Similar Network Research Articles

Comparative analysis of classic network vs. nanogel junction network in konjac glucomannan/kappa carrageenan hybrid hydrogels

The three-dimensional network architecture of hydrogels significantly influences their mechanical and physical properties; therefore, understanding them is essential for designing optimized hydrogel-based biomaterials. This study presents a comparative analysis of two hybrid hydrogels composed of konjac glucomannan (KGM) and kappa carrageenan (KCAR) with the same stiffness (5.2–5.7 kPa and 1.6–1.7 kPa) thus similar cross-linking density but different network architectures: a classic network formed by extended polysaccharide interactions and a nanogel junction network where nanoscale cross-linked KCAR (KCAR-NGs) links KGM chains. The mechanical behavior, dissolution, and diffusion characteristics were examined, revealing that the classic network demonstrates superior tensile resistance, elongation, and solvent-induced swelling resistance, leading to slower dissolution rates and higher viscosity. Conversely, the nanogel junction network offers higher permeability for small molecules and faster dissolution, suggesting a more open network structure. These findings highlight the nanogel-based hydrogels' advantages for biomedical applications requiring stability, permeability, and rapid dissolution without high temperatures or chelating agents. This study underscores the potential of nanogel junction networks to balance hydrogel stiffness and permeability, advancing the design of hydrogel-based biomaterials.

Critical assessment of the validity of quasi-static pore network modeling in the application of underground hydrogen storage

This study explores the suitability of quasi-static pore-network modeling for simulating the transport of hydrogen in networks with box-shaped pores and square cylinder throats. The dynamic pore-network modeling results are compared with quasi-static pore-network modeling, and a good agreement is observed when the simulations reach steady-state, for a capillary number of Nc≤10−7. This finding suggests that the quasi-static approach can be used as a reliable and efficient method for studying hydrogen transport in similar networks.

Genome Mining Leads to the Identification of a Stable and Promiscuous Baeyer-Villiger Monooxygenase from a Thermophilic Microorganism.

Baeyer-Villiger monooxygenases (BVMOs) are NAD(P)H-dependent flavoproteins that convert ketones to esters and lactones. While these enzymes offer an appealing alternative to traditional Baeyer-Villiger oxidations, these proteins tend to be either too unstable or exhibit too narrow of a substrate scope for implementation as industrial biocatalysts. Here, sequence similarity networks were used to search for novel BVMOs that are both stable and promiscuous. Our genome mining led to the identification of an enzyme from Chloroflexota bacterium (strain G233) dubbed ssnBVMO that exhibits i) the highest melting temperature of any naturally sourced BVMO (62.5 °C), ii) a remarkable kinetic stability across a wide range of conditions, similar to those of PAMO and PockeMO, iii) optimal catalysis at 50 °C, and iv) a broad substrate scope that includes linear aliphatic, aromatic, and sterically bulky ketones. Subsequent quantitative assays using propiophenone demonstrated >95 % conversion. Several fusions were also constructed that linked ssnBVMO to a thermostable phosphite dehydrogenase. These fusions can recycle NADPH and catalyze oxidations with sub-stoichiometric quantities of this expensive cofactor. Characterization of these fusions permitted identification of PTDH-L1-ssnBVMO as the most promising protein that could have utility as a seed sequence for enzyme engineering campaigns aiming to develop biocatalysts for Baeyer-Villiger oxidations.

Volume-based structural connectome of epilepsy partialis continua in Rasmussen's encephalitis.

