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Nanoparticle tracking analysis (NTA) is a single particle tracking technique that in principle provides a more direct measure of particle size distribution compared to dynamic light scattering (DLS). Here, we demonstrate how statistical mixture distribution analysis can be used in combination with NTA to quantitatively characterize the amount and extent of particle binding in a mixture of nanomaterials. The combined approach is used to study the binding of gold nanoparticles to two types of phospholipid vesicles, those containing and lacking the model ion channel peptide gramicidin A. This model system serves as both a proof of concept for the method and a demonstration of the utility of the approach in studying nano-bio interactions. Two diffusional models (Stokes-Einstein and Kirkwood-Riseman) were compared in the determination of particle size, extent of binding, and nanoparticle:vesicle binding ratios for each vesicle type. The combination of NTA and statistical mixture distributions is shown to be a useful method for quantitative assessment of the extent of binding between particles and determination of binding ratios.

Rationale: Patients with asthma were advised to avoid coronavirus disease (COVID-19) and comply with medication during the COVID-19 pandemic. Respiratory tract infection is a common cause of asthma exacerbations. There has not been evidence suggesting the link between COVID-19 and asthma exacerbation, especially in places with dramatic responses in infection control with universal masking and aggressive social distancing. Objectives: To assess the number for admissions of asthma exacerbations in January to April 2020 in Hong Kong with reference to admission in the past 5 years. Methods: Admission records of asthma exacerbations were retrieved from the Clinical Data Analysis and Reporting System. Patients aged 18 years or older with a known history of asthma admitted for asthma exacerbation were included. Log-linear was used to model count, with year and masking used as covariate and further analysis on ambient temperature and length of hospital stays. Fisher’s exact test was used to compare the mortality rate and mechanical ventilation between the periods. Admissions for myocardial infarction, ischemic stroke, and gastric ulcer were included as controls. Results: The number of admissions for asthma exacerbations significantly decreased by 53.2% (95% confidence interval [CI], 50.4–55.8%) in 2020 compared with monthly average admission in 2015–2019, with a higher magnitude of decrease compared with control diagnoses. Admissions for asthma exacerbations decreased by 2.0% (95% CI, 1.8–2.2%) with every 1°C (1.8°F) increase in temperature and by 0.8% with every 1% increase in masking (95% CI, 0.8–0.9%). Conclusions: Hospitalization number for asthma exacerbations significantly decreased in early 2020, with similar length of stay. This was observed with concomitant practice of universal masking and social distancing during the COVID-19 pandemic in Hong Kong. We proposed that universal masking and social distancing reduced respiratory viral infection, leading to fewer hospital admissions for asthma exacerbations.

We explore a hierarchical generalized latent factor model for discrete and bounded response variables and in particular, binomial responses. Specifically, we develop a novel two-step estimation procedure and the corresponding statistical inference that is computationally efficient and scalable for the high dimension in terms of both the number of subjects and the number of features per subject. We also establish the validity of the estimation procedure, particularly the asymptotic properties of the estimated effect size and the latent structure, as well as the estimated number of latent factors. The results are corroborated by a simulation study and for illustration, the proposed methodology is applied to analyze a dataset in a gene-environment association study.

Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful disease prevention and control require not only awareness of the current disease situation, but also the ability to understand disease dynamics, all of which rely on collection of data at strategically chosen locations during surveillance seasons. In particular, knowledge about the location of the disease front is foundational for deploying disease counter measures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future surveillance season. We also devise an optimal surveillance system design at the disease front that takes into account practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for white-nose syndrome in the contiguous US.

The present study investigated the effect of blood group on cochlear function in a large participant sample across different age groups. The study hypothesis was that participants with blood group O would show relatively reduced cochlear function as reflected in otoacoustic emission (OAE) measures. Data were collected from transient evoked otoacoustic emission (TEOAE), distortion product otoacoustic emission (DPOAE), DPOAE input/output (I/O) function, and spontaneous otoacoustic emission (SOAE) recordings. Four hundred and sixty-three normal hearing adults aged 20–59 years among the four ABO blood groups participated in the study. TEOAE and DPOAE amplitudes did not reveal significant differences for participants with blood group O compared with participants with non-O blood groups. No significant differences in I/O function categories were found among participants with different blood groups. SOAE prevalence was also not significantly different across blood groups. However, previously reported age and gender differences for OAE variables were confirmed. Participants with blood group O were not found to have significantly reduced cochlear function, based on OAE measures. Results from the current study do not support the hypothesis that normal hearing individuals with different ABO blood groups differ in level of cochlear function.

