Plasmonic Scattering Imaging of Surface-Bonded Nanoparticles at the Solution-Solid Interface.

Imaging nanoscale objects at interfaces is essential for revealing surface-tuned mechanisms in chemistry, physics, and life science. Plasmonic-based imaging, a label-free and surface-sensitive technique, has been widely used for studying the chemical and biological behavior of nanoscale objects at interfaces. However, direct imaging of surface-bonded nanoscale objects remains challenging due to uneven image backgrounds. Here, we present a new surface-bonded nanoscale object detection microscopy that eliminates strong background interference by reconstructing accurate scattering patterns at different positions. Our method effectively functions at low signal-to-background ratios, allowing for optical scattering detection of surface-bonded polystyrene nanoparticles and severe acute respiratory syndrome coronavirus 2 pseudovirus. It is also compatible with other imaging configurations, such as bright-field imaging. This technique complements existing methods for dynamic scattering imaging and broadens the applications of plasmonic imaging techniques for high-throughput sensing of surface-bonded nanoscale objects, enhancing our understanding of the properties, composition, and morphology of nanoparticles and surfaces at the nanoscale.

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Factors Influencing Retention among Regional, Rural and Remote Undergraduate Nursing Students in Australia: A Systematic Review of Current Research Evidence.

BACKGROUND: This systematic review aimed to explore the factors influencing retention among regional, rural, and remote undergraduate nursing students who were enrolled in Australian universities. METHODS: Mixed-methods systematic review. A+ Education, CINAHL, Education Resources Information Center (ERIC), Education Research Complete, JBI EBP database, Journals@Ovid, Medline, PsycINFO, PubMed, and Web of Science were systematically searched from September 2017 to September 2022 to identify eligible English-language studies. The methodological quality of the included studies was critically assessed using the Joanna Briggs Institute's critical appraisal tools. Descriptive analysis with a convergent segregated approach was conducted to synthesize and integrate the results from the included studies. RESULTS: Two quantitative and four qualitative studies were included in this systematic review. Both the quantitative and qualitative findings demonstrated that additional academic and personal support was essential for improving retention among undergraduate nursing students from regional, rural, and remote areas in Australia. The qualitative synthesis also highlighted many internal (e.g., personal qualities, stress, ability to engage with classes and institutions, time management, lack of confidence, cultural well-being, and Indigenous identity) and external factors (e.g., technical difficulties, casual tutors, different competing demands, study facilities, and financial and logistical barriers) that influenced retention among undergraduate nursing students from regional, rural, and remote areas in Australia. CONCLUSIONS: This systematic review demonstrates that identifying potentially modifiable factors could be the focus of retention support programs for undergraduate nursing students. The findings of this systematic review provide a direction for the development of retention support strategies and programs for undergraduate nursing students from regional, rural and remote areas in Australia.

The role of school organizational conditions in teacher psychological resilience and stress during COVID-19 pandemic: A moderated mediation model.

Educational revisions facilitate the relief of teacher stress by means of enhancing school organizational conditions. However, limited research has explored the effects of school organizational conditions on teacher stress in China. Using a sample of 734 primary and secondary school teachers from 30 provinces or municipalities of China, this study examined the effects of school organizational conditions on teacher stress in China, with a particular focus on the mediating role of psychological resilience and moderating role of perceived COVID-19 crisis strength. The results demonstrated that school organizational conditions were negatively associated with teacher stress. Furthermore, psychological resilience partially mediated the relation between school organizational conditions and teacher stress. In addition, perceived COVID-19 crisis strength significantly moderated the direct and indirect relations between school organizational conditions and teacher stress. The relations between school organizational conditions and teacher stress and between school organizational conditions and psychological resilience were stronger for teachers who perceived low levels of COVID-19 crisis strength. However, the indirect relation between psychological resilience and stress was stronger for teachers who perceived high levels of COVID-19 crisis strength. Implications have been provided accordingly.

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing.

