Similar Network Research Articles

Extractor-attention-predictor network for quantitative photoacoustic tomography

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting–expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

Exploring morphological similarity and randomness in Alzheimer’s disease using adjacent grey matter voxel-based structural analysis

BackgroundAlzheimer’s disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer’s disease.MethodsHere, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer’s disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer’s disease was assessed by stepwise regression.ResultsCompared to cognitively normal participants, individuals with Alzheimer’s disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer’s disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer’s disease.ConclusionsOur study suggested that individuals with Alzheimer’s disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer’s disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer’s disease.

A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network

In response to the global challenge of climate change and the shift away from fossil fuels, the accurate prediction of wind power generation is crucial for optimizing grid operations and managing energy storage. This study introduces a novel approach by integrating the proportional–integral–derivative (PID) control theory into wind power forecasting, employing a three-dimensional gated neural (TGN) unit designed to enhance error feedback mechanisms. The proposed empirical mode decomposition (EMD)–convolutional neural network (CNN)–three-dimensional gated neural network (TGNN) framework starts with the pre-processing of wind data using EMD, followed by feature extraction via a CNN, and time series forecasting using the TGN unit. This setup leverages proportional, integral, and differential control within its architecture to improve adaptability and response to dynamic wind patterns. The experimental results show significant improvements in forecasting accuracy; the EMD–CNN–TGNN model outperforms both traditional models like autoregressive integrated moving average (ARIMA) and support vector regression (SVR), and similar neural network approaches, such as EMD–CNN–GRU and EMD–CNN–LSTM, across several metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). These advancements substantiate the model’s effectiveness in enhancing the precision of wind power predictions, offering substantial implications for future renewable energy management and storage solutions.

CHDmap: One Step Further Toward Integrating Medicine-Based Evidence Into Practice

Abstract Evidence-based medicine, rooted in randomized controlled trials, offers treatment estimates for the average patient but struggles to guide individualized care. This challenge is amplified in complex conditions like congenital heart disease due to disease variability and limited trial applicability. To address this, medicine-based evidence was proposed to synthesize information for personalized care. A recent article introduced a patient similarity network, CHDmap, which represents a promising technical rendition of the medicine-based evidence concept. Leveraging comprehensive clinical and echocardiographic data, CHDmap creates an interactive patient map representing individuals with similar attributes. Using a k-nearest neighbor algorithm, CHDmap interactively identifies closely resembling patient groups based on specific characteristics. These approximate matches form the foundation for predictive analyses, including outcomes like hospital length of stay and complications. A key finding is the tool’s dual capacity: not only did it corroborate clinical intuition in many scenarios, but in specific instances, it prompted a reevaluation of cases, culminating in an enhancement of overall performance across various classification tasks. While an important first step, future versions of CHDmap may aim to expand mapping complexity, increase data granularity, consider long-term outcomes, allow for treatment comparisons, and implement artificial intelligence–driven weighting of various input variables. Successful implementation of CHDmap and similar tools will require training for practitioners, robust data infrastructure, and interdisciplinary collaboration. Patient similarity networks may become valuable in multidisciplinary discussions, complementing clinicians’ expertise. The symbiotic approach bridges evidence, experience, and real-life care, enabling iterative learning for future physicians.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

Debunking wasp pollination: Wasps are comparable to bees in terms of plant interactions, body pollen and single‐visit pollen deposition

Abstract Wasps are understudied in their contribution to pollination services. In order to better understand the ecological contribution of wasp communities to plant pollination, we conducted three studies to compare bees and wasps by (1) the plant communities visited in agricultural and prairie environments, (2) body pollen composition as an indirect measure of pollination and (3) a single‐visit deposition study as a direct measure of pollination in the plant species complex Solidago canadensis. We found wasps visit a distinct early‐blooming exotic plant community from bees but have overlapping use of late‐blooming native plants and similar network interaction metrics. This suggests wasps may fill an ecological niche distinct from bees, while also serving as common native plant visitors. We also found that body pollen density and pollen fidelity in four and five (out of six) wasp families, respectively, were comparable to or greater than the bee family Colletidae. Three wasp families also showed comparable to or greater visit fidelity than two (out of five) bee families examined. These results suggest wasps may carry and potentially deliver substantial pollen to specific plants. In addition, controlled single‐visit deposition revealed the paper wasp Polistes fuscatus deposits a similar amount of pollen grains as the bumble bee Bombus impatiens, suggesting surprisingly high pollination efficiency that warrants further investigation. Our multi‐pronged study shows the importance of investigating the role of wasps as pollinators, especially by focusing on direct plant‐pollination metrics to determine wasp pollination efficacy.

Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity.

Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.

