Scalable Algorithms for Laplacian Pseudo-inverse Computation

The pseudo-inverse of a graph Laplacian matrix, denoted as $L^\dagger$, finds extensive application in various graph analysis tasks. Notable examples include the calculation of electrical closeness centrality, determination of Kemeny's constant, and evaluation of resistance distance. However, existing algorithms for computing $L^\dagger$ are often computationally expensive when dealing with large graphs. To overcome this challenge, we propose novel solutions for approximating $L^\dagger$ by establishing a connection with the inverse of a Laplacian submatrix $L_v$. This submatrix is obtained by removing the $v$-th row and column from the original Laplacian matrix $L$. The key advantage of this connection is that $L_v^{-1}$ exhibits various interesting combinatorial interpretations. We present two innovative interpretations of $L_v^{-1}$ based on spanning trees and loop-erased random walks, which allow us to develop efficient sampling algorithms. Building upon these new theoretical insights, we propose two novel algorithms for efficiently approximating both electrical closeness centrality and Kemeny's constant. We extensively evaluate the performance of our algorithms on five real-life datasets. The results demonstrate that our novel approaches significantly outperform the state-of-the-art methods by several orders of magnitude in terms of both running time and estimation errors for these two graph analysis tasks. To further illustrate the effectiveness of electrical closeness centrality and Kemeny's constant, we present two case studies that showcase the practical applications of these metrics

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

ObjectiveTo establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.MethodsThe cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.ResultsThe area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).ConclusionMachine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

PurposeTo implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.Materials and MethodsA total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant.ResultsThe majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas.ConclusionThe radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies

Diffusional Channeling in the Sulfate-Activating Complex: Combined Continuum Modeling and Coarse-Grained Brownian Dynamics Studies

Enzymes required for sulfur metabolism have been suggested to gain efficiency by restricted diffusion (i.e., channeling) of an intermediate APS2– between active sites. This article describes modeling of the whole channeling process by numerical solution of the Smoluchowski diffusion equation, as well as by coarse-grained Brownian dynamics. The results suggest that electrostatics plays an essential role in the APS2– channeling. Furthermore, with coarse-grained Brownian dynamics, the substrate channeling process has been studied with reactions in multiple active sites. Our simulations provide a bridge for numerical modeling with Brownian dynamics to simulate the complicated reaction and diffusion and raise important questions relating to the electrostatically mediated substrate channeling in vitro, in situ, and in vivo

A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer’s Disease

Abstract There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer’s Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways

A Rice CPYC-Type Glutaredoxin OsGRX20 in Protection against Bacterial Blight, Methyl Viologen and Salt Stresses

Glutaredoxins (GRXs) belong to the antioxidants involved in the cellular stress responses. In spite of the identification 48 GRX genes in rice genomes, the biological functions of most of them remain unknown. Especially, the biological roles of members of GRX family in disease resistance are still lacking. Our proteomic analysis found that OsGRX20 increased by 2.7-fold after infection by bacterial blight. In this study, we isolated and characterized the full-length nucleotide sequences of the rice OsGRX20 gene, which encodes a GRX family protein with CPFC active site of CPYC-type class. OsGRX20 protein was localized in nucleus and cytosol, and its transcripts were expressed predominantly in leaves. Several stress- and hormone-related motifs putatively acting as regulatory elements were found in the OsGRX20 promoter. Real-time quantitative PCR analysis indicated that OsGRX20 was expressed at a significantly higher level in leaves of a resistant or tolerant rice genotype, Yongjing 50A, than in a sensitive genotype, Xiushui 11, exposed to bacterial blight, methyl viologen, heat, and cold. Its expression could be induced by salt, PEG-6000, 2,4-D, salicylic acid, jasmonic acid, and abscisic acid treatments in Yongjing 50A. Overexpression of OsGRX20 in rice Xiushui 11 significantly enhanced its resistance to bacterial blight attack, and tolerance to methyl viologen and salt stresses. In contrast, interference of OsGRX20 in Yongjing 50A led to increased susceptibility to bacterial blight, methyl viologen and salt stresses. OsGRX20 restrained accumulation of superoxide radicals in aerial tissue during methyl viologen treatment. Consistently, alterations in OsGRX20 expression affect the ascorbate/dehydroascorbate ratio and the abundance of transcripts encoding four reactive oxygen species scavenging enzymes after methyl viologen-induced stress. Our results demonstrate that OsGRX20 functioned as a positive regulator in rice tolerance to multiple stresses, which may be of significant use in the genetic improvement of rice resistance

