Early-life tobacco smoke exposure and stroke risk: a prospective study of 341,783 and 352,737 UK Biobank participants.

INTRODUCTION: Stroke is a life-threatening condition that causes a major medical burden globally. The currently used methods for the prevention or prediction of stroke have certain limitations. Exposure to tobacco in early life, including smoking during adolescence and maternal smoking during pregnancy, can affect adolescent development and lead to several negative outcomes. However, the association between early-life tobacco exposure and stroke is not known. METHODS: In this prospective cohort study, for the analyses involving exposure to maternal smoking during pregnancy and age of smoking initiation, we included 304,984 and 342,893 participants, respectively., respectively from the UK Biobank. Cox proportional hazard regression model and subgroup analyses were performed to investigate the association between early-life tobacco exposure and stroke. Mediation analyses were performed to identify the mediating role of biological aging in the association between early tobacco exposure and stroke. RESULTS: Compared with participants whose mothers did not smoke during pregnancy, participants whose mothers smoked during pregnancy showed an 11% increased risk of stroke (HR: 1.11, 95% CI: 1.05-1.18, P < 0.001). Compared with participants who never smoked, participants who smoked during adulthood, adolescence and childhood showed a 22%, 24%, and 38% increased risk of stroke during their adulthood, respectively. Mediation analysis indicated that early-life tobacco exposure can cause stroke by increasing biological aging. CONCLUSION: This study reveals that exposure to tobacco during early life is associated with an increased risk of experiencing a stroke, and increased biological aging can be the underlying mechanism.

Surface-Enriched Room-Temperature Liquid Bismuth for Catalytic CO2 Reduction.

Bismuth-based electrocatalysts are effective for carbon dioxide (CO2) reduction to formate. However, at room temperature, these materials are only available in solid state, which inevitably suffers from surface deactivation, declining current densities, and Faradaic efficiencies. Here, the formation of a liquid bismuth catalyst on the liquid gallium surface at ambient conditions is shown as its exceptional performance in the electrochemical reduction of CO2 (i.e., CO2RR). By doping a trace amount of bismuth (740 ppm atomic) in gallium liquid metal, a surface enrichment of bismuth by over 400 times (30 at%) in liquid state is obtained without atomic aggregation, achieving 98% Faradic efficiency for CO2 conversion to formate over 80 h. Ab initio molecular simulations and density functional theory calculations reveal that bismuth atoms in the liquid state are the most energetically favorable sites for the CO2RR intermediates, superior to solid Bi-sites, as well as joint GaBi-sites. This study opens an avenue for fabricating high-performing liquid-state metallic catalysts that cannot be reached by elementary metals under electrocatalytic conditions.

An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion.

INTRODUCTION: Distinguishing between malignant pleural effusion (MPE) and benign pleural effusion (BPE) poses a challenge in clinical practice. We aimed to construct and validate a combined model integrating radiomic features and clinical factors using computerized tomography (CT) images to differentiate between MPE and BPE. METHODS: A retrospective inclusion of 315 patients with pleural effusion (PE) was conducted in this study (training cohort: n = 220; test cohort: n = 95). Radiomic features were extracted from CT images, and the dimensionality reduction and selection processes were carried out to obtain the optimal radiomic features. Logistic regression (LR), support vector machine (SVM), and random forest were employed to construct radiomic models. LR analyses were utilized to identify independent clinical risk factors to develop a clinical model. The combined model was created by integrating the optimal radiomic features with the independent clinical predictive factors. The discriminative ability of each model was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). RESULTS: Out of the total 1,834 radiomic features extracted, 15 optimal radiomic features explicitly related to MPE were picked to develop the radiomic model. Among the radiomic models, the SVM model demonstrated the highest predictive performance [area under the curve (AUC), training cohort: 0.876, test cohort: 0.774]. Six clinically independent predictive factors, including age, effusion laterality, procalcitonin, carcinoembryonic antigen, carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE), were selected for constructing the clinical model. The combined model (AUC: 0.932, 0.870) exhibited superior discriminative performance in the training and test cohorts compared to the clinical model (AUC: 0.850, 0.820) and the radiomic model (AUC: 0.876, 0.774). The calibration curves and DCA further confirmed the practicality of the combined model. CONCLUSION: This study presented the development and validation of a combined model for distinguishing MPE and BPE. The combined model was a powerful tool for assisting in the clinical diagnosis of PE patients.