Rasmussen's encephalitis is a rare, progressive neurological inflammatory with hemispheric brain atrophy. Epilepsy partialis continua (EPC) is a diagnostic clinical condition in patients with Rasmussen's encephalitis. However, the incidence of EPC in the natural course of Rasmussen's encephalitis is only about 50%. The majority of experts hold the belief that EPC is associated with dysfunction in the motor cortex, yet the whole pathogenesis remains unclear. We hypothesize that there is a characteristic topological discrepancy between groups with EPC and without EPC from the perspective of structural connectome. To this end, we described the structural MRI findings of 20 Rasmussen's encephalitis cases, 11 of which had EPC, and 9 of which did not have EPC (NEPC), and 20 healthy controls. We performed voxel-based morphometry to evaluate the alterations of grey matter volume. Using a volume-based structural covariant network, the hub distribution and modularity were studied at the group level. Based on the radiomic features, an individual radiomics structural similarity network was constructed for global topological properties, such as small-world index, higher path length, and clustering coefficient. And then, the Pearson correlation was used to delineate the association between duration and topology properties. In the both EPC and NEPC groups, the volume of the motor cortex on the affected side was significantly decreased, but putamen atrophy was most pronounced in the EPC group. Hubs in the EPC group consisted of the executive network, and the contralateral putamen was the hub in the NEPC group with the highest betweenness centrality. Compared to the NEPC, the EPC showed a higher path length and clustering coefficient in the structural similarity network. Moreover, the function of morphological network integration in EPC patients was diminished as the duration of Rasmussen's encephalitis increased. Our study indicates that motor cortex atrophy may not be directly related to EPC patients. Whereas atrophy of the putamen, and a more regularized configuration may contribute to the generation of EPC. The findings further suggest that the putamen could potentially serve as a viable target for controlling EPC in patients with Rasmussen's encephalitis.

Activating mutations remodel the chromatin accessibility landscape to drive distinct regulatory networks in KMT2A-rearranged acute leukemia.

Activating FLT3 and RAS mutations commonly occur in leukemia with KMT2A-gene rearrangements (KMT2A-r). However, how these mutations cooperate with the KMT2A-r to remodel the epigenetic landscape is unknown. Using a retroviral acute myeloid leukemia (AML) mouse model driven by KMT2A::MLLT3, we show that FLT3 ITD , FLT3 N676K , and NRAS G12D remodeled the chromatin accessibility landscape and associated transcriptional networks. Although the activating mutations shared a common core of chromatin changes, each mutation exhibits unique profiles with most opened peaks associating with enhancers in intronic or intergenic regions. Specifically, FLT3 N676K and NRAS G12D rewired similar chromatin and transcriptional networks, distinct from those mediated by FLT3 ITD . Motif analysis uncovered a role for the AP-1 family of transcription factors in KMT2A::MLLT3 leukemia with FLT3 N676K and NRAS G12D , whereas Runx1 and Stat5a/Stat5b were active in the presence of FLT3 ITD . Furthermore, transcriptional programs linked to immune cell regulation were activated in KMT2A-r AML expressing NRAS G12D or FLT3 N676K , and the expression of NKG2D-ligands on KMT2A-r cells rendered them sensitive to CAR T cell-mediated killing. Human KMT2A-r AML cells could be pharmacologically sensitized to NKG2D-CAR T cells by treatment with the histone deacetylase inhibitor LBH589 (panobinostat) which caused upregulation of NKG2D-ligand levels. Co-treatment with LBH589 and NKG2D-CAR T cells enabled robust AML cell killing, and the strongest effect was observed for cells expressing NRAS G12D . Finally, the results were validated and extended to acute leukemia in infancy. Combined, activating mutations induced mutation-specific changes in the epigenetic landscape, leading to changes in transcriptional programs orchestrated by specific transcription factor networks.

MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.

PIWI-interacting RNAs (piRNAs) are a typical class of small non-coding RNAs, which are essential for gene regulation, genome stability and so on. Accumulating studies have revealed that piRNAs have significant potential as biomarkers and therapeutic targets for a variety of diseases. However current computational methods face the challenge in effectively capturing piRNA-disease associations (PDAs) from limited data. In this study, we propose a novel method, MRDPDA, for predicting PDAs based on limited data from multiple sources. Specifically, MRDPDA integrates a deep factorization machine (deepFM) model with regularizations derived from multiple yet limited datasets, utilizing separate Laplacians instead of a simple average similarity network. Moreover, a unified objective function to combine embedding loss about similarities is proposed to ensure that the embedding is suitable for the prediction task. In addition, a balanced benchmark dataset based on piRPheno is constructed and a deep autoencoder is applied for creating reliable negative set from the unlabeled dataset. Compared with three latest methods, MRDPDA achieves the best performance on the pirpheno dataset in terms of the five-fold cross validation test and independent test set, and case studies further demonstrate the effectiveness of MRDPDA.

NEW AML TOOLS: ANALYZING ETHEREUM CRYPTOCURRENCY TRANSACTIONS USING A BAYESIAN CLASSIFIER

The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a «critical» risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.

