Cost-effective mitigation of nitrogen pollution from global croplands.

Cropland is a main source of global nitrogen pollution1,2. Mitigating nitrogen pollution from global croplands is a grand challenge because of the nature of non-point-source pollution from millions of farms and the constraints to implementing pollution-reduction measures, such as lack of financial resources and limited nitrogen-management knowledge of farmers3. Here we synthesize 1,521 field observations worldwide and identify 11 key measures that can reduce nitrogen losses from croplands to air and water by 30-70%, while increasing crop yield and nitrogen use efficiency (NUE) by 10-30% and 10-80%, respectively. Overall, adoption of this package of measures on global croplands would allow the production of 17 ± 3 Tg (1012 g) more crop nitrogen (20% increase) with 22 ± 4 Tg less nitrogen fertilizer used (21% reduction) and 26 ± 5 Tg less nitrogen pollution (32% reduction) to the environment for the considered base year of 2015. These changes could gain a global societal benefit of 476 ± 123 billion US dollars (USD) for food supply, human health, ecosystems and climate, with net mitigation costs of only 19 ± 5 billion USD, of which 15 ± 4 billion USD fertilizer saving offsets 44% of the gross mitigation cost. To mitigate nitrogen pollution from croplands in the future, innovative policies such as a nitrogen credit system (NCS) could be implemented to select, incentivize and, where necessary, subsidize the adoption of these measures.

Atomic Au3Cu Palisade Interlayer in Core@Shell Nanostructures for Efficient Kirkendall Effect Mediation.

Plasmonic Cu@semiconductor heteronanocrystals (HNCs) have many favorable properties, but the synthesis of solid structures is often hindered by the nanoscale Kirkendall effect. Herein, we present the use of an atomically thin Au3Cu palisade interlayer to reduce lattice mismatch and mediate the Kirkendall effect, enabling the successive topological synthesis of Cu@Au3Cu@Ag, Cu@Au3Cu@Ag2S, and further transformed solid Cu@Au3Cu@CdS core-shell HNCs via cation exchange. The atomically thin and intact Au3Cu palisade interlayer effectively modulates the diffusion kinetics of Cu atoms as demonstrated by experimental and theoretical investigations and simultaneously alleviates the lattice mismatch between Cu and Ag as well as Cu and CdS. The Cu@Au3Cu@CdS HNCs feature exceptional crystallinity and atomically organized heterointerfaces between the plasmonic metal and the semiconductor. This results in the efficient plasmon-induced injection of hot electrons from Cu@Au3Cu into the CdS shell, enabling the Cu@Au3Cu@CdS HNCs to achieve high activity and selectivity for the photocatalytic reduction of CO2 to CO.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis. METHODS: Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). RESULTS: By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples. CONCLUSION: This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Improving regional medical laboratory center report quality through a report recall management system.

OBJECTIVES: Currently, most medical laboratories do not have a dedicated software for managing report recalls, and relying on traditional manual methods or laboratory information system (LIS) to record recall data is no longer sufficient to meet the quality management requirements in the large regional laboratory center. The purpose of this article was to describe the research process and preliminary evaluation results of integrating the Medical Laboratory Electronic Record System (electronic record system) laboratory report recall function into the iLab intelligent management system for quality indicators (iLab system), and to introduce the workflow and methods of laboratory report recall management in our laboratory. METHODS: This study employed cluster analysis to extract commonly used recall reasons from laboratory report recall records in the electronic record system. The identified recall reasons were validated for their applicability through a survey questionnaire and then incorporated into the LIS for selecting recall reasons during report recall. The statistical functionality of the iLab system was utilized to investigate the proportion of reports using the selected recall reasons among the total number of reports, and to perform visual analysis of the recall data. Additionally, we employed P-Chart to establish quality targets and developed a "continuous improvement process" electronic flow form. RESULTS: The reasons for the recall of laboratory reports recorded in the electronic recording system were analyzed. After considering the opinions of medical laboratory personnel, a total of 12 recall reasons were identified, covering 73.05 % (1854/2538) of the recalled laboratory reports. After removing data of mass spectra lab with significant anomalies, the coverage rate increased to 82.66 % (1849/2237). The iLab system can generate six types of statistical graphs based on user needs, including statistical time, specialty labs (or divisions), test items, reviewers, reasons for report recalls, and distribution of the recall frequency of 0-24 h reports. The control upper limit of the recall rate of P-Chart based on laboratory reports can provide quality targets suitable for each professional group at the current stage. Setting the five stages of continuous process improvement reasonably and rigorously can effectively achieve the goal of quality enhancement. CONCLUSIONS: The enhanced iLab system enhances the intelligence and sustainable improvement capability of the recall management of laboratory reports, thus improving the efficiency of the recall management process and reducing the workload of laboratory personnel.

dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations.

