mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network (GNN) AI models have been widely used to analyze graph-structure datasets and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data by node and edge ranking analysis for signaling flow/cascade inference. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert them into graph-structure data for individual samples, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. We evaluated the mosGraphGen using both multi-omics datasets of cancer and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/Multi-OmicGraphBuilder/mosGraphGen.

circRNA6448-14/miR-455-3p/OTUB2 axis stimulates glycolysis and stemness of esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with high incidence. This study aimed to reveal the complete circRNA-miRNA-mRNA regulatory network in ESCC and validate its function mechanism. METHOD: Expression of OTU Domain-Containing Ubiquitin Aldehyde-Binding Protein 2 (OTUB2) in ESCC was analyzed by bioinformatics to find the binding sites between circRNA6448-14 and miR-455-3p, as well as miR-455-3p and OTUB2. The binding relationships were verified by RNA Immunoprecipitation (RIP) and dual-luciferase assay. The expressions of circRNA6448-14, miR-455-3p, and OTUB2 were detected by quantitative real-time polymerase chain reaction (qRT-PCR). MTT assay measured cell viability, and the spheroid formation assay assessed the ability of stem cell sphere formation. Western blot (WB) determined the expression of marker proteins of stem cell surface and rate-limiting enzyme of glycolysis. The Seahorse XFe96 extracellular flux analyzer measured the rate of extracellular acidification rate and cellular oxygen consumption. Corresponding assay kits assessed cellular glucose consumption, lactate production, and adenosine triphosphate (ATP) generation. RESULTS: In ESCC, circRNA6448-14 and OTUB2 were highly expressed in contrast to miR-455-3p. Knocking down circRNA6448-14 could prevent the glycolysis and stemness of ESCC cells. Additionally, circRNA6448-14 enhanced the expression of OTUB2 by sponging miR-455-3p. Overexpression of OTUB2 or silencing miR-455-3p reversed the inhibitory effect of knockdown of circRNA6448-14 on ESCC glycolysis and stemness. CONCLUSION: This research demonstrated that the circRNA6448-14/miR-455-3p/OTUB2 axis induced the glycolysis and stemness of ESCC cells. Our study revealed a novel function of circRNA6448-14, which may serve as a potential therapeutic target for ESCC.

The influence of Life's Essential 8 on the link between socioeconomic status and depression in adults: a mediation analysis.

BACKGROUND: Individuals with low socioeconomic status (SES) are at a higher risk of developing depression. However, evidence on the role of cardiovascular health (CVH) in this chain is sparse and limited. The purpose of this research was to assess the mediating role of Life's Essential 8 (LE8), a recently updated measurement of CVH, in the association between SES and depression according to a nationally representative sample of adults. METHODS: Data was drawn from the National Health and Nutrition Examination Survey (NHANES) in 2013-2018. Multivariate logistic regression analysis was applied to analyze the association of SES (measured via the ratio of family income to poverty (FIPR), occupation, educational level, and health insurance) and LE8 with clinically relevant depression (CRD) (evaluated using the Patient Health Questionnaire (PHQ-9)). Multiple linear regression analysis was performed to analyze the correlation between SES and LE8. Mediation analysis was carried out to explore the mediating effect of LE8 on the association between SES and CRD. Moreover, these associations were still analyzed by sex, age, and race. RESULTS: A total of 4745 participants with complete PHQ-9 surveys and values to calculated LE8 and SES were included. In the fully adjusted model, individuals with high SES had a significantly higher risk of CRD (odds ratio = 0.21; 95% confidence interval: 0.136 to 0.325, P < 0.01) compared with those with low SES. Moreover, LE8 was estimated to mediate 22.13% of the total association between SES and CRD, and the mediating effect of LE8 varied in different sex and age groups. However, the mediating effect of LE8 in this chain was significant in different sex, age, and racial subgroups except for Mexican American (MA) individuals. CONCLUSION: The results of our study suggest that LE8 could mediate the association between SES and CRD. Additionally, the mediating effect of LE8 in this chain could be influenced by the race of participants.

Boosting interpersonal emotion regulation through facial imitation: functional neuroimaging foundations.

