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In this study, Co2P/CoP hybrid nanoparticles (NPs) imbedded on the surface of core-shell metal-organic frameworks (MOFs) derived three-dimensional N, P co-doped graphitized carbon (Co2P/CoP@NPGC) are prepared via direct pyrolysis of P-containing MOF precursors. P dopant dosage is tailored to adjust active sites and crystalline phases of Co2P/CoP@NPGC. The active Co2P and CoP NPs and the synergistic effect from the Co-Nx/C and Co-P/C active sites and porous NPGC make the dominant contributions to the ORR/OER. For ORR, the half-wave potential of Co2P/CoP@NPGC-1 is 0.93V, which is superior to that of Pt/C (E1/2 = 0.875 V). As for OER, Co2P/CoP@NPGC-1 displays a lower overpotential (ƞ = 340 mV) compared to RuO2 (ƞ = 380 mV, at 10 mA cm-2). The Co2P@CoOOH heterojunction guarantees intrinsic conductivity. Furthermore, doping with N and P can modify the surface electronic structure of catalyst to lower the energy of oxygen adsorption and dissociation, which are beneficial to enhance the ORR and OER activity. Additionally, its bifunctional activity parameter (ΔE) for ORR and OER is only 0.64 V, which is lower than that of Pt/C and RuO2 (0.76 V). Therefore, this work proposes a new sight into constructing a competitive core-shell MOFs derived electrocatalyst for ORR/OER.

Objective To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists. Materials and methods A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I). Results The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time. Conclusions Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.

Microchemical technology shows prospect in the process intensification field of carbon capture and storage, however, there remain great challenges in microchemical technology for gas-liquid mass transfer with counter-current flow. In this work, we proposed a micro-apparatus with confined structure for gas-liquid counter-current flow mass transfer. The flow patterns on the confined structure, steady flow and bead flow, were observed, furthermore, the critical flow rate between them was measured. The influences of confined structure geometry, surface tension and viscosity of liquid on the meniscus at the gas-liquid interface were studied. The operation window of micro-apparatus with different types of confined structures was examined. The liquid loading and gas loading of micro-apparatus with S1005 were tested to be 230 μL/min and 500 mL/min, respectively. The system of absorption of pure CO2 in a 1 M NaOH solution was used to study the mass transfer. The average liquid side mass transfer coefficient of 25.1 × 10−5 m/s was measured at liquid flow rate of 30 μL/min and gas flow rate of 300 μL/min, which proves that the micro-apparatus achieves the process intensification of CO2 absorption.

Organophosphate esters (OPEs) are increasingly used as flame retardants and plasticizers in various products. Most of them are physically mixed rather than chemical bonded to the polymeric products, leading to OPEs being readily released into the surrounding environment. Due to their relatively high solubility and mobility, OPEs are ubiquitous in the aquatic environment and may pose potential hazards to human health and aquatic organisms. This review systematically summarized the fate and distribution of OPEs in the aquatic environment and the potential effects of OPEs on humans. Data analysis shows that the concentrations of OPEs vary widely in various types of aquatic environments, including surface water (range: 25–3671 ng/L), drinking water (4–719 ng/L), and wastewater (104–29,800 ng/L). The results of human exposure assessments via aquatic products and drinking water ingestion indicate that all OPEs pose low, but not negligible, risks to human health. In addition, the limitations of previous studies are summarized, and the outlook is provided. This review provides valuable information on the occurrence and distribution of OPEs in the aquatic environment.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is the causative agent of coronavirus disease 2019 (COVID-19). Lung lesions are considered to be the main damage caused by SARSCoV-2 infection. In addition, liver injury has also been reported to occur during the course of the disease in severe cases. However, the effect of antiviral treatment on liver injury in critically ill patients is not yet clear. Methods We retrospectively evaluated the effect of antiviral treatment and antiviral drug arbidol on liver injury in COVID-19 critically ill patients. Baseline characteristics were collected from patients who were admitted to intensive care units of Tongji Hospital in Wuhan, China, and confounders were balanced by propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analyses. Results Both the PSM (OR=2.77; 95% CI: 1.03, 7.48; P=0.045) and the IPTW-adjusted (OR=2.33; 95% CI: 1.02, 5.34; P=0.047) results showed that COVID-19 critically ill patients receiving antiviral treatment had a significantly higher risk of liver injury. However, arbidol treatment did not have a significant effect on liver injury (IPTW: OR=2.11; 95% CI: 0.79, 5.67; P=0.14). Conclusions Our results show that although arbidol treatment does not seem to be significantly associated with liver injury complications, the overall use of antiviral drugs increases the risk of liver injury for critically ill patients with COVID-19. Antiviral drugs are widely used to treat COVID-19, but we recommend that for critically ill patients, antiviral treatment should be used with caution considering both effectiveness and potential adverse effects.

