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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

In Vivo Evolution of CTX-M-215, a Novel Narrow-Spectrum ß-Lactamase in an Escherichia coli Clinical Isolate Conferring Resistance to Mecillinam.

Here, we report a novel narrow-spectrum ß-lactamase CTX-M-215 identified in an Escherichia coli clinical isolate in China and conferring high-level resistance to mecillinam but not to cefotaxime. CTX-M-215 differed from CTX-M-125, a CTX-M extended-spectrum ß-lactamase (ESBL), by an N132D substitution, which decreased hydrolytic activities toward penicillins and cephalosporins except for mecillinam. High similarity was observed between CTX-M-215- and CTX-M-125-bearing plasmids, carried by different isolates in the same patient, indicating in vivo evolution of CTX-M-215 from CTX-M-125.

Tiotropium in Early-Stage Chronic Obstructive Pulmonary Disease.

BACKGROUND: Patients with mild or moderate chronic obstructive pulmonary disease (COPD) rarely receive medications, because they have few symptoms. We hypothesized that long-term use of tiotropium would improve lung function and ameliorate the decline in lung function in patients with mild or moderate COPD. METHODS: In a multicenter, randomized, double-blind, placebo-controlled trial that was conducted in China, we randomly assigned 841 patients with COPD of Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 1 (mild) or 2 (moderate) severity to receive a once-daily inhaled dose (18 µg) of tiotropium (419 patients) or matching placebo (422) for 2 years. The primary end point was the between-group difference in the change from baseline to 24 months in the forced expiratory volume in 1 second (FEV1) before bronchodilator use. Secondary end points included the between-group difference in the change from baseline to 24 months in the FEV1 after bronchodilator use and the between-group difference in the annual decline in the FEV1 before and after bronchodilator use from day 30 to month 24. RESULTS: Of 841 patients who underwent randomization, 388 patients in the tiotropium group and 383 in the placebo group were included in the full analysis set. The FEV1 in patients who received tiotropium was higher than in those who received placebo throughout the trial (ranges of mean differences, 127 to 169 ml before bronchodilator use and 71 to 133 ml after bronchodilator use; P<0.001 for all comparisons). There was no significant amelioration of the mean (±SE) annual decline in the FEV1 before bronchodilator use: the decline was 38±6 ml per year in the tiotropium group and 53±6 ml per year in the placebo group (difference, 15 ml per year; 95% confidence interval [CI], -1 to 31; P=0.06). In contrast, the annual decline in the FEV1 after bronchodilator use was significantly less in the tiotropium group than in the placebo group (29±5 ml per year vs. 51±6 ml per year; difference, 22 ml per year [95% CI, 6 to 37]; P=0.006). The incidence of adverse events was generally similar in the two groups. CONCLUSIONS: Tiotropium resulted in a higher FEV1 than placebo at 24 months and ameliorated the annual decline in the FEV1 after bronchodilator use in patients with COPD of GOLD stage 1 or 2. (Funded by Boehringer Ingelheim and others; Tie-COPD ClinicalTrials.gov number, NCT01455129 .).

Design, syntheses and evaluations of novel indole derivatives as orally selective estrogen receptor degraders (SERD).

Most estrogen receptor positive (ER +) breast cancers depend on ER signaling pathway to develop. Clinical application of SERD fulvestrant effectively degraded ER, blocked its function and prolonged progression free survival of ER + breast cancer patients. However, current SERD suffers from limited bioavailability, therefore is given as intramuscular (IM) injection. In this paper, we report herein a novel indole series compounds with nanomolar range ER degradation potencies and oral systemic exposures. Selected compounds suppressed tumor growth in vivo in ER + MCF7 breast cancer CDX model via p.o. administration. All those data supported further optimizations of this analog to develop preclinical candidate as oral SERD for ER + breast cancer's treatment.

Design, synthesis, and evaluation of a novel macrocyclic anti-EV71 agent.

We describe here the design, synthesis, and evaluation of a macrocyclic peptidomimetic as a potent agent targeting enterovirus A71 (EV71). The compound has a 15-membered macrocyclic ring in a defined conformation. Yamaguchi esterification reaction was used to close the 15-membered macrocycle instead of the typical Ru-catalyzed ring-closing olefin metathesis reaction. The crystallographic characterization of the complex between this compound and its target, 3C protease from EV71, validated the design and paved the way for the generation of a new series of anti-EV71 agents.

Multi-Frequency Based Direction-of-Arrival Estimation for 2q-Level Nested Radar & Sonar Arrays.

Direction finding is a hot research area in radar and sonar systems. In the case of q ≥ 2, the 2qth-order cumulant based direction of arrival (DOA) estimation algorithm for the 2q-level nested array can achieve high resolution performance. A virtual 2qth-order difference co-array, which contains O(N2q) virtual sensors in the form of a uniform linear array (ULA), is yielded and the Gaussian noise is eliminated. However, some virtual elements are separated by the holes among the 2qth-order difference co-array and cannot be fully used. Even though the application of the multi-frequency method for minimum frequency separation (MFMFS) can fill the holes with low computation complexity, it requires that the number of frequencies must increase with the number of holes. In addition, the signal spectra have to be proportional for all frequencies, which is hard to satisfy when the number of holes is large. Aiming at this, we further propose a multi-frequency method for a minimum number of frequencies (MFMNF) and discuss the best frequency choice under two specific situations. Simulation results verify that, compared with the MFMFS method, the proposed MFMNF method can use only one frequency to fill all the holes while achieving a longer virtual array and the DOA estimation performance is, therefore, improved.

Discovery of aminocyclohexene analogues as selective and orally bioavailable hNav1.7 inhibitors for analgesia.

hNav1.7 receives a lot of attention owing to its attractive mechanism of action in pain processing pathway. We have previously reported our design of a novel series of tetrahydropyridine analogues towards hNav1.7 selective inhibitors. Herein, we disclose further efforts to the optimization of hit compound (-)-6, which led to the identification of aminocyclohexene analogues (-)-9 and (-)-17 with good potency, high selectivity, and minimal CYP inhibition. Both compounds (-)-9 and (-)-17 demonstrated improved pharmacokinetic profiles in rats, and robust efficacy in rat formalin-induced nociception and spinal nerve ligation (SNL) models.

Discovery and optimization of selective FGFR4 inhibitors via scaffold hopping.

Introduction of a Michael acceptor on a flexible scaffold derived from pan-FGFR inhibitors has successfully yielded a novel series of highly potent FGFR4 inhibitors with selectivity over FGFR1. Due to reduced lipophilicity and aromatic ring count, this series demonstrated improved solubility and permeability. However, plasma instability and fast metabolism limited its potential for in vivo studies. Efforts have been made to address these problems, which led to the discovery of compound (-)-11 with improved stability, CYP inhibition, and good activity/selectivity for further optimization.