Correlation Patterns Research Articles

The place of subjective emptiness in the structure of personality

BackgroundSubjective emptiness is a transdiagnostic clinical dimension related to suicide, distress, and other maladaptive outcomes. Although it is typically conceptualized as a symptom of borderline personality disorder, it has a different pattern of correlates than other symptoms of that phenotype and is present in individuals with other diagnoses. The goals of this study were to replicate and extend previous findings regarding the place of emptiness as a standalone construct within a relatively comprehensive array of personality features.MethodA sample of 992 anonymous participants (M = 45.37 years, SD = 16.53) who were census-matched to the US population completed an online survey including measures of emptiness, normal-range and maladaptive trait domains and aspects, as well as interpersonal values and problems.ResultsOverall, the pattern of correlations suggests that people who report feelings of emptiness tend to have negative emotions, be socially withdrawn, and are low in energy.ConclusionThese findings highlight the relevance of subjective emptiness beyond the borderline category, replicate previous findings regarding the personality and psychopathology correlates of subjective emptiness, and portray a particular profile of personality that represents a risk factor for the experience of subjective emptiness.

Religious involvement over time: Assessing measurement invariance of the Religious Involvement scale in Ukraine

This study examines the measurement invariance of the Religious Involvement Scale (RI scale) in Ukraine. Using data from the European Social Survey (ESS) and a nationwide survey conducted just before the full-scale Russian invasion in 2022, we provide a detailed analysis of the RI scale in the Ukrainian context. Previous research using multigroup confirmatory factor analysis (MGCFA) across countries lacked a focused analysis on Ukraine, especially after the Revolution of Dignity. We used MGCFA to test the RI scale across different ESS waves and the 2022 Ukrainian survey. The metric invariance of the scale across all time points was not confirmed. We also tested results excluding data from occupied territories and found similar patterns. Partial metric invariance was observed for the period from 2004 to 2013. Our findings indicate that full metric invariance was only achieved for 2006-2013, with scalar invariance not achieved. In addition, the surveys conducted in 2004 and after 2014 had somewhat different patterns of correlations between scale components compared to other waves of the ESS. We suggest that political dynamics have influenced the social aspects of religiosity in Ukraine; therefore, it is important to consider the political context when assessing religious involvement. Our preliminary findings highlight the importance of socio-political factors in measuring and interpreting religiosity.

Joint Short-term Power Forecasting of Hydro-Wind-Photovoltaic Considering Spatiotemporal Delay of Weather Processes

Regional integrated energy systems (RIES) represented by hydro, wind, and solar energy are crucial avenues for future clean energy development, fully leveraging their complementarity can facilitate the orderly supply of renewable energy, significantly enhance the level of new energy consumption, and make outstanding contributions to achieving carbon neutrality goals. However, run-of-river hydropower (RHP), wind power (WP), and photovoltaic (PV) are affected by the chaotic characteristics of the weather system, with significant random fluctuations, and their output characteristics have significant heterogeneity, which brings a big challenge for the complementary coordinated scheduling. The high-accuracy power prediction of RHP, WP, and PV in the region is one of the key means for solving the above problems. To meet this challenge, by analyzing the spatio-temporal correlation properties between weather processes and station output, a novel joint prediction framework for short-term power forecasting (STPF) of regional hydro-wind-PV clusters is proposed. Firstly, to solve the problem of significant heterogeneity of water, wind, and solar, a Differentiated Spatio-Temporal Mixture Network (DSTMN) is proposed, which differentially encodes the weather and power information at multiple locations, enabling the extraction of common features while preserving their specificities, which addresses the difficulty of representing the inherent correlation patterns between wide-area meteorological information and heterogeneous energy sources. Secondly, to further explore the spatio-temporal correlation between meteorological information and power information, to improve the forecasting performance of the model, a spatio-temporal-lagged correlation (STLC) that can quantify their spatio-temporal delays is proposed. By analyzing the correlation characteristics between regional grid numerical weather prediction (NWP) information and station output at different spatial and temporal scales, the optimal spatial location and delay time of NWP inputs under the current scenario are determined for each station. Based on this, the best NWP scene probabilistic transfer model is established based on the Rank Bayesian Ensemble (RBE) method. By fitting the conditional probability distribution of the current best NWP spatiotemporal location and the 2nd day’s best NWP spatiotemporal location, providing a reliable input for the STPF model. Finally, a case study with 164 hydro-wind-solar stations in China demonstrates the effectiveness of the proposed method. Specifically, we achieved an accuracy improvement of 0.46% — 11.03% on the 2nd day of forecasting compared to benchmark methods.

Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions.

Associations between serum levels of liver function biomarkers and all-cause and cause-specific mortality: a prospective cohort study

BackgroundThe liver plays critical roles in human health. Circulating level of liver function biomarkers may associate with the long-term and short-term mortality in general population.MethodsWe used data from US National Health and Nutrition Examination Survey 1988–94 and 1999–2014. People aged ≥ 20 years with measured serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), total bilirubin (TB), and albumin (ALB) at baseline were included. All-cause and cause-specific mortality was identified from the National Death Index through 31 December 2015. Additive Cox regression models were applied to assess the correlation patterns between the serum level of these analytes and mortality risk.ResultsA total of 44,508 participants were included; among them, 9,721 deaths occurred during a mean follow-up of 12.5 years. A “J-shaped” correlation was found between serum levels of ALT, AST, and TB and all-cause mortality. The risk of mortality monotonically increased with increasing GGT and ALP levels when their levels exceeded the valley points. A “L-shaped” correlation was found between the serum level of ALB and all-cause mortality. The correlation patterns were comparable among deaths from different causes and were consistent in subgroup and sensitivity analyses. While the integration of all six liver function biomarkers did not yield an optimal predictive performance for mortality (area under ROC curve = 0.706), it demonstrated a significantly better performance compared to any single biomarker..ConclusionCirculating liver function biomarkers showed diverse nonlinear correlations with mortality and may be utilized as part of a screening process to help identify individuals who may be at elevated risk of mortality.

Ordinal pattern-based analysis of an opposition-controlled turbulent channel flow

We conduct a numerical study on the drag-reduction mechanism of an opposition- controlled turbulent channel flow from the viewpoint of a symbolic dynamics approach. The effect of the virtual wall formed by the opposition control is maximised at the location of the detection plane $y_d^+ \approx 10$ . At this wall-normal location, the local link strength of the self-loop of network nodes representing the negative correlation pattern between the streamwise and wall-normal velocity fluctuations is maximised in the uncontrolled flow. In the controlled case, the multiscale complexity–entropy causality plane and the spatial permutation entropy at $y_d^+ \approx 10$ indicate that the drag-reduction effect is attributed to the reduction of the region where streaks actively coalesce and separate and the suppression of the regeneration cycle in the region near the wall.

The relative effects of climatic drivers and phenotypic integration on phenotypic plasticity of a globally invasive plant

IntroductionUnderstanding the constraints of phenotypic plasticity can provide insights into the factors that limit or influence the capacity of an organism to respond to changing environments. However, the relative effects of external and internal factors on phenotypic plasticity remain largely unexplored. Phenotypic integration, the pattern of correlations among traits, is recognized as an important internal constraint to plasticity. Phenotypic plasticity is critical in facilitating the acclimation of invasive species to the diverse environments within their introduced ranges. Consequently, these species serve as ideal models for investigating phenotypic plasticity and its underlying determinants.MethodsHere, we collected seeds of a global salt marsh invader Spartina alterniflora from seven invasive populations covering the entire latitudinal range in China. These populations were cultivated in two common gardens located at the southern and northern range margins, respectively. We quantified plasticity and variation therein for plant height, shoot density, first flowering day and inflorescence biomass (on a per capita basis). These traits have direct or indirect effects on invasiveness. We examined the relationships between traits plasticity with climatic conditions at site of origin (external factor) and phenotypic integration (internal factor).ResultsWe found that plasticity differed according to the trait being measured, and was higher for a trait affecting fitness. Phenotypic variance increased with latitude and temperature at the site of origin was the primary factor affecting phenotypic variation. These results indicated that external abiotic factors directly affected the selection on phenotypic plasticity of S. alterniflora.DiscussionOur study provides a unique viewpoint on assessing the importance of identifying influential factors and mechanisms underlying phenotypic plasticity. Understanding these factors and mechanisms is a critical indicator for invasive and other cosmopolitan species’ responses, establishment, persistence, and distribution under climate change.