Background Laryngeal squamous cell carcinoma(LSCC) is a common type of malignant tumors of the head and neck. At present, the early biological markers of laryngeal cancer are still the focus of attention by otolaryngologists.Golgi membrane protein 1 (GOLM1) is a transmembrane glycoprotein present in the cis-Golgi complex.In recent years, successive findings have revealed the carcinogenic effects of GOLM1.However, the biological impact of GOLM1 on LSCC has not been clarified.Results In this study, GOLM1 expression was found to be significantly correlated with the progression and prognosis of LSCC. By silencing GOLM1, the Wnt/β-catenin pathway was downregulated, thereby inhibiting cell proliferation, migration, and invasion, as well as the progress of EMT, and promoting TU686 cell apoptosis. Conclusions Our study revealed the influence of GOLM1 through the Wnt/β-catenin pathway on the biological behavior of LSCC. GOLM1 is thus a potential target for the diagnosis and treatment of LSCC.

This paper addresses logarithmic vector quantizers with dynamic sensitivity design for stochastic continuous time linear system with a feedback control law. Logarithmic vector quantization is adopted so that the coarsest density of quantization and data compression efficiency are both guaranteed. In addition, the problem of quantizer saturation is treated with the mechanism of dynamic sensitivity. The dynamic of the sensitivity during “zoom-in” / “zoom-out” stages is proposed. It is shown that with the proposed algorithm, a single input continuous time linear system can be stabilized in the sense of mean square by quantized feedback control via adopting sensitivity varying algorithm. Results from theoretical analysis are verified through simulation at the end of the paper.

Public concerns are essential for megainfrastructure project success, meanwhile profoundly influencing stakeholder performance. To understand the dynamics of public concerns and the project...

To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). In this retrospective study, CT images and clinical information (age, sex and medical history) from 1020 participants were collected and divided into a single-centre training set (n = 760; age: 55.8 ± 13.4 years; men: 500), a single-centre testing set (n = 134; age: 53.1 ± 14.3 years; men: 90), and two independent multicentre testing sets from two different hospitals (n = 62, age: 57.97 ± 11.88, men: 41; n = 64, age: 57.40 ± 13.36, men: 35). A Faster Region–based CNN (Faster R-CNN) model was applied to integrate CT images and clinical information. Then, a result merging technique was used to convert 2D inferences into 3D lesion results. The diagnostic performance was assessed on the basis of the receiver operating characteristic (ROC) curve, free-response ROC (fROC) curve, precision, recall (sensitivity), F1-score, and diagnosis time. The classification performance was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity. The CNN model showed improved performance on fresh, healing, and old fractures and yielded good classification performance for all three categories when both clinical information and CT images were used compared to the use of CT images alone. Compared with experienced radiologists, the CNN model achieved higher sensitivity (mean sensitivity: 0.95 > 0.77, 0.89 > 0.61 and 0.80 > 0.55), comparable precision (mean precision: 0.91 > 0.87, 0.84 > 0.77, and 0.95 > 0.70), and a shorter diagnosis time (average reduction of 126.15 s). A CNN model combining CT images and clinical information can automatically detect and classify rib fractures with good performance and feasibility in actual clinical practice. • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.

Background: Difficult tracheal intubation is a problem commonly encountered by anesthesiologists in the clinic. Methods: In this retrospective study, case-level clinical data and computed tomography images of 96 infants with Pierre-Robin syndrome were included in the analysis. First, computed tomography images were labeled by a clinically experienced physician. Then color space conversion, binarization, contour acquisition, and area calculation processing were performed on the annotated files. Finally, we calculated the correlation coefficient between the seven clinical factors and tracheal intubation difficulty, and the difference in each risk factor under tracheal intubation difficulty. Results: The absolute value of the correlation coefficient between throat area and tracheal intubation difficulty is 0.54, and the difference of throat area under tracheal intubation difficulty is significant. Body surface area, weight and gender also show significant difference under tracheal intubation difficulty. Conclusions: There is a significant correlation between throat area and tracheal intubation difficulty in infants with Pierre-Robin syndrome. Body surface area, weight and gender may have an impact on tracheal intubation difficulty in infants with Pierre-Robin syndrome.