Empathic function, which is primarily manifested by facial imitation, is believed to play a pivotal role in interpersonal emotion regulation for mood reinstatement. To explore this association and its neural substrates, we performed a questionnaire survey (study l) to identify the relationship between empathy and interpersonal emotion regulation; and a task-mode fMRI study (study 2) to explore how facial imitation, as a fundamental component of empathic processes, promotes the interpersonal emotion regulation effect. Study 1 showed that affective empathy was positively correlated with interpersonal emotion regulation. Study 2 showed smaller negative emotions in facial imitation interpersonal emotion regulation (subjects imitated experimenter's smile while followed the interpersonal emotion regulation guidance) than in normal interpersonal emotion regulation (subjects followed the interpersonal emotion regulation guidance) and Watch conditions. Mirror neural system (e.g. inferior frontal gyrus and inferior parietal lobe) and empathy network exhibited greater activations in facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation condition. Moreover, facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation exhibited increased functional coupling from mirror neural system to empathic and affective networks during interpersonal emotion regulation. Furthermore, the connectivity of the right orbital inferior frontal gyrus-rolandic operculum lobe mediated the association between the accuracy of facial imitation and the interpersonal emotion regulation effect. These results show that the interpersonal emotion regulation effect can be enhanced by the target's facial imitation through increased functional coupling from mirror neural system to empathic and affective neural networks.

Maximum correentropy-based robust Square-root Cubature Kalman Filter for vehicular cooperative navigation.

As the core method of cooperative navigation, relative positioning plays a key role in realizing intelligent vehicle driving and vehicle self-assembling network collaboration algorithms. However, when the contamination rate of measurement noise is high, the performance of filtering will be seriously affected. To better address the filtering performance degradation problem due to noise contamination, this paper proposes a vehicular cooperative localization method based on the Maximum Correentropy Robust Square-root Cubature Kalman Filter (MCSCKF). The algorithm not only retains the advantages of Square-root Cubature Kalman Filter (SCKF) but also has strong robustness to non-Gaussian noise. The experimental results of tightly integrated vehicular cooperative navigation show that compared with the Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF), the localization accuracy of MCSCKF is improved by 35.08% and 31.83%, respectively, which verified the effectiveness in improving the accuracy and robustness of the relative position estimation.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling.

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.

Dexmedetomidine Improves Anxiety-like Behaviors in Sleep-Deprived Mice by Inhibiting the p38/MSK1/NFκB Pathway and Reducing Inflammation and Oxidative Stress.

(1) Background: Sleep deprivation (SD) triggers a range of neuroinflammatory responses. Dexmedetomidine can improve sleep deprivation-induced anxiety by reducing neuroinflammatory response but the mechanism is unclear; (2) Methods: The sleep deprivation model was established by using an interference rod device. An open field test and an elevated plus maze test were used to detect the emotional behavior of mice. Mouse cortical tissues were subjected to RNA sequence (RNA-seq) analysis. Western blotting and immunofluorescence were used to detect the expression of p38/p-p38, MSK1/p-MSK1, and NFκBp65/p- NFκBp65. Inflammatory cytokines were detected using enzyme-linked immunosorbent assay (ELISA); (3) Results: SD triggered anxiety-like behaviors in mice and was closely associated with inflammatory responses and the MAPK pathway (as demonstrated by transcriptome analysis). SD led to increased expression levels of p-p38, p-MSK1, and p-NFκB. P38 inhibitor SB203580 was used to confirm the important role of the p38/MSK1/NFκB pathway in SD-induced neuroinflammation. Dexmedetomidine (Dex) effectively improves emotional behavior in sleep-deprived mice by attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex, mainly by inhibiting the activation of the p38/MSK1/NFκB pathway; (4) Conclusions: Dex inhibits the activation of the p38/MSK1/NFκB pathway, thus attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex of mice.

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition.

Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best.

Correction: Crystalline matrix-activated spin-forbidden transitions of engineered organic crystals.

Correction for 'Crystalline matrix-activated spin-forbidden transitions of engineered organic crystals' by Heming Zhang et al., Phys. Chem. Chem. Phys., 2023, 25, 11102-11110, DOI: https://doi.org/10.1039/d3cp00187c.

Empagliflozin ameliorates diabetic cardiomyopathy via regulated branched-chain amino acid metabolism and mTOR/p-ULK1 signaling pathway-mediated autophagy.

