Understanding nonlinear and synergistic effects of the built environment on urban vibrancy in metro station areas

Transit-oriented development (TOD) has long been recognized as a significant model for prospering urban vibrancy. However, most studies on TOD and urban vibrancy do not consider temporal differences or the nonlinear effects involved. This study applies the gradient boosting decision tree (GBDT) model to metro station areas in Wuhan to explore the nonlinear and synergistic effects of the built-environment features on urban vibrancy during different times. The results show that (1) the effects of the built-environment features on the vibrancy around metro stations differ over time;(2) the most critical features affecting vibrancy are leisure facilities, floor area ratio, commercial facilities, and enterprises;(3) there are approximately linear or complex nonlinear relationships between the built-environment features and the vibrancy;and (4) the synergistic effects suggest that multimodal is more effective at leisure-dominated stations, high-density development is more effective at commercial-dominated stations, and mixed development is more effective at employment-oriented stations. The findings suggest improved planning recommendations for the organization of rail transport to improve the vibrancy of metro station areas.

Asymmetric dynamic risk transmission between financial stress and monetary policy uncertainty: thinking in the post-covid-19 world

Considering the dramatically increasing impact of the COVID-19 outbreak on monetary policy and the uncertainty in the financial system, we aim to examine the dynamic asymmetric risk transmission between financial stress and monetary policy uncertainty. Our sample covers 30 years of data. We first employ the conventional Granger causality test to examine the average relationship between financial stress and monetary policy uncertainty, and the results cannot provide evidence of causality between them. However, from an asymmetric perspective, we further detect the strongly apparent existence of the asymmetric structure of causality between them. Finally, we conduct further research on the asymmetric impacts from a time-varying perspective. The time-varying test finds that this relationship can be influenced by major events, especially the dot-com bubble, the 2009 financial crisis, and the current COVID-19 pandemic. Thus, one can learn more information about the influencing mechanism between financial stress and monetary policy with our work, which may be beneficial for making better decisions in the future.

Financial stress spillover network across Asian countries in the context of COVID-19

Using the dynamic connectedness framework of Antonakakis et al. (2020), this paper explores the financial stress spillover characteristic across nine Asian countries during major economic, political and public health emergency events, especially during COVID-19. We first find a substantial increase in the intensity of total financial stress spillover across nine Asian countries during COVID-19. Second, there are clear differences in the financial stress spillover networks across Asian countries during different economic and political events. In particular, in the first three months after the outbreak of COVID-19, there was considerable month-to-month variation in the financial stress spillover network. Singapore and Japan are the major net transmitter and receiver of financial stress shocks, respectively, during all considered events. During COVID-19, China, as the first country to detect and contain COVID-19, is the strongest net financial stress shock receiver in March 2020, but transmitted net financial stress shocks in February 2020, when the epidemic in China is serious.

How much urban air quality is affected by local emissions: A unique case study from a megacity in the Pearl River Delta, China

In March 2022, the resurgence of COVID-19 cases in Shenzhen, a megacity in the Pearl River Delta (PRD) region of China, led to unusual restrictions on anthropogenic activities within a single city, in contrast to the restrictions COVID-19 caused on a national scale at the beginning of 2020. In this unique event, we found that only under unfavorable meteorological conditions did substantial urban local emission reductions have an impact on air pollutant changes (−42.4%–6.6%), whereas the deweathered changes were very small (−8.3%–3.4%) under favorable meteorological conditions. Primary anthropogenic pollutants, such as NO2, toluene, BC, and primary organic aerosol (POA), responded most considerably to emission reductions from early morning to noon during unfavorable meteorological days;for secondary organic aerosol (SOA), regulating the daytime total oxidant (Ox = O3 + NO2) was found to be more effective than controlling its precursors within the city scale, whereas secondary nitrate displayed the opposite trend. Since Ox changed little during the urban lockdown despite the remarkable decrease in precursors, it is emphasized that regionally coordinated control of VOCs and NOx is necessary to effectively reduce Ox levels. In addition, Shenzhen's NOx emission reduction efforts should be sustained in order to control PM2.5 and O3 pollution synergistically for long-term attainment.