The American Dream-the perception that upward social mobility depends on effort-is a central cultural ethos in the United States. The belief in upward social mobility is not unique to Americans, and cultural groups across the world endorse it to varying degrees. The current study aims to examine cross-cultural trends in perceived mobility and to test possible mechanisms that may explain changes in perceived mobility. Using a dataset of over 1.4 million participants across 167 countries from 2005 to 2019, we comprehensively document cultural variations in perceived mobility. Citizens in Bhutan, Qatar, and Uzbekistan reported the highest levels of perceived mobility, and the United States ranked 107. We further examined the trajectories of perceived mobility across a 15-year timespan and found rapid declines in perceived mobility in countries experiencing sociopolitical crises (e.g., in Syria and Hong Kong). Multilevel analyses revealed high-income individuals are 32% (95% confidence interval, CI [24%, 39%]) more likely to perceive mobility than low-income individuals, and the level of disparity did not decrease over time. Preregistered time-series analyses showed education privatization and economic condition Granger-cause perceived mobility, but these temporal associations showed heterogeneity across countries. Overall, we performed the world's largest global monitor of perceived mobility and discussed how the cultural value of perceived mobility unfolds in social, economic, and political contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Coronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease.To apply a metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration to investigate the effect of border restriction as a public health measure to limit outbreak of coronavirus disease 2019.We apply a modified metapopulation SEIR model with inspected migration with simulating population migration, and incorporating parameters such as efficiency of custom inspection in blocking infected travelers in the model. The population sizes were retrieved from government reports, while the number of COVID-19 patients were retrieved from Hong Kong Department of Health and China Centre for Disease Control (CDC) data. The RComplete border closure can help to reduce the cumulative COVID-19 case number and mortality in Hong Kong by 13.99% and 13.98% respectively. To prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local REarly complete travel restriction is effective in reducing cumulative cases and mortality. However, additional anti-COVID-19 measures to reduce local R

Background Amiodarone is one of the most commonly used anti-arrhythmic agents. Amiodarone pulmonary toxicity is a potentially fatal adverse effect associated with amiodarone use. Previous studies on the epidemiology and risk factors for amiodarone pulmonary toxicity showed diverse results. Methods A multicenter retrospective cohort study was conducted to identify clinic-epidemiologic markers associated with amiodarone pulmonary toxicity for development of a prediction rule. Patients taking amiodarone who were managed in 3 centres in Hong Kong from 2005 to 2015 were included in this study. Penalized logistic regression was used to model the outcome as it is rare. Results A total of 34 cases with amiodarone pulmonary toxicity were identified among 1786 patients taking amiodarone for at least 90 days from 2005 to 2015. The incidence of amiodarone pulmonary toxicity was estimated to be 1.9%. The risk factors for amiodarone pulmonary toxicity included advanced age (OR 1.047, 95% CI 1.010–1.085, p = 0.013), ventricular arrhythmia (OR 2.703, 95% CI 1.053–6.935, p = 0.039), underlying lung disease (OR 2.511, 95% CI 1.146–5.501, p = 0.021) and cumulative dose of amiodarone (OR 4.762, 95% CI 1.310–17.309 p = 0.018). Conclusions The incidence of amiodarone pulmonary toxicity in Chinese patients in Hong Kong is estimated to be 1.9% in this study. Age, underlying lung disease, ventricular arrhythmia and cumulative dose of amiodarone are associated with the development of amiodarone pulmonary toxicity. A prediction rule was developed to inform the risk of developing amiodarone pulmonary toxicity.

Background Coronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease. Objective To apply a metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration to investigate the effect of border restriction as a public health measure to limit outbreak of coronavirus disease 2019. Methods We apply a modified metapopulation SEIR model with inspected migration with simulating population migration, and incorporating parameters such as efficiency of custom inspection in blocking infected travelers in the model. The population sizes were retrieved from government reports, while the number of COVID-19 patients were retrieved from Hong Kong Department of Health and China Centre for Disease Control (CDC) data. The R0 was obtained from previous clinical studies. Results Complete border closure can help to reduce the cumulative COVID-19 case number and mortality in Hong Kong by 13.99% and 13.98% respectively. To prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local R0 to below 1.6 was necessary, apart from having complete border closure. Conclusions Early complete travel restriction is effective in reducing cumulative cases and mortality. However, additional anti-COVID-19 measures to reduce local R0 to below 1.6 are necessary to prevent COVID-19 cases from overwhelming hospital isolation facilities.

Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource-intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT-PCR results. Our final model had high predictive power and only included geographic location and rRT-PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north-central states and the south-eastern region of Alaska. Lower rRT-PCR Ct-values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct-value) using the rRT-PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.

