Network science: Ising states of matter.

Network science provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learning has raised significant interest, the full prediction power of network science has not yet been systematically explored in this context. Here we fill this gap by providing an in-depth statistical, combinatorial, geometrical, and topological characterization of 2D Ising snapshot networks (IsingNets) extracted from Monte Carlo simulations of the 2D Ising model at different temperatures, going across the phase transition. Our analysis reveals the complex organization properties of IsingNets in both the ferromagnetic and paramagnetic phases and demonstrates the significant deviations of the IsingNets with respect to randomized null models. In particular percolation properties of the IsingNets reflect the existence of the symmetry between configurations with opposite magnetization below the critical temperature and the very compact nature of the two emerging giant clusters revealed by our persistent homology analysis of the IsingNets. Moreover, the IsingNets display a very broad degree distribution and significant degree-degree correlations and weight-degree correlations demonstrating that they encode relevant information present in the configuration space of the 2D Ising model. The geometrical organization of the critical IsingNets is reflected in their spectral properties deviating from the one of the null model. This work reveals the important insights that network science can bring to the characterization of phases of matter. The set of tools described hereby can be applied as well to numerical and experimental data.

A cross-sectional, multicenter survey of the prevalence and influencing factors for migraine in epilepsy.

OBJECTIVES: Epilepsy and migraine are common chronic neurological disease. Epidemiologic studies and shared pathophysiology and treatment suggest that these two diseases overlap. However, migraine is often underestimated among patients with epilepsy. This study aimed to evaluate the prevalence of migraine and identify the related influencing factors among adult patients with epilepsy. METHODS: Adult patients with epilepsy were recruited at the outpatient epilepsy clinic of 13 tertiary hospitals in China from February to September 2022. ID Migraine questionnaire was applied to evaluate for migraine. Both univariable and multivariable logistic regression models were used to explore the influencing factors of migraine. RESULTS: A total of 1326 patients with epilepsy were enrolled in this study. The prevalence of migraine among patients with epilepsy was 19.2% (254/1326). In the multivariable analysis, being female (OR = 1.451, 95% CI: 1.068-1.975; p = 0.018), focal and focal to bilateral tonic-clonic seizures (OR = 1.583, 95% CI: 1.090-2.281; p = 0.015), and current seizure attack in the last 3 months (OR = 1.967, 95% CI: 1.282-3.063; p = 0.002) were the influencing factors for migraine. However, <10% of patients with epilepsy received analgesics for migraine. SIGNIFICANCE: Approximately 20% of patients with epilepsy screened positive for migraine. Being female, focal and focal to bilateral tonic-clonic seizures, and current seizure attack in the last 3 months were the influencing factors for migraine. Neurologists should pay more attention to the screening and management of the migraine among patients with epilepsy in China. PLAIN LANGUAGE SUMMARY: Epilepsy and migraine are common chronic neurological disease with shared pathophysiological mechanisms and therapeutic options. However, migraine is often underestimated among patients with epilepsy. This multicenter study aimed to evaluate the prevalence of migraine and current status of treatment. In this study, approximately 20% of patients with epilepsy screened positive for migraine. Female, focal and focal to bilateral tonic-clonic seizures, and current seizure attack in the last 3 months were identified as independent influencing factors for migraine. Despite the high prevalence, the treatment for migraine was not optimistic, neurologists should pay more attention to the screening and management of migraine.

Multi-omics analysis-based macrophage differentiation-associated papillary thyroid cancer patient classifier.