Land Use Change and Its Impact on the Quality of the Ecological Environment in Xinjiang

Xinjiang is a key area for implementing the Belt and Road Initiative, as well as an ecological security barrier in Northwest China. In this study, the remote sensing ecological index (RSEI) was constructed on the basis of the Google Earth Engine (GEE) platform combined with multiperiod (MODIS) remote sensing imagery to analyze the land use changes in Xinjiang and their impacts on the ecological and environmental quality from 2000 to 2023. The results are as follows. (1) The areas of land use changes in Xinjiang are concentrated mainly in the oasis areas around the mountain ranges and the Tarim Basin. (2) The quality of the overall ecological environment of Xinjiang showed an insignificant linear decreasing trend, with an annual rate of change of −0.0009 a−1 (p > 0.05). (3) The spatial distribution of the RSEI in Xinjiang revealed a significant positive spatial correlation and clustering pattern, which was high in the west and low in the east. (4) The increase in the cultivated land area had a positive effect on improving the quality of the ecological environment in Xinjiang. When the cultivated land area increased by 10%, the area of improvement in the quality of the ecological environment increased by 8.8%.

Selection of spillover channels in spatial dynamic panel models using heterogeneous shrinkage on spatial parameters

ABSTRACT This paper proposes a Bayesian approach to estimating heterogeneous spatial dynamic panel models, subject to possible shrinkage on spatial dependence parameters. This amounts to heterogeneous selection of candidate spatial weight matrices that represent different spillover channels. The shrinkage methods include both the traditional and more flexible ones that allow the shrinkage strength to vary across spatial parameters. Monte Carlo results indicate that when the true model has a relatively low proportion of nonzero spatial parameters, flexible shrinkage in general leads to lower average root mean squared errors in estimating these parameters. An empirical study using this approach shows that there exists substantial heterogeneity in spillover channels across counties that determine the correlation patterns of county COVID-19 vaccination rates in four states in the United States.

Characteristics of putative keystones in the healthy adult human gut microbiota as determined by correlation network analysis.

Keystone species are thought to play a critical role in determining the structure and function of microbial communities. As they are important candidates for microbiome-targeted interventions, the identification and characterization of keystones is a pressing research goal. Both empirical as well as computational approaches to identify keystones have been proposed, and in particular correlation network analysis is frequently utilized to interrogate sequencing-based microbiome data. Here, we apply an established method for identifying putative keystone taxa in correlation networks. We develop a robust workflow for network construction and systematically evaluate the effects of taxonomic resolution on network properties and the identification of keystone taxa. We are able to identify correlation network keystone species and genera, but could not detect taxa with high keystone potential at lower taxonomic resolution. Based on the correlation patterns observed, we hypothesize that the identified putative keystone taxa have a stabilizing effect that is exerted on correlated taxa. Correlation network analysis further revealed subcommunities present in the dataset that are remarkably similar to previously described patterns. The interrogation of available metatranscriptomes also revealed distinct transcriptional states present in all putative keystone taxa. These results suggest that keystone taxa may have stabilizing properties in a subset of community members rather than global effects. The work presented here contributes to the understanding of correlation network keystone taxa and sheds light on their potential ecological significance.

Changes in IL-6, IL-12, IL-5, IL-10 and TGF-β1 Concentration in Patients with Treatment-Resistant Schizophrenia (TRS) Following Electroconvulsive Therapy (ECT)-A Pilot Study.

Background/Objectives: Treatment-resistant schizophrenia (TRS) may be considered as a neuro-immune disorder. Electroconvulsive therapy (ECT) remains an important therapeutic option for patients with TRS, however, its impact on cytokine profile is barely investigated. Therefore, this study attempts to establish associations between serum cytokines IL-6, IL-12, IL-5, IL-10 and TGF-β1 changes (pre- and post-ECT) and the effectiveness of ECT in TRS patients. The second aim is to search for correlations between serum concentrations of the above specified cytokines and psychometric assessments of clinical schizophrenia symptoms. Methods: The cytokine concentrations were measured in eight TRS patients on psychopharmacological treatment prior to and following ECT and in 13 control subjects. Psychopathology assessment was based on the Positive and Negative Syndrome Scale (PANSS). Results: Prior to ECT, IL-10 concentration was significantly higher in TRS patients, while IL-5 was decreased in comparison to the controls. A significant concentration decrease in the pro-inflammatory cytokines IL-6 (p = 0.012), IL-12 (p = 0.049) and anti-inflammatory IL-10 (p = 0.012) post-ECT vs. pre-ECT was observed, whereas concentrations of IL-5 and TGF-β1 did not significantly change. Also, a significant decrease in schizophrenia symptoms measured by the PANSS post-ECT was found. Furthermore, the pattern of correlations between PANSS scores and cytokine concentrations was different when comparing levels pre- and post-ECT. Additionally, correlations between changes in PANSS scores and cytokine concentrations were found. Conclusions: These results may indicate the probable impact of electroconvulsive therapy on the balance between pro- and anti-inflammatory cytokines, which may correspond to a neurobiological therapeutic effect of ECT in TRS patients.