BACKGROUND: Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features. RESULTS: From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs. CONCLUSION: The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.

Analysis of GATA transcription factors and their expression patterns under abiotic stress in grapevine (Vitis vinifera L.).

BACKGROUND: GATA transcription factors are type IV zinc-finger proteins that play key roles in plant growth and responses to environmental stimuli. Although these proteins have been studied in model plants, the related studies of GATA gene family under abiotic stresses are rarely reported in grapevine (Vitis vinifera L.). RESULTS: In the current study, a total of 23 VviGATA genes were identified in grapevine and classified into four groups (I, II, III, and IV), based on phylogenetic analysis. The proteins in the same group exhibited similar exon-intron structures and conserved motifs and were found to be unevenly distributed among the thirteen grapevine chromosomes. Accordingly, it is likely that segmental and tandem duplication events contributed to the expansion of the VviGATA gene family. Analysis of cis-acting regulatory elements in their promoters suggested that VviGATA genes respond to light and are influenced by multiple hormones and stresses. Organ/tissue expression profiles showed tissue specificity for most of the VviGATA genes, and five were preferentially upregulated in different fruit developmental stages, while others were strongly induced by drought, salt and cold stress treatments. Heterologously expressed VamGATA5a, VamGATA8b, VamGATA24a, VamGATA24c and VamGATA24d from cold-resistant V. amurensis 'Shuangyou' showed nuclear localization and transcriptional activity was shown for VamGATA5a, VamGATA8b and VamGATA24d. CONCLUSIONS: The results of this study provide useful information for GATA gene function analysis and aid in the understanding of stress responses in grapevine for future molecular breeding initiatives.

Rapid detection of SARS-CoV-2 with CRISPR-Cas12a.

The recent outbreak of betacoronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is responsible for the Coronavirus Disease 2019 (COVID-19) global pandemic, has created great challenges in viral diagnosis. The existing methods for nucleic acid detection are of high sensitivity and specificity, but the need for complex sample manipulation and expensive machinery slow down the disease detection. Thus, there is an urgent demand to develop a rapid, inexpensive, and sensitive diagnostic test to aid point-of-care viral detection for disease monitoring. In this study, we developed a clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR associated proteins (Cas) 12a-based diagnostic method that allows the results to be visualized by the naked eye. We also introduced a rapid sample processing method, and when combined with recombinase polymerase amplification (RPA), the sample to result can be achieved in 50 minutes with high sensitivity (1-10 copies per reaction). This accurate and portable detection method holds a great potential for COVID-19 control, especially in areas where specialized equipment is not available.

Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform.

OBJECTIVE: A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening. METHODS: This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control. RESULTS: The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913). CONCLUSIONS: The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.

Quantitative PCR of string-test collected gastric material: A feasible approach to detect Helicobacter pylori and its resistance against clarithromycin and levofloxacin for susceptibility-guided therapy.

BACKGROUND: As the reduced eradication rate of Helicobacter pylori (H. pylori), we introduced string-test and quantitative PCR (qPCR) for susceptibility-guided therapy innovatively. The practicality of the string test was evaluated. METHODS: It was an open-label, non-randomized, parallel, single-center study, in which subjects tested by 13 C- urea breath test (UBT) and string-qPCR were enrolled. Based on the results of string-qPCR, we calculated clarithromycin and levofloxacin resistance rates and gave 13 C-UBT positive patients 14 days susceptibility-guided bismuth quadruple therapy. In the empirical therapy group, we retrospectively analyzed the treatment results of 13 C-UBT positive patients also treated with bismuth quadruple at Shenzhen Luohu People's Hospital from January 2021 to May 2022. The eradication rate was compared between susceptibility-guided therapy and empirical therapy groups. RESULTS: The diagnosis of H. pylori infection using the string-qPCR had an overall concordance rate of 95.9% with the 13 C-UBT results. Based on the results of string-qPCR, the clarithromycin and levofloxacin resistance rates were 26.1% and 31.8%, respectively. The patients who were given 14 days susceptibility-guided bismuth-based quadruple therapy achieved a high H. pylori eradication rate of 91.8%. Retrospective analysis of patient treatment data from January 2021 to May 2022 available in the hospital database revealed an overall success rate of 82.3% for those who received empirical bismuth-based quadruple therapies, which is marginally significantly lower than that of the string-qPCR susceptibility-guided group (p = 0.084). CONCLUSION: The high treatment success rate of 91.8% indicates that the string-qPCR test is a valuable and feasible approach for clinical practice to help improve H. pylori treatment success rate.