BACKGROUND: The reclassification of Papillary Thyroid Carcinoma (PTC) is an area of research that warrants attention. The connection between thyroid cancer, inflammation, and immune responses necessitates considering the mechanisms of differential prognosis of thyroid tumors from an immunological perspective. Given the high adaptability of macrophages to environmental stimuli, focusing on the differentiation characteristics of macrophages might offer a novel approach to address the issues related to PTC subtyping. METHODS: Single-cell RNA sequencing data of medullary cells infiltrated by papillary thyroid carcinoma obtained from public databases was subjected to dimensionality reduction clustering analysis. The RunUMAP and FindAllMarkers functions were utilized to identify the gene expression matrix of different clusters. Cell differentiation trajectory analysis was conducted using the Monocle R package. A complex regulatory network for the classification of Immune status and Macrophage differentiation-associated Papillary Thyroid Cancer Classification (IMPTCC) was constructed through quantitative multi-omics analysis. Immunohistochemistry (IHC) staining was utilized for pathological histology validation. RESULTS: Through the integration of single-cell RNA and bulk sequencing data combined with multi-omics analysis, we identified crucial transcription factors, immune cells/immune functions, and signaling pathways. Based on this, regulatory networks for three IMPTCC clusters were established. CONCLUSION: Based on the co-expression network analysis results, we identified three subtypes of IMPTCC: Immune-Suppressive Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (ISMPTCC), Immune-Neutral Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (INMPTCC), and Immune-Activated Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (IAMPTCC). Each subtype exhibits distinct metabolic, immune, and regulatory characteristics corresponding to different states of macrophage differentiation.

Machine learning based on SEER database to predict distant metastasis of thyroid cancer.

OBJECTIVE: Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant metastasis or are at high risk as early as possible. This paper aimed to predict distant metastasis of thyroid cancer through the construction of machine learning models to provide a reference for clinical diagnosis and treatment. MATERIALS & METHODS: Data on demographic and clinicopathological characteristics of thyroid cancer patients between 2010 and 2015 were extracted from the National Institutes of Health (NIH) Surveillance, Epidemiology, and End Results (SEER) database. Our research used univariate and multivariate logistic models to screen independent risk factors, respectively. Decision Trees (DT), ElasticNet (ENET), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Radial Basis Function Support Vector Machine (RBFSVM) and seven machine learning models were compared and evaluated by the following metrics: the area under receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), sensitivity(also called recall), specificity, precision, accuracy and F1 score. Interpretable machine learning was used to identify possible correlation between variables and distant metastasis. RESULTS: Independent risk factors for distant metastasis, including age, gender, race, marital status, histological type, capsular invasion, and number of lymph nodes metastases were screened by multifactorial regression analysis. Among the seven machine learning algorithms, RF was the best algorithm, with an AUC of 0.948, sensitivity of 0.919, accuracy of 0.845, and F1 score of 0.886 in the training set, and an AUC of 0.960, sensitivity of 0.929, accuracy of 0.906, and F1 score of 0.908 in the test set. CONCLUSIONS: The machine learning model constructed in this study helps in the early diagnosis of distant thyroid metastases and helps physicians to make better decisions and medical interventions.

Prevalence and risk factors of anxiety and depression in adult patients with epilepsy: a multicenter survey-based study.

Background: Depression and anxiety are the most common psychiatric comorbidities in patients with epilepsy (PWE). However, they are often unrecognized and consequently untreated. Objective: The study was conducted to evaluate the prevalence and risk factors of anxiety and depression among Chinese adult PWE. Design: Cross-sectional study. Methods: Adult PWE were recruited from 13 tertiary epilepsy centers from February to September 2022. Generalized Anxiety Disorder-7 and Neurological Disorders Depression Inventory for Epilepsy were applied to evaluate anxiety and depression, respectively. Both univariate and multivariate logistic regression analyses models were performed to explore the risk factors of anxiety and depression. Results: A total of 1326 PWE were enrolled in this study. The prevalence of anxiety and depression was 31.45% and 27.30%, respectively. Being female [odds ratio (OR) = 1.467, 95% CI: 1.134-1.899; p = 0.004], focal and focal to bilateral tonic-clonic seizures (TCSZ) (OR = 1.409, 95% CI: 1.021-1.939; p = 0.036), and seizure occurrence in the last 3 months (OR = 1.445, 95% CI: 1.026-2.044; p = 0.036) were the risk factors for anxiety. Focal and focal to bilateral TCSZ (OR = 1.531, 95% CI: 1.094-2.138; p = 0.013) and seizure occurrence in the last 3 months (OR = 1.644, 95% CI: 1.130-2.411; p = 0.010) were the risk factors for depression. In addition, for every 1-year increment of age, the odds of developing depression were decreased by 3.8% (p = 4.12e-5). Nevertheless, up to 70% of PWE did not receive any treatment for comorbidity. Conclusion: There were approximately 30% of PWE screened positive for anxiety or depression. Both focal and focal to bilateral TCSZ and seizure occurrence in the last 3 months were estimated as risk factors for anxiety and depression. However, the current status of treatment was not optimal.