Electrochemical immunosensor nanoarchitectonics with the Ag-rGO nanocomposites for the detection of receptor-binding domain of SARS-CoV-2 spike protein.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a grave threat to human life and health, it is essential to develop an efficient and sensitive detection method to identify infected individuals. This study described an electrode platform immunosensor to detect SARS-CoV-2-specific spike receptor-binding domain (RBD) protein based on a bare gold electrode modified with Ag-rGO nanocomposites and the biotin-streptavidin interaction system. The Ag-rGO nanocomposites was obtained by chemical synthesis and characterized by electrochemistry and scanning electron microscope (SEM). Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to record the electrochemical signals in the electrode modification. The differential pulse voltammetry (DPV) results showed that the limit of detection (LOD) of the immunosensor was 7.2 fg mL-1 and the linear dynamic detection range was 0.015 ~ 158.5 pg mL-1. Furthermore, this sensitive immunosensor accurately detected RBD in artificial saliva with favorable stability, specificity, and reproducibility, indicating that it has the potential to be used as a practical method for the detection of SARS-CoV-2.

Serological survey of SARS-CoV-2 in companion animals in China.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can be transmitted from human to companion animals. The national wide serological surveillance against SARS-CoV-2 was conducted among pet animals, mainly in cats and dogs, 1 year after the first outbreak of COVID-19 in China. All sera were tested for SARS-CoV-2 IgG antibodies using an indirect enzyme linked immunosorbent assay (ELISA) based on the receptor binding domain (RBD) of spike protein. This late survey takes advantage of the short duration of the serological response in these animals to track recent episode of transmission. A total of 20,592 blood samples were obtained from 25 provinces across 7 geographical regions. The overall seroprevalence of SARS-CoV-2 infections in cats was 0.015% (2/13397; 95% confidence intervals (CI): 0.0, 0.1). The virus infections in cats were only detected in Central (Hubei, 0.375%) and Eastern China (Zhejiang, 0.087%) with a seroprevalence estimated at 0.090 and 0.020%, respectively. In dogs, the seroprevalence of SARS-CoV-2 infections was 0.014% (1/7159; 95% CI: 0.0, 0.1) in the entire nation, seropositive samples were limited to Beijing (0.070%) of Northern China with a prevalence of 0.054%. No seropositive cases were discovered in other geographic regions, nor in other companion animals analyzed in this study. These data reveal the circulation of SARS-CoV-2 in companion animals, although transmission of the virus to domestic cats and dogs is low in China, continuous monitoring is helpful for the better understand of the virus transmission status and the effect on animals.

Electrochemical immunosensor nanoarchitectonics with the Ag-rGO nanocomposites for the detection of receptor-binding domain of SARS-CoV-2 spike protein

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a grave threat to human life and health, it is essential to develop an efficient and sensitive detection method to identify infected individuals. This study described an electrode platform immunosensor to detect SARS-CoV-2-specific spike receptor-binding domain (RBD) protein based on a bare gold electrode modified with Ag-rGO nanocomposites and the biotin-streptavidin interaction system. The Ag-rGO nanocomposites was obtained by chemical synthesis and characterized by electrochemistry and scanning electron microscope (SEM). Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to record the electrochemical signals in the electrode modification. The differential pulse voltammetry (DPV) results showed that the limit of detection (LOD) of the immunosensor was 7.2 fg mL−1 and the linear dynamic detection range was 0.015 ~ 158.5 pg mL−1. Furthermore, this sensitive immunosensor accurately detected RBD in artificial saliva with favorable stability, specificity, and reproducibility, indicating that it has the potential to be used as a practical method for the detection of SARS-CoV-2.

PLFace: Progressive learning for face recognition with mask bias.

The outbreak of the COVID-19 coronavirus epidemic has promoted the development of masked face recognition (MFR). Nevertheless, the performance of regular face recognition is severely compromised when the MFR accuracy is blindly pursued. More facts indicate that MFR should be regarded as a mask bias of face recognition rather than an independent task. To mitigate mask bias, we propose a novel Progressive Learning Loss (PLFace) that achieves a progressive training strategy for deep face recognition to learn balanced performance for masked/mask-free faces recognition based on margin losses. Particularly, our PLFace adaptively adjusts the relative importance of masked and mask-free samples during different training stages. In the early stage of training, PLFace mainly learns the feature representations of mask-free samples. At this time, the regular sample embeddings shrink to the prototype. In the later stage of training, PLFace converges on mask-free samples and further focuses on masked samples until the masked sample embeddings are also gathered in the center of the class. The entire training process emphasizes the paradigm that normal samples shrink first and masked samples gather afterward. Extensive experimental results on popular regular and masked face benchmarks demonstrate the superiority of our PLFace over state-of-the-art competitors.

Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method

The purpose of this paper is to explore whether the categorical Economic Policy Uncertainty (EPU) indices are predictable for the volatility of carbon futures, in the mixed data sampling (MIDAS) regression framework. The prediction methods include the MIDAS-RV model, the MIDAS models extended by individual categorical EPU index, combination prediction approaches, the MIDAS models extended by dimensionality reduction techniques as well as the machine learning methods on the basis of MIDAS model and Markov regime switching method. We find firstly that categorical EPU indices are predictable for carbon futures volatility, but the predictive power of individual categorical EPU indices is not robust. Secondly, machine learning methods, especially the machine learning method considering the Markov regime switching structure, help to obtain valid information from multiple categorical EPU indices and produce robust and superior prediction accuracy for carbon futures volatility. The results of the extension analysis also found that machine learning methods, especially the machine learning method considering the Markov regime switching structure help to produce higher investment performance and more accurate long-term carbon futures volatility forecasts. Meanwhile, we also find the advantages of the MIDAS based machine learning methods over the traditional AR based machine learning methods. Finally, the forecasting performance of the machine learning method which considering Markov regime switching structure are superior during both the low and high volatility regimes and even during the COVID-19 pandemic.

A film-lever actuated switch technology for multifunctional, on-demand, and robust manipulation of liquids.

A lab-on-a-chip system with Point-of-Care testing capability offers rapid and accurate diagnostic potential and is useful in resource-limited settings where biomedical equipment and skilled professionals are not readily available. However, a Point-of-Care testing system that simultaneously possesses all required features of multifunctional dispensing, on-demand release, robust operations, and capability for long-term reagent storage is still a major challenge. Here, we describe a film-lever actuated switch technology that can manipulate liquids in any direction, provide accurate and proportional release response to the applied pneumatic pressure, as well as sustain robustness during abrupt movements and vibrations. Based on the technology, we also describe development of a polymerase chain reaction system that integrates reagent introduction, mixing and reaction functions all in one process, which accomplishes "sample-in-answer-out" performance for all clinical nasal samples from 18 patients with Influenza and 18 individual controls, in good concordance of fluorescence intensity with standard polymerase chain reaction (Pearson coefficients > 0.9). The proposed platform promises robust automation of biomedical analysis, and thus can accelerate the commercialization of a range of Point-of-Care testing devices.

Identification of three conserved linear B cell epitopes on the SARS-CoV-2 spike protein.

Spike (S) glycoprotein is the most significant structural protein of SARS-CoV-2 and a key target for neutralizing antibodies. In light of the on-going SARS-CoV-2 pandemic, identification and screening of epitopes of spike glycoproteins will provide vital progress in the development of sensitive and specific diagnostic tools. In the present study, NTD, RBD, and S2 genes were inserted into the pcDNA3.1(+) vector and designed with N-terminal 6× His-tag for fusion expression in HEK293F cells by transient transfection. Six monoclonal antibodies (4G, 9E, 4B, 7D, 8F, and 3D) were prepared using the expressed proteins by cell fusion technique. The characterization of mAbs was performed by indirect -ELISA, western blot, and IFA. We designed 49 overlapping synthesized peptides that cover the extracellular region of S protein in which 6 amino acid residues were offset between adjacent (S1-S49). Peptides S12, S19, and S49 were identified as the immunodominant epitope regions by the mAbs. These regions were further truncated and the peptides S12.2 286TDAVDCALDPLS297, S19.2 464FERDISTEIYQA475, and S49.4 1202ELGKYEQYIKWP1213 were identified as B- cell linear epitopes for the first time. Alanine scans showed that the D467, I468, E471, Q474, and A475 of the epitope S19.2 and K1205, Q1208, and Y1209 of the epitope S49.4 were the core sites involved in the mAbs binding. The multiple sequence alignment analysis showed that these three epitopes were highly conserved among the variants of concern (VOCs) and variants of interest (VOIs). Taken together, the findings provide a potential material for rapid diagnosis methods of COVID-19.

How does the COVID-19 outbreak affect the causality between gold and the stock market? New evidence from the extreme Granger causality test.