BACKGROUND: Empagliflozin, a sodium-glucose co-transporter 2 inhibitor (SGLT2i), has been reported to significantly reduce the risk of heart failure in multiple clinical studies. However, the underlying mechanisms remain elusive. This study aimed to investigate the effect of empagliflozin on branched-chain amino acid (BCAA) metabolism in diabetic cardiomyopathy. METHODS: Thirty male 8-week KK Cg-Ay/J mice were used to study diabetic cardiomyopathy; here, 15 were used as the model group, and the remaining 15 were administered empagliflozin (3.75 mg/kg/day) by gavage daily for 16 weeks. The control group consisted of fifteen male 8-week C57BL/6J mice, whose blood glucose and body weight were measured simultaneously with the diabetic mice until 16 weeks without additional intervention. Echocardiography and histopathology were performed to evaluate cardiac structure and function. Proteomic sequencing and biogenic analysis were performed on mouse hearts. Parallel Reaction Monitoring and western blotting were performed to validate the expression levels of differentially expressed proteins. RESULTS: The results showed that empagliflozin improved ventricular dilatation and ejection fraction reduction in diabetic hearts, as well as the elevation of myocardial injury biomarkers hs-cTnT and NT-proBNP. At the same time, empagliflozin alleviates myocardial inflammatory infiltration, calcification foci deposition, and fibrosis caused by diabetes. The results of the proteomics assay showed that empagliflozin could improve the metabolism of various substances, especially promoting the BCAA metabolism of diabetic hearts by up-regulating PP2Cm. Furthermore, empagliflozin could affect the mTOR/p-ULK1 signaling pathway by reducing the concentration of BCAA in diabetic hearts. When mTOR/p-ULK1 protein was inhibited, ULK1, the autophagy initiation molecule, increased. Moreover, autophagy substrate p62 and autophagy marker LC3B were significantly reduced, indicating that the autophagy activity of diabetes inhibition was reactivated. CONCLUSIONS: Empagliflozin may attenuate diabetic cardiomyopathy-related myocardial injury by promoting the catabolism of BCAA and inhibiting mTOR/p-ULK1 to enhance autophagy. These findings suggest that empagliflozin could be a potential candidate drug against BCAA increase and could be used for other cardiovascular diseases with a metabolic disorder of BCAA.

Crystalline matrix-activated spin-forbidden transitions of engineered organic crystals.

Spin-forbidden excitation is an efficient way to obtain triplet excitons directly from the ground state of organic semiconductors. According to perturbation theory under the framework of Fermi's golden rule, this process requires spin-orbit coupling (SOC) and the transition dipole moment (TDM) to be combined through an intermediate state that mixes the initial and final states. While previous research has focused mostly on enhancing SOC, little attention has been paid to engineering the coupling between SOC and the TDM in organic materials. In this study, a series of engineered crystals were designed by doping guest molecules into host organic crystals. The confinement of the guest molecule within a crystalline matrix of the host provides a strong intermolecular interaction to couple both SOC and the TDM. This in turn activates the spin-forbidden excitation directly from the ground state to a "dark" triplet state. Based on a comparison of different engineered crystals, strong intermolecular interaction is identified to induce a distortion of the ligands and further enhancing the spin-forbidden excitation. This work outlines a strategy for designing spin-forbidden excitation.

Performance and mechanism of Pb2+ and Cd2+ ions' adsorption via modified antibiotic residue-based hydrochar.

This study investigated the hydrochar-based porous carbon prepared by combining the technical route of hydrothermal carbonization (HTC) + chemical activation. The hydrochar morphology was adjusted by changing the activation reaction conditions and adding metal salts. Experiments showed that the activation of KHCO3 significantly increased the specific surface area and pore size of the hydrochar. Besides, oxygen-rich groups on the surface of the activated hydrochar interacted with heavy metal ions to achieve efficient adsorption. The activated hydrothermal carbon adsorption capacity for Pb2+ and Cd2+ ions reached 289 and 186 mg/g, respectively. The adsorption mechanism study indicated that the adsorption of Pb2+ and Cd2+ was related to electrostatic attraction, ion exchange, and complexation reactions. The "HTC + chemical activation" technology was environmentally friendly and effectively implemented antibiotic residues. Carbon materials with high adsorption capacity can be prepared so that biomass resources can be utilized with excessive value, as a consequence presenting technical assistance for the comprehensive disposal of organic waste in the pharmaceutical industry and establishing a green and clean production system.

The role of periodontitis in the link between alpha-tocopherol intake and cognitive performance: A mediation analysis in older adults.

