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Conventional CO2 capture technologies often focus on the simple capture process, while the absorption and high-value utilization of carbon capture products have profound significance, especially when combined with the utilization of biomass energy. Novel ammonia-based CO2 capture coupled with selective silicon dissolution was proposed with process regulation of external fields (i.e. magnetic field, microwave field, ultrasound field) used to prepare nano silica. The results show that the magnetic field plays a dual role of “vibration stirring” and adding energy (11.2 J/mol) on the formation and dispersion of nano silica. The specific surface area of nano-silica is increased from 140.24 m2/g to 280 m2/g by microwave field (250 W), and its “internal heating” promotes the formation of crystal nuclei. The ultrasonic field reduces the primary particle size of nano-silica by 3.17 nm, and inhibits the particle growth through cavitation and mechanical vibration. It would provide parameter support and optimization guidance for the low-cost high-quality nano silica for high-value utilization of ammonia-based CO2 capture products.

Purpose: This study explored the influence of team member exchange on employees’ knowledge hiding behaviors via job embeddedness and work alienation, with learning goal orientation acting as the boundary condition. Method: ology: This study adopted a quantitative multi-study research methodology to validate the proposed hypotheses, combining a time-lagged field study with 459 in-service employees and a scenario-based experiment with 128 university students at a northern university in China. Findings: In Study 1 (field study), team-member exchange was negatively associated with knowledge hiding, and job embeddedness and work alienation mediated this relationship. Perceptions of learning goal orientation can amplify the effect of team-member exchange on job embeddedness and work alienation, which in turn reduces knowledge hiding behaviors. A subsequent experiment (Study 2) almost replicated and supported these findings, but work alienation did not play a role as an intermediary in the relationship between team member exchange and knowledge hiding behavior. Practical implications: Managers should stimulate social exchanges among team members to inhibit knowledge hiding behaviors and prioritize individuals exhibiting higher learning goal orientations when deciding whom to hire. Originality: This research identifies and rationalizes how (underlying mechanisms) and when (contingencies) team-member exchange can make a difference in employees’ knowledge hiding behaviors, expanding and advancing further research on the knowledge hiding phenomenon from a team perspective.

Acanthamoeba keratitis is a rare parasitic infection of the cornea that can lead to permanent blindness if not diagnosed and treated promptly. We collected data on the incidences of Acanthamoeba keratitis from 20 countries and calculated an annual incidence of 23,561 cases, with the lowest rates in Tunisia and Belgium, and the highest in India. We analyzed 3755 Acanthamoeba sequences from the GenBank database across Asia, Europe, North America, South America, and Oceania and genotyped them into T1, T2, T3, T4, T5, T10, T11, T12, and T15. Many genotypes possess different characteristics, yet T4 is the most prevalent genotype. As efficient treatment against Acanthamoeba remains lacking, prevention from early diagnosis via staining, PCR, or in vivo confocal microscopy (IVCM) becomes significant for the condition’s prognosis. IVCM is the most recommended approach for the early detection of Acanthamoeba. If IVCM is unavailable, PCR should be used as an alternative.

Objectives: To analyze the epidemiologic features, culture positivity, the fungal spectrum of different sites of ocular infection in North China over 20 years from 2001 to 2020. Methods: 11, 635 patients suspected of ocular fungal infection were reviewed. The demographic profile, fungal positive culture rate among different sites, the distribution, and trends of main pathogens among cornea and intraocular fluid were analyzed. Results: Among 11, 635 samples, the positive culture rate of ocular fungal infection was 23.6%. Most of samples (83.1%) were from cornea, and their culture positivity was 26.9%. Fungal keratitis occurred more often during the harvesting season (October to December; 34.0%) than in other seasons (average: 22.0%). Fusarium sp. (53.2%), Aspergillus sp. (15.9%) and Alternaria sp. (12.5%) were the most common fungal species of ocular mycotic infections in the past two decades in north China. 2562 organisms were identified from cornea, of which 1443 (56.3%) were Fusarium sp., 403 (15.7%) and 329 (12.8%) were Aspergillus sp., and Alternaria sp., respectively. Of the 120 fungi isolated from the intraocular fluid, the most common was Aspergillus sp. (33.3%), followed by Fusarium sp. (24.2%) and Candida sp. (15.0%). Conclusions: Fusarium sp., Aspergillus sp. and Alternaria sp. were the most common organisms in cases of fungal keratitis, while Aspergillus sp., Fusarium sp. and Candida sp. were the most frequent isolates for fungal endophthalmitis.