Western Diet-Induced Nonalcoholic Fatty Liver Disease Mice Mimic the Key Transcriptomic Signatures Observed in Humans

Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease characterized by the accumulation of fat in the liver in the absence of excessive alcohol consumption or a secondary cause of hepatic steatosis. The prevalence of NAFLD is increasing worldwide and its management has become a public health concern. Animal models are traditionally used to elucidate disease mechanisms and identify potential drug targets; however, their translational aspects in human diseases have not been fully established. This study aimed to clarify the utility of animal models for translational research by assessing their relevance to human diseases using gene expression analysis. Weighted gene co-expression network analysis of liver tissues from Western diet (WD)-induced NAFLD mice was performed to identify the modules associated with disease progression. Moreover, the similarity of the gene co-expression network across species was evaluated using module preservation analysis. Nineteen disease-associated modules were identified. The brown module was positively associated with disease severity, and functional analyses indicated that it may be involved in inflammatory responses in immune cells. Moreover, the gene co-expression network of the brown module was highly preserved in human NAFLD liver gene expression datasets. These results indicate that WD-induced NAFLD mice have similar gene co-expression networks (especially genes associated with inflammatory responses) to humans and are thought to be a useful experimental tool for preclinical research on NAFLD.

Shared and distinct cortical morphometric alterations in five neuropsychiatric symptoms of Parkinson’s disease

Neuropsychiatric symptoms (including anxiety, depression, apathy, impulse–compulsive behaviors and hallucinations) are among the most common non-motor features of Parkinson’s disease. Whether these symptoms should be considered as a direct consequence of the pathophysiologic mechanisms of Parkinson’s disease is controversial. Morphometric similarity network analysis and epicenter mapping approach were performed on T1-weighted images of 505 patients with Parkinson’s disease and 167 age- and sex-matched healthy participants from Parkinson’s Progression Markers Initiative database to reveal the commonalities and specificities of distinct neuropsychiatric symptoms. Abnormal cortical co-alteration pattern in patients with neuropsychiatric symptoms was in somatomotor, vision and frontoparietal regions, with epicenters in somatomotor regions. Apathy, impulse–compulsive behaviors and hallucinations shares structural abnormalities in somatomotor and vision regions, with epicenters in somatomotor regions. In contrast, the cortical abnormalities and epicenters of anxiety and depression were prominent in the default mode network regions. By embedding each symptom within their co-alteration space, we observed a cluster composed of apathy, impulse–compulsive behaviors and hallucinations, while anxiety and depression remained separate. Our findings indicate different structural mechanisms underlie the occurrence and progression of different neuropsychiatric symptoms. Based upon these results, we propose that apathy, impulse-compulsive behaviors and hallucinations are directly related to damage of motor circuit, while anxiety and depression may be the combination effects of primary pathophysiology of Parkinson’s disease and psychosocial causes.

Online stimulation of the prefrontal cortex during practice increases motor variability and modulates later cognitive transfer: a randomized, double-blinded and sham-controlled tDCS study

The benefits of learning a motor skill extend to improved task-specific cognitive abilities. The mechanistic underpinnings of this motor-cognition relationship potentially rely on overlapping neural resources involved in both processes, an assumption lacking causal evidence. We hypothesize that interfering with prefrontal networks would inhibit concurrent motor skill performance, long-term learning and associated cognitive functions dependent on similar networks (transfer). We conducted a randomised, double-blinded, sham-controlled brain stimulation study using transcranial direct current stimulation (tDCS) in young adults spanning over three weeks to assess the role of the prefrontal regions in learning a complex balance task and long-term cognitive performance. Balance training combined with active tDCS led to higher performance variability in the trained task as compared to the sham group, impacting the process of learning a complex task without affecting the learning rate. Furthermore, active tDCS also positively influenced performance in untrained motor and cognitive tasks. The findings of this study help ascertaining the networks directly involved in learning a complex motor task and its implications on cognitive function. Hence, opening up the possibility of harnessing the observed frontal networks involved in resource mobilization in instances of aging, brain lesion/injury or dysfunction.

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA–disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA–disease associations. Our experimental results on the Human microRNA Disease Database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers.