Background: Epidemiological evidence on alpha (α)-tocopherol intake and cognitive performance in older individuals is controversial and the effect of periodontitis in this chain is sparse and limited. The goal of this study was to characterize the association between α-tocopherol intake and cognitive performance and the mediating role of periodontitis in a nationally representative sample of older adults. Methods: Data from the National Health and Nutrition Examination Survey (NHANES), 2011-2014, were used. Multivariate logistic regression analysis was performed to explore the association of α-tocopherol intake, periodontal measures (mean attachment loss [AL] and mean probing depth [PD]), and clinical periodontitis defined by the European Workshop in Periodontology with poor cognitive performance evaluated by Consortium to Establish a Registry for Alzheimer's disease (CERAD); the animal fluency test (AFT); and the Digit Symbol Substitution test (DSST) and the correlation between α-tocopherol intake and clinical periodontitis. Multiple linear regression analysis was used to explore the relationship between α-tocopherol intake and periodontal measures. Mediation analysis was used to test the effects of periodontal measures on the association between α-tocopherol intake and cognitive measures. Results: A total of 1,749 older participants (≥60 years of age) with complete periodontal diagnosis, dietary retrospective survey, and cognitive tests were included. In the fully adjusted model, the odds ratio (OR) with 95% confidence interval (CI) of CERAD score, AFT score and DSST score were 0.214 (0.137-0.327), 0.378 (0.241-0.585) and 0.298 (0.169-0.512) for the highest versus lowest tertile of α-tocopherol intake, respectively. And participants with clinical periodontitis were more likely to exhibit lower DSST score (OR = 1.689; 95 CI%: 1.018-2.771) than those without periodontitis. Mean AL (OR = 1.296; 95 CI%: 1.102-1.524) and PD (OR = 1.667; 95 CI%: 1.18-2.363) were negatively correlated with DSST, and were estimated to mediate 9.1 and 8.2% of the total association between α-tocopherol intake and cognitive performance, respectively. Conclusion: Finding of the present study suggested that participants with low α-tocopherol intake were at higher risk for developing cognitive decline. Moreover, periodontitis mediated the association between α-tocopherol intake and cognitive performance.

Dynamic stability of a blue multi-resonance thermally activated delayed fluorescence molecule without damaging the luminescence properties.

The instability of blue emitters is one of the shortcomings of organic light-emitting diodes (OLEDs) in industrial applications. This instability is intrinsically associated with the basic transitions and reactions in the excited states. In this work, using the framework of Fermi's golden rule and DFT/TDDFT, the mechanisms of the transitions and reactions of a typical boron based multi-resonance thermally activated delayed fluorescence emitter involving the excited states were investigated. A dynamic stability mechanism describing recycling between the dissociation of the molecular structure in the T1 state and restoration in the S0 state dominated by steric effects was discovered. Applying knowledge of this mechanism, a small modification was made to the molecular structure, and the stability was increased without degrading other luminescence properties such as the luminescence color, FWHM, reverse intersystem crossing, fluorescence quantum yield, and internal quantum yield.

Interaction Between Memory Load and Experimental Design on Brain Connectivity and Network Topology.

The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation. While providing many insights into brain functions, the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal. Recent studies utilized one single long run for task trials of the same condition, the so-called continuous design, to investigate functional connectivity based on task functional magnetic resonance imaging. Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment, which has been examined for working memory, sensory, motor, and semantic task experiments in previous research. But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks. This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks, by using repeated-measures analysis of variance. Across 50 young healthy adults, behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load. Imaging results revealed that the cingulo-opercular, fronto-parietal, and default model networks were associated with not only task activation, but significant main effects of design and load as well as their interaction on intra- and inter-network functional connectivity and global network topology. Moreover, a significant behavior-brain association was identified for the continuous design. This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.

Cortical connectivity gradients and local timescales during cognitive states are modulated by cognitive loads.

Although resting-state fMRI studies support that human brain is topographically organized regarding localized and distributed processes, it is still unclear about the task-modulated cortical hierarchy in terms of distributed functional connectivity and localized timescales. To address, current study investigated the effect of cognitive load on cortical connectivity gradients and local timescales in the healthy brain using resting state fMRI as well as 1- and 2-back working memory task fMRI. The results demonstrated that (1) increased cognitive load was associated with lower principal gradient in transmodal cortices, higher principal gradient in primary cortices, decreased decay rate and reduced timescale variability; (2) global properties including gradient variability, timescale decay rate, timescale variability and network topology were all modulated by cognitive load, with timescale variability related to behavioral performance; and (3) at 2-back state, the timescale variability was indirectly and negatively linked with global network integration, which was mediated by gradient variability. In conclusion, current study provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states, which could contribute to better understanding about cognitive load theory and brain disorders with cognitive dysfunction.

