Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

Negative emotions mediate the association between the topology of the complex brain network and smartphone use disorder: A resting-state EEG study.

Background: Increasing research has examined the factors related to smartphone use disorder. However, limited research has explored its neural basis. Aims: We aimed to examine the relationship between the topology of the resting-state electroencephalography (rs-EEG) brain network and smartphone use disorder using minimum spanning tree analysis. Furthermore, we examined how negative emotions mediate this relationship. Methods: This study included 113 young, healthy adults (mean age = 20.87 years, 46.9% males). Results: The results showed that the alpha- and delta-band kappas and delta-band leaf fraction were positively correlated with smartphone use disorder. In contrast, the alpha-band diameter was negatively correlated with smartphone use disorder. Negative emotions fully mediated the relationship between alpha-band kappa and alpha-band diameter and smartphone use disorder. Furthermore, negative emotions partially mediated the relationship between delta-band kappa and smartphone use disorder. The findings suggest that excessive scale-free alpha- and delta-band brain networks contribute to the emergence of smartphone use disorder. In addition, the findings also demonstrate that negative emotions and smartphone use disorder share the same neural basis. Negative emotions play a mediating role in the association between topological deviations and smartphone use disorder. Discussion: To the best of our knowledge, this is the first study to examine the neural basis of smartphone use disorder from the perspective of the topology of the rs-EEG brain network. Therefore, neuromodulation may be a potential intervention for smartphone use disorder.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Fast clustering algorithm based on MST of representative points.

Minimum spanning tree (MST)-based clustering algorithms are widely used to detect clusters with diverse densities and irregular shapes. However, most algorithms require the entire dataset to construct an MST, which leads to significant computational overhead. To alleviate this issue, our proposed algorithm R-MST utilizes representative points instead of all sample points for constructing MST. Additionally, based on the density and nearest neighbor distance, we improved the representative point selection strategy to enhance the uniform distribution of representative points in sparse areas, enabling the algorithm to perform well on datasets with varying densities. Furthermore, traditional methods for eliminating inconsistent edges generally require prior knowledge about the number of clusters, which is not always readily available in practical applications. Therefore, we propose an adaptive method that employs mutual neighbors to identify inconsistent edges and determine the optimal number of clusters automatically. The experimental results indicate that the R-MST algorithm not only improves the efficiency of clustering but also enhances its accuracy.

Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data.

As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.

The dynamic network of industries in US stock market: Evidence of GFC, COVID-19 pandemic and Russia-Ukraine war.

We investigate the topology of sectoral returns in the US stock market using minimum spanning tree (MST) analysis. We examine four distinct time periods: the full period, the Global Financial Crisis (GFC), the COVID-19 pandemic, and the Russia-Ukraine war period. By comparing the static results across these periods, we identify differences in the network structure. Additionally, a rolling window analysis is conducted to explore the time-varying nature of the MST. We employ a TVP-VAR based connectedness framework to ensure a robust analysis of the sectoral return linkages. Our main findings are summarized as follows: First, the structure of the MST varies in different periods, with distinct crisis period structures. During the GFC, the industrial sector dominated clustering, whereas COVID-19 affected the financial, IT, and industrial sectors. The Russia-Ukraine war period showed clustering centered on materials, except in the industrial sector. These varying structures may explain the different characteristics of each crisis. Second, both static and rolling window analyses highlight the significance of the industrial sector in the US stock market. Third, the utilities sector exhibits the lowest centrality measures, indicating its minimal importance and lack of relationships with other industries. These findings provide valuable insights into the interrelationships among industries in the US stock market. Market participants can leverage these findings to enhance their understanding and improve their portfolio management. By utilizing this information, investors can develop optimal diversification strategies to maximize returns and minimize risk.

Data, instance sets, and instances generator for the Hop-Constrained Minimum Spanning Tree problem, the Delay-Constrained Minimum Spanning Tree problem, and their bi-objective variants.