The dynamic nature of percolation on networks with triadic interactions.

Percolation establishes the connectivity of complex networks and is one of the most fundamental critical phenomena for the study of complex systems. On simple networks, percolation displays a second-order phase transition; on multiplex networks, the percolation transition can become discontinuous. However, little is known about percolation in networks with higher-order interactions. Here, we show that percolation can be turned into a fully fledged dynamical process when higher-order interactions are taken into account. By introducing signed triadic interactions, in which a node can regulate the interactions between two other nodes, we define triadic percolation. We uncover that in this paradigmatic model the connectivity of the network changes in time and that the order parameter undergoes a period doubling and a route to chaos. We provide a general theory for triadic percolation which accurately predicts the full phase diagram on random graphs as confirmed by extensive numerical simulations. We find that triadic percolation on real network topologies reveals a similar phenomenology. These results radically change our understanding of percolation and may be used to study complex systems in which the functional connectivity is changing in time dynamically and in a non-trivial way, such as in neural and climate networks.

The role of tetraspanins pan-cancer.

Tetraspanins (TSPANs) are a class of four-transmembrane segmented proteins. The precise functions of TSPANs and their roles in pan-cancer are unclear. In this work, we analyzed TSPAN1, TSPAN10, TSPAN11, TSPAN12, TSPAN13, TSPAN14, TSPAN15, TSPAN16, TSPAN17, TSPAN18, TSPAN18-AS1, TSPAN19, TSPAN2, TSPAN3, TSPAN31, TSPAN32, TSPAN33, TSPAN4, TSPAN5, TSPAN6, TSPAN7, TSPAN8, TSPAN9, TSPAN9-IT1 (24 TSPAN family genes) in relation to tumor characteristics from 11,057 TCGA samples across 33 cancer types. On 24 TSPAN family genes, multidimensional studies were conducted, including gene differential expression, immunological subtype analysis, clinical analysis, stemness indices analysis, drug sensitivity analysis, alteration analysis, and multi-omics validation (including ATAC-seq validation, single-cell sequencing validation, and other external validation). Genes were differentially expressed in 33 cancers, and several of them showed high consistency in terms of tumor characteristics. In particular, the potential roles of TSPAN15 and TSPAN1 in cancer deserve further attention.

A Clinical Predictive Model of Central Lymph Node Metastases in Papillary Thyroid Carcinoma.

Background: Thyroid carcinoma is one of the most common endocrine tumors, and papillary thyroid carcinoma (PTC) is the most common pathological type. Current studies have reported that PTC has a strong propensity for central lymph node metastases (CLNMs). Whether to prophylactically dissect the central lymph nodes in PTC remains controversial. This study aimed to explore the risk factors and develop a predictive model of CLNM in PTC. Methods: A total of 2,554 patients were enrolled in this study. The basic information, laboratory examination, characteristics of cervical ultrasound, genetic test, and pathological diagnosis were collected. The collected data were analyzed by univariate logistic analysis and multivariate logistic analysis. The risk factors were evaluated, and the predictive model was constructed of CLNM. Results: The multivariate logistic analysis showed that Age (p < 0.001), Gender (p < 0.001), Multifocality (p < 0.001), BRAF (p = 0.027), and Tumor size (p < 0.001) were associated with CLNM. The receiver operating characteristic curve (ROC curve) showed high efficiency with an area under the ROC (AUC) of 0.781 in the training group. The calibration curve and the calibration of the model were evaluated. The decision curve analysis (DCA) for the nomogram showed that the nomogram can provide benefits in this study. Conclusion: The predictive model of CLNM constructed and visualized based on the evaluated risk factors was confirmed to be a practical and convenient tool for clinicians to predict the CLNM in PTC.

