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Abstract The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strategy against COVID-19. In the current study, we utilized islet organoids derived from human embryonic stem cells (hESCs), animal models and COVID-19 patients to discover that fibroblast growth factor 7 (FGF7) enhances ACE2 expression within the islets, facilitating SARS-CoV-2 infection and resulting in impaired insulin secretion. Using hESC-derived islet organoids, we demonstrated that FGF7 interacts with FGF receptor 2 (FGFR2) and FGFR1 to upregulate ACE2 expression predominantly in β cells. This upregulation increases both insulin secretion and susceptibility of β cells to SARS-CoV-2 infection. Inhibiting FGFR counteracts the FGF7-induced ACE2 upregulation, subsequently reducing viral infection and replication in the islets. Furthermore, retrospective clinical data revealed that diabetic patients with severe COVID-19 symptoms exhibited elevated serum FGF7 levels compared to those with mild symptoms. Finally, animal experiments indicated that SARS-CoV-2 infection increased pancreatic FGF7 levels, resulting in a reduction of insulin concentrations in situ. Taken together, our research offers a potential regulatory strategy for ACE2 by controlling FGF7, thereby protecting islets from SARS-CoV-2 infection and preventing the progression of diabetes in the context of COVID-19.

Abstract The accurate perception of multiple flight parameters, such as the angle of attack, angle of sideslip, and airflow velocity, is essential for the flight control of micro air vehicles, which conventionally rely on arrays of pressure or airflow velocity sensors. Here, we present the estimation of multiple flight parameters using a single flexible calorimetric flow sensor featuring a sophisticated structural design with a suspended array of highly sensitive vanadium oxide thermistors. The proposed sensor achieves an unprecedented velocity resolution of 0.11 mm·s−1 and angular resolution of 0.1°. By attaching the sensor to a wing model, the angles of attack and slip were estimated simultaneously. The triaxial flight velocities and wing vibrations can also be estimated by sensing the relative airflow velocity due to its high sensitivity and fast response. Overall, the proposed sensor has many promising applications in weak airflow sensing and flight control of micro air vehicles.

IntroductionThe prognosis for glioma is generally poor, and the 5-year survival rate for patients with this disease has not shown significant improvement over the past few decades. Parkinson’s disease (PD) is a prevalent movement disorder, ranking as the second most common neurodegenerative disease after Alzheimer’s disease. Although Parkinson’s disease and glioma are distinct diseases, they may share certain underlying biological pathways that contribute to their development.ObjectiveThis study aims to investigate the involvement of genes associated with Parkinson’s disease in the development and prognosis of glioma.MethodsWe obtained datasets from the TCGA, CGGA, and GEO databases, which included RNA sequencing data and clinical information of glioma and Parkinson’s patients. Eight machine learning algorithms were used to identify Parkinson-Glioma feature genes (PGFGs). PGFGs associated with glioma prognosis were identified through univariate Cox analysis. A risk signature was constructed based on PGFGs using Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) method. We subsequently validated its predictive ability using various methods, including ROC curves, calibration curves, KM survival analysis, C-index, DCA, independent prognostic analysis, and stratified analysis. To validate the reproducibility of the results, similar work was performed on three external test datasets. Additionally, a meta-analysis was employed to observe the heterogeneity and consistency of the signature across different datasets. We also compared the differences in genomic variations, functional enrichment, immune infiltration, and drug sensitivity analysis based on risk scores. This exploration aimed to uncover potential mechanisms of glioma occurrence and prognosis.ResultsWe identified 30 PGFGs, of which 25 were found to be significantly associated with glioma survival. The prognostic signature, consisting of 19 genes, demonstrated excellent predictive performance for 1-, 2-, and 3-year overall survival (OS) of glioma. The signature emerged as an independent prognostic factor for glioma overall survival (OS), surpassing the predictive performance of traditional clinical variables. Notably, we observed differences in the tumor microenvironment (TME), levels of immune cell infiltration, immune gene expression, and drug resistance analysis among distinct risk groups. These findings may have significant implications for the clinical treatment of glioma patients.ConclusionThe expression of genes related to Parkinson’s disease is closely associated with the immune status and prognosis of glioma patients, potentially regulating glioma pathogenesis through multiple mechanisms. The interaction between genes associated with Parkinson’s disease and the immune system during glioma development provides novel insights into the molecular mechanisms and targeted therapies for glioma.