Hole-Free Nested Array with Three Sub-ULAs for Direction of Arrival Estimation.

Sparse arrays are of deep concern due to their ability to identify more sources than the number of sensors, among which the hole-free difference co-array (DCA) with large degrees of freedom (DOFs) is a topic worth discussing. In this paper, we propose a novel hole-free nested array with three sub-uniform line arrays (NA-TS). The one-dimensional (1D) and two-dimensional (2D) representations demonstrate the detailed configuration of NA-TS, which indicates that both nested array (NA) and improved nested array (INA) are special cases of NA-TS. We subsequently derive the closed-form expressions for the optimal configuration and the available number of DOFs, concluding that the DOFs of NA-TS is a function of the number of sensors and the number of the third sub-ULA. The NA-TS possesses more DOFs than several previously proposed hole-free nested arrays. Finally, the superior direction of arrival (DOA) estimation performance based on the NA-TS is supported by numerical examples.

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN.

This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation.

Lanthanide-doped upconversion glass-ceramic photocatalyst fabricated from fluorine-containing waste for the degradation of organic pollutants.

Fluorine-containing waste is one kind of hazardous waste characteristic by hard disposal and utilization, it is an attractive way to prepare for fluoride-based luminescent matrix. In this work, to realize the high value-added utilization of fluorine-containing waste and reduce cost of the raw materials for preparation near-infrared (NIR) glass-ceramic (GC) photocatalyst, the pure fluoride of luminescent matrix was replaced by introducing fluorine-containing waste. The waste contained NIR GC photocatalyst was synthesis by the method of facile in-situ etching of an upconversion GC with HCl, which possesses core-shell structure, where the GC micro-powder including optically active centers lanthanides doped CaF2 nanocrystals are displayed as the core, and the BiOCl is as the superficial coating. The upconversion emission performance of CaF2 based luminescent matrix in photocatalyst is not weakened with HCl etching. NIR GC photocatalyst has high methyl orange and enrofloxacin degradation rate of 86 % and 82 % over 180 min after NIR light irradiation, respectively. The UV-Vis-NIR photocatalytic activity was enhanced degradation rate (93 % in 15 min) of enrofloxacin compared with those of commercial P25 and BiOCl. In addition, the photocatalyst had stable photocatalytic activity and it also can be regenerated. The study provided references for high value-added utilization fluorine-containing waste.

Direction of Arrival Estimation of Generalized Nested Array via Difference-Sum Co-Array.

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference-sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance.

Circulating asprosin, irisin, and abdominal obesity in Chinese patients with type 2 diabetes mellitus: a case-control study.

INTRODUCTION: Studies have suggested that serum asprosin and irisin were involved in type 2 diabetes mellitus (T2DM) and obesity. This study evaluated circulating levels of asprosin and irisin and their associations with anthropometric and metabolic parameters, especially the visceral fat area (VFA) in T2DM patients with abdominal obesity (AO). MATERIAL AND METHODS: In this case-control study, 131 patients with T2DM were grouped into an AO group (n = 68) and a non-AO group (NAO) (n = 63) based on their VFA. Anthropometric and metabolic parameters as well as serum asprosin and irisin levels were measured and compared between the 2 groups. RESULTS: Compared to the NAO group, the AO group had significantly higher serum asprosin and irisin concentrations (3.67 ± 1.76 ng/mL vs. 2.85 ± 0.90 ng/mL, p = 0.001; 154.62 ± 61.87 pg/mL vs. 130.54 ± 34.89 pg/mL, p = 0.008, respectively) and greater VFA (p < 0.001). Serum asprosin in the AO group was positively associated with weight, waist circumference (WC), hipline, body mass index, fasting blood glucose (FBG), glycated haemoglobin (HbA1c), VFA, subcutaneous fat area, and total abdominal fat area (TAFA), and the serum irisin concentration in the AO group was positively correlated with WC, waist-to-hip ratio (WHR), VFA, and TAFA and negatively correlated with FBG. Stepwise logistic regression analysis suggested that FBG and VFA were independent factors positively associated with serum asprosin, and that FBG was independently, negatively associated with serum irisin, while VFA was independently, positively associated with serum irisin. CONCLUSIONS: Elevated serum asprosin and irisin levels in T2DM patients with AO and their correlations with other metabolic parameters suggest that both are potential therapeutic agents/targets in treating obesity and its related disorders.