A different approach to similarity networks in Archaeology - Similarity Network Fusion

A different approach to similarity networks in Archaeology - Similarity Network Fusion

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography

IntroductionThis study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with Bipolar disorder and Major depressive disorder, comparing the neuropathology of these disorders.ObjectivesThis study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with Bipolar disorder and Major depressive disorder, comparing the neuropathology of these disorders.Methods A total of 116 participants (35 patients diagnosed with bipolar disorder(BD), 39 patients diagnosed with Major depressive disorder(MDD), and 42 people who are healthy-control groups(HC)) were enrolled for this study. Depression and anxiety were evaluated with using State-Trait Anxiety Inventory (STAI) and Beck Depression Inventory (BDI). Graph theory‑based source‑level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands.Results At the global level, patients with BD and MDD showed higher strength (p = 0.001) and CC (p = 0.001), and lower PL (p < 0.001) in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus(p < 0.001), left isthmus cingulate(p < 0.001), bilateral paracentral(p < 0.001), and left superior frontal(p < 0.001); however, patients with MDD showed higher nodal CC only in the right precuneus(p < 0.001) compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs.ConclusionsThis study suggest that both patients have similar network changes at the global level, but they have different network changes at the nodal level. Also, the higher nodal CCs in the high beta band might indicate the regions became more connected with their neighbors in accordance with the severity of depressive and anxious states. This study found a significant correlation between cortical network state and anxiety-related psychological measure in BD patients. Our source-level cortical network indices might contribute to the understanding of the neuropathological mechanisms in these two disorders.Disclosure of InterestNone Declared

In Vitro Characterization of an O-Specific Glycosyltransferase Involved in Flagellin Glycosylation.

Glycosyltransferases play a fundamental role in the biosynthesis of glycoproteins and glycotherapeutics. In this study, we investigated protein glycosyltransferase FlgGT1, belonging to the GT2 family. The GT2 family includes cysteine S-glycosyltransferases involved in antimicrobial peptide biosyntheses, sharing conserved catalytic domains while exhibiting diverse C-terminal domains. Our in vitro studies revealed that FlgGT1 recognizes structural motifs rather than specific amino acid sequences when glycosylating the flagellin protein Hag. Notably, FlgGT1 is selective for serine or threonine O-glycosylation over cysteine S-glycosylation. Molecular dynamics simulations provided insights into the structural basis of FlgGT1's ability to accommodate various sugar nucleotides as donor substrates. Mutagenesis experiments on FlgGT1 demonstrated that truncating the relatively large C-terminal domain resulted in a loss of flagellin glycosylation activity. Our classification based on sequence similarity network analysis and AlphaFold2 structural predictions suggests that the acquisition of the C-terminal domain is a key evolutionary adaptation conferring distinct substrate specificities on glycosyltransferases within the GT2 family.

Network Approach to Work Addiction: A Cross-Cultural Study

We aimed to estimate and compare networks of work addiction among Norwegian and Polish working individuals. The Bergen Work Addiction Scale was used to measure work addiction in four samples, two comprising responses of working Norwegians ( n1 = 16,426; n2 = 764) and two comprising responses of working Poles ( n3 = 719; n4 = 711). The networks were estimated jointly with the fused graphic lasso method. Additionally, we estimated symptoms’ centrality, symptoms’ predictability, and networks’ stability, as well as quantitatively compared the four networks. The results showed highly similar networks across the four samples. There were several strong direct relationships between symptoms. The most and the least central symptoms were Relapse and Mood modification, respectively. Mean symptom predictability varied between 22.6 and 28.3% across samples. We discussed the possible intervention strategies based on the obtained results.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

Substrate specificity mapping of fungal CAZy AA3_2 oxidoreductases

BackgroundOxidative enzymes targeting lignocellulosic substrates are presently classified into various auxiliary activity (AA) families within the carbohydrate-active enzyme (CAZy) database. Among these, the fungal AA3 glucose–methanol–choline (GMC) oxidoreductases with varying auxiliary activities are attractive sustainable biocatalysts and important for biological function. CAZy AA3 enzymes are further subdivided into four subfamilies, with the large AA3_2 subfamily displaying diverse substrate specificities. However, limited numbers of enzymes in the AA3_2 subfamily are currently biochemically characterized, which limits the homology-based mining of new AA3_2 oxidoreductases. Importantly, novel enzyme activities may be discovered from the uncharacterized parts of this large subfamily.ResultsIn this study, phylogenetic analyses employing a sequence similarity network (SSN) and maximum likelihood trees were used to cluster AA3_2 sequences. A total of 27 AA3_2 proteins representing different clusters were selected for recombinant production. Among them, seven new AA3_2 oxidoreductases were successfully produced, purified, and characterized. These enzymes included two glucose dehydrogenases (TaGdhA and McGdhA), one glucose oxidase (ApGoxA), one aryl alcohol oxidase (PsAaoA), two aryl alcohol dehydrogenases (AsAadhA and AsAadhB), and one novel oligosaccharide (gentiobiose) dehydrogenase (KiOdhA). Notably, two dehydrogenases (TaGdhA and KiOdhA) were found with the ability to utilize phenoxy radicals as an electron acceptor. Interestingly, phenoxy radicals were found to compete with molecular oxygen in aerobic environments when serving as an electron acceptor for two oxidases (ApGoxA and PsAaoA), which sheds light on their versatility. Furthermore, the molecular determinants governing their diverse enzymatic functions were discussed based on the homology model generated by AlphaFold.ConclusionsThe phylogenetic analyses and biochemical characterization of AA3_2s provide valuable guidance for future investigation of AA3_2 sequences and proteins. A clear correlation between enzymatic function and SSN clustering was observed. The discovery and biochemical characterization of these new AA3_2 oxidoreductases brings exciting prospects for biotechnological applications and broadens our understanding of their biological functions.