The causal relationship between gold and stocks has been widely studied, while their causality and the long- and short-run characteristic of this relationship have not been examined under different shocks. The purpose of this paper is to fill this gap. Meanwhile, considering the impact of the COVID-19 outbreak on gold and stock markets, we also aim to investigate whether the relationship changes after this epidemic. With invoking the time- and frequency-domain extreme Granger causality tests, we find that a significant causality between gold and stock usually comes from extreme shocks, displaying as the long-term causality running from gold shocks to stock shocks while the fickle impact of stock shocks on gold shocks. Besides, empirical results suggest that the causality between gold and stock shocks is greatly promoted after this epidemic. The present study is useful for investors and policymakers, as it has reference significance when dealing with subsequent extreme shocks or events.

Differential diagnosis of the infection caused by wild-type or CD2v-deleted ASFV strains by quantum dots-based immunochromatographic assay.

African swine fever (ASF), a highly contagious and lethal disease, poses a tremendous threat and burden to the swine industry worldwide. Lack of available vaccines or treatments leaves rapid diagnosis as the key tool to control the disease. Quantum dots (QDs) are unique fluorescent semiconductor nanoparticles, highly versatile for biological applications. In this study, we developed a quantum dots-based fluorescent immunochromatographic assay (QDs-FICA) using CD2v as the diagnosis antigen to detect ASFV antibodies. The titre of the test strip was 1 : 5·12 × 105 . In addition, the strip was highly specific to anti-ASFV serum and had no cross-reaction with CSFV, PPV, PRRSV, PCV-2, PRV and FMDV. Moreover, a comparative test of 71 clinical samples showed that the coincidence rate was 85·92% between the test strip and the commercial ELISA kit (coated with p30, p62 and p72). The QDs-FICA can be used to detect ASFV antibodies, which is meaningful for the surveillance, control and purification of ASF.

A Universal Fluorescent Immunochromatography Assay Based on Quantum Dot Nanoparticles for the Rapid Detection of Specific Antibodies against SARS-CoV-2 Nucleocapsid Protein.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the pathogenic agent leading to COVID-19. Due to high speed of transmission and mutation rates, universal diagnosis and appropriate prevention are still urgently needed. The nucleocapsid protein of SARS-CoV-2 is considered more conserved than spike proteins and is abundant during the virus' life cycle, making it suitable for diagnostic applications. Here, we designed and developed a fluorescent immunochromatography assay (FICA) for the rapid detection of SARS-CoV-2-specific antibodies using ZnCdSe/ZnS QDs-conjugated nucleocapsid (N) proteins as probes. The nucleocapsid protein was expressed in E.coli and purified via Ni-NTA affinity chromatography with considerable concentration (0.762 mg/mL) and a purity of more than 90%, which could bind to specific antibodies and the complex could be captured by Staphylococcal protein A (SPA) with fluorescence displayed. After the optimization of coupling and detecting conditions, the limit of detection was determined to be 1:1.024 × 105 with an IgG concentration of 48.84 ng/mL with good specificity shown to antibodies against other zoonotic coronaviruses and respiratory infection-related viruses (n = 5). The universal fluorescent immunochromatography assay simplified operation processes in one step, which could be used for the point of care detection of SARS-CoV-2-specific antibodies. Moreover, it was also considered as an efficient tool for the serological screening of potential susceptible animals and for monitoring the expansion of virus host ranges.

Psychological Impact and Workload of COVID-19 on Healthcare Workers in China During the Early Time of the Pandemic: A Cross-sectional Study.

OBJECTIVE: This study aimed to investigate the organization, workload, and psychological impact of COVID-19 on healthcare workers from the domestic Medical Aid Teams (MATs) sent to Wuhan in China. METHODS: Leaders and members of MATs involved in the care for COVID-19 patients were invited to participate in a study by completing 2 separate self-report questionnaires from April 1 to 24, 2020. RESULTS: A total of 9 MAT leaders were involved and 464 valid questionnaires were collected from 140 doctors and 324 nurses. Mean age of the doctors and nurses were 39.34 ± 6.70 (26∼58 years old) and 31.88 ± 5.29 (21∼52 years old), with 72 (15.5%) being males. Nurses were identified as an independent risk factor (HR 1.898; P = 0.001) for a day working time in the multivariate analysis. The proportions of psychological consulting received among nurses were higher than those among doctors (49.7 vs 30.0%, P < 0.001). More than 50% of the anesthetists and emergency doctors who have received psychological consulting thought that it was effective according to self-evaluation. CONCLUSIONS: This study focused on healthcare workers' situation during the early period of the pandemic. Nurses worked longer than doctors. The effectiveness of psychological consulting depends on the physicians' specialties and the working conditions of the nurses and psychological consulting targeting different specialties need to be improved.