Coherent Signal DOA Estimation for MIMO Radar under Composite Background of Strong Interference and Non-Uniform Noise.

To address the problems of low accuracy and low robustness of the conventional algorithm in estimating the direction of arrival (DOA) of coherent signals against a composite background of strong interference and non-uniform noise, in this paper, a coherent signal DOA estimation algorithm based on fixed projection blocking is proposed in conjunction with a multi-input multi-output (MIMO) radar. The covariance matrix of the received signal is first decomposed by eigenvalues, and a fixed projection matrix orthogonal to the interference guidance vector is constructed as the interference blocking matrix. Then, the received array signal is pre-processed to re-form the covariance matrix, and this matrix is rendered decoherent through a Toeplitz reconstruction. Finally, the reconstructed covariance matrix is estimated by DOA using the propagation operator algorithm to reduce the complexity. The simulation verifies that the proposed algorithm has a better robustness and higher accuracy than conventional algorithms for the DOA estimation of coherent signals in composite backgrounds.

Efficient Near-Infrared Response Antibacterial Ceramics Based on the Method of Facile In Situ Etching Upconversion Glass-Ceramics.

As the world is faced with the coronavirus disease 2019 (COVID-19) pandemic, photocatalytic antibacterial ceramics can reduce the consumption of disinfectants and improve the safety of the public health environment. However, these antibacterial ceramics are often limited by poor stability and low light utilization efficiency. Herein, an antibacterial ceramic was developed via the method of facile in situ etching of upconversion glass-ceramics (UGC) (FIEG) with HCl, in which the BiOCl nanosheets were in situ grown on the surface of GC to improve its stability and antibacterial activity. The results suggest that the upconversion antibacterial ceramics can harvest and utilize near-infrared (NIR) photons efficiently, which display notable antibacterial activity for Escherichia coli (E. coli) under NIR (≥780 nm) and visible light (420-780 nm) irradiation, with a maximum inactivation rate of 7.5 log in 30 min. Meanwhile, in the cycle experiment, more than 6 log inactivation of E. coli was achieved using an antibacterial ceramic sheet after 2-h NIR light irradiation, and the stability of the antibacterial ceramic was discussed. Furthermore, the reactive species, fluorescence-based live/dead cells, and cell structure of bacteria were analyzed to verify the antibacterial mechanism. This study provides a promising strategy for the construction of efficient and stable antibacterial ceramics.

Hydrogen production from the air.

Green hydrogen produced by water splitting using renewable energy is the most promising energy carrier of the low-carbon economy. However, the geographic mismatch between renewables distribution and freshwater availability poses a significant challenge to its production. Here, we demonstrate a method of direct hydrogen production from the air, namely, in situ capture of freshwater from the atmosphere using hygroscopic electrolyte and electrolysis powered by solar or wind with a current density up to 574 mA cm-2. A prototype of such has been established and operated for 12 consecutive days with a stable performance at a Faradaic efficiency around 95%. This so-called direct air electrolysis (DAE) module can work under a bone-dry environment with a relative humidity of 4%, overcoming water supply issues and producing green hydrogen sustainably with minimal impact to the environment. The DAE modules can be easily scaled to provide hydrogen to remote, (semi-) arid, and scattered areas.

The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise.

Due to the discontinuity of ocean waves and mountains, there are often multipath propagation effects and obvious pulse characteristics in low-altitude detection. If the conventional direction of arrival (DOA) estimation method is directly used for direction finding, it will lead to a large error. In view of serious misalignment in the DOA estimation of multipath signals under the background of impulse noise, a DOA estimation method based on spatial difference and a modified projection subspace algorithm is proposed in this paper. Firstly, the covariance matrix of the received data vector is used for spatial difference to eliminate the multipath effects of low-altitude targets. Secondly, the modified projection matrix is constructed using the signal source estimated with the least squares criterion and then used for modifying the covariance matrix, thus eliminating the cross-covariance matrices that affect the estimation accuracy. Finally, the modified covariance matrix is used for the DOA estimation of targets. Simulations show that the proposed algorithm achieves a higher accuracy in the DOA estimation of low-altitude targets than conventional algorithms under two common impulse noise models, without requiring prior knowledge of impulse noise.