Ral GEF with the PH Domain and SH3 Binding Motif 1 Regulated by Splicing Factor Junction Plakoglobin and Pyrimidine Metabolism Are Prognostic in Uterine Carcinosarcoma.

Uterine carcinosarcoma (UCS) is a highly invasive malignant tumor that originated from the uterine epithelium. Many studies suggested that the abnormal changes of alternative splicing (AS) of pre-mRNA are related to the occurrence and metastasis of the tumor. This study investigates the mechanism of alternative splicing events (ASEs) in the tumorigenesis and metastasis of UCS. RNA-seq of UCS samples and alternative splicing event (ASE) data of UCS samples were downloaded from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases, several times. Firstly, we performed the Cox regression analysis to identify the overall survival-related alternative splicing events (OSRASEs). Secondly, a multivariate model was applied to approach the prognostic values of the risk score. Afterwards, a coexpressed network between splicing factors (SFs) and OSRASEs was constructed. In order to explore the relationship between the potential prognostic signaling pathways and OSRASEs, we fabricated a network between these pathways and OSRASEs. Finally, validations from multidimension platforms were used to explain the results unambiguously. 1,040 OSRASEs were identified by Cox regression. Then, 6 OSRASEs were incorporated in a multivariable model by Lasso regression. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was 0.957. The risk score rendered from the multivariate model was corroborated to be an independent prognostic factor (P < 0.001). In the network of SFs and ASEs, junction plakoglobin (JUP) noteworthily regulated RALGPS1-87608-AT (P < 0.001, R = 0.455). Additionally, RALGPS1-87608-AT (P = 0.006) showed a prominent relationship with distant metastasis. KEGG pathways related to prognosis of UCS were selected by gene set variation analysis (GSVA). The pyrimidine metabolism (P < 0.001, R = -0.470) was the key pathway coexpressed with RALGPS1. We considered that aberrant JUP significantly regulated RALGPS1-87608-AT and the pyrimidine metabolism pathway might play a significant part in the metastasis and prognosis of UCS.

Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks.

The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.

Higher-order percolation processes on multiplex hypergraphs.

Higher-order interactions are increasingly recognized as a fundamental aspect of complex systems ranging from the brain to social contact networks. Hypergraphs as well as simplicial complexes capture the higher-order interactions of complex systems and allow us to investigate the relation between their higher-order structure and their function. Here we establish a general framework for assessing hypergraph robustness and we characterize the critical properties of simple and higher-order percolation processes. This general framework builds on the formulation of the random multiplex hypergraph ensemble where each layer is characterized by hyperedges of given cardinality. We observe that in presence of the structural cutoff the ensemble of multiplex hypergraphs can be mapped to an ensemble of multiplex bipartite networks. We reveal the relation between higher-order percolation processes in random multiplex hypergraphs, interdependent percolation of multiplex networks, and K-core percolation. The structural correlations of the random multiplex hypergraphs are shown to have a significant effect on their percolation properties. The wide range of critical behaviors observed for higher-order percolation processes on multiplex hypergraphs elucidates the mechanisms responsible for the emergence of discontinuous transition and uncovers interesting critical properties which can be applied to the study of epidemic spreading and contagion processes on higher-order networks.

Co-expression Analysis of Genes and Tumor-Infiltrating Immune Cells in Metastatic Uterine Carcinosarcoma.

Uterine carcinosarcoma (UCS) is a malignant tumor with a high tendency to invasion and metastasis. However, the underlying invasion and metastasis mechanisms of UCS remain poorly understood. Genetic alteration and tumor-infiltrating immune cells play important roles in tumorigenesis, progression, and metastasis. To better understand the underlying mechanisms of UCS, we screened tumor-infiltrating immune cells by applying CIBERSORT algorithm and constructed nomograms to predict the prognosis of UCS patients based on metastasis-specific tumor-infiltrating immune cells and genes, and demonstrated their utility by the high AUC values. Combining gene co-expression and experimental validation results, we propose a potential mechanism of AK8, MPZ, and mast cells activated might play important parts in UCS metastasis.

