Identification of potential molecular mechanism related to craniofacial dysmorphism caused by FOXI3 deficiency.

BACKGROUND: Hemifacial macrosomia (HFM, OMIM 164210) is a complex and highly heterogeneous disease. FORKHEAD BOX I3 (FOXI3) is a susceptibility gene for HFM, and mice with loss of function of Foxi3 did exhibit a phenotype similar to craniofacial dysmorphism. However, the specific pathogenesis of HFM caused by FOXI3 deficiency remains unclear till now. METHOD: In this study, we first constructed a Foxi3 deficiency (Foxi3-/- ) mouse model to verify the craniofacial phenotype of Foxi3-/- mice, and then used RNAseq data for gene differential expression analysis to screen candidate pathogenic genes, and conducted gene expression verification analysis using quantitative real-time PCR. RESULTS: By observing the phenotype of Foxi3-/- mice, we found that craniofacial dysmorphism was present. The results of comprehensive bioinformatics analysis suggested that the craniofacial dysmorphism caused by Foxi3 deficiency may be involved in the PI3K-Akt signaling pathway. Quantitative real-time PCR results showed that the expression of PI3K-Akt signaling pathway-related gene Akt2 was significantly increased in Foxi3-/- mice. CONCLUSION: The craniofacial dysmorphism caused by the deficiency of Foxi3 may be related to the expression of Akt2 and PI3K-Akt signaling pathway. This study laid a foundation for understanding the function of FOXI3 and the pathogenesis and treatment of related craniofacial dysmorphism caused by FOXI3 dysfunction.

Unveiling the best predictive models for early­onset metastatic cancer: Insights and innovations (Review).

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early­onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu.

Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus-subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience.

Multi-Parameter Optimization of Rubidium Laser Optically Pumped Magnetometers with Geomagnetic Field Intensity.

Rubidium laser optically pumped magnetometers (OPMs) are widely used magnetic sensors based on the Zeeman effect, laser pumping, and magnetic resonance principles. They measure the magnetic field by measuring the magnetic resonance signal passing through a rubidium atomic gas cell. The quality of the magnetic resonance signal is a necessary condition for a magnetometer to achieve high sensitivity. In this research, to obtain the best magnetic resonance signal of rubidium laser OPMs in the Earth's magnetic field intensity, the experiment system of rubidium laser OPMs is built with a rubidium atomic gas cell as the core component. The linewidth and amplitude ratio (LAR) of magnetic resonance signals is utilized as the optimization objective function. The magnetic resonance signals of the magnetometer experiment system are experimentally measured for different laser frequencies, radio frequency (RF) intensities, laser powers, and atomic gas cell temperatures in a background magnetic field of 50,765 nT. The experimental results indicate that optimizing these parameters can reduce the LAR by one order of magnitude. This shows that the optimal parameter combination can effectively improve the sensitivity of the magnetometer. The sensitivity defined using the noise spectral density measured under optimal experimental parameters is 1.5 pT/Hz1/2@1 Hz. This work will provide key technical support for rubidium laser OPMs' product development.

Osteopontin Promotes Macrophage M1 Polarization by Activation of the JAK1/STAT1/HMGB1 Signaling Pathway in Nonalcoholic Fatty Liver Disease.

Background and Aims: Osteopontin (OPN) is reported to be associated with the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the function of OPN in NAFLD is still inconclusive. Therefore, our aim in this study was to evaluate the role of OPN in NAFLD and clarify the involved mechanisms. Methods: We analyzed the expression change of OPN in NAFLD by bioinformatic analysis, qRT-PCR, western blotting and immunofluorescence staining. To clarify the role of OPN in NAFLD, the effect of OPN from HepG2 cells on macrophage polarization and the involved mechanisms were examined by FACS and western blotting. Results: OPN was significantly upregulated in NAFLD patients compared with normal volunteers by microarray data, and the high expression of OPN was related with disease stage and progression. OPN level was also significantly increased in liver tissue samples of NAFLD from human and mouse, and in HepG2 cells treated with oleic acid (OA). Furthermore, the supernatants of OPN-treated HepG2 cells promoted the macrophage M1 polarization. Mechanistically, OPN activated the janus kinase 1(JAK1)/signal transducers and activators of transcription 1 (STAT1) signaling pathway in HepG2 cells, and consequently HepG2 cells secreted more high-mobility group box 1 (HMGB1), thereby promoting macrophage M1 polarization. Conclusions: OPN promoted macrophage M1 polarization by increasing JAK1/STAT1-induced HMGB1 secretion in hepatocytes.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

OBJECTIVE: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI). METHODS: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities. RESULTS: Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality. CONCLUSION: Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.

Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity.

Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70-0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65-0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.

The feasibility and efficacy of a community-based multifactorial intervention to improve the cardiovascular risk factor control among patients with type 2 diabetes: A 2-year cluster randomized trial.

TRIAL DESIGN: Our study is to investigate the feasibility and effectiveness of multiple cardiovascular factors intervention (MFI) in type 2 diabetes patients in China's primary care setting. METHODS: We performed a cluster randomized trial to compare the proportion of patients achieved the targets between usual care group (control, 9 sites, n = 868) and MFI group (8 sites, n = 739) among patients with type 2 diabetes in primary care setting. Logistic regression model with random effects was used to estimate the association of the effect of intervention and the proportion achieved the targets. RESULTS: At baseline, the end of 1 year, and 2 years follow-up, the proportion of patients achieved all 3 target goals (HbA1c < 7.0%, blood pressure < 130/80 mm Hg and low-density lipoprotein cholesterol < 2.6 mmol/L) were 5.7%, 5.9%, 5.7% in the control group and 5.9%, 10.6%, 12.3% in the MFI group. After adjusting sex, age, diabetes duration, body mass index, HbA1c, blood pressure, and low-density lipoprotein cholesterol at baseline, there was no difference between the 2 groups (OR (95% CI): 1.27 (0.38-4.27) and 1.86 (0.79-4.38) for the first year and second year, respectively). When stratified by payment method, the patients with medical insurance or public expenses had a higher proportion achieved target goals (6.9% vs 16.4%, OR (95% CI): 2.30 (1.04-5.08)) in the second year. CONCLUSIONS: The controlling of cardiovascular risk factor targets remains suboptimal among patients with type 2 diabetes in primary care setting. MFI in type 2 diabetes improved cardiovascular disease risk profile, especially in the patients with medical insurance.

Identification of a novel pyroptosis-related gene signature correlated with the prognosis of diffuse glioma patients.

BACKGROUND: Diffuse glioma is the most common primary tumor of the central nervous system and has a poor prognosis. Recently, a new type of programmed cell death (PCD), pyroptosis, has been found to be widely involved in the process of tumor diseases. However, the expression of pyroptosis-related genes (PRGs) in diffuse gliomas and their relationship with prognosis have rarely been evaluated. METHODS: In this study, we obtained RNA sequencing and clinical data from the Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas (CGGA) of diffuse glioma patients. Simultaneously, differentially expressed PRGs between TCGA-Glioma tumor samples and the normal brain samples from the Genome Tissue Expression (GTEx) were investigated. Besides, univariate and multivariate Cox regression analysis were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve and principal component analysis (PCA) was undertaken to assess the prognostic capacity of the signature. Gene set enrichment analyses (GSEA) and single sample GSEA (ssGSEA) were used to further understand the molecular mechanisms and the difference of immune microenvironment. External validation of two separate cohorts from the CGGA database was then performed. RESULTS: Caspase 3 (CASP3) and interleukin-18 (IL18) were identified as potential prognostic biomarkers. A novel prognostic model was constructed to predict diffuse glioma patients' overall survival (OS) time. Patients in high-risk subgroup had shorter survival than those with high-risk with P<0.0001. GSEA and ssGSEA showed the activation of immune-related pathways and the extensive infiltration of immune cells [such as cytotoxic T cells, dendritic cells (DC), natural killer T cell (NKT), induced regulatory T cells (iTreg), naturally occurring regulatory T cells (nTreg)] in high-risk subgroup. CONCLUSIONS: A novel two-PRGs prognostic signature based on gene expression was identified, which could predict diffuse glioma patients' OS time. Pyroptosis may be involved in the establishment of immune microenvironment in diffuse glioma.

A Soft-Path Solution to Risk Reduction by Modeling Medical Waste Disposal Center Location-Allocation Optimization.

The risk of medical waste pollution and huge demand of daily medical waste disposal pose great difficulties to medical waste management. Establishing medical waste disposal centers (MWDCs) is considered one of the ways to reduce the environmental and public risk of medical waste pollution. However, how to serve the medical waste disposal demand in optimal MWDCs' locations is a key challenge due to the complexity of the whole system and relationships among stakeholders. This article develops a soft-path solution for reducing risks as well as mitigating the related costs by optimizing the MWDC location-allocation problem. A risk mitigation-oriented bilevel equilibrium optimization model is developed for modeling the Stackelberg game behavior between the local government and the medical institutions. The objectives of the local government are minimizing the total risk of loss, the subsidy costs, and the investment cost of building the MWDCs, while minimizing the disposal and transportation costs are the objectives at the medical institution level. Fuzzy random variables are introduced by combining insufficient historical data with expert knowledge via consulting surveys to describe the coexisting uncertainties in the data. To solve the model, a hybrid approach combined with the interactive fuzzy programming technique and an Entropy-Boltzmann selection-based genetic algorithm are designed and tested. The Chengdu Medical Waste Disposal Centers Planning Project is used as a practical application. The results show that it is possible to achieve a balanced market with higher economic efficiency and significantly reduced risk through an appropriate principle of interactive actions between the bilevel stakeholders.

Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model.

De-carbonization of the transport sector is an important pathway to climate-change mitigation and presents the potential for future lower emissions. To assess the potential quantitatively under different optimization measures, this paper presents a hybrid model combining an integrated machine learning model with the scenario analysis. We compare the training accuracy of the back-propagation neural networks (BPNN), Gaussian process regression (GPR), and support vector machine (SVM) fitting model with different training datasets. The results indicate that the performance of the SVM model is superior to other methods. And the particle swarm optimization (PSO) algorithm is then used to optimize hyper-parameters of the SVM model. Two scenarios including business as usual (BAU) and best case (BC) are set according to the current trends and target trends of driving factors identified by the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. Finally, to find the de-carbonization potentials in the transport sector, the PSO-SVM model is applied to predict transport emissions from 2015 to 2030 under two scenarios. Results show that transport emissions reduce by about 131.36 million tons during 2015-2020 and 372.86 million tons during 2021-2025 in the BC scenario. The findings can effectively track, test, and predict the achievement of policy goals and provide practical guidance for de-carbonization development.

A novel speed model for safety evaluation of freeway alignment in Euclidean 3D space.

Objective: Operating speed is a critical indicator to evaluate consistency of road alignment and safety. Although extensive studies have been conducted on developing operating speed models, few researchers have considered the interactive influence of horizontal and vertical alignment in 3D space. The purpose of this study is to develop a speed model based on 3D alignment in Euclidean space rather than traditional horizontal and vertical alignment. Methods: According to the curve theory of differential geometry, a novel method to estimate operating speed is proposed in our study using 3D space curvature instead of traditional horizontal or vertical parameters to describe the spatial geometric properties for a freeway alignment. Speeds of 54 different alignment segments are observed to develop the speed model. Several observing sites of each segment are selected beforehand, and the speeds of more than 300 vehicles in each site are observed. Space curvature is used as an important index to estimate operating speed. Results: The findings of this study indicated that both horizontal alignment and vertical alignment contribute to space curvature. Space curvature mainly affects direction control operating performance. However, vehicles overcome the effects of gravity along the vertical alignment in the z direction. Results indicate that operating speed exponentially declines with space curvature and that quadratic parabola decline with vertical grade. Conclusions: It can be concluded that there is a clear correlation between velocity and spatial curvature, which is proved by variance analysis. The estimation results of the speed models are reliable as tested using a real engineering example. The study would provide a scientific basis for safety evaluation of freeway alignment.

Complex network analysis of bilateral international investment under de-globalization: Structural properties and evolution.

As many countries are now seeking to protect their own markets rather than indulge in global trade, this paper examines whether this type of de-globalization behavior has been having any effect on international investment relationships through a systematic analysis of international investment network (IIN) in 127 economies from 2005 to 2016. Unlike previous studies that only analyzed portfolio investment data, the bilateral international investment data were estimated using a matrix-based iteration approach, and the IIN established using complex network theory. Using bilateral international investment data made the results more reliable and somewhat closer to reality. To analyze the structural properties and evolution of the IIN, complex network indicators including a new one named node similarity were developed. The node similarity is defined as the proportion of common relationships of the current economy between two successive years which is useful to reveal the dynamics of the IIN. This paper finds that there are heterogenous and hierarchal properties in the IIN, several economies had a wide range of international investment partners, while most others had only a small range of investment partners and were more likely to form tight groups within the network. The economies in the IIN were tending towards smaller but closer communities, a new trend of regional financial cooperation was developing. The IIN is divided into more communities over time while the top active and central economies often locate in different communities. These findings imply that the structure of the IIN is changing geographically during the de-globalization rather than independent with regions. The regional cooperation has made positive effect on the international investment. The governments should ensure that they continue to support liberal financial policies and to promote better regional financial cooperation.

Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network.