Joint Estimation Method of DOD and DOA of Bistatic Coprime Array MIMO Radar for Coherent Targets Based on Low-Rank Matrix Reconstruction.

Based on low-rank matrix reconstruction theory, this paper proposes a joint DOD and DOA estimation method for coherent targets with bistatic coprime array MIMO radar. Unlike the conventional vectorization, the proposed method processed the coprime array with virtual sensor interpolation, which obtained a uniform linear array to generate the covariance matrix. Then, we reconstructed the Toeplitz matrix and established a matrix optimization recovery model according to the kernel norm minimization theory. Finally, the reduced dimension multiple signal classification algorithm was applied to estimate the angle of the coherent targets, with which the automatic pairing of DOD and DOA could be realized. With the same number of physical sensors, the proposed method expanded the array aperture effectively, so that the degree of freedom and angular resolution could be improved significantly for coherent signals. However, the effectiveness of the method was largely limited by the signal-to-noise ratio. The superiority and effectiveness of the method were proved using simulation experiments.

Prognostic Role of Chronic Obstructive Pulmonary Disease and Asthma Physiology Score for in-Hospital and 1-year Mortality in Patients with Acute Exacerbations of COPD.

BACKGROUND AND OBJECTIVES: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) often lead to high mortality. Chronic obstructive pulmonary disease and asthma physiology score (CAPS) is a simple clinical severity score. The aim of this study was to explore whether CAPS could be an effective predictor for in-hospital and 1-year mortality in AECOPD patients. Methods. We used CAPS to grade all patients and record their clinical characteristics. The receiver operator characteristic (ROC) curve was used to determine the cut-off of CAPS that discriminated survivors and non-survivors. Univariate and multivariate logistic regression analyses and Cox regression analyses were used to identify the risk factors for in-hospital and 1-year mortality, respectively. Results. 240 patients were enrolled in our study; 18 patients died during hospitalization and 29 patients died during the 1-year follow-up. Compared with in-hospital survivors, those who died were older (80.83 ± 6.06 vs. 76.94 ± 8.30 years old, P = 0.019) and had a higher percentage of congestive heart failure (61.1% vs. 14.4%, P < 0.001), higher CAPS levels (31.11 ± 10.05 vs. 16.49 ± 7.11 points, P < 0.001), and a lower BMI (19.48 ± 3.26 vs. 21.50 ± 3.86, P = 0.032). The area under the ROC curve of CAPS for in-hospital death was 0.91 (95% CI: 0.85-0.96) with a sensitivity of 0.889 and a specificity of 0.802 for a cut-off point of 21 points. CAPS ≥21 points was an independent risk factor for in-hospital mortality after adjustment for relative risk (RR) (RR = 13.28, 95% CI: 1.97-89.53, P = 0.008). Univariate Cox regression analysis showed that a CAPS ≥21 points (HR = 4.07, 95% CI: 1.97-8.44) was a risk factor for 1-year mortality. However, multivariate Cox regression analysis showed that CAPS (HR = 2.24, 95% CI: 0.90-5.53) was not associated with 1-year mortality. CONCLUSION: A CAPS ≥21 points was a strong and independent risk factor for in-hospital mortality in AECOPD patients and CAPS had no impact on the 1-year mortality in patients with acute exacerbations of COPD after discharge.

Discovery and preclinical profile of LX-039, a novel indole-based oral selective estrogen receptor degrader (SERD).

We previously described the discovery of a novel indole series compounds as oral SERD for ER positive breast cancer treatment. Further SAR exploration focusing on substitutions on indole moiety of compound 12 led to the discovery of a clinical candidate LX-039. We report herein its profound anti-tumor activity, desirable ER antagonistic characteristics combined with favorable pharmacokinetic and preliminary safety properties. LX-039 is currently in clinical trial (NCT04097756).