The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the "Delta" virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

Prevalence and associated factors of suicidal ideation among college students during the COVID-19 pandemic in China: a 3-wave repeated survey.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic, a major public health crisis, harms individuals' mental health. This 3-wave repeated survey aimed to examine the prevalence and correlates of suicidal ideation at different stages of the COVID-19 pandemic in a large sample of college students in China. METHODS: Using a repeated cross-sectional survey design, we conducted 3 online surveys of college students during the COVID-19 pandemic at 22 universities in Guandong, China. The 3 surveys were conducted during the outbreak period (T1: 3 February to 10 February 2020, N = 164,101), remission period (T2: 24 March to 3 April 2020, N = 148,384), and normalized prevention and control period (T3: 1 June to 15 June 2020, N = 159,187). Suicidal ideation was measured by the ninth item of the Patient Health Questionnaire-9. A range of suicide-related factors was assessed, including sociodemographic characteristics, depression, anxiety, insomnia, pre-existing mental health problems, and COVID-19-related factors. RESULTS: The prevalence of suicidal ideation was 8.5%, 11.0% and 12.6% at T1, T2, and T3, respectively. Male sex (aOR: 1.35-1.44, Ps < 0.001), poor self-perceived mental health (aOR: 2.25-2.81, Ps < 0.001), mental diseases (aOR: 1.52-2.09, P < 0.001), prior psychological counseling (aOR: 1.23-1.37, Ps < 0.01), negative perception of the risk of the COVID-19 epidemic (aOR: 1.14-1.36, Ps < 0.001), depressive symptoms (aOR: 2.51-303, Ps < 0.001) and anxiety symptoms (aOR: 1.62-101.11, Ps < 0.001) were associated with an increased risk of suicidal ideation. CONCLUSION: Suicidal ideation appeared to increase during the COVID-19 pandemic remission period among college students in China. Multiple factors, especially mental health problems, are associated with suicidal ideation. Psychosocial interventions should be implemented during and after the COVID-19 pandemic to reduce suicide risk among college students.

Front Cover: Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS‐COV‐2 Spike Protein (Chem. Eur. J. 6/2022)

Although the prefusion conformation of the spike protein of SARS‐COV‐2 can be stabilized alone, a spontaneously and energy‐friendly centripetal movement of the receptor binding domain occurs when spike protein binds to ACE2. During the binding process, several residues, especially Phe329 and Phe515, play a significant role in the allosteric effect. As a result, two potential cleavage sites S1/S2 and S2′ are exposed on the surface. More information can be found in the Research Article by A. Zhang, J. Fu et al. (DOI: 10.1002/chem.202104215).

Changes in Bulk and Rhizosphere Soil Microbial Diversity and Composition Along an Age Gradient of Chinese Fir (Cunninghamia lanceolate) Plantations in Subtropical China.

Soil microorganisms play key roles in biogeochemical cycling in forest ecosystems. However, whether the responses of microbial community with stand development differed in rhizosphere and bulk soils remains unknown. We collected rhizosphere and bulk soil in Chinese fir plantations with different stand ages (7a, 15a, 24a, and 34a) in subtropical China, and determined bacterial and fungal community variation via high-throughput sequencing. The results showed that soil bacterial, but not fungal, community diversity significantly differed among stand ages and between rhizosphere and bulk soils (p < 0.05). The differences in Shannon-Wiener and Simpson's indices between rhizosphere and bulk soil varied with stand age, with significant higher soil bacterial diversity in rhizosphere than bulk soils in 7a and 34a plantations (p < 0.05), but there were no significant difference in soil bacterial diversity between rhizosphere and bulk soils in 15a and 24a plantations (p > 0.05). Soil microbial community composition varied significantly with stand age but not between the rhizosphere and bulk soil. The dominant bacterial phyla at all ages were Acidobacteria and Proteobacteria, while the dominant fungal phyla were Ascomycota and Basidiomycota in both rhizosphere and bulk soil. They showed inconsistent distribution patterns along stand age gradient (7-34a) in the rhizosphere and bulk soil, suggesting distinct ecological strategy (r-strategist vs. k-strategist) of different microbial taxa, as well as changes in the microenvironment (i.e., nutrient stoichiometry and root exudates). Moreover, bacterial and fungal community composition in rhizosphere and bulk soil were governed by distinct driving factors. TP and NH4 +-N are the two most important factors regulating bacterial and fungal community structure in rhizosphere soil, while pH and NO3 --N, DON, and TN were driving factors for bacterial and fungal community structure in bulk soil, respectively. Collectively, our results demonstrated that the changes in microbial diversity and composition were more obvious along stand age gradients than between sampling locations (rhizosphere vs. bulk soil) in Chinese fir plantations.

Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS-COV-2 Spike Protein.

Invited for the cover of this issue are Aiqian Zhang, Jianjie Fu, Guibin Jiang, and co-workers at the Chinese Academy of Sciences. The image depicts the molecular recognition of human angiotensin-converting enzyme 2 by the SARS-COV-2 spike protein. Read the full text of the article at 10.1002/chem.202104215.

Optical Tracking of the Interfacial Dynamics of Single SARS-CoV-2 Pseudoviruses.

The frequent detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in healthcare environments, accommodations, and wastewater has attracted great attention to the risk of viral transmission by environmental fomites. However, the process of SARS-CoV-2 adsorption to exposed surfaces in high-risk environments remains unclear. In this study, we investigated the interfacial dynamics of single SARS-CoV-2 pseudoviruses with plasmonic imaging technology. Through the use of this technique, which has high spatial and temporal resolution, we tracked the collision of viruses at a surface and differentiated their stable adsorption and transient adsorption. We determined the effect of the electrostatic force on virus adhesion by correlating the solution and surface chemistry with the interfacial diffusion velocity and equilibrium position. Viral adsorption was found to be enhanced in real scenarios, such as in simulated saliva. This work not only describes a plasmonic imaging method to examine the interfacial dynamics of a single virus but also provides direct measurements of the factors that regulate the interfacial adsorption of SARS-CoV-2 pseudovirus. Such information is valuable for understanding virus transport and environmental transmission and even for designing anticontamination surfaces.

Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.

A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak.

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model's prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) ≈ SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.

Perinatal Depression of Exposed Maternal Women in the COVID-19 Pandemic in Wuhan, China.

Objective: This study aims to investigate perinatal depression in women who gave birth during the COVID-19 pandemic in Wuhan, and to evaluate the effect of the pandemic on perinatal depression prevalence. Methods: A cross-sectional investigation was conducted into women hospitalized for delivery in Hubei Maternity and Child Healthcare Hospital from December 31, 2019 to March 22, 2020, a period which encompasses the entire time frame of the COVID-19 pandemic in Wuhan. The Edinburgh Postnatal Depression Scale (EPDS) was adopted to evaluate perinatal depression status. A Chi-square test and logistic regression model were utilized for data analysis. Results: A total of 2,883 participants were included, 33.71% of whom were found to suffer from depressive symptoms. In detail, 27.02%, 5.24%, and 1.46% were designated as having mild, moderate, and severe depressive symptoms, respectively. The perinatal depression prevalence increased as the COVID-19 pandemic worsened. Compared to the period from December 31, 2019 to January 12, 2020, perinatal depression risk significantly decreased within the 3 weeks of March 2-22, 2020 (1st week: OR = 0.39, 95% CI: 0.20, 0.78; 2nd week: OR = 0.35, 95% CI: 0.17, 0.73; and 3rd week: OR = 0.48, 95% CI: 0.25, 0.94); and the postnatal depression risk significantly rose within the four weeks of January 27-February 23, 2020 (1st week: OR = 1.78, 95% CI: 1.18, 2.68; 2nd week: OR = 2.03, 95% CI: 1.35, 3.04; 3rd week: OR = 1.48, 95% CI: 1.02, 2.14; and 4th week: OR = 1.73, 95% CI: 1.20, 2.48). Conclusion: The dynamic change of perinatal depression was associated with the progression of the COVID-19 pandemic among new mothers who were exposed to the pandemic. An elevated risk of postnatal depression was also observed during the COVID-19 pandemic.