Inducing spin polarization via Co doping in the BiVO4 cell to enhance the built-in electric field for promotion of photocatalytic CO2 reduction.

The efficiency of CO2 photocatalytic reduction is severely limited by inefficient separation and sluggish transfer. In this study, spin polarization was induced and built-in electric field was strengthened via Co doping in the BiVO4 cell to boost photocatalytic CO2 reduction. Results showed that owing to the generation of spin-polarized electrons upon Co doping, carrier separation and photocurrent production of the Co-doped BiVO4 were enhanced. CO production during CO2 photocatalytic reduction from the Co-BiVO4 was 61.6 times of the BiVO4. Notably, application of an external magnetic field (100 mT) further boosted photocatalytic CO2 reduction from the Co-BiVO4, with 68.25 folds improvement of CO production compared to pristine BiVO4. The existence of a built-in electric field (IEF) was demonstrated through density functional theory (DFT) simulations and kelvin probe force microscopy (KPFM). Mechanism insights could be elucidated as follows: doping of magnetic Co into the BiVO4 resulted in increased the number of spin-polarized photo-excited carriers, and application of a magnetic field led to an augmentation of intrinsic electric field due to a dipole shift, thereby extending carrier lifetime and suppressing charges recombination. Additionally, HCOO- was a crucial intermediate in the process of CO2RR, and possible pathways for CO2 reduction were proposed. This study highlights the significance of built-in electric fields and the important role of spin polarization for promotion of photocatalytic CO2 reduction.

Biomass-Derived Carbon Quantum Dots Chemical Bonding with Sn-Doped Bi2 O2 CO3 Endowed Fast Carriers' Dynamics and Stabilized Oxygen Vacancies for Efficient Decontamination of Combined Pollutants.

Surface defects on photocatalysts could promote carrier separation and generate unsaturated sites for chemisorption and reactant activation. Nevertheless, the inactivation of oxygen vacancies (OVs) would deteriorate catalytic activity and limit the durability of defective materials. Herein, bagasse-derived carbon quantum dots (CQDs) are loaded on the Sn-doped Bi2 O2 CO3 (BOC) via hydrothermal procedure to create Bi─O─C chemical bonding at the interface, which not only provides efficient atomic-level interfacial electron channels for accelerating carriers transfer, but also enhances durability. The optimized Sn-BOC/CQDs-2 achieves the highest photocatalytic removal efficiencies for levofloxacin (LEV) (88.7%) and Cr (VI) (99.3%). The elimination efficiency for LEV and Cr (VI) from the Sn-BOC/CQDs-2 is maintained at 55.1% and 77.0% while the Sn-BOC is completely deactivated after four cycle tests. Furthermore, the key role of CQDs in stabilization of OVs is to replace OVs as the active center of H2 O and O2 adsorption and activation, thereby preventing reactant molecules from occupying OVs. Based on theoretical calculations of the Fukui index and intermediates identification, three possible degradation pathways of LEV are inferred. This work provides new insight into improving the stability of defective photocatalysts.

Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases.

Purpose: While deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario. Methods: We collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny. Results: Dose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%). Conclusion: This study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.

From Nonlinear Dominant System to Linear Dominant System: Virtual Equivalent System Approach for Multiple Variable Self-Tuning Control System Analysis.

The stability and convergence analysis of a multivariable stochastic self-tuning system (STC) is very difficult because of its highly nonlinear structure. In this paper, based on the virtual equivalent system method, the structural nonlinear or nonlinear dominated multivariable self-tuning system is transformed into a structural linear or linear dominated system, thus simplifying the stability and convergence analysis of multivariable STC systems. For the control process of a multivariable stochastic STC system, parameter estimation is required, and there may be three cases of parameter estimation convergence, convergence to the actual value and divergence. For these three cases, this paper provides four theorems and two corollaries. Given the theorems and corollaries, it can be directly concluded that the convergence of parameter estimation is a sufficient condition for the stability and convergence of stochastic STC systems but not a necessary condition, and the four theorems and two corollaries proposed in this paper are independent of specific controller design strategies and specific parameter estimation algorithms. The virtual equivalent system theory proposed in this paper does not need specific control strategies, parameters and estimation algorithms but only needs the nature of the system itself, which can judge the stability and convergence of the self-tuning system and relax the dependence of the system stability convergence criterion on the system structure information. The virtual equivalent system method proposed in this paper is proved to be effective when the parameter estimation may have convergence, convergence to the actual value and divergence.

Deep residual neural-network-based robot joint fault diagnosis method.

A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain error, offset error and malfunction for both sensors and actuators, respectively. First, a deep residual network fault diagnosis model is derived by stacking small convolution cores and increasing the core size. meanwhile, the gaussian white noise is injected into the fault data set to verify the noise immunity for the proposed deep residual network. Furthermore, a simulation is conducted, where different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), long-term memory network (LTMN) and deep residual neural network (DRNN) are compared, and the simulation results show the accuracy of fault diagnosis for robot system using DRNN is higher, meanwhile, DRNN needs less model training time. Visualization analysis proved the feasibility and effectiveness of the proposed method for robot joint sensor and actuator fault diagnosis using DRNN method.

The Cellular and Viral circRNAome Induced by Respiratory Syncytial Virus Infection.