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

Since pressure while drilling (PWD) has the disadvantages of single-point measurement and high cost of application, a micro-measurer based on MEMS (micro-electromechanical systems) sensor technology, which can measure downhole temperature, pressure, magnetic field, and dynamic signal, has been developed to achieve real-time, efficient, and accurate measurement of multiple parameters in the well. The kernel circuit system is the core of measurement and control, and the shell plays the role in protecting the kernel circuit. The shell of the micro-measurer is made of preferably selected materials with high-temperature and high-pressure resistance, corrosion resistance, small size, and low density, which can adapt to working in drilling fluid for a long time. The micro-measurer uses integrated interfaces on the shell to enable communication between the host computer and measuring machine. Based on the field test, both the functional integrity and data measurement accuracy of the micro-measurer are verified. Through analysis of the measured data, the profile of the downhole temperature field is constructed. The physical phenomena reflected by the measured magnetic field signal and dynamic signal are consistent with the actual working conditions observed in the test. Hence, as a new microchip measuring device, the micro-measurer can better serve the drilling engineering field and provide technical support for real-time measurement of downhole parameters in the future.

IntroductionModifiable lifestyle factors are considered key to the control of cardiometabolic diseases. This study aimed to explore the association between multiple lifestyle factors and cardiometabolic multimorbidity.MethodsA total of 14,968 participants were included in this cross-sectional exploratory study (mean age 54.33 years, range 45–91; 49.6% male). Pearson's Chi-square test, logistic regression, and latent class analysis were employed.ResultsWe found that men with 4–5 high-risk lifestyle factors had a 2.54-fold higher risk (95% CI: 1.60–4.04) of developing multimorbidity compared to males with zero high-risk lifestyle factors. In an analysis of dietary behavior, we found that in women compared to men, over-eating (OR = 1.94, P < 0.001) and intra-meal water drinking (OR = 2.15, P < 0.001) were more likely to contribute to the development of cardiometabolic multimorbidity. In an analysis of taste preferences, men may be more sensitive to the effect of taste preferences and cardiometabolic multimorbidity risk, particularly for smoky (OR = 1.71, P < 0.001), hot (OR = 1.62, P < 0.001), and spicy (OR = 1.38, P < 0.001) tastes. Furthermore, “smoking and physical activity” and “physical activity and alcohol consumption” were men's most common high-risk lifestyle patterns. “Physical activity and dietary intake” were women's most common high-risk lifestyle patterns. A total of four common high-risk dietary behavior patterns were found in both males and females.ConclusionsThis research reveals that the likelihood of cardiometabolic multimorbidity increases as high-risk lifestyle factors accumulate. Taste preferences and unhealthy dietary behaviors were found to be associated with an increased risk of developing cardiometabolic multimorbidity and this association differed between genders. Several common lifestyle and dietary behavior patterns suggest that patients with cardiometabolic multimorbidity may achieve better health outcomes if those with certain high-risk lifestyle patterns are identified and managed.

In this paper, a stepwise synthesis method was used to synthesize a glycine acid Schiff base copper complex catalyst to catalyze carbonyl hydrazine removal of DO in feedwater. The effects of reaction temperature, pH, mass concentration of catalyst, and carbonyl hydrazine dosage on the catalytic deoxygenation performance were investigated and the optimum conditions were determined. When the reaction temperature is 45 ℃, the dosage of carbonyl hydrazine is 2 times the theoretical amount, the initial pH of the solution is 8, and mass concentration of catalyst is 0.01 g/L, the removal rate of DO reaches 94.8% after 5 minutes of reaction. At the same time, the structure of the catalyst was characterized by elemental analysis, IR, and UV spectrometry. Cu and glycine Schiff base ligand are coordinated, and —C=N—, —COO—, Ar—O— participate in the coordination with Cu. The complex can react with DO quickly at low and room temperatures for removing DO in water.

Epilepsy, a functional disease caused by abnormal discharge of neurons, has attracted the attention of neurologists due to its complex characteristics. N6-methyladenosine (m6A) is a reversible mRNA modification that plays essential role in various biological processes. Nevertheless, no previous study has systematically evaluated the role of m6A regulators in epilepsy. Here, using gene expression screening in the Gene Expression Omnibus GSE143272, we identified seven significant m6A regulator genes in epileptic and non-epileptic patients. The random forest (RF) model was applied to the screening, and seven m6A regulators (HNRNPC, WATP, RBM15, YTHDC1, YTHDC2, CBLL1, and RBMX) were selected as the candidate genes for predicting the risk of epilepsy. A nomogram model was then established based on the seven-candidate m6A regulators. Decision curve analysis preliminarily showed that patients with epilepsy could benefit from the nomogram model. The consensus clustering method was performed to divide patients with epilepsy into two m6A patterns (clusterA and clusterB) based on the selected significant m6A regulators. Principal component analysis algorithms were constructed to calculate the m6A score for each sample to quantify the m6A patterns. Patients in clusterB had higher m6A scores than those in clusterA. Furthermore, the patients in each cluster had unique immune cell components and different cell death patterns. Meanwhile, based on the M6A classification, a correlation between epilepsy and glucose metabolism was laterally verified. In conclusion, the m6A regulation pattern plays a vital role in the pathogenesis of epilepsy. The research on m6A regulatory factors will play a key role in guiding the immune-related treatment, drug selection, and identification of metabolism conditions and mechanisms of epilepsy in the future.