Defects of solar panels can easily cause electrical accidents. The YOLO v5 algorithm is improved to make up for the low detection efficiency of the traditional defect detection methods. Firstly, it is improved on the basis of coordinate attention to obtain a LCA attention mechanism with a larger target range, which can enhance the sensing range of target features in addition to fully capturing feature information; secondly, the weighted bidirectional feature pyramid is used to balance the feature information with excessive pixel differences by assigning different weights, which is more conducive to multi-scale Fast fusion of features; finally, the usual coupled head of YOLO series is replaced with decoupled head, so that the task branch can be performed more accurately and the detection accuracy can be improved. The results of comparative experiments on the solar panel defect detection data set show that after the improvement of the algorithm, the overall precision is increased by 1.5%, the recall rate is increased by 2.4%, and the mAP is up to 95.5%, which is 2.5% higher than that before the improvement. It can more accurately determine whether there are defects, standardize the quality of solar panels, and ensure electrical safety.

Summary: Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection. Motivation: Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay.

Summary: Background: Fungal keratitis (FK) is a leading cause of corneal blindness in developing countries due to poor clinical recognition and laboratory identification. Here, we aimed to identify the distinct clinical signature of FK and develop a diagnostic model to differentiate FK from other types of infectious keratitis. Methods: We reviewed the electronic health records (EHRs) of all patients with suspected infectious keratitis in Beijing Tongren Hospital from January 2011 to December 2021. Twelve clinical signs of slit-lamp images were assessed by Lasso regression analysis and collinear variables were excluded. Three models based on binary logistic regression, random forest classification, and decision tree classification were trained for FK diagnosis and employed for internal validation. Independent external validation of the models was performed in a cohort of 420 patients from seven different ophthalmic centers to evaluate the accuracy, specificity, and sensitivity in real world. Findings: Three diagnostic models of FK based on binary logistic regression, random forest classification, and decision tree classification were established and internal validation were achieved with the mean AUC of 0.916, 0.920, and 0.859, respectively. The models were well-calibrated by external validation using a prospective cohort including 210 FK and 210 non-FK patients from seven eye centers across China. The diagnostic model with the binary logistic regression algorithm classified the external validation dataset with a sensitivity of 0.907 (0.774, 1.000), specificity 0.899 (0.750, 1.000), accuracy 0.905 (0.805, 1.000), and AUC 0.903 (0.808, 0.998). Interpretation: Our model enables rapid identification of FK, which will help ophthalmologists to establish a preliminary diagnosis and to improve the diagnostic accuracy in clinic. Funding: The Open Research Fund from the National Key Research and Development Program of China (2021YFC2301000) and the Open Research Fund from Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing Tongren Hospital, Beihang University & Capital Medical University (BHTR-KFJJ-202001) supported this study.

Summary: We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset used in the original publication or a custom dataset. Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes.For complete details on the use and execution of this protocol, please refer to Chen et al. (2022). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Muscarinic acetylcholine receptors (mAChRs) are widely expressed in various effector cells and have been proved to play vital roles in smooth muscle contraction and digestive secretion. However, there are relatively few literatures revealing the roles of mAChRs in inflammatory processes, and its underlying regulatory mechanisms have not been elucidated. Taking the advantages of live imaging of zebrafish, we found that inhibition of mAChRs resulted in increased neutrophils recruitment and proinflammatory cytokines expression, whereas activation of mAChRs led to opposite outcome. Subsequently, we found that mAChRs regulated the expression of arginases (args), and pharmacological intervention of args level could reverse the effects of mAChRs on neutrophils migration and cytokines expression, suggesting that args are important downstream proteins of mAChRs that mediate the regulation of inflammatory response. In this study, we identified args as novel downstream proteins of mAChRs in inflammatory responses, providing additional evidence for system immune regulation of cholinergic receptors.

Silica is widely applied in industrial fields due to its good properties, while preparation of high-quality SiO2 by carbonization is an important way to utilize CO2. The experiment was carried out in a microbubble/bubbling experimental device, with its mass transfer kinetics of carbonization analyzed according to the pH-t curves. Under microbubble conditions, with the increasing temperature of 20∼60°C, the specific surface area, oil absorption value and particle size of silica varies at 336.80∼231.29 m2/g, 1.12∼1.81 ml/g and 16.49∼14.60 μm, respectively, which is similar as that with bubble. Aging at 20∼90 °C, they involve in the range of 35.82∼128.76 m2/g, 1.59∼2.44 ml/g and 23.15∼14.46 μm. With total concentration of solution >0.5 M, the concentration increases, the specific surface area and particle size of microbubbles are larger than those of the bubbling silica, and oil absorption value increases at 1.81∼2.44 ml/g. The kinetic analysis shows that it is more consistent with theoretical value, which can be judged as the secondary-reaction process. The microbubbles increase the CO2 utilization rate and volumetric mass transfer coefficient of carbonization by 2-4 times, while the liquid-phase mass transfer coefficient decreases. It indicates that microbubble mass transfer is achieved by significantly increasing the specific surface area.