Seroepidemiology of Neosporosis in Various Animals in the Qinghai-Tibetan Plateau.

Neosporosis is a worldwide infectious disease caused by intracellular parasite Neospora caninum that is a major pathogen of abortion in cattle and neurological disorders in other hosts. However, limited data are available on animals exposed to N. caninum in the Qinghai-Tibetan Plateau Area (QTPA), and little is known about whether animals in the plateau area play an important role in the epidemiology of N. caninum. Therefore, indirect ELISAs based on a combination of NcSAG1 and NcGRA7 antigens were developed to examine both N. caninum-specific IgG and IgM antibodies in Tibetan sheep, yak, cow, pig, cattle, horse, chicken, camel, and donkey from the QTPA in this study. The results showed that all current species present- IgG and IgM-positive animals, and that the overall seroprevalence of N. caninum were 18.6 (703/3,782) and 48.1% (1,820/3,782) for the IgG and IgM antibodies, respectively. Further analysis found significant differences from different altitudes in IgG in Tibetan sheep and IgM in the yak. Hence, the present serological results indicate that the tested animal populations in the QTPA are suffering from N. caninum infections or have become carriers of N. caninum antibodies. To the best of our knowledge, this is the first report on current N. caninum-infected animals in the QTPA, the first epidemiology of neosporosis in cow and camel in China, and the first record of N. caninum IgM antibodies in all the surveyed animals in China. This study provides the latest valuable data on the epidemiology of neosporosis in China and in plateau areas of the world.

Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients.

Industrial applications of fault detection and diagnosis face great challenges as they require not only accurate identification of faulty statuses but also the effective understandability of the results. In this paper, a two-step robust and understandable fault detection and diagnosis framework is developed to address this challenge by exploiting denoising sparse autoencoder and smooth integrated gradients. Specifically, denoising sparse autoencoder(DSAE) is utilized to detect faults in the first step. DSAE is more robust to noise corruption and has better generalization performance compared to the existing autoencoder-based methods. In the second step, smooth integrated gradients(SIG) is used to diagnose the root-cause variables of the faults detected. Smooth integrated gradients can provide a denoising effect on the feature importance. The proposed framework is evaluated through an application to the Tennessee Eastman process. As proved in the experiments, the presented DSAE-SIG method not only achieves higher diagnosis accuracy but also successfully identifies the potential root-cause variables of process disturbances.

Propofol Inhibits Thyroid Cancer Cell Proliferation, Migration, and Invasion by Suppressing SHH and PI3K/AKT Signaling Pathways via the miR-141-3p/BRD4 Axis.

Objective: This study explores the effect and mechanism of propofol for thyroid tumor. Methods: Culture human normal thyroid cells Nthy-ori 3-1 and thyroid cancer cell line TPC-1. TPC-1 cells were divided into the propofol group (treated with propofol), miR-141-3p group (transfected with the miR-141-3p mimic), negative control group (transfected with miR-NC), miR-141-3p + pcDNA-BRD4 group (transfected with the miR-141-3p mimic and pcDNA-BRD4), miR-141-3p + pcDNA group (transfected with the miR-141-3p mimic and pcDNA), siBRD4 group (transfected with siBRD4), and si-control group (transfected with si-control). The detection of miR-141-3p and BRD4 expression in cells was done by RT-qPCR, and the dual-luciferase reporter gene method and western blotting were used to verify the targeting relationship between miR-141-3p and BRD4. MTT method was used to test cell proliferation, transwell method was used to test cell migration and invasion, and western blotting was used to test SHH, GLI1, p-PI3K, and p-AKT protein expression. Results: Compared with Nthy-ori 3-1 cells, the expression of miR-141-3p in TPC-1 cells was markedly decreased. Propofol treatment and excessive expression of miR-141-3p could influence the phenotype of TPC-1 cells. BRD4 is one of the target genes of miR-141-3p, and its expression is negatively regulated by miR-141-3p. Overexpression of BRD4 can partially reverse the restraining effect of miR-141-3p on the TPC-1 cell phenotype. Both miR-141-3p and BRD4 can regulate the activity of SHH and PI3K/AKT signaling pathways. Conclusion: Propofol can inhibit the activity of SHH and PI3K/AKT pathways by targeting downregulating BRD4 through miR-141-3p, thereby inhibiting the phenotype of TPC-1 cells.