Network Reconstruction Research Articles

Prioritization of potential pharmacological targets for the development of anti-hepatocarcinoma drugs modulating the extrinsic apoptosis pathway: the reconstruction and analysis of associative gene networks help

Prioritization of potential pharmacological targets for the development of anti-hepatocarcinoma drugs modulating the extrinsic apoptosis pathway: the reconstruction and analysis of associative gene networks help

Gene networks for use in metabolomic data analysis of blood plasma from patients with postoperative delirium

Postoperative delirium (POD) is considered one of the most severe complications, resulting in impaired cognitive function, extended hospitalization, and higher treatment costs. The challenge of early POD diagnosis becomes particularly significant in cardiac surgery cases, as the incidence of this complication exceeds 50 % in certain patient categories. While it is known that neuroinflammation, neurotransmitter imbalances, disruptions in neuroendocrine regulation, and interneuronal connections contribute significantly to the development of POD, the molecular, genetic mechanisms of POD in cardiac surgery patients, along with potential metabolomic diagnostic markers, remain in adequately understood. In this study, blood plasma was collected from a group of patients over 65 years old after cardiac surgery involving artificial circulation. The collected samples were analyzed for sphingomyelin content and quantity using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS) me thods. The analysis revealed four significantly different sphingomyelin contents in patients with POD compared to those who did not develop POD (control group). Employing gene network reconstruction, we perceived a set of 82 regulatory enzymes affiliated with the genetic coordination of the sphingolipid metabolism pathway. Within this set, 47 are assumed to be regulators of gene expression, governing the transcription of enzymes pivotal to the metabolic cascade. Complementing this, an additional assembly of 35 regulators are considered to be regulators of activity, degradation, and translocation dynamics of enzymes integral to the aforementioned pathway. Analysis of the overrepresentation of diseases with which these regulatory proteins are associated showed that the regulators can be categorized into two groups, associated with cardiovascular pathologies (CVP) and neuropsychiatric diseases (NPD), respectively. The regulators associated with CVP are expectedly related to the effects on myocardial tissue during surgery. It is hypothesized that dysfunction of NPD-associated regulators may specifically account for the development of POD after cardiac surgery. Thus, the identified regulatory genes may provide a basis for planning further experiments, in order to study disorders at the level of expression of these genes, as well as impaired function of proteins encoded by them in patients with POD. The identified significant sphingolipids can be considered as potential markers of POD.

Robust and efficient network reconstruction in complex system via adaptive signal lasso

Robust and efficient network reconstruction in complex system via adaptive signal lasso

Hypergraph reconstruction from uncertain pairwise observations

The network reconstruction task aims to estimate a complex system’s structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities—the pairwise case. Here, using Bayesian inference, we investigate the general problem of reconstructing a network in which higher-order interactions are also present. We study a minimal example of this problem, focusing on the case of hypergraphs with interactions between pairs and triplets of vertices, measured imperfectly and indirectly. We derive a Metropolis-Hastings-within-Gibbs algorithm for this model to highlight the unique challenges that come with estimating higher-order models. We show that this approach tends to reconstruct empirical and synthetic networks more accurately than an equivalent graph model without higher-order interactions.

Reconstruction and Layer Division of Unknown Multilayer Networks

Reconstruction and Layer Division of Unknown Multilayer Networks

Parietal‐temporal structural network reorganization is associated with pathological load and axonal damage in Alzheimer’s disease: a post‐mortem MRI and pathology study.