Renormalization group theory of percolation on pseudofractal simplicial and cell complexes.

Simplicial complexes are gaining increasing scientific attention as they are generalized network structures that can represent the many-body interactions existing in complex systems ranging from the brain to high-order social networks. Simplicial complexes are formed by simplicies, such as nodes, links, triangles, and so on. Cell complexes further extend these generalized network structures as they are formed by regular polytopes, such as squares, pentagons, etc. Pseudofractal simplicial and cell complexes are a major example of generalized network structures and they can be obtained by gluing two-dimensional m-polygons (m=3 triangles, m=4 squares, m=5 pentagons, etc.) along their links according to a simple iterative rule. Here we investigate the interplay between the topology of pseudofractal simplicial and cell complexes and their dynamics by characterizing the critical properties of link percolation defined on these structures. By using the renormalization group we show that the pseudofractal simplicial and cell complexes have a continuous percolation threshold at p_{c}=0. When the pseudofractal structure is formed by polygons of the same size m, the transition is characterized by an exponential suppression of the order parameter P_{∞} that depends on the number of sides m of the polygons forming the pseudofractal cell complex, i.e., P_{∞}∝pexp(-α/p^{m-2}). Here these results are also generalized to random pseudofractal cell complexes formed by polygons of different number of sides m.

Identification of prognostic and metastasis-related alternative splicing signatures in hepatocellular carcinoma.

As the most common neoplasm in digestive system, hepatocellular carcinoma (HCC) is one of the most important leading cause of cancer deaths worldwide. Its high-frequency metastasis and relapse rate lead to the poor survival of HCC patients. However, the mechanism of HCC metastasis is still unclear. Alternative splicing events (ASEs) have a great effect in cancer development, progression and metastasis. We downloaded RNA sequencing and seven types of ASEs data of HCC samples, in order to explore the mechanism of ASEs underlying tumorigenesis and metastasis of HCC. The data were taken from the The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. Univariate Cox regression analysis was used to determine a total of 3197 overall survival-related ASEs (OS-SEs). And based on five OS-SEs screened by Lasso regression, we constructed a prediction model with the Area Under Curve of 0.765. With a good reliability of the model, the risk score was also proved to be an independent predictor. Among identified 390 candidate SFs, Y-box protein 3 (YBX3) was significantly correlated with OS and metastasis. Among 177 ASEs, ATP-binding cassette subfamily A member 6 (ABCA6)-43162-AT and PLIN5-46808-AT were identified both associated with OS, bone metastasis and co-expressed with SFs. Then we identified primary bile acid biosynthesis as survival-related (KEGG) pathway by Gene Set Variation Analysis (GSVA) and univariate regression analysis, which was correlated with ABCA6-43162-AT and PLIN5-46808-AT. Finally, we proposed that ABCA6-43162-AT and PLIN5-46808-AT may contribute to HCC poor prognosis and metastasis under the regulation of aberrant YBX3 through the pathway of primary bile acid biosynthesis.

Phase junction enhanced photocatalytic activity of Ga2O3 nanorod arrays on flexible glass fiber fabric.

Ga2O3 nanostructures hold great potential applications in photocatalytic fields due to their stability, high efficiency and environmental friendliness. The construction of phase junction has been proved to be one of the most effective strategies for enhancing Ga2O3 photocatalytic activity. However, the influence of the formation process at the interface of the phase junction on the photocatalytic activity of Ga2O3 nanostructures is far less well understood. In this work, for the first time, large-area Ga2O3 nanorod arrays (NRAs) with controllable α/ß phase junction were prepared in situ on a flexible glass fiber fabric by a facile and environmentally friendly three-step method. The α/ß-Ga2O3 phase junction NRAs exhibit an ultra-high photocatalytic degradation rate of 97% during Ultraviolet (UV) irradiation for 60 min, which is attributed to a unique phase junction promoting efficient charge separation. However, the photocatalytic activity of α/ß-Ga2O3 phase junction NRAs is not evident in the early phase transition, possibly due to the presence of defects acting as charge recombination centers.