The Comparison of Biomolecular Changes of Epstein-Barr Virus Infection in Nasopharyngeal Epithelial Cells Using Confocal Raman Microspectroscopy.

BACKGROUND: Due to its unique fingerprinting properties, Confocal Raman microscopy (CRM) can be used to examine the biomolecular changes of viruses invading and manipulating host cells. Recently, the biochemical changes due to the invasion and infection of B lymphocyte cells, nerve cells, and epithelial cells by Epstein-Barr virus (EBV) have been reported. However, biomolecular changes in nasopharyngeal epithelial cells that result from EBV infection are still poorly understood. METHODS: In continuation of our prior investigation of EBV infection in nasopharyngeal epithelial cells, we tried to expound on biomolecular changes in EBV-infected nasopharyngeal epithelial cells using Raman microspectroscopy. EBV has two life cycles, latent infection and lytic replication. We have established latent and lytic infection models at the cellular level. In order to understand the characteristics of the two patterns of EBV infection, we used Raman spectroscopy to identify the changes in biomolecules of EBV latent cells (CNE2, CNE2-EBV) and lytic cells (NPEC1-BMI1-CN, NPEC1-BMI1-EBV). RESULTS: During latent infection, levels of glycogen, protein, and lipid molecules in the cell increased while levels of nucleic acid and collagen molecules decreased. Molecular levels of glycogen, proteins, and nucleic acids are reduced during lytic infection. We found that molecular levels of nucleic acid decreased during two different periods of infection, whereas levels of other biomolecules showed the opposite trend. Glycogen, proteins, lipids, nucleic acids, and other molecules are associated with alterations in cellular biochemical homeostasis. These changes correspond to unique Raman spectra in infected and uninfected cells associated with specific biomolecules that have been proven. These molecules are mainly responsible for cellular processes such as cell proliferation and apoptosis. The Raman signatures of these biomolecular changes depend on the different phases of viral infection. CONCLUSIONS: Therefore, by using CRM, it is possible to discern details in the progression of EBV infection in nasopharyngeal epithelial cells at the molecular level.

Comprehensive analysis of aberrantly expressed circRNAs, mRNAs and lncRNAs in patients with nasopharyngeal carcinoma.

BACKGROUND: The location of nasopharyngeal cancer is hidden, so it is difficult to diagnose at an early stage. In this study, we aimed to investigate the expression profiles of circRNAs, mRNAs and IncRNAs and to provide some basis for further studies. METHODS: Expression profiles of circRNAs, mRNAs, and lncRNAs were analyzed using microarray techniques. The differentially expressed ncRNA was calculated by bioinformatics. RESULTS: A total of 3048 circRNAs, 2179 lncRNAs, and 2015 mRNAs were detected to be significantly differentially expressed in NPC. The most upregulated circRNAs, lncRNAs, and mRNAs were hsa-circ-0067562, NONHSAT232922.1, and HOXB13, respectively. And, the most downregulated circRNAs, lncRNAs, and mRNAs were hsa_circ_0078837, lnc-TTC8-4:3, and LTF, respectively. The number of upregulated DE lncRNAs was more than twice than those downregulated. Our data showed that 80.44% of pairs of lncRNAs and cis-mRNAs demonstrated positive correlations. For lncRNAs and trans-mRNAs pairs, 53.7% of pairs showed positive correlation. LncRNA-mediated cis regulation is a prevalent regulatory mode in the development of nasopharyngeal carcinoma. CR1, LRMP and SORBS2 are predicted to be mediated not only by cis-acting lncRNA modes of action, but also by trans-acting lncRNA mechanisms. Additionally, we constructed a diagnostic prediction model with a high sensitivity and specificity. CONCLUSION: Our study characterized the landscape of circRNAs, mRNAs and lncRNAs in NPC tissue and provided novel insights into the molecular mechanisms of NPC.

The Impact of Elsinoë ampelina Infection on Key Metabolic Properties in Vitis vinifera 'Red Globe' Berries via Multiomics Approaches.