AbstractBackgroundThe characteristic spatial pattern of Aβ and p‐tau distribution suggests that pathology spreads from brain regions exhibiting pathology to interconnected regions through large‐scale axonal networks. Advances in MRI and graph theory have made it possible to study brain network organization. The current study aims to assess whether local proteinopathy and/or axonal degeneration contributes to regionally altered network measures of integration and segregation in clinically‐defined and pathologically‐confirmed Alzheimer’s disease (AD) and control brain donors.MethodPostmortem In‐situ 3T‐MRI (3DT1 and DTI) data were collected for 25 AD (6F/19M, 66y±11y) and 14 control (7F/7M, 72y±8y) brain donors. Following probabilistic tractography and structural network reconstruction, graph theoretical measures of integration (eigenvector centrality) and segregation (clustering coefficient) were calculated. After autopsy, the middle frontal gyrus (MFG), middle temporal gyrus (MTG), precuneus and middle hippocampus were immunostained for amyloid‐beta (Aβ), phosphorylated tau (p‐tau) and neurofilament light (NfL), and quantitative area% load was determined. Clinical Dementia Rating (CDR) scores were obtained from clinical records. Group differences were assessed with linear models and regional associations with Spearman and Pearson correlations, corrected for age, gender, and postmortem delay.ResultIn AD, no differences were observed in Aβ, p‐tau and NfL load among the selected neocortical regions, the hippocampus had lower Aβ and NfL load. Compared to controls, the AD group showed lower integration (p = 0.078) of the MTG, associated with higher CDR scores (r = ‐0.50;p = 0.035), more p‐tau pathology (r = ‐0.61;p = 0.002) and axonal damage (r = ‐0.64;p = 0.001). Furthermore, higher segregation (p = 0.006) of the MTG was observed and associated with higher p‐tau burden (r = 0.51;p = 0.015). In contrast, the precuneus showed higher integration (p = 0.038) and lower segregation (p = 0.023) in AD compared to controls, associated with less axonal damage (r = ‐0.44;p = 0.036) and less p‐tau pathology (r = 0.46;r = 0.033) respectively. No effects were found for the MFG and hippocampus.ConclusionIn AD decedents, a reorganization of the structural network is observed, predominantly in the parietal‐temporal region; while the temporal region is more disconnected, the parietal region is more strongly connected. Our work furthermore shows that this network (dis)integration is driven by p‐tau pathology and axonal damage, and associates with cognitive outcome.

A systematic approach to pixel-level crack detection and localization with a feature fusion attention network and 3D reconstruction

Automated detection and localization of surface cracks by using deep learning and computer vision (CV) techniques are conducive to efficient structural condition assessment. However, insufficient feature resolution in deep architecture networks leads to misclassification and inaccurate segmentation of cracks. Furthermore, accurately localizing cracks within the overall structure is crucial for understanding the extent of damage to structural safety and durability. To address these challenges, this paper proposes a systematic approach for pixel-level surface crack detection and localization. The approach is based on a novel Feature Pyramid and Attention Feature Fusion Network (FPAFFN) and 3D reconstruction techniques. Two attention mechanisms named Attention Refinement Module (ARM) and Feature Fusion Module (FFM) are embedded in FPAFFN to strengthen the extraction and fusion of semantic and detailed information. With the cracks annotated images produced by FPAFFN, COLMAP and an open Multi-View Stereo are adapted to reconstruct the 3D model of the cracked structural components. Experimental studies on benchmark datasets CRACK500 and Cracktree200 and manually captured images validate the effectiveness of FPAFFN. The ARM and FFM embedded in FPAFFN enhance the feature learning efficiency and lead to a 3 % rise in the mean intersection ratio (mIoU). FPAFFN demonstrates remarkable generalization capability in detecting images with disturbances and outperforming other well-known crack detection methods in delineating crack edges and details. A subsequent experimental study on a damaged concrete slab shows that the proposed approach is capable of systematically detecting and localizing cracks. Therefore, it is promising to apply the proposed approach to practical structural inspection.

A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing.

Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Bioinformatics analysis of primary biliary cholangitis key genes and molecular mechanisms

Objective: To extract the differentially expressed key genes of primary biliary cholangitis (PBC) using bioinformatics methods, so as to provide information for further study into the mechanism. Methods: The GSE119600 dataset was downloaded from the GEO database to obtain differentially expressed genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for differentially expressed genes. Protein-protein interaction (PPI) network reconstruction, Cytoscape software visualization, and core gene screening were performed. The area under the receiver operating characteristic curve (ROC AUC) was used to assess the diagnostic effectiveness of genes and plot the pROC software package. The x-Cell software was used to calculate the enrichment score of 34 immune cells in each sample. Finally, four key genes (PSMA4, PSMA1, PSMB1, and PSMA3) were selected. Blood samples were analyzed using the qPCR method. Results:: A total of 373 immune-related differentially expressed genes were identified. Eight genes (PSMC6, PSMB2, PSMB1, PSMA3, PSMA4, PSMA1, PSMD7, and PSMB5) were screened from the 178 nodes and 596 edges as hub genes of the PPI network, which were significantly related to amino acid metabolism, hematopoietic stem cell differentiation, cell cycle, and immune processes. PSMA4, PSMA1, PSMB1, and PSMA3 were defined as immunological biomarkers for PBC with an AUC value of the ROC curve > 0.7. Immunoinfiltrating cell analysis showed that the proportion of eosinophils was significantly higher in PBC patients compared to the control group, whereas the proportion of CD4+ memory T cells, plasma cells, Th2 cells, and cDC cells was significantly lower in PBC patients than the control group. Plasma cells were associated with all four immunological biomarkers. Seven PBC patients and seven healthy subjects were selected for peripheral blood qPCR validation, which demonstrates that PSMB1, PSMA3, PSMA1, and PSMA4 levels were significantly lower in PBC patients than healthy subjects, with a statistically significant difference. Conclusion:: Bioinformatics screened eight key genes, of which four were key immunological markers and may serve as a basis for clinical diagnosis and mechanism exploration.

Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan.

Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the Acidithiobacillus genus, along with the process of energy production, remain areas that necessitate further research and elucidation. In this study, a novel strain of sulfur-oxidizing bacterium, Acidithiobacillus Ameehan, was isolated. Several physiological characteristics of the strain Ameehan were verified and its complete genome sequence was presented in the study. Besides, the first genome-scale metabolic network model (AMEE_WP1377) was reconstructed for Acidithiobacillus Ameehan to gain a comprehensive understanding of the metabolic capacity of the strain.The characteristics of Acidithiobacillus Ameehan included morphological size and an optimal growth temperature range of 37-45°C, as well as an optimal growth pH range of pH 2.0-8.0. The microbe was found to be capable of growth when sulfur and K2O6S4 were supplied as the energy source and electron donor for CO2 fixation. Conversely, it could not utilize Na2S2O3, FeS2, and FeSO4·7H2O as the energy source or electron donor for CO2 fixation, nor could it grow using glucose or yeast extract as a carbon source. Genome annotation revealed that the strain Ameehan possessed a series of sulfur oxidizing genes that enabled it to oxidize elemental sulfur or various reduced inorganic sulfur compounds (RISCs). In addition, the bacterium also possessed carbon fixing genes involved in the incomplete Calvin-Benson-Bassham (CBB) cycle. However, the bacterium lacked the ability to oxidize iron and fix nitrogen. By implementing a constraint-based flux analysis to predict cellular growth in the presence of 71 carbon sources, 88.7% agreement with experimental Biolog data was observed. Five sulfur oxidation pathways were discovered through model simulations. The optimal sulfur oxidation pathway had the highest ATP production rate of 14.81 mmol/gDW/h, NADH/NADPH production rate of 5.76 mmol/gDW/h, consumed 1.575 mmol/gDW/h of CO2, and 1.5 mmol/gDW/h of sulfur. Our findings provide a comprehensive outlook on the most effective cellular metabolic pathways implicated in sulfur oxidation within Acidithiobacillus Ameehan. It suggests that the OMP (outer-membrane proteins) and SQR enzymes (sulfide: quinone oxidoreductase) have a significant impact on the energy production efficiency of sulfur oxidation, which could have potential biotechnological applications.

Urban rainstorm and waterlogging scenario simulation based on SWMM under changing environment.

Urban rainstorm and waterlogging occurred more frequently in recent years, causing huge economic losses and serious social harms. Accurate rainstorm and waterlogging simulation is of significant value for disaster prevention and mitigation. This paper proposed a numerical model for urban rainstorm and waterlogging based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS), and the model was applied in Lianhu district of Xi'an city of China. Furthermore, the effects of rainfall characteristics, pipe network implementation level and urbanization level on waterlogging were explored from the perspectives of spatial distribution of waterlogging points, drainage capacity of pipe network and surface runoff generation and confluence. The results show that: (1) with the increase of rainfall recurrence period, the peak of total water accumulating volume, the average decline rate of water accumulating volume and the number of waterlogging nodes increase; (2) optimizing the pipe diameter can shorten the average overload time of the pipe network from the entire pipe network, but for a single pipe, optimizing the pipe diameter may lead to overloading of unoptimized downstream pipeline; (3) the lower the imperviousness, the less the number of waterlogging nodes and average time of water accumulating, and (4) the west, northwest and southwest areas are relatively affected by the imperviousness, only improving the underlying surface conditions has limited influence on waterlogging in the study area. This study can provide reference for urban waterlogging prevention and reduction and pipe network reconstruction.

Functional Hydrogel Co-Remolding Migration and Differentiation Microenvironment for Severe Spinal Cord Injury Repair.