This article proposes a benchmark instance generator for the Hop-Constrained Minimum Spanning Tree problem, the Delay-Constrained Minimum Spanning Tree problem, and their bi-objective variants. The generator is developed in C++ and does not uses external libraries, being understandable, easy-to-read, and easy-to-use. Furthermore, the generator employs five parameters that makes possible to generate personalized benchmark instances for these problems. We also describe 640 benchmark instances that were previously used in computational experiments in the literature. Lastly, we include raw results obtained from computational experiments with the described benchmark instances. We hope that the data introduced in this article can foster the development and the evaluation of new algorithms for solving constrained minimum spanning tree problems.

A Nine-Gene Expression Signature Distinguished a Patient with Chronic Lymphocytic Leukemia Who Underwent Prolonged Periodic Fasting.

Background and Objectives: This study aimed to investigate the causes of continuous deep fluctuations in the absolute lymphocyte count (ALC) in an untreated patient with Chronic Lymphocytic Leukemia (CLL), who has had a favorable prognosis since the time of diagnosis. Up until now, the patient has voluntarily chosen to adopt a predominantly vegetarian and fruitarian diet, along with prolonged periods of total fasting (ranging from 4 to 39 days) each year. Materials and Methods: For this purpose, we decided to analyze the whole transcriptome profiling of peripheral blood (PB) CD19+ cells from the patient (#1) at different time-points vs. the same cells of five other untreated CLL patients who followed a varied diet. Consequently, the CLL patients were categorized as follows: the 1st group comprised patient #1 at 20 different time-points (16 time-points during nutrition and 4 time-points during fasting), whereas the 2nd group included only one time point for each of the patients (#2, #3, #4, #5, and #6) as they followed a varied diet. We performed microarray experiments using a powerful tool, the Affymetrix Human Clariom™ D Pico Assay, to generate high-fidelity biomarker signatures. Statistical analysis was employed to identify differentially expressed genes and to perform sample clustering. Results: The lymphocytosis trend in patient #1 showed recurring fluctuations since the time of diagnosis. Interestingly, we observed that approximately 4-6 weeks after the conclusion of fasting periods, the absolute lymphocyte count was reduced by about half. The gene expression profiling analysis revealed that nine genes were statistically differently expressed between the 1st group and the 2nd group. Specifically, IGLC3, RPS26, CHPT1, and PCDH9 were under expressed in the 1st group compared to the 2nd group of CLL patients. Conversely, IGHV3-43, IGKV3D-20, PLEKHA1, CYBB, and GABRB2 were over-expressed in the 1st group when compared to the 2nd group of CLL patients. Furthermore, clustering analysis validated that all the samples from patient #1 clustered together, showing clear separation from the samples of the other CLL patients. Conclusions: This study unveiled a small gene expression signature consisting of nine genes that distinguished an untreated CLL patient who followed prolonged periods of total fasting, maintaining a gradual growth trend of lymphocytosis, compared to five untreated CLL patients with a varied diet. Future investigations focusing on patient #1 could potentially shed light on the role of prolonged periodic fasting and the implication of this specific gene signature in sustaining the lymphocytosis trend and the favorable course of the disease.

MOMS: A pipeline for scaffolding using multi-optical maps.

Here, we report a new multi-optical maps scaffolder (MOMS) aiming at utilizing complementary information among optical maps labelled by distinct enzymes. This pipeline was designed for data structure organization, scaffolding by path traversal, gap-filling and molecule reuse of optical maps. Our testing showed that this pipeline has uncapped enzyme tolerance in scaffolding. This means that there are no inbuilt limits as to the number of maps generated by different enzymes that can be utilized by MOMS. For the genome assembly of the human GM12878 cell line, MOMS significantly improved the contiguity and completeness with an up to 144-fold increase of scaffold N50 compared with initial assemblies. Benchmarking on the genomes of human and O. sativa showed that MOMS is more effective and robust compared with other optical-map-based scaffolders. We believe this pipeline will contribute to high-fidelity chromosome assembly and chromosome-level evolutionary analysis.

[Effect of electroconvulsive therapy on brain functional network in major depressive disorder].

Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

Multimodal multilayer network centrality relates to executive functioning.

Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.

We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyse the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph. We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the subgraph based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.

RabbitTClust: enabling fast clustering analysis of millions of bacteria genomes with MinHash sketches.