Association Between Extracellular Superoxide Dismutase Activity and 1-Year All-Cause Mortality in Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Prospective Cohort Study.

Background and Objectives: Accumulating evidence suggests that oxidative stress is involved in the development of chronic obstructive pulmonary disease (COPD) and its progression. Activity of extracellular superoxide dismutase (ecSOD), the only extracellular enzyme eliminating superoxide radicals, has been reported to decline in acute exacerbations of COPD (AECOPD). However, the association between serum ecSOD activity and 1-year all-cause mortality in AECOPD patients remains unclear. The objective of our study was to explore the usefulness of ecSOD activity on admission in AECOPD as an objective predictor for 1-year all-cause mortality. Methods: We measured serum ecSOD activity in AECOPD patients on admission in a prospective cohort study. We also recorded their laboratory and clinical data. Multivariate Cox regression was used to analyze the association between ecSOD activity and the risk of 1-year all-cause mortality. Restricted cubic spline curves were used to visualize the relationship between ecSOD activity and the hazard ratio of 1-year all-cause mortality. Results: A total of 367 patients were followed up for 1 year, and 29 patients died during a 1-year follow-up period. Compared with survivors, the non-survivors were older (79.52 ± 8.39 vs. 74.38 ± 9.34 years old, p = 0.004) and had increased levels of tobacco consumption (47.07 ± 41.67 vs. 33.83 ± 31.79 pack-years, p = 0.037). Having an ecSOD activity ≤ 98.8 U/ml was an independent risk factor of 1-year all-cause mortality after adjustment for baseline differences, clinical variables and comorbidities [hazard ratio = 5.51, 95% confidence interval (CI): 2.35-12.95, p < 0.001]. Conclusion: Lower serum ecSOD activity was a strong and independent predictor of 1-year all-cause mortality in AECOPD patients.

Optimal multi-stage group partition for efficient coronavirus screening.

The outbreak of COVID-19 has affected the economy worldwide due to entire countries being on lockdown. This has been highly challenging for governments facing constraints in terms of time and resources related to the availability of testing kits for the virus. This paper develops an optimal method for multiple-stage group partition for coronavirus screening using a dynamic programming approach. That is, in each stage, a group of people is divided into a certain number of subgroups, each will be tested as a whole. Only the subgroup(s) tested positive will be further divided into smaller subgroups in the next stage or individuals at the last stage. Our multiple-stage group partition scheme is able to minimize the total number of test kits and the number of stages. Our scheme can help solve the test kit shortage problem and save time. Finally, numerical examples with useful managerial insights for further investigation are presented. The results confirm the advantages of the multi-stage sampling method over the existing binary tree method.

DOA Estimation Method Based on Improved Deep Convolutional Neural Network.

For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which "1" indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input.

Discovery and optimization of selective RET inhibitors via scaffold hopping.

Aberrant alterations of rearranged during transfection (RET) have been identified as actionable drivers of multiple cancers, including thyroid carcinoma and lung cancer. Currently, several approved multikinase inhibitors such as vandetanib and cabozantinib demonstrate clinical activity in patients with RET-rearranged or RET-mutant cancers. However, the observed response rates are only modest and the 'off-target' toxicities resulted from the inhibition of other kinases is also a concern. Herein, we designed and synthesized a series of RET inhibitors based on the structure of selective RET inhibitor BLU-667 and investigated their biological activities. We identified compound 9 as a novel potent and selective RET inhibitor with improved drug-like properties. Compound 9 exhibits a selective inhibitory profile with an inhibitory concentration 50 (IC50) of 1.29 nM for RET and 1.97 (RET V804M) or 0.99 (RET M918T) for mutant RETs. The proliferation of Ba/F3 cells transformed with NSCLC related KIF5B-RET fusion was effectively suppressed by compound 9 (IC50 = 19 nM). Additionally, compound 9 displayed less 'off-target' effects than BLU-667. In mouse xenograft models, compound 9 repressed tumor growth driven by KIF5B-RET-Ba/F3 cells in a dose-dependent manner. Based on its exceptional kinase selectivity, good potency and high exposure in tumor tissues, compound 9 represents a promising lead for the discovery of RET directed therapeutic agents and the study of RET-driven tumor biology.