Grape anthracnose caused by Elsinoë ampelina (Shear) is one of the most serious fungal diseases that lead to the quality reduction and yield losses of grape (Vitis vinifera 'Red Globe') berries. In the present study, metabolome and transcriptome analyses were conducted using grape berries in the field after infection with E. ampelina at 7, 10, and 13 days to identify the metabolic properties of berries. In total, 132 metabolites with significant differences and 6,877 differentially expressed genes were detected and shared by three comparisons. The analyses demonstrated that phenylpropanoid, flavonoid, stilbenoid, and nucleotide metabolisms were enriched in E. ampelina-infected grape berries but not amino acid metabolism. Phenolamide, terpene, and polyphenole contents also accumulated during E. ampelina infection. The results provided evidence of the enhancement of secondary metabolites such as resveratrol, α-viniferin, ε-viniferin, and lignins involved in plant defense. The results showed the plant defense-associated metabolic reprogramming caused by E. ampelina infection in grape berry and provided a global metabolic mechanism under E. ampelina stimulation.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.

BACKGROUND: Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas. METHODS: Clinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression. RESULTS: For the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score. CONCLUSIONS: The radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.

Performance evaluation of an automatic chemiluminescence immune platform for SARS-CoV-2 neutralizing antibody after vaccination in real world.

OBJECTIVE: Reliable high-throughput serological assays for SARS-CoV-2 antibodies present an important role in the strength and duration of immunity after vaccination. The study investigated the analytical and clinical performances of neutralizing antibodies (NTAb) assay by chemiluminescent (CLIA), and SARS-CoV-2 neutralizing antibody after vaccination in real world. METHODS: The analytical performances of CLIA for SARS-CoV-2 NTAb were evaluated, followed by the sensitivity and specificity identified with a PRNT test from 50 volunteers. Then, a cohort of vaccine recipients (n = 37) were tracked with SARS-CoV-2 NTAb assay at prior to vaccination, one, three and six months post two doses. In real world, a total of 737 cases were recruited from physical examination center in Shenzhen Luohu People's Hospital (from Jun to August 2021) to analyze vaccination status. RESULTS: Serological assays on the CLIA were found with excellent characteristics including imprecision, repeatability and linearity. Besides, it was robust to icterus, lipemia and hemolysis. The good sensitivity and specificity were obtained at 98% and 100%, respectively. NTAb results showed a high correlation with PRNT50 titers (r 0.61). Until July 2021, the BBIBP-CorV (76.3%) and Sinovac CoronaVac (20.5%) were the predominant vaccines injection in Shenzhen, China. Adolescent less than 18 years was the main unvaccinated group (52.1%). The seropositive rate of inactive SRAR-CoV-2 vaccines exceeded 97% after inoculation. The NTAb generated by Sinovac CoronaVac with the schedule of 0-56 days was found significantly lower than that by BBIBP-CorV (P < 0.001). The follow-up of NTAb changes in a cohort and the dynamic variation of NTAb in real world disclosed steep downward by almost three times for NTAb level occurred at three months post twice vaccinations. The seropositive ratio was at least 50% over 6 months. CONCLUSIONS: SARS-CoV-2 neutralizing antibodies assay show excellent analytical and clinical performances, and a high correlation with neutralizing activity. Anti-epidemic measures and the urgent trial of SARS-CoV-2 vaccine was calling for adolescents.

Dynamic observation of SARS-CoV-2 IgM, IgG, and neutralizing antibodies in the development of population immunity through COVID-19 vaccination.

BACKGROUND: Currently, mass vaccine inoculation against coronavirus disease-2019 (COVID-19) has been being implemented globally. Rapid and the large-scale detection of serum neutralizing antibodies (NAbs) laid a foundation for assessing the immune response against SARS-CoV-2 infection and vaccine. Additional assessments include the duration of antibodies and the optimal time for a heightened immune response. METHODS: The performance of five surrogate NAbs-three chemiluminescent immunoassay (CLIA) and two enzyme-linked immunosorbent assays (ELISAs)-and specific IgM and IgG assays were compared using COVID-19-vaccinated serum (n = 164). Conventional virus neutralization test (cVNT) was used as a criterion and the diagnostic agreement and correlation of the five assays were evaluated. We studied the antibody responses after the two-dose vaccine in volunteers up to 6 months. RESULTS: The sensitivity and specificity of five surrogate NAb assays ranged from 84% to 100%. Our cVNT results indicated great consistency with the surrogate assays. At 28 days after primary vaccination, the seropositivities of the NAbs, IgG, and IgM were 6%, 4%, and 13%, respectively. After the booster dose, seropositivities reached 14%, 65%, and 97%, respectively. Six months after receipt of the second dose, the NAb positive rate was eventually maintained at 66%. In all COVID-19 convalescents, patients were detected with 100% NAb sat three months after discharge. CONCLUSION: COVID-19 vaccine induced a humoral immune response lasting at least six months. Rapid serological detection was used as a proxy for identifying changes in immunity levels and as a guide to whether an individual may require a booster vaccination.