Decarbonizing the inland container fleet with carbon cap-and-trade scheme

Market-based measures, like the carbon cap-and-trade (CCT) scheme, can effectively reduce emissions and promote green technology by internalizing the external costs in the shipping industry. Under the CCT scheme, the government sets the quantity commitment of carbon emissions for a unit of shipping work, and the excessive quantity must be traded from the inner shipping market. To cope with carbon regulation, carriers can adjust their ship deployment plan and adopt new engine technologies to support low-carbon fuels. This paper proposes a bi-level programming model to investigate the effects of the CCT scheme on the decisions of carriers in a competitive inland container shipping network. At the upper level, carriers adjust their own ship deployment plan and the choice of technology and fuel type. At the lower level, the shippers between different origins and destinations choose the shipping lines with the given ship deployment plans of carriers. Two kinds of equilibrium are considered in the proposed problem: the competitive equilibrium of carriers on freight shipment in the inland shipping network and the trading equilibrium of carbon quotas. The former equilibrium can be captured by a traffic assignment model in a similar transit transportation network, and the latter equilibrium follows the idea of the demand–supply equilibrium of carbon quotas in the traditional market. The two equilibria interact since the freight distribution affects the shipping revenue of each ship and the trading equilibrium is related to the total cost of the ship. The properties of the optimal choice of technology and fuel type are theoretically studied. We find that the optimal choice of fuel technology for each ship assigned to any shipping line at any given CCT must be Pareto optimal in the sense of cost-related and emission-relative measures. Based on the result, the optimal solution of the proposed model can be calculated by a sampling-based method. Numerical examples based on the Yangtze River are further adopted to illustrate the proposed model and conclusions.

Altered individual-level morphological similarity network in children with growth hormone deficiency

BackgroundAccumulating evidences indicate regional grey matter (GM) morphology alterations in pediatric growth hormone deficiency (GHD); however, large-scale morphological brain networks (MBNs) undergo these patients remains unclear.ObjectiveTo investigate the topological organization of individual-level MBNs in pediatric GHD.MethodsSixty-one GHD and 42 typically developing controls (TDs) were enrolled. Inter-regional morphological similarity of GM was taken to construct individual-level MBNs. Between-group differences of topological parameters and network-based statistics analysis were compared. Finally, association relationship between network properties and clinical variables was analyzed.ResultsCompared to TDs, GHD indicated a disturbance in the normal small-world organization, reflected by increased Lp, γ, λ, σ and decreased Cp, Eglob (all PFDR < 0.017). Regarding nodal properties, GHD exhibited increased nodal profiles at cerebellum 4-5, central executive network-related left inferior frontal gyrus, limbic regions-related right posterior cingulate gyrus, left hippocampus, and bilateral pallidum, thalamus (all PFDR < 0.05). Meanwhile, GHD exhibited decreased nodal profiles at sensorimotor network -related bilateral paracentral lobule, default-mode network-related left superior frontal gyrus, visual network -related right lingual gyrus, auditory network-related right superior temporal gyrus and bilateral amygdala, right cerebellum 3, bilateral cerebellum 10, vermis 1-2, 3, 4-5, 6 (all PFDR < 0.05). Furthermore, serum markers and behavior scores in GHD group were correlated with altered nodal profiles (P ≤ 0.046, uncorrected).ConclusionGHD undergo an extensive reorganization in large-scale individual-level MBNs, probably due to abnormal cortico-striatal-thalamo-cerebellum loops, cortico-limbic-cerebellum, dorsal visual-sensorimotor-striatal, and auditory-cerebellum circuitry. This study highlights the crucial role of abnormal morphological connectivity underlying GHD, which might result in their relatively slower development in motor, cognitive, and linguistic functional within behavior problem performance.

Drug repositioning by collaborative learning based on graph convolutional inductive network

Motivation:Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction. Results:To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at https://github.com/BioMLab/DRCLN.