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model.

This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns

In this study, we construct China's aggregate sentiment indicator (SsPCA) based on the method of Huang et al. (2021a), which employs a new dimension reduction method of scaled principal component analysis (PCA), to aggregate useful information from individual sentiment proxies, and further examine its return predictability for the Chinese stock market. The empirical evidence suggests that SsPCA significantly improves the prediction accuracy for stock market returns both in and out of the sample, and also obtains considerable economic gain for a mean-variance investor. Additionally, the forecasting effect of SsPCA is superior to that of SPCA and SPLS, evaluated using the traditional PCA and partial least square methods, respectively. Moreover, relative to the period of the bull market, SsPCA exhibits better ability in forecasting stock market returns during the bear market. Finally, special events, such as the outbreak of coronavirus disease 2019 (COVID-19), also affect the predictive performance of the sentiment indicator.

Forecasting volatility of EUA futures: New evidence

In this paper, we judge the predictability of EUA's own short- and long-term asymmetry, extreme observations, as well as jump components on its volatility by comparing the GARCH mixed frequency data sampling model and its asymmetry, extreme observation and jump extensions. The in-sample estimation results show that both long- and short-term asymmetries, extreme observations, and jump information have substantial effect on the EUA volatility. The out-of-sample forecast assessment results further illustrate the predictability of these volatility components on EUA volatility. Specially, among all asymmetric extension models, the model extended by both short-term asymmetry, long-term asymmetry and long-term leverage has better predictive performance. Among all extreme observation extension models, the model extended by only short-term extreme observations has better predictive performance. Among all jump extension models, the model extended by only the short-term jump information and the model extended by both the short-term and long-term jump information perform better, and their forecasting performance outperform all the other extension models in most cases. These findings are robust even if the assessment method, the rolling window length and the lag order are changed. It is worth mentioning that the advantage of these extension models in predicting EUA volatility is mainly seen in periods of low volatility. However, even during periods of COVID-19 pandemic, the predictive performance of the two well-performing jump extension models cannot be underestimated.

Screening and identification of B cell epitope of the nucleocapsid protein in SARS-CoV-2 using the monoclonal antibodies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes the coronavirus disease (COVID-19). It is confirmed that nucleocapsid (N) protein is closely related to viral pathogenesis, modulation of host immune response, RNA transcription, and replication and virus packaging. Therefore, the N protein is a preponderant antigen target for virus detection. The codon-optimized N gene was designed according to the encoding characteristics of insect cells and inserted into pFastBacTM1 vector with 6 × His-tag-fused N protein for expression in insect sf21 cells. Six anti-N mAbs (4G3, 5B3, 12B6, 18C7-A2, 21H10-A3, 21H10-E9) were prepared by recombinant N protein. The mAbs showed high titers, antibody affinity, and reactivity with the SARS-CoV-2 N protein. Then, fourteen overlapped peptides that covered the intact N protein were synthesized (N1-N14). Peptide N14 was identified as the main linear B-cell epitope region via peptide-ELISA and dot-blot assay, and this region was truncated gradually until mapping the peptide 401-DFSKQLQQ-408. Simultaneously, compared with the sequence of variants of concern (VOCs) and variants of interest (VOIs) strains among the several countries, epitope 401-DFSKQLQQ-408 is very conservative among them. The findings provide new guidance for the design and detection of COVID-19 targets. KEY POINTS: • The N protein was optimized according to the insect cell codon preference and was highly expressed. • The monoclonal antibodies prepared in this study were shown high antibody titers and high affinity. • Monoclonal antibodies were used to map the epitope 401-408 amino acids of N protein for the first time in this study.

Oil price volatility predictability: New evidence from a scaled PCA approach

We introduce the scaled principal component analysis (sPCA) method to forecast oil volatility, and compare it with two commonly used dimensionality reduction methods: principal component analysis (PCA) and partial least squares (PLS). By combining the simple autoregressive model with the three dimensionality reduction methods, we obtain several interesting and notable findings. First, the model with the sPCA diffusion index performs substantially better than the competing models based on the out-of-sample Roos2 test. Moreover, the model with the sPCA diffusion index consistently demonstrates superior forecasting power compared with the other models under different macroeconomic conditions (e.g., business cycle recessions and expansions, high- and low-volatility levels, and the COVID-19 pandemic). Furthermore, the findings of our study are strongly robust to various robustness tests, such as alternative forecasting window sizes and different lags of model selection.