Designing and Implementation of a Digitalized Intersectoral Discharge Management System and Its Effect on Readmissions: Mixed Methods Approach.

Digital transformation offers new opportunities to improve the exchange of information between different health care providers, including inpatient, outpatient and care facilities. As information is especially at risk of being lost when a patient is discharged from a hospital, digital transformation offers great opportunities to improve intersectoral discharge management. However, most strategies for improvement have focused on structures within the hospital. This study aims to evaluate the implementation of a digitalized discharge management system, the project "Optimizing instersectoral discharge management" (SEKMA, derived from the German Sektorübergreifende Optimierung des Entlassmanagements), and its impact on the readmission rate. A mixed methods design was used to evaluate the implementation of a digitalized discharge management system and its impact on the readmission rate. After the implementation, the congruence between the planned (logic model) and the actual intervention was evaluated using a fidelity analysis. Finally, bivariate and multivariate analyses were used to evaluate the effectiveness of the implementation on the readmission rate. For this purpose, a difference-in-difference approach was adopted based on routine data of hospital admissions between April 2019 and August 2019 and between April 2022 and August 2022. The department of vascular surgery served as the intervention group, in which the optimized discharge management was implemented in April 2022. The departments of internal medicine and cardiology formed the control group. Overall, 26 interviews were conducted, and we explored 21 determinants, which can be categorized into 3 groups: "optimization potential," "barriers," and "enablers." On the basis of these results, 19 strategies were developed to address the determinants, including a lack of networking among health care providers, digital information transmission, and user-unfriendliness. On the basis of these strategies, which were prioritized by 11 hospital physicians, a logic model was formulated. Of the 19 strategies, 7 (37%; eg, electronic discharge letter, providing mobile devices to the hospital's social service, and generating individual medication plans in the format of the national medication plan) have been implemented in SEKMA. A survey on the fidelity of the application of the implemented strategies showed that 3 of these strategies were not yet widely applied. No significant effect of SEKMA on readmissions was observed in the routine data of 14,854 hospital admissions (P=.20). This study demonstrates the potential of optimizing intersectoral collaboration for patient care. Although a significant effect of SEKMA on readmissions has not yet been observed, creating a digital ecosystem that connects different health care providers seems to be a promising approach to ensure secure and fast networking of the sectors. The described intersectoral optimization of discharge management provides a structured template for the implementation of a similar local digital care networking infrastructure in other care regions in Germany and other countries with a similarly fragmented health care system.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

Abstract 7354: Proteogenomic and metabolomic characterization of acute myeloid leukemia

Abstract Acute myeloid leukemia (AML) is a blood malignancy of poor prognosis with marked heterogeneity. To elucidate the underlying mechanisms that drive AML as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) effort, we performed comprehensive genomics, transcriptomics, proteomics including multiple post-translational modifications (phosphorylation, acetylation, and glycosylation), metabolomics, and lipidomics characterization of 173 treatment-naïve AML patients. Applying the similarity network fusion method on both transcriptomics and proteomics data, we identified 8 proteogenomic clusters. These clusters recapitulate specific recurrent mutations and established clinical subtypes available within the cohort. We used single-cell RNAseq data as a reference to perform immune component analysis for collected bulk samples. The result reveals that our proteogenomic clustering also captures the variations of AML differentiation hierarchies including CD14+ monocyte-like and GMP-like AML. To assess the complex disease nature of AML, we performed functional analysis for each cluster to reveal interplay between multiple genomic aberrations such as NPM1, FLT3-ITD, DNMT3A mutations, complex chromosomal alterations, and the leukemia cell differentiation. Importantly, the multi-omics analysis performed not only connects previously identified molecular drivers and cell differentiation variations within AML, but also links them with observed cancer metabolomic reprogramming with their drug response implications. Moreover, our study also identified site-specific post-translational modifications previously not known in AML, highlighting the valuable insights and clinical relevance of these newly identified clusters. Citation Format: Alec Chu, Yi Hsiao, Yamei Deng, Chenwei Wang, Jennifer Kyle, Yongchao Dou, James C. Pino, Camilo Posso, Leanne Henry, Lijun Chen, Ginny Xiaohe Li, Tung-Shing Mamie Lih, Yifat Geffen, Fengchao Yu, Li Ding, Gil Omenn, Chandan Kumar, Saravana M. Dhanasekaran, Elie Traer, Jeffrey W. Tyner, Hui Zhang, Tao Liu, Sara Gosline, Bing Zhang, Arul Chinnaiyan, Alexey I. Nesvizhskii, Marcin Cieslik, The CPTAC Consortium. Proteogenomic and metabolomic characterization of acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7354.