Research on the Optimization and Development of Low Carbon Energy Economy from the Perspective of Water Conservancy Engineering

In order to explore the optimal development path of low-carbon energy economy in universities, this paper proposes a new method based on complex network theory to describe the global evolution and transformation characteristics of multivariate time series correlation patterns. In order to more intuitively see the impact of product extraction inputs as endogenous factors on economic growth, this article explores the relationship between economic growth and product extraction inputs by constructing a Hamiltonian function and using optimization methods. When considering whether the extraction input share of products with different extraction ratios is different, this article found the relationship between the total extraction input of products with different extraction ratios and the input share of green and low-carbon products, and analyzed consumers' preferences; Extractive investment of products under budget constraints and policy constraints. In addition, this article uses the dynamic equation of household utility function and environmental quality to prove that the extractive input of products not only promotes carbon reduction, but also has a positive effect on promoting economic growth. The simulation results show that the low-carbon economic development model based on energy efficiency proposed in this paper can effectively improve the development path of low-carbon energy economy.

Ichthyobodo necator infection as a first record in cultured Dicentrarchus labrax: morphomolecular characterization and infestation patterns in correlation to water quality parameters

Ichthyobodo necator is a damaging and economically important protozoan parasite; however, its clinical patterns and incidence, correlated to environmental factors, are still poorly understood. This study provides the first report of Ichthyobodo necator infestation among Dicentrarchus labrax. We recorded significant mortalities in the cultured D. labrax of average size (5.92 g weight, and 11.55 cm length) in El-Max Station, National Institute of Oceanography and Fisheries, NIOF, Egypt, during the period extending from early summer to early fall (June to October) 2023. Behavioral and gross lesions of the infested fish were recorded; besides, dead and moribund fish were collected. Wet smears of the skin and gill of the affected fish were examined by compound light microscopy. Moreover, water samples from the affected ponds were collected and examined for their physicochemical parameters. The bodonid biflagellated Ichthyobodo necator (Henneguy) C. Pinto, 1928 was isolated from the skin and gills and identified based on specific morphological characteristics. DNA sequencing of PCR products and phylogenetic analysis confirmed identification, with clustering of the current isolates with I. necator isolates from different fish species. The multivariate analysis revealed a positive correlation between the prevalence and infestation intensity of Ichthyobodo, increased temperature levels, decreased dissolved oxygen DO, and also correlated with higher ammonia, and pH values. Additionally, the elevated concentrations of iron, calcium, magnesium, zinc, and copper in pond water were positively correlated with infestation and mortalities. These water parameter levels promote respiratory distress, accelerated prevalence, and infestation intensities of I. necator, particularly during the infestation peak in August and September. This study bridges the knowledge gap regarding I. necator parasite infestations and their correlation with water quality parameters.

Yearly Assessment of Weather and Air Quality Impact on Respiratory Disease Hospitalizations: Faial Island (Azores) Study