In the background of incubation network, the paper studies how incubated enterprises gain cognitive social capital through embedding in the network, obtain intangible resources effectively in the resource-sharing environment and improve their innovation performance. We construct and analyze the theoretical model of cognitive social capital, intangible resource acquisition and innovation performance. The incubated enterprises need to strengthen cognitive social capital accumulation through incubation network; it effects the ability of intangible resource acquisition, then helps start-ups improve innovation performance.

Background Difficult tracheal intubation is a common problem encountered by anesthesiologists in the clinic. This study was conducted to assess the difficulty of tracheal intubation in infants with Pierre Robin syndrome (PRS) by incorporating computed tomography (CT) to guide airway management for anesthesia. Methods In this retrospective study, we analyzed case-level clinical data and CT images of 96 infants with PRS. First, a clinically experienced physician labeled CT images, after which the color space conversion, binarization, contour acquisition, and area calculation processing were performed on the annotated files. Finally, the correlation coefficient between the seven clinical factors and tracheal intubation difficulty, as well as the differences in each risk factor under tracheal intubation difficulty were calculated. Results The absolute value of the correlation coefficient between the throat area and tracheal intubation difficulty was 0.54; the observed difference was statistically significant. Body surface area, weight, and gender also showed significant difference under tracheal intubation difficulty. Conclusions There is a significant correlation between throat area and tracheal intubation difficulty in infants with PRS. Body surface area, weight and gender may have an impact on tracheal intubation difficulty in infants with PRS.

The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of MLL2, and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status (p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.

A Gram-stain-variable, aerobic, rod-shaped, endospore-forming strain R196T) was isolated from internal tissues of roots of Cymbidium goeringii. Cells were motile with peritrichous flagella. The colonies were light pink on tryptone soya agar medium. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain R196T fell into a phylogenetic cluster belonging to the genus Paenibacillus . Strain R196T was closely related to Paenibacillus cavernae C4-5T and Paenibacillus contaminans CKOBP-6T with 93.6 and 93.3% sequence similarities, respectively. The major cellular polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, three unidentified phospholipids, two unidentified aminophospholipids and an unidentified aminolipid. The dominant respiratory quinone was MK-7. The main cellular fatty acids were anteiso-C15 : 0 (53.01%), C16 : 0 (13.04%) and iso-C16 : 0 (10.80%). The genome size of R196T was 9.45 Mb, containing 7617 predicted protein-coding genes, with a DNA G+C content of 57.7 mol%. Based on the results of phenotypic, chemotaxonomic and whole-genome analyses, strain R196T represents a novel species of the genus Paenibacillus , for which the name Paenibacillus cymbidii sp. nov. is proposed. The type strain is R196T (=ACCC 61713T=KCTC 33718T).

Background: Due to limited immunological profiles of high-grade serous ovarian cancer (HGS-OvCa), we aimed to characterise its molecular features to determine whether a specific subset that can respond to immunotherapy exists. Methods: A training cohort of 418 HGS-OvCa samples from TCGA was analysed by consensus non-negative matrix factorization. We correlated the expression patterns with the presence of immune cell infiltrates, immune regulatory molecules and other genomic or epigenetic features. Two independent cohorts containing 482 HGS-OvCa samples and in vitro experiments were used for validation. Findings: We identified immune and normal groups where the former was enriched in signatures that reflect immune cells, infiltration, and PD-1 signalling (all, P

Objective To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and methods This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 × 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

This paper addresses logarithmic quantizers with dynamic sensitivity design for continuous time linear system with a quantized feedback control law. The dynamic of the sensitivity during “Zoom-in” / “Zoom-out” stages is proposed. It is shown that with the proposed algorithm, a linear system can be stabilized by quantized feedback control via adopting sensitivity varying algorithm. It is shown that the advantage of logarithmic quantization is sustained while achieving stability. At the end of this paper the results will be verified by simulation.