Circular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention. DNA virus infections have been reported to induce modifications in cellular circRNA transcriptomes and express viral circRNAs. However, the identification and expression of cellular and viral circRNAs are unknown in the context of respiratory syncytial virus (RSV), a human RNA virus with no effective treatments or vaccines. Here, we report a comprehensive identification of the cellular and viral circRNAs induced by RSV infection in A549 cells with high-throughput sequencing. In total, 53,719 cellular circRNAs and 2,280 differentially expressed cellular circRNAs were identified. Trend analysis further identified three significant expression pattern clusters, which were related to the antiviral immune response according to gene enrichment analysis. Subsequent results showed that not only RSV infection but also poly(I·C) treatment and another RNA virus infection induced the upregulation of the top 10 circRNAs from the focused cluster. The top 10 circRNAs generally inhibit RSV replication in turn. Moreover, 1,254 viral circRNAs were identified by the same circRNA sequencing. The induced expression of viral circRNAs by RSV infection was found not only in A549 cells but also in HEp-2 cells. Additionally, we profiled the general characteristics of both cellular and viral circRNAs such as back-splicing signals, etc. Collectively, RSV infection induced the differential expression of cellular circRNAs, some of which affected RSV infection, and RSV also expressed viral circRNAs. Our study reveals novel layers of host-RSV interactions and identifies cellular or viral circRNAs that may be novel therapeutic targets or biomarkers. IMPORTANCE Noncoding RNAs (ncRNAs) demonstrate substantial roles in cell-virus interactions. Circular RNAs (circRNAs) are a newly identified class of ncRNAs that have gained increased attention recently. DNA virus infections have been reported to induce modifications in cellular circRNA transcriptomes and express viral circRNAs. However, the identification and expression of cellular and viral circRNAs are unknown in the context of respiratory syncytial virus (RSV), a human RNA virus with no effective treatments or vaccines. Here, we report a comprehensive identification of the cellular and viral circRNAs induced by RSV infection by high-throughput sequencing. We revealed that RSV infection induces the differential expression of cellular circRNAs, some of which affected RSV infection, and that RSV also expresses viral circRNAs. Our study reveals novel layers of host-RSV interactions and identifies cellular or viral circRNAs that may be novel therapeutic targets or biomarkers.

Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN.

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.

Clinical features and short-term outcomes of elderly patients with COVID-19.

BACKGROUND: The outbreak of Coronavirus Disease 2019 (COVID-19) has become a global public health emergency. METHODS: 204 elderly patients (≥60 years old) diagnosed with COVID-19 in Renmin Hospital of Wuhan University from January 31st to February 20th, 2020 were included in this study. Clinical endpoint was in-hospital death. RESULTS: Of the 204 patients, hypertension, diabetes, cardiovascular disease, and chronic obstructive pulmonary disease (COPD) were the most common coexisting conditions. 76 patients died in the hospital. Multivariate analysis showed that dyspnea (hazards ratio (HR) 2.2, 95% confidence interval (CI) 1.414-3.517; p < 0.001), older age (HR 1.1, 95% CI 1.070-1.123; p < 0.001), neutrophilia (HR 4.4, 95% CI 1.310-15.061; p = 0.017) and elevated ultrasensitive cardiac troponin I (HR 3.9, 95% CI 1.471-10.433; p = 0.006) were independently associated with death. CONCLUSION: Although so far the overall mortality of COVID-19 is relatively low, the mortality of elderly patients is much higher. Early diagnosis and supportive care are of great importance for the elderly patients of COVID-19.

Comparison of geometrical uncertainties in the radiotherapy for various treatment sites with two different immobilization marking methods.

OBJECTIVE: The skin marking method (SMM) and bow-form-ruler marking method (BFRM) are two commonly used patient marking methods in mainland China. This study aims to evaluate SMM and BFRM by comparing the inter-fraction setup errors from using these two methods together with vacuum cushion immobilization in patients underwent radiotherapy for different treatment sites. MATERIALS AND METHODS: Eighteen patients diagnosed with pelvic, abdominal and thoracic malignant tumors (with 6 patients per treatment site) were enrolled in this prospective study. All patients were immobilized with vacuum cushion. Each patient was marked by both SMM and BFRM before computed tomography (CT) simulation. Target location was verified by cone beam CT images with displacements assessed prior to each sampled treatment session. The localization errors in three translational and three rotational directions were recorded and analyzed. RESULTS: Images from 108 fractions in 18 patients produced 324 translational and 324 rotational comparisons for SMM and BFRM. The setup errors of all treatment sites showed no difference in two marking methods in any directions (p > 0.05). In subgroups of treatment site analysis, SMM significantly lessened the lateral and yaw setup errors compared to BFRM in the pelvic sites (0.39±1.85 mm vs -1.28±1.13 mm, p < 0.01 and -0.19±0.59° vs -0.61±0.59°, p < 0.05). However, in the abdominal subgroup, BFRM was superior to SMM for reduced vertical errors (0.17±2.73 mm vs 2.28±3.16 mm, p < 0.05). For the underweight or obese patients (with Body Mass Index, BMI < 18.5 or BMI≥24), SMM resulted in less yaw errors compared to BFRM (-0.05±0.38° vs -0.43±0.48°, p < 0.05). No significant difference between SMM and BFRM in setup errors of normal weighted patients (18.5≤BMI < 24) was observed for all three studied treatment sites. CONCLUSIONS: This study shows no significant difference in patient setup errors for various treatment sites between SMM and BFRM in general. SMM may be suitable for the pelvic tumor and patients with BMI < 18.5 or BMI≥24, while BFRM is recommended for the abdominal tumor sites.

Probing the benzofuroquinolinium derivative as a potent antibacterial agent through the inhibition of FtsZ activity.

A benzofuroquinolinium derivative that exhibits excellent cell division inhibitory effect was discovered through cell-based screening approach. This compound possesses potent antimicrobial activity against both Gram-positive and Gram-negative bacteria including the drug-resistant strains. In addition, this compound is able to restore MRSA susceptibility to beta-lactam antibiotics. The biochemical results suggest that the compound inhibits bacterial cell division through the disruption of GTPase activity and the polymerization of FtsZ, which is probably the mechanism of antibacterial activity.