Building upon our previous research conducted at weekly and monthly intervals, this study investigates the yearly dynamics of weather conditions and air quality on respiratory diseases, specifically tailored to Faial Island in the Azores. Expanding our analysis to a yearly basis allows for a more comprehensive understanding of long-term trends and seasonal variations in hospital admissions. Drawing upon extensive daily data spanning from 2008 to 2019, sourced from the Statistics Service of Hospital da Horta, the Meteorological Observatory Príncipe Alberto de Mónaco, and the Air Quality Monitoring Network of the Azores, we examined the differentiated relationships between yearly meteorological variables, such as temperature, dew point, and wind direction, and respiratory health outcomes. Additionally, we explored the persistent influence of air quality indicators, including suspended particulate matter with an aerodynamic diameter of 10 micrometers (µm) or less (PM10), sulfur dioxide (SO<sub>2)</sub>, nitrogen dioxide (NO<sub>2)</sub>, and ozone (O<sub>3</sub>), across different seasons and years. By incorporating yearly data into our analysis, this study aims to provide a comprehensive and nuanced understanding of the interplay between environmental factors and respiratory disease hospitalizations, thereby informing targeted interventions and public health strategies in insular regions. The principal component analysis (PCA) applied to the yearly data reveals interesting correlation patterns between meteorological variables and hospitalizations for respiratory diseases such as asthma, pneumonia, and rhinitis. Hospital admissions were found to increase during periods of lower temperatures and smaller temperature amplitudes, suggesting that prolonged cold conditions may exacerbate symptoms of these diseases. Additionally, atmospheric pollutant concentrations, such as suspended particles PM10, SO<sub>2</sub>, NO<sub>2</sub>, and O<sub>3</sub>, significantly varied across seasons, with a notable influence on the exacerbation of respiratory diseases. In insular regions like the Azores, these findings highlight the importance of targeted public health strategies to mitigate the impact of environmental factors on respiratory health.

Multi-stage early Cretaceous magmatism in the Tongling region, lower Yangtze metallogenic belt (E. China)

ABSTRACT A combined investigation of geochemistry and geochronology was carried out for Early Cretaceous magmatic rocks in Tongling, centre of the Middle and Lower Yangtze River. The magmatic activity can be divided into two stages, peaking at ~140 Ma and ~127 Ma. The previous show high Sr and Sr/Y, low Y, and the later have low Sr, Sr/Y and La/Yb. There are correlation patterns between Sr-Nd isotopes and SiO2, and samples show little variation of V and Cr with large variation of Rb, which suggest the effects of partial melting of the diverse source and magma mixing/crustal assimilation were more important than fractional crystallization. The broad variation of εNd(t) and εHf(t) values can be attributed to binary mixing of YC lower crust with slab-derived or enriched mantle melts. The TDM2 of Nd and Hf, Zr saturation temperature, and magma oxygen fugacity display increased trend from ~140 Ma to ~127 Ma. Based on these signatures, origin model of the Early Cretaceous intrusions in Tongling was proposed. Affected by Pacific Plate subduction, following metasomatism by slab-derived fluid/melts, basaltic magma derived from lithospheric mantle underplated the lower crust. Meanwhile, partial melting of the lower crust formed the intermediate-felsic rocks of the first stage. As subduction continued, roll-back of the subducted slab resulted in extension, causing lithospheric thinning and the upwelling of hot asthenosphere. The second stage intrusions were the result of the reworking of the Meso-Neoproterozoic crust in the extension tectonic setting.

Integrated assessment of urban green infrastructure multifunctionality: Insights from Stavanger

Optimised contributions of green infrastructure (GI) to urban ecosystem services are strongly related to its multifunctionality. The challenge, however, is that the concept of multifunctionality still needs to be transformed into an operationalised assessment to evaluate current performance, which is instrumental in supporting spatial planning and policy strategies. Using the case of Stavanger City (Norway), the study conducted a spatial assessment of the multifunctionality of the urban green infrastructure. The study used a comprehensive set of 27 function indicators estimated for each of the 156 spatial units classified by their type, age, size, and biophysical characteristics. Correlation patterns among indicators and how the average and effective multifunctionality related to unit characteristics were analysed using correlation and multivariate approaches.The study demonstrated weak correlations between function indicators but revealed some potential trade-offs and function bundles. Notably, bundles related to tree cover (e.g. C sequestration, stormwater retention) had negative relationships with facilitation measures. There was a large overlap in functions between GI types associated with public green spaces and parks. Moreover, the characteristics of green infrastructure units, like size and age, primarily affected multifunctionality through effects on function indicators. Regarding the city-wide multifunctionality, we found some turnover and subsetting of functions among units, supporting multifunctionality at larger spatial scales. However, the average contributions from different GI types were similar. The study highlights the need to understand correlation patterns among function indicators and function bundles as critical to benefit from synergies and avoid unintentional trade-offs when designing and managing urban green areas.