Identification of the significant factors of traffic crashes has been a primary concern of the transportation safety research community for many years. A fatal-injury crash is a comprehensive result influenced by multiple variables involved at the moment of the crash scenario, the main idea of this paper is to explore the process of significant factors identification from a multi-objective optimization (MOP) standpoint. It proposes a data-driven model which combines the Non-dominated Sorting Genetic Algorithm (NSGA-II) with the Neural Network (NN) architecture to efficiently search for optimal solutions. This paper also defines the index of Factor Significance (Fs) for quantitative evaluation of the significance of each factor. Based on a set of three year data of crash records collected from three main interstate highways in the Washington State, the proposed method reveals that the top five significant factors for a better Fatal-injury crash identification are 1) Driver Conduct, 2) Vehicle Action, 3) Roadway Surface Condition, 4) Driver Restraint and 5) Driver Age. The most sensitive factors from a spatiotemporal perspective are the Hour of Day, Most Severe Sobriety, and Roadway Characteristics. The method and results in this paper provide new insights into the injury pattern of highway crashes and may be used to improve the understanding of, prevention of, and other enforcement efforts related to injury crashes in the future.

Carbon emission allowance allocation with a mixed mechanism in air passenger transport.

Air passenger transport carbon emissions have become a great challenge for both governments and airlines because of rapid developments in the aviation industry in recent decades. In this paper, a mixed mechanism composed of a cap-and-trade mechanism and a carbon tax mechanism is developed to assist governments in allocating carbon emission allowances to airlines operating on the routes. Combined this mixed mechanism with an equilibrium strategy, a bi-level multi-objective model is proposed for an air passenger transport carbon emission allowance allocation problem, in which a government is considered as a leader and the airlines as the followers. An interactive solution approach integrating a genetic algorithm and an interactive evolutionary mechanism is designed to search for satisfactory solutions of the proposed model. A case study is then presented to show its practicality and efficiency in mitigating carbon emissions. Sensitivity analyses under different tradable and taxable levels are also conducted, which can give the government insights as to the tradeoffs between lowering carbon intensity and improving airlines' operations. The computational results demonstrate that the mixed mechanism can assist greatly in carbon emission mitigation for air passenger transport and therefore, it should be established as part of air passenger transport carbon emission policies.

A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis.

The mixed multinomial logit (MNL) approach, which can account for unobserved heterogeneity, is a promising unordered model that has been employed in analyzing the effect of factors contributing to crash severity. However, its basic assumption of using a linear function to explore the relationship between the probability of crash severity and its contributing factors can be violated in reality. This paper develops a generalized nonlinear model-based mixed MNL approach which is capable of capturing non-monotonic relationships by developing nonlinear predictors for the contributing factors in the context of unobserved heterogeneity. The crash data on seven Interstate freeways in Washington between January 2011 and December 2014 are collected to develop the nonlinear predictors in the model. Thirteen contributing factors in terms of traffic characteristics, roadway geometric characteristics, and weather conditions are identified to have significant mixed (fixed or random) effects on the crash density in three crash severity levels: fatal, injury, and property damage only. The proposed model is compared with the standard mixed MNL model. The comparison results suggest a slight superiority of the new approach in terms of model fit measured by the Akaike Information Criterion (12.06 percent decrease) and Bayesian Information Criterion (9.11 percent decrease). The predicted crash densities for all three levels of crash severities of the new approach are also closer (on average) to the observations than the ones predicted by the standard mixed MNL model. Finally, the significance and impacts of the contributing factors are analyzed.

Equilibrium strategy-based optimization method for the coal-water conflict: A perspective from China.

Environmental water problems have become increasingly severe, with the coal-water conflict becoming one of the most difficult issues in large scale coal mining regions. In this paper, a bi-level optimization model based on the Stackelberg-Nash equilibrium strategy with fuzzy coefficients is developed to deal with environmental water problems in large scale coal fields, in which both the groundwater quality and quantity are considered. Using the proposed model, and fully considering the relationship between the authority and the collieries and also the equilibrium between economic development and environmental protection, an environmental protection based mining quotas competition mechanism is established. To deal with the inherent uncertainties, the model is defuzzified using a possibility measure, and a solution approach based on the Karush-Kuhn-Tucker condition is designed to search for the solutions. A case study is presented to demonstrate the practicality and efficiency of the model, and different constraint violation risk levels and related results are also obtained. The results showed that under the environmental protection based mining quotas competition mechanism, collieries attempt to conduct environmentally friendly exploitation to seek greater mining quotas. This demonstrates the practicality and efficiency in the proposed model of reducing the coal-water conflict. Finally, a comprehensive discussion is provided and some propositions is given as a foundation for the proposed management recommendations.