Spinal cord injury (SCI) activates nestin+ neural stem cells (NSCs), which can be regarded as potential seed cells for neuronal regeneration. However, the lesion microenvironment seriously hinders the migration of the nestin+ cells to the lesion epicenter and their differentiation into neurons to rebuild neural circuits. In this study, a photosensitive hydrogel scaffold is prepared as drug delivery carrier. Genetically engineered SDF1α and NT3 are designed and the scaffold is binary modified to reshape the lesion microenvironment. The binary modified scaffold can effectively induce the migration and neuronal differentiation of nestin+ NSCs in vitro. When implanted into a rat complete SCI model, many of the SCI-activated nestin+ cells migrate into the lesion site and give rise to neurons in short-term. Meanwhile, long-term repair results also show that implantation of the binary modified scaffold can effectively promote the maturation, functionalization and synaptic network reconstruction of neurons in the lesion site. In addition, animals treated with binary scaffold also showed better improvement in motor functions. The therapeutic strategy based on remolding the migration and neuronal differentiation lesion microenvironment provides a new insight into SCI repair by targeting activated nestin+ cells, which exhibits excellent clinical transformation prospects.

Incremental learning-based optimal design of BFN kernel for online spacecraft disturbance rejection control

Low earth orbit (LEO) space environment is glutted with unknown and uncertain disturbance, which impede the regular operation and dynamic stability of spacecraft. In recent decades, Basis Function Network (BFN) has attracted much attention benefiting from its potential adaptability for unknown dynamic disturbance. However, it also suffers from the shortcomings of parameter drift, under fitting and long time consuming, which restricts its applications in practical space missions. This paper proposes a new online network reconstruction algorithm, which enables BFN a better excitation effect and more rapid response with less time consumption. The algorithm selects less but more relevant basis functions than routine neural network to approximate the disturbance, which consequently helps to achieve a better characterization effect. Two BFN selection strategies based on iterative learning and batch learning are proposed to further increase the efficiency of online network reconstruction. Detailed theoretical derivation proves the feasibility of the new algorithm. Numerical simulation of a real space station shows that the algorithm has strong adaptability and time efficiency under the interference of structural uncertainty.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Three-dimensional reconstruction of cotton plant with internal canopy occluded structure recovery

The inner leaves of crop canopies are obscured by outer branches and leaves, leading to information loss and observation difficulty of the occluded canopy structure using modern crop monitoring techniques. It has restricted the development of phenotypic analysis and precision agriculture. In this paper, we propose a neural network approach to reconstruct the occluded structure of crop canopies with an RGB-D sensor. Taking the cotton plant as the object of study, we propose a novel Cascade Leaf Segmentation and Completion Network (CLSCN) to reconstruct the occluded leaf images and propose a Fragmental Leaf Point–cloud Reconstruction Algorithm (FLPRA) to complete the missing point clouds. By combining the Instance Segmentation Network (ISN), Generative Adversarial Network (GAN) and Point-cloud Reconstruction Algorithm (PRA), the three-dimensional models of cotton plants with both completed internal and external structures of the canopy are smoothly reconstructed. Firstly, we collect a large number of leaf images and point clouds of cotton plants using an RGB-D sensor with the top view and construct a manually labeled cotton leaf dataset for training and evaluation. Secondly, a network named CLSCN is cascading constructed with an Instance Segmentation Network (ISN) and a Generative Adversarial Network (GAN), and the two parts of CLSCN are separately trained with our constructed dataset to output complete cotton leaves. Thirdly, with the fusion of the completed RGB images output by cascaded network segmentation and the point clouds captured by RGB-D sensor, the proposed FLPRA is used to filter, reconstruct, fuse and register the cotton canopy leaf point clouds, and to obtain the whole cotton canopy point-clouds with inner occluded structure recovery. Finally, the CLSCN and FLPRA are validated using the validation dataset of cotton leaf. The test results indicate that the front-end ISN of the proposed CLSCN can generate high-quality cotton leaf masks, with FID scores less than 35 and mIoU up to 84.65%. Additionally, the back-end GAN of CLSCN can complete the occluded leaves with an accuracy of over 94%. The reconstruction accuracy of the final three-dimensional model of the cotton canopy is as high as 82.70%. Therefore, the proposed neural network and algorithm effectively solve the problem of incomplete canopy point cloud caused by the occlusion of outer leaves and provide an effective way to recover the complete three-dimensional structure of crop canopy with internal occlusion. It is a meaningful theoretical and technical support to realize real-time crop status observation and precise field management in agriculture production.