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.0 TB in FASTA format, within only 34 min on a 128-core workstation. Our results further identify 1269 redundant genomes, with identical nucleotide content, in the RefSeq bacterial genomes database.

Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN.

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network's total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF's becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.

Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network.

BACKGROUND: Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD. METHOD: Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively. RESULT: The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R2 value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction). CONCLUSION: We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD.

Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic.

The spatial scan statistics based on the Poisson and binomial models are the most common methods to detect spatial clusters in disease surveillance. These models rely on Monte-Carlo simulation which are time consuming. Moreover, frequently, datasets present over-dispersion which cannot be handled by them. Thus, we have the following goals. First, we propose irregularly shaped spatial scan for the Bell, Poisson, and binomial. The Bell distribution has just one parameter but it is capable of handling over-dispersed datasets. Second, we apply these scan statistics to big maps. A fast version, without Monte-Carlo simulation, for the proposed Poisson and binomial scans is introduced. Intensive simulation studies are carried out to assess the quality of the proposals. In addition, we show the time improvement of the fast scan versions over their traditional ones. Finally, we end the paper with an application on the detection of irregular shape small nodules in a medical image. Supplementary Information: The online version contains supplementary material available at 10.1007/s12561-022-09353-7.

Differential expression and cross-correlation between global regulator and pho regulon genes involved in decision-making under phosphate stress.

The differential gene expression under phosphate stress conditions leads to cross-talk between the global regulator, pho regulon, and metabolic genes. Promoter activity analysis of the selected 23 genes reveals the dynamic nature of real-time gene expression under different phosphate conditions. The expression profiles of the global regulator (rpoD, soxR, soxS, arcB, and fur), pho regulon (phoH, phoR, phoB, and ugpB), and metabolic genes (sdh, pfkA, ldh) varied significantly on phosphate level variation. Under stress conditions, soxR switches expression partners and co-expresses with rpoS instead of soxS. The partner-switching behavior of the genes under a challenging environment represents the intelligence of functional execution and ensures cell survival. The dynamic expression profile of the selected genes applies a time-lagged correlation to provide insight into the differential gene interaction between time-shifted expression profiles. Under different phosphate conditions, the minimum spanning tree graph revealed a different clustering pattern of selected genes depending on the computed distance and its proximity to other promoters.

Effect of electroconvulsive therapy on brain functional network in major depressive disorder / 生物医学工程学杂志

Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

Graph Analysis of Functional Brain Topology Using Minimum Spanning Tree in Subjective Cognitive Decline.

BACKGROUND: Subjects with subjective cognitive decline (SCD) are proposed as a potential population to screen for Alzheimer's disease (AD). OBJECTIVE: Investigating brain topologies would help to mine the neuromechanisms of SCD and provide new insights into the pathogenesis of AD. METHODS: Objectively cognitively unimpaired subjects from communities who underwent resting-state BOLD-fMRI and clinical assessments were included. The subjects were categorized into SCD and normal control (NC) groups according to whether they exhibited self-perceived cognitive decline and were worried about it. The minimum spanning tree (MST) of the functional brain network was calculated for each subject, based on which the efficiency and centrality of the brain network organization were explored. Hippocampal/parahippocampal volumes were also detected to reveal whether the early neurodegeneration of AD could be seen in SCD. RESULTS: A total of 49 subjects in NC and 95 subjects in SCD group were included in this study. We found the efficiency and centrality of brain network organization, as well as the hippocampal/parahippocampal volume were preserved in SCD. Besides, SCD exhibited normal cognitions, including memory, language, and execution, but increased depressive and anxious levels. Interestingly, language and execution, instead of memory, showed a significant positive correlation with the maximum betweenness centrality of the functional brain organization and hippocampal/parahippocampal volume. Neither depressive nor anxious scales exhibited correlations with the brain functional topologies or hippocampal/parahippocampal volume. CONCLUSION: SCD exhibited preserved efficiency and centrality of brain organization. In clinical practice, language and execution as well as depression and anxiety should be paid attention in SCD.

GUBS: Graph-Based Unsupervised Brain Segmentation in MRI Images.

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.