Copper Ion Attenuated the Antiproliferative Activity of Di-2-pyridylhydrazone Dithiocarbamate Derivative; However, There Was a Lack of Correlation between ROS Generation and Antiproliferative Activity

The use of chelators for cancer treatment has been an alternative option. Dithiocarbamates have recently attracted considerable attention owning to their diverse biological activities; thus, the preparation of new dithiocarbamate derivatives with improved antitumor activity and selectivity as well as probing the underlying molecular mechanism are required. In this study, di-2-pyridylhydrazone dithiocarbamate S-propionic acid (DpdtpA) and its copper complex were prepared and characterized, and its antiproliferative activity was evaluated. The proliferation inhibition assay showed that DpdtpA exhibited excellent antiproliferative effect in hepatocellular carcinoma (IC50 = 1.3 ± 0.3 μM for HepG2, and 2.5 ± 0.6 μM for Bel-7402). However, in the presence of copper ion, the antiproliferative activity of DpdtpA was dramatically attenuated (20–30 fold) owing to the formation of copper chelate. A preliminarily mechanistic study revealed that reactive oxygen species (ROS) generation mediated the antiproliferative activity of DpdtpA, and accordingly induced apoptosis, DNA cleavage, and autophagy. Surprisingly, the cytotoxicity of DpdtpA copper complex (DpdtpA–Cu) was also involved in ROS generation; however, a paradoxical relation between cellular ROS level and cytotoxicity was observed. Further investigation indicated that DpdtpA could induce cell cycle arrest at the S phase; however, DpdtpA–Cu lacked this effect, which explained the difference in their antiproliferative activity

Palmitic Acid Curcumin Ester Facilitates Protection of Neuroblastoma against Oligomeric Aβ40 Insult

Background/Aims: The generation of reactive oxygen species (ROS) caused by amyloid-β (Aβ) is considered to be one of mechanisms underlying the development of Alzheimer’s disease. Curcumin can attenuate Aβ-induced neurotoxicity through ROS scavenging, but the protective effect of intracellular curcumin on neurocyte membranes against extracellular Aβ may be compromised. To address this issue, we synthesized a palmitic acid curcumin ester (P-curcumin) which can be cultivated on the cell membrane and investigated the neuroprotective effect of P-curcumin and its interaction with Aβ. Methods: P-curcumin was prepared through chemical synthesis. Its structure was determined via nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS). An MTT assay was used to assess Aβ cytotoxicity and the protective effect of P-curcumin on SH-SY5Y cells. The effect of P-curcumin on Aβ-induced ROS production in vitro and in vivo were assessed based on changes in dichlorofluorescein (DCF) fluorescence. A spectrophotometric method was employed to detect lipid peroxidation. To mimic the interaction of P-curcumin on cell membranes with Aβ, liposomes were prepared by thin film method. Finally, the interactions between free P-curcumin and P-curcumin cultivated on liposomes and Aβ were determined via spectrophotometry. Results: A novel derivative, palmitic acid curcumin ester was prepared and characterized. This curcumin, cultivated on the membranes of neurocytes, may prevent Aβ-mediated ROS production and may inhibit the direct interaction between Aβ and the cellular membrane. Furthermore, P-curcumin could scavenge Aβ-mediated ROS as curcumin in vitro and in vivo, and had the potential to prevent lipid peroxidation. Morphological analyses showed that P-curcumin was better than curcumin at protecting cell shape. To examine P-curcumin’s ability to attenuate direct interaction between Aβ and cell membranes, the binding affinity of Aβ to curcumin and P-curcumin was determined. The association constants for free P-curcumin and curcumin were 7.66 × 104 M-1 and 7.61 × 105 M-1, respectively. In the liposome-trapped state, the association constants were 3.71 × 105 M-1 for P-curcumin and 1.44× 106 M-1 for curcumin. With this data, the thermodynamic constants of P-curcumin association with soluble Aβ (ΔH, ΔS, and ΔG) were also determined. Conclusion: Cultivated curcumin weakened the direct interaction between Aβ and cell membranes and showed greater neuroprotective effects against Aβ insult than free curcumin