The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder.

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

Multimodal state-dependent connectivity analysis of arousal and autonomic centers in the brainstem and basal forebrain.

Vigilance is a continuously altering state of cortical activation that influences cognition and behavior and is disrupted in multiple brain pathologies. Neuromodulatory nuclei in the brainstem and basal forebrain are implicated in arousal regulation and are key drivers of widespread neuronal activity and communication. However, it is unclear how their large-scale brain network architecture changes across dynamic variations in vigilance state (i.e., alertness and drowsiness). In this study, we leverage simultaneous EEG and 3T multi-echo functional magnetic resonance imaging (fMRI) to elucidate the vigilance-dependent connectivity of arousal regulation centers in the brainstem and basal forebrain. During states of low vigilance, most of the neuromodulatory nuclei investigated here exhibit a stronger global correlation pattern and greater connectivity to the thalamus, precuneus, and sensory and motor cortices. In a more alert state, the nuclei exhibit the strongest connectivity to the salience, default mode, and auditory networks. These vigilance-dependent correlation patterns persist even after applying multiple preprocessing strategies to reduce systemic vascular effects. To validate our findings, we analyze two large 3T and 7T fMRI datasets from the Human Connectome Project and demonstrate that the static and vigilance-dependent connectivity profiles of the arousal nuclei are reproducible across 3T multi-echo, 3T single-echo, and 7T single-echo fMRI modalities. Overall, this work provides novel insights into the role of neuromodulatory systems in vigilance-related brain activity.

The Correlation Patterns of miRNA Expression with Targeted mRNA Transcripts in Glioma Patients with Wild-Type and Mutated Isocitrate Dehydrogenase (IDH) Genotypes.

Low-grade gliomas are divided into two main genetic phenotypes based on the presence or absence of mutations in the isocitrate dehydrogenase (IDH) genes. The mutated IDH phenotype (IDHmut), in contrast to the wild-type phenotype (IDHwt), is characterized by a more positive response to pharmacological intervention and a significantly longer survival time. In this study, we analyzed the differential co-expression of 225,000 microRNA-mRNA pairs at the level of correlations between microRNA levels and their potential mRNA targets. Analysis of the associative relationships of individual representatives of the selected pairs revealed that the level of mRNAs encoded by the ELN, ARL4C, C9orf64, PLAT, and FKBP9 genes associated with aggressive progression of glioma was increased in the IDHwt group. Meanwhile, the levels of miRNA-182, miRNA-455, and miRNA-891a associated with the negative prognosis in glioma were generally increased in the IDHmut group. Most (16/21) of the detected 21 microRNA-mRNA pairs with significant difference in regulation between IDHwt and IDHmut glioma samples had a weak or moderate positive correlation in IDHmut samples and a negative correlation in IDHwt samples. Therefore, our findings indicate that glioma samples from the IDHmut group with a positive prognosis potentially have a significantly less pronounced ability to microRNA-mediated regulation. We further suggest that such physiological disorders can lead to reduced tumor viability, resulting in an increased ability of the host to resist the spread of a malignant transformation of this genetic phenotype.

ME[formula omitted]A: A Multimodal Entity Entailment framework for multimodal Entity Alignment

Current methods for multimodal entity alignment (MEA) primarily rely on entity representation learning, which undermines entity alignment performance because of cross-KG interaction deficiency and multimodal heterogeneity. In this paper, we propose a Multimodal Entity Entailment framework of multimodal Entity Alignment task, ME3A, and recast the MEA task as an entailment problem about entities in the two KGs. This way, the cross-KG modality information directly interacts with each other in the unified textual space. Specifically, we construct the multimodal information in the unified textual space as textual sequences: for relational and attribute modalities, we combine the neighbors and attribute values of entities as sentences; for visual modality, we map the entity image as trainable prefixes and insert them into sequences. Then, we input the concatenated sequences of two entities into the pre-trained language model (PLM) as an entailment reasoner to capture the unified fine-grained correlation pattern of the multimodal tokens between entities. Two types of entity aligners are proposed to model the bi-directional entailment probability as the entity similarity. Extensive experiments conducted on nine MEA datasets with various modality combination settings demonstrate that our ME3A effectively incorporates multimodal information and surpasses the performance of the state-of-the-art MEA methods by 16.5% at most.