Performance comparison of two nucleic acid amplification systems for SARS-CoV-2 detection: A multi-center study.

BACKGROUND: Many rapid nucleic acid testing systems have emerged to halt the development and spread of COVID-19. However, so far relatively few studies have compared the diagnostic performance between these testing systems and conventional detection systems. Here, we performed a retrospective analysis to evaluate the clinical detection performance between SARS-CoV-2 rapid and conventional nucleic acid detection system. METHODS: Clinical detection results of 63,352 oropharyngeal swabs by both systems were finally enrolled in this analysis. Sensitivity (SE), specificity (SP), and positive and negative predictive value (PPV, NPV) of both systems were calculated to evaluate their diagnostic accuracy. Concordance between these two systems were assessed by overall, positive, negative percent agreement (OPA, PPA, NPA) and κ value. Sensitivity of SARS-CoV-2 rapid nucleic acid detection system (Daan Gene) was further analyzed with respect to the viral load of clinical specimens. RESULTS: Sensitivity of Daan Gene was slightly lower than that of conventional detection system (0.86 vs. 0.979), but their specificity was equivalent. Daan Gene had ≥98.0% PPV and NPV for SARS-CoV-2. Moreover, Daan Gene demonstrated an excellent test agreement with conventional detection system (κ = 0.893, p = 0.000). Daan Gene was 99.31% sensitivity for specimens with high viral load (Ct < 35) and 50% for low viral load (Ct ≥ 35). CONCLUSIONS: While showing an analytical sensitivity slightly below than that of conventional detection system, rapid nucleic acid detection system may be a diagnostic alternative to rapidly identify SARS-CoV-2-infected individuals with high viral loads and a powerful complement to current detection methods.

Two-Dimensional All-in-One Sulfide Monolayers Driving Photocatalytic Overall Water Splitting.

For realizing scalable solar hydrogen synthesis, the development of visible-light-absorbing photocatalysts capable of overall water splitting is essential. Metal sulfides can capture visible light efficiently; however, their utilization in water splitting has long been plagued by the poor resilience against hole oxidation. Herein, we report that the ZnIn2S4 monolayers with dual defects (Ag dopants and nanoholes) accessed via cation exchange display stoichiometric H2 and O2 evolution in pure water under visible light irradiation. In-depth characterization and modeling disclose that the dual-defect structure endows the ZnIn2S4 monolayers with optimized light absorption and carrier dynamics. More significantly, the dual defects cooperatively function as active sites for water oxidation (Ag dopants) and reduction (nanoholes), thus leading to steady performance in photocatalytic overall water splitting without the assistance of cocatalysts. This work demonstrates a feasible way for fulfilling "all-in-one" photocatalyst design and manifests its great potential in addressing the stability issues associated with sulfide-based photocatalysts.

Genome-wide identification and expression analysis of the B-box transcription factor gene family in grapevine (Vitis vinifera L.).

BACKGROUND: B-box (BBX) zinc-finger transcription factors play important roles in plant growth, development, and stress response. Although these proteins have been studied in model plants such as Arabidopsis thaliana or Oryza sativa, little is known about the evolutionary history or expression patterns of BBX proteins in grapevine (Vitis vinifera L.). RESULTS: We identified a total of 25 VviBBX genes in the grapevine genome and named them according to the homology with Arabidopsis. These proteins were classified into five groups on the basis of their phylogenetic relationships, number of B-box domains, and presence or absence of a CCT domain or VP motif. BBX proteins within the same group showed similar exon-intron structures and were unevenly distributed in grapevine chromosomes. Synteny analyses suggested that only segmental duplication events contributed to the expansion of the VviBBX gene family in grapevine. The observed syntenic relationships between some BBX genes from grapevine and Arabidopsis suggest that they evolved from a common ancestor. Transcriptional analyses showed that the grapevine BBX genes were regulated distinctly in response to powdery mildew infection and various phytohormones. Moreover, the expression levels of a subset of BBX genes in ovules were much higher in seedless grapevine cultivars compared with seeded cultivars during ovule development, implying a potential role in seed abortion. Additionally, VviBBX8, VquBBX15a and VquBBX29b were all located in the nucleus and had transcriptional activity except for VquBBX29b. CONCLUSIONS: The results of this study establish the genome-wide analysis of the grapevine BBX family and provide a framework for understanding the biological roles of BBX genes in grapevine.