: The basal squamous subtype and luminal infiltrated subtype, which showed low response rates for IMCIs, had the highest levels of immune infiltration. The neuronal subtypes, which showed the highest response rates to IMCIs, had low ICILs. The modules of interest and key genes were determined based on topological overlap measurement, clustering results, and inclusion criteria. Modules highly correlated with ICILs were mainly enriched in immune responses and epithelial–mesenchymal transition. After screening the key genes in the modules, five candidate biomarkers (CD48, SEPT1, ACAP1, PPP1R16B, and IL16) were selected by unsupervised clustering. The key genes were inversely associated with tumor purity and were mostly expressed in the basal squamous subtype and luminal infiltrated subtypes.Interpretation: Patients with high ICILs may benefit the least from treatment with IMCIs. Five key genes could predict ICILs in BLCA, and their high expression suggested that the response rate to IMCIs may decrease.

CLINE: a web-tool for the comparison of biological dendrogram structures

BackgroundWhen visually comparing the results of hierarchical clustering, the differences in the arrangements of components are of special interest. However, in a biological setting, identifying such differences becomes less straightforward, as the changes in the dendrogram structure caused by permuting biological replicates, do not necessarily imply a different biological interpretation. Here, we introduce a visualization tool to help identify biologically similar topologies across different clustering results, even in the presence of replicates.ResultsHere we introduce CLINE, an open-access web application that allows users to visualize and compare multiple dendrogram structures, by visually displaying the links between areas of similarity across multiple structures. Through the use of a single page and a simple user interface, the user is able to load and remove structures form the visualization, change some aspects of their display and set the parameters used to match cluster topology across consecutive pairs of dendrograms.ConclusionsWe have implemented a web-tool that allows the users to visualize different dendrogram structures, showing not only the structures themselves, but also linking areas of similarity across multiple structures. The software is freely available at http://mizuguchilab.org/tools/cline/. Also, the source code, documentation and installation instructions are available on GitHub at https://github.com/RodolfoAllendes/cline/.

Topological analysis in Monte Carlo simulation for uncertainty propagation

Abstract. This paper proposes and demonstrates improvements for the Monte Carlo simulation for uncertainty propagation (MCUP) method. MCUP is a type of Bayesian Monte Carlo method aimed at input data uncertainty propagation in implicit 3-D geological modeling. In the Monte Carlo process, a series of statistically plausible models is built from the input dataset of which uncertainty is to be propagated to a final probabilistic geological model or uncertainty index model. Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUP. These differences are interpreted as analogous to population heterogeneity. The source of this heterogeneity is traced to be the non-linear relationship between plausible datasets' variability and plausible model's variability. Non-linearity is shown to mainly arise from the effect of the geometrical rule set on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces topological heterogeneity and challenges the underlying assumption of homogeneity which global uncertainty estimates rely on. To address this issue, a method for topological analysis applied to the plausible model suite in MCUP is introduced. Boolean topological signatures recording lithological unit adjacency are used as n-dimensional points to be considered individually or clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic examples with varying levels of confidence in the structural input data. Results indicate that topological signatures constitute a powerful discriminant to address plausible model heterogeneity. Basic topological signatures appear to be a reliable indicator of the structural behavior of the plausible models and provide useful geological insights. Moreover, ignoring heterogeneity was found to be detrimental to the accuracy and relevance of the probabilistic geological models and uncertainty index models. Highlights. Monte Carlo uncertainty propagation (MCUP) methods often produce topologically distinct plausible models. Plausible models can be differentiated using topological signatures. Topologically similar probabilistic geological models may be obtained through topological signature clustering.