FEATURES OF INCOME FORMATION OF TELECOMMUNICATIONS MARKET ORGANIZATIONS

The theoretical and methodological research presented in the scientific paper aims to identify the potential growth of the revenue level of participants in the telecommunications market, and the identification of potential opportunities and threats to ensure their economic security in order to define effective directions for the communications industry development in the long run.As a result of the research, based on the identification of potential business opportunities, given the availability of potential and competence resources, it has been established that small participants in the communication market (operators) are not able to provide the appropriate level and continuity of service, therefore a significant part of customers switches to using the services of large market participants (providers), where the largest percentage of service provision is for wired Internet services.The research was conducted on the materials of national telecommunications enterprises’ activities results. The methodical basis of the research was general and special methods, namely: system analysis - for comprehensive consideration of strategic landmarks of telecommunications enterprises’ activity; generalization and scientific abstraction - to study the level of readiness of enterprises for changes in management in the direction of innovative development; statistical analysis (method of standard deviation), synthesis and comparison - for the study of the domestic telecommunications market, in diagnosing the current state and trends of innovative development of domestic telecommunications enterprises, identifying priority development directions of innovative decisions in the direction of a revenue level formation of participants in the telecommunications market. The main aspect of the results approved that an important and global task today for the telecommunication market development remains the implementation of start-ups for the reorganization, reconstruction, optimization and improvement of telecommunication networks, as a result of which there should be an innovative transition to the use of modern switching equipment in the direction of ensuring the high level of service for several numbering zones at once and maintaining accessibility to a wide range of services, which will make it possible to achieve a high level of profitability in the near future.

Writers and readers of H3K9me2 form distinct protein networks during the cell cycle that include candidates for H3K9 mimicry.

Histone H3 lysine 9 methylation (H3K9me), which is written by the Euchromatic Histone Lysine Methyltransferases EHMT1 and EHMT2 and read by the heterochromatin protein 1 (HP1) chromobox (CBX) protein family, is dysregulated in many types of cancers. Approaches to inhibit regulators of this pathway are currently being evaluated for therapeutic purposes. Thus, knowledge of the complexes supporting the function of these writers and readers during the process of cell proliferation is critical for our understanding of their role in carcinogenesis. Here, we immunopurified each of these proteins and used mass spectrometry to define their associated non-histone proteins, individually and at two different phases of the cell cycle, namely G1/S and G2/M. Our findings identify novel binding proteins for these writers and readers, as well as corroborate known interactors, to show the formation of distinct protein complex networks in a cell cycle phase-specific manner. Furthermore, there is an organizational switch between cell cycle phases for interactions among specific writer-reader pairs. Through a multi-tiered bioinformatics-based approach, we reveal that many interacting proteins exhibit histone mimicry, based on an H3K9-like linear motif. Gene ontology analyses, pathway enrichment, and network reconstruction inferred that these comprehensive EHMT and CBX-associated interacting protein networks participate in various functions, including transcription, DNA repair, splicing, and membrane disassembly. Combined, our data reveals novel complexes that provide insight into key functions of cell cycle-associated epigenomic processes that are highly relevant for better understanding these chromatin-modifying proteins during cell cycle and carcinogenesis.

Neural network reconstruction of cosmology using the Pantheon compilation

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in artificial neural networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.

A novel multi-omics data analysis of dose-dependent and temporal changes in regulatory pathways due to chemical perturbation: a case study on caffeine

Comprehensive analysis of multi-omics data can reveal alterations in regulatory pathways induced by cellular exposure to chemicals by characterizing biological processes at the molecular level. Data-driven omics analysis, conducted in a dose-dependent or dynamic manner, can facilitate comprehending toxicity mechanisms. This study introduces a novel multi-omics data analysis designed to concurrently examine dose-dependent and temporal patterns of cellular responses to chemical perturbations. This analysis, encompassing preliminary exploration, pattern deconstruction, and network reconstruction of multi-omics data, provides a comprehensive perspective on the dynamic behaviors of cells exposed to varying levels of chemical stimuli. Importantly, this analysis is adaptable to any number of omics layers, including site-specific phosphoproteomics. We implemented this analysis on multi-omics data obtained from HepG2 cells exposed to a range of caffeine doses over varying durations and identified six response patterns, along with their associated biomolecules and pathways. Our study demonstrates the effectiveness of the proposed multi-omics data analysis in capturing multidimensional patterns of cellular response to chemical perturbation, enhancing understanding of pathway regulation for chemical risk assessment.