Biosynthesis of the benzylpyrrolidine precursor in anisomycin by a unique ThDP-dependent enzyme

Anisomycin (compound 1), a multifunctional pyrrolidine antibiotic, primarily inhibits protein biosynthesis by binding to the ribosome. Upon binding to the ribosome, the para-phenol moiety of anisomycin inserts completely into the hydrophobic crevice of the A-site and blocks the access of the incoming aminoacyl-tRNAs, disrupting peptide bond formation. Hence, the para-methoxyphenyl group serves as a starting point for developing novel anisomycin analogs with potent antifungal and insecticidal properties. However, the activation and condensation mechanism of phenylpyruvic acid has not yet been elucidated. In this study, genetic manipulations of aniP and its homologue siAniP confirmed their indispensable role in 1 biosynthesis. Bioinformatics analysis suggested that AniP and siAniP function as transketolase. siAniP was found to catalyzed condensation between 4-hydroxyphenylpyruvic acid (3) and glyceraldehyde (GA), initiating pyrrolidine synthesis. siAniP was specific for aromatic keto acids and tolerant of aliphatic and aromatic aldehydes, and was able to catalyze the asymmetric intermolecular condensation of two keto acids, leading to the formation of 24 α-hydroxy ketone. To the best of our knowledge, siAniP is the first TK that catalyzes the transfer of a C2 ketol and symmetrical intermolecular coupling using aromatic keto acids as donor substrates. Structural analysis, docking model construction, and site-directed mutagenesis identified that I220, H275, R322 and W391 were crucial for substrate binding. Moreover, sequence similarity network (SSN)-based genome neighborhood network (GNN) analyses of AniP suggested the widespread occurrence of the AniP-like-mediated reaction in the biosynthesis of 1 and its analogs, particularly in the assembly of benzylpyrrolidine. These findings not only expand the repertoire of TKs but also provide a potent biocatalyst that could be used for the structural innovation of 1 and its derivatives.

MOSDNET: A multi-omics classification framework using simplified multi-view deep discriminant representation learning and dynamic edge GCN with multi-task learning

The integration of multi-omics data offers a robust approach to understanding the complexity of diseases by combining information from various biological levels, such as genomics, transcriptomics, proteomics, and metabolomics. This integrated approach is essential for a comprehensive understanding of disease mechanisms and for developing more effective diagnostic and therapeutic strategies. Nevertheless, most current methodologies fail to effectively extract both shared and specific representations from omics data. This study introduces MOSDNET, a multi-omics classification framework that effectively extracts shared and specific representations. This framework leverages Simplified Multi-view Deep Discriminant Representation Learning (S-MDDR) and Dynamic Edge GCN (DEGCN) to enhance the accuracy and efficiency of disease classification. Initially, MOSDNET utilizes S-MDDR to establish similarity and orthogonal constraints for extracting these representations, which are then concatenated to integrate the multi-omics data. Subsequently, MOSDNET constructs a comprehensive view of the sample data by employing patient similarity networks. By incorporating similarity networks into DEGCN, MOSDNET learns intricate network structures and node representations, which enables superior classification outcomes. MOSDNET is trained through a multitask learning approach, effectively leveraging the complementary knowledge from both the data integration and classification components. After conducting extensive comparative experiments, we have conclusively demonstrated that MOSDNET outperforms leading state-of-the-art multi-omics classification models in terms of classification accuracy. Simultaneously, we employ MOSDNET to identify pivotal biomarkers within the multi-omics data, providing insights into disease etiology and progression.

SVMBN: A new method for predicting metabolite-disease associations based on biological networks and machine learning

Identifying metabolite-disease associations is of paramount significance. With the advancement of research, computational methods have surpassed traditional experiments in efficiency. Nevertheless, current computational methods often overlook the integration of multiomics data, and the performance of the predictive models used is limited. To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately. Second, the metabolite and disease similarity networks are combined to obtain the original link features. Third, nonnegative Matrix Factorization (NMF) is applied to extract effective features from the original features, thereby reducing noise. Finally, Support Vector Machine (SVM) is utilized to predict potential associations between metabolites and diseases. Experimental results demonstrate that the SVMBN algorithm achieves an average AUC of 0.98 in 5-fold cross-validation, indicating its superiority over other methods. Furthermore, case studies prove that the SVMBN algorithm can accurately forecast the relationships between metabolites and diseases.

Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data

Due to its strong dependence on weather conditions, photovoltaic (PV) power is highly intermittent in nature. In light of this, this paper introduces a PV power prediction model based on similar-day clustering and temporal convolutional network (TCN) with bidirectional long short-term memory (BiLSTM) model. Firstly, in the data preprocessing stage, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is used to decompose and denoise the original PV power data to obtain subsequences of different frequencies. Subsequently, the sample entropy (SE) method is incorporated to reconstruct the subsequences and generate low-frequency and high-frequency subsequences with more pronounced temporal features. In addition, a self-organizing map (SOM) with the K-means algorithm is used to categorize historical days into four weather types: sunny, cloudy, rainy, and intermittent. Through grey relational analysis (GRA), meteorological factors significantly affecting PV power prediction are identified to construct a multidimensional feature set. During the model prediction phase, historical PV power data and related meteorological impact factors are input into the TCN-BiLSTM model to perform multi-data-driven PV power prediction. Finally, the predicted results of each subsequence are linearly combined to obtain the ultimate PV power prediction. The proposed model is compared with convolutional neural network (CNN)-LSTM, LSTM, and TCN models, achieving a reduction in the root mean square error (RMSE) of 0.129 kW, 0.238 kW, and 0.257 kW, respectively. This demonstrates that the proposed model exhibits superior overall performance in time series modeling and information capture, enhancing the understanding of seasonal, periodic, and irregular patterns in PV power generation data.

3D chromatin-based variant-to-gene maps across 57 human cell types reveal the cellular and genetic architecture of autoimmune disease susceptibility.

A portion of the genetic basis for many common autoimmune disorders has been uncovered by genome-wide association studies (GWAS), but GWAS do not reveal causal variants, effector genes, or the cell types impacted by disease-associated variation. We have generated 3D genomic datasets consisting of promoter-focused Capture-C, Hi-C, ATAC-seq, and RNA-seq and integrated these data with GWAS of 16 autoimmune traits to physically map disease-associated variants to the effector genes they likely regulate in 57 human cell types. These 3D maps of gene cis-regulatory architecture are highly powered to identify the cell types most likely impacted by disease-associated genetic variation compared to 1D genomic features, and tend to implicate different effector genes than eQTL approaches in the same cell types. Most of the variants implicated by these cis-regulatory architectures are highly trait-specific, but nearly half of the target genes connected to these variants are shared across multiple autoimmune disorders in multiple cell types, suggesting a high level of genetic diversity and complexity among autoimmune diseases that nonetheless converge at the level of target gene and cell type. Substantial effector gene sharing led to the common enrichment of similar biological networks across disease and cell types. However, trait-specific pathways representing potential areas for disease-specific intervention were identified. To test this, we pharmacologically validated squalene synthase, a cholesterol biosynthetic enzyme encoded by the FDFT1 gene implicated by our approach in MS and SLE, as a novel immunomodulatory drug target controlling inflammatory cytokine production by human T cells. These data represent a comprehensive resource for basic discovery of gene cis-regulatory mechanisms, and the analyses reported reveal mechanisms by which autoimmune-associated variants act to regulate gene expression, function, and pathology across multiple, distinct tissues and cell types.

Human adolescent brain similarity development is different for paralimbic versus neocortical zones

Adolescent development of human brain structural and functional networks is increasingly recognized as fundamental to emergence of typical and atypical adult cognitive and emotional proodal magnetic resonance imaging (MRI) data collected from N [Formula: see text] 300 healthy adolescents (51%; female; 14 to 26 y) each scanned repeatedly in an accelerated longitudinal design, to provide an analyzable dataset of 469 structural scans and 448 functional MRI scans. We estimated the morphometric similarity between each possible pair of 358 cortical areas on a feature vector comprising six macro- and microstructural MRI metrics, resulting in a morphometric similarity network (MSN) for each scan. Over the course of adolescence, we found that morphometric similarity increased in paralimbic cortical areas, e.g., insula and cingulate cortex, but generally decreased in neocortical areas, and these results were replicated in an independent developmental MRI cohort (N [Formula: see text] 304). Increasing hubness of paralimbic nodes in MSNs was associated with increased strength of coupling between their morphometric similarity and functional connectivity. Decreasing hubness of neocortical nodes in MSNs was associated with reduced strength of structure-function coupling and increasingly diverse functional connections in the corresponding fMRI networks. Neocortical areas became more structurally differentiated and more functionally integrative in a metabolically expensive process linked to cortical thinning and myelination, whereas paralimbic areas specialized for affective and interoceptive functions became less differentiated, as hypothetically predicted by a developmental transition from periallocortical to proisocortical organization of the cortex. Cytoarchitectonically distinct zones of the human cortex undergo distinct neurodevelopmental programs during typical adolescence.