Dynamic Topic Networks to Evaluate Systemic Risk in Financial Markets

The study proposes a Dynamic Topic Network (DTN) approach to assess systemic risk in financial markets, utilizing a combination of topic modeling and network analysis. By employing Latent Dirichlet Allocation (LDA) to analyze news articles, the study extracts topics that are then used to construct topic similarity networks over time. The results obtained highlight the interconnectedness of topics, allowing for the correlation of abnormal behaviors with volatility in financial markets. Using the 2015–2016 stock market selloff and the COVID-19 pandemic as case studies, the study demonstrates how the DTN approach can provide insights into abnormal movements in the Dow Jones Industrial Average and predict the gradual recovery of the market following such events. From a risk management perspective, the analysis can be conducted on a daily basis with new data to predict real-time systemic risk in financial markets, providing valuable information for decision-makers in managing financial stability and mitigating potential losses

Flying Ad-Hoc Networks (FANETs): A Review

INTRODUCTION: FANETs are a type of wireless communication network consisting of Unmanned Aerial Vehicles (UAVs) or drones that work collaboratively to process data and attain optimal results. These networks have achieved significant attention due to their potential applications in diverse engineering fields. The paper provides a comprehensive analysis of FANET, covering various aspects related to its classification, architecture, communication types, mobility models, challenges, characteristics, and design. It also discusses the importance of routing protocols and topology in FANETs. Furthermore, this paper identifies and presents open issues and challenges in the field of FANETs, urging researchers to focus on exploring and addressing these essential parameters and research areas. 
 OBJECTIVES: This paper will aims to promote further investigation and advancement in the field of FANETs and similar networks, enabling researchers to explore and overcome the challenges to unleash the full potential of these UAV-based ad-hoc networks shortly.
 METHODS: The data used in this paper was gathered from various research papers. A brief comparison among FANETs, MANETs, and VANETs has been shown and highlighted the main points. This paper also elaborates the general architecture, mobility models, routing, routing protocols in FANETs.
 RESULTS: It was discovered that the use of both deterministic and probabilistic techniques is suggested to enhance the performance and efficiency of FANETs. By combining these methods, the paper suggests that better results can be achieved in terms of network reliability, adaptability, and overall performance. 
 CONCLUSION: This paper discusses the importance of routing protocols and topology in FANETs. Furthermore, this paper identifies and presents open issues and challenges in the field of FANETs, urging researchers to focus on exploring and addressing these essential parameters and research areas.

Two multimodal neuroimaging subtypes of obsessive-compulsive disorder disclosed by semi-supervised machine learning

BackgroundObsessive-compulsive disorder (OCD) is a highly heterogeneous mental condition with a diverse symptom. Existing studies classified OCD on the basis of conventional phenomenology-based taxonomy ignoring the fact that the same subtype identified in accordance with clinical symptom may have different mechanisms and treatment responses. MethodsThis research involved 50 medicine-free patients with OCD and 50 matched healthy controls (HCs). All the participants were subjected to structural and functional magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) and amplitude of low frequency fluctuation (ALFF) were used to evaluate gray matter volume (GMV) and spontaneous neuronal activities at rest respectively. Similarity network fusion (SNF) was utilized to integrate GMVs and spontaneous neuronal activities, and heterogeneity by discriminant analysis was applied to characterise OCD subtypes. ResultsTwo OCD subtypes were identified: Subtype 1 exhibited decreased GMVs (i.e., left inferior temporal gyrus, right supplementary motor area and right lingual gyrus) and increased ALFF value (i.e., right orbitofrontal cortex), whereas subtype 2 exhibited increased GMVs (i.e., left cuneus, right precentral gyrus, left postcentral gyrus and left hippocampus) and decreased ALFF value (i.e., right caudate nucleus). Furthermore, the altered GMVs was negatively correlated with abnormal ALFF values in both subtype 1 and 2. LimitationsThis study requires further validation via a larger, independent dataset and should consider the potential influences of psychotropic medication on OCD patients' brain activities. ConclusionsResults revealed two reproducible subtypes of OCD based on underlying multimodal neuroimaging and provided new perspectives on the classification of OCD.