Aggressive angiomyxoma (AAM) is a rare mesenchymal tumor primarily growing in the soft tissue of the pelvis and perineum in women of reproductive age. It is a benign tumor that still has a probability of being accompanied by localized invasion. Although negative margins of resection are difficult to achieve due to the invasive nature of the tumor and the lack of a well-defined capsule, the first line of treatment for AAM is surgery. The diagnosis of AAM is difficult to make due to a lack of specific manifestations and specific tumor markers. In this study, we reported a case of aggressive angiomyxoma in a 2-year-old girl that rarely develops in the skull with craniocerebral compression. The patient initially had a mass on her head that attracted the attention of her family, and then she began to have episodic headaches. Surgery was performed after hospitalization, and the tumor recurred 1 year after the operation, around the originally affected skull.

The Yellow River Basin in Inner Mongolia (YRBIM) has witnessed major changes in land use/land cover (LULC), which have had an impact on the basin’s ecosystem, in the context of fast economic development and urbanization. This study set out to investigate the ecological risk and key driving forces in the basin as LULC evolves. In order to evaluate the ecological risk of the basin and use a geographic detector model to understand the causes of its spatial heterogeneity, we built a landscape ecological risk index (ERI) model based on changes in LULC from 1990 to 2020. The findings indicate that between 1990 and 2020, LULC modifications led to the transfer of several land types to a small number of land types, all of which have since changed into other land types. With high risk areas primarily located in the Hobq Desert, the Hetao irrigation area, and some portions of the Mu Us Sandy Land, the ecological risk level in the basin is gradually decreasing. Human activities are the main cause of the regional variation of ecological risk in the basin, with topography and climate coming in second and third. The Yellow River Basin’s ecological danger and environmental quality have only received a limited amount of analysis to date. This study is a crucial resource for the development of civilization and ecological restoration in the region.

Synthetic aperture radar (SAR), as an active microwave sensor, can inevitably receive radio frequency interference (RFI) generated by various electromagnetic equipment. When the SAR system receives RFI, it will affect SAR imaging and limit the application of SAR images. As a kind of RFI mitigation method, notch filtering method is a classical method with high efficiency and robust performance. However, the notch filtering methods pay no attention to the protection of useful signals. This paper proposed a modified 2-D notch filter based on image segmentation for RFI mitigation with signal-protected capability. (1) The adaptive gamma correction (AGC) approach was utilized to enhance the SAR image with RFI in the range-frequency and azimuth-time domain. (2) The modified selective binary and Gaussian filtering regularized level set (SBGFRLS) model was utilized to further process the image after AGC to accurately extract the contour of the useful signals with interference, which is more conducive to protecting the useful signals without interference. (3) The Generalized Singular Value Thresholding (GSVT) based low-rank sparse decomposition (LRSD) model was utilized to separate the RFI signals and the useful signals. Then, the useful signals were restored to the raw data. The simulation experiments and measured data experiments show that the proposed method can effectively mitigate RFI and protect the useful signals whether there are RFI with single source or multiple sources.

Obesity is a serious public health issue worldwide. Growing evidence demonstrates the efficacy of the ketogenic diet (KD) for weight loss, but there may be some adverse side effects such as dyslipidemia and hepatic steatosis. Aerobic exercise is a widely recognized approach for improving these metabolic markers. Here we explored the combined impacts of KD and moderate aerobic exercise for an 8-week intervention on body weight and fat loss, serum biomarkers, and hepatic lipid metabolism in a mouse model of high-fat diet-induced obesity. Both KD and KD combined with exercise significantly reduced body weight and fat mass. No significant adverse effects of KD were observed in serum biomarkers or hepatic lipid storage, except for an increase in circulating triglyceride level. However, aerobic exercise lowered serum triglyceride levels, and further ameliorated serum parameters, and hepatic steatosis in KD-fed mice. Moreover, gene and protein expression analysis indicated that KD combined with exercise was associated with increased expression of lipolysis-related genes and protein levels, and reduced expression of lipogenic genes relative to KD without exercise. Overall, our findings for mice indicate that further work on humans might reveal that KD combined with moderate aerobic exercise could be a promising therapeutic strategy for obesity.

Cu–Cr–Co combinatorial multilayer thin-films were prepared by a high-throughput ion beam sputtering system. Based on the thickness ratio among the individual nanoscale monolayers (Cu, Cr, Co), the resulting stoichiometry covered the entire phase diagram. The chemical composition and structure of Cu–Cr–Co combinatorial chip upon solid-state reaction were studied by lab-based micro-X-ray fluorescence (μ-XRF) and high-throughput synchrotron X-ray diffraction (XRD), respectively. A composition-structure map for Cu–Cr–Co combinatorial chip was developed through automated data analysis employing hierarchical clustering techniques. The structural evolution of Cu–Cr–Co combinatorial chip as a function of heat-treatment temperature, time, and modulation period were studied systematically. Furthermore, the effect of the elemental distribution in-depth direction was investigated to gain more insights regarding phase transformation. This work provides an efficient method and new perspectives for the design and optimization of the composition and structure of high-performance thin-films.

It is widely considered that photocatalysis is an effective and eco-friendly method of dealing with organic pollutants dissolved in water. Nonetheless, photocatalysts still have some drawbacks, such as poor visible-light absorption, easy recombination of photogenerated charge carriers, and limited active sites. In this study, bismuth ferrite coupled with biochar material (BiFeO3/BC) was simply synthesized, and its photocatalysis reactivity was systemically examined under an irradiation of λ > 400 nm. The experimental results showed that under a relatively acidic environment, the removal rate of tetracycline hydrochloride reached 95%. Using a variety of characterization investigations, we analyzed the morphology structure and chemical composition of BiFeO3/BC. In consideration of simple preparation and high respondence toward visible light, further explorations of BiFeO3/BC and its properties and optimized degradation conditions are worthwhile.

The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 parameters that may affect the occurrence of complex downhole accidents, the mud overflow and leakage are predicted. The prediction results of the model are compared with the prediction results of a conventional BP neural network, and verified by the real drilling data. The results show that the MAE, MSE, and RMSE of the GA-BP neural network model are improved by 2.91%, 4.48%, and 10.93%, respectively, compared with the BP neural network model, and the prediction quality is higher. Moreover, the amount of mud overflow and leakage predicted by using the GA-BP neural network matches well with the pattern of mud overflow and leakage data in real drilling, which proves the effectiveness and accuracy of the GA-BP neural network in overflow and leakage prediction.

As an open system, synthetic aperture radar (SAR) inevitably receives radio frequency interference (RFI) generated by electromagnetic equipment in the same band. The existence of RFI seriously affects SAR signal processing and image interpretation. In recent years, many algorithms and models related to RFI mitigation have been proposed. However, most of that focus on effectively mitigating the RFI is insufficient to protect the useful signals. This article proposes a mitigation method of RFI with a signal-protected capability. (1) The kurtosis coefficient is used to detect RFI pulse-by-pulse, and the echoes containing RFI are stored in matrix form. (2) The preliminary extraction of RFI is complete by low-rank sparse decomposition of the echo matrix containing RFI. (3) For the secondary separation of RFI, the accurate position of RFI in the preliminary extraction results is located by the fuzzy C-means clustering; then, we separate the RFI and the remaining useful signals again and reconstruct the useful signals to complete the mitigation work. The proposed method can further protect useful signals while effectively removing interference through the secondary separation of RFI. Experimental results based on simulated and measured data verify the performance and potential of the proposed method.

Summary: Infectious endophthalmitis is a growing concern that causes irreversible intraocular tissue and optic nerve damage. Here, we report a theranostic nanoplatform based on UiO-66-NH2 to combine photodynamic therapy (PDT) and lipopolysaccharide (LPS) targeting through polypeptide (YVLWKRKRKFCFI-NH2) modification. Toluidine blue (TB), serving as a photosensitizer (PS), is loaded into UiO-66-NH2 nanoparticles (NPs) for both self-imaging and PDT functions. The accelerated TB release at pH 5.5 compared to pH 7.4 demonstrates the responsive drug release feature. In vitro antibacterial tests, including contact incubation-agar plate counting, bacterial live/dead staining, and scanning electron microscopy observations, confirm the utility of the TB@UiO-66-NH2@PEP&PEG NPs (UTPP NPs) in biofilms removal and sterilization of Pseudomonas aeruginosa and Staphylococcus epidermidis. Furthermore, the UTPP NPs also show good biocompatibility toward human retinal pigment epithelial cells, as well as in situ self-imaging property. Finally, an in vivo infectious endophthalmitis model supports the application of the UTPP NPs in infectious endophthalmitis treatment.