Background: Coronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease.Methods: A novel metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration was applied to investigate the effect of border restriction between Hong Kong and mainland China on the epidemiological characteristics of COVID-19 in Hong Kong. Isolation facilities occupancy was also studied.Results: At R0 is set to be inversely correlated with temperature of 2.2, the cumulative COVID-19 cases in Hong Kong can be reduced by 13.99% (from 29,163 to 25,084) with complete border closure. At an in-patient mortality of 1.4%, the number of deaths can be reduced from 408 to 351 (57 lives saved). However, border closure alone was insufficient to prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local R0 to below 1.6 was necessary. Conclusions: As a public health measure to tackle COVID-19, border restriction is effective in reducing cumulative cases and mortality.

Based upon regulatory and food industry-driven food safety standards, there is a need for rapid, accurate methods for assessing sanitary conditions. A commonly used assay is based on the assessment of the biochemical molecule adenosine triphosphate (ATP). A more recent assay, the total adenylate homologue-based (AXP) assay, targets the cumulative presence of ATP and its dephosphorylated homologues, adenosine diphosphate and adenosine monophosphate. Yet there is little information that compares the practical performance of these two assays. This work examined these two assay types with a comparative study in a grade A dairy foods processing plant and a licensed and inspected meat processing facility. A total of 1,920 concomitant analyses were conducted with main variables of assay type, processing facility type, and hygiene zone category. Statistical process control methodology was used to calculate 95% confidence control limits; data beyond those limits were considered contamination events. Results demonstrated that overall, the AXP assay detected contamination events approximately two times more often than the assay based on ATP only. This increase in the rate of contamination event detection was especially prevalent in the meat processing facility, where across all hygienic zones, there were 38 versus 85 contaminations events detected for the ATP and AXP assays, respectively. Across hygiene zones, the AXP data displayed either an equal or an increased incidence of soil detection compared with data from the ATP assay. This study provides applied evidence that assays solely dependent on ATP concentrations are less able to detect soil contaminants under conditions that favor ATP dephosphorylation reactions. HIGHLIGHTS

With the rapid advances of data acquisition techniques, spatio-temporal data are becoming increasingly abundant in a diverse array of disciplines. Here we develop spatio-temporal regression methodology for analyzing large amounts of spatially referenced data collected over time, motivated by environmental studies utilizing remotely sensed satellite data. In particular, we specify a semiparametric autoregressive model without the usual Gaussian assumption and devise a computationally scalable procedure that enables the regression analysis of large datasets. We estimate the model parameters by quasi maximum likelihood and show that the computational complexity can be reduced from cubic to linear of the sample size. Asymptotic properties under suitable regularity conditions are further established that inform the computational procedure to be efficient and scalable. A simulation study is conducted to evaluate the finite-sample properties of the parameter estimation and statistical inference. We illustrate our methodology by a dataset with over 2.96 million observations of annual land surface temperature and the comparison with an existing state-of-the-art approach highlights the advantages of our method.

Background Chronic respiratory diseases are risk factors for severe disease in coronavirus disease 2019 (COVID-19). Respiratory tract infection is one of the commonest causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). There has not been evidence suggesting the link between COVID-19 and AECOPD, especially in places with dramatic responses in infection control with universal masking and aggressive social distancing. Methods This is a retrospective study to assess the number of admissions of AECOPD in the first three months of 2020 in Queen Mary Hospital with reference to the admissions in past five years. Log-linear model was used for statistical inference of covariates, including percentage of masking, air quality health index and air temperature. Results The number of admissions for AECOPD significantly decreased by 44.0% (95% CI 36.4%–52.8%, p, Highlights • Admissions for COPD exacerbation decrease during COVID-19 period in Hong Kong. • Decrease in COPD admission is observed with universal masking and social distancing. • Universal masking and social distancing prevent respiratory tract infection. • Masking reduces respiratory tract infection postulate to decrease COPD exacerbation.

To examine the impact of the second legal ban on synthetic cannabinoids (SCs) in the UK in February 2013, we surveyed the UK legal high market just before and after the change in legislation, looking for new SCs. The technique gas chromatography–mass spectrometry (GC–MS) in electron ionization mode, most widely applied for analysis, was found to be insufficient for the identification of several SCs, and therefore liquid chromatography–high-resolution mass spectrometry (LC–HR-MS) was required. LC–HR-MS(/MS) measurements of the protonated molecules and product ions allowed the detection of up to 27 compounds as the third-generation SCs in the samples analysed as part of this study, including two unknown compounds that were tentatively identified as F2201 and dealkyl-SDB-006. Our results showed that banned compounds were removed from the market on the day when the ban was in place, and were replaced by other SCs immediately after the ban. In only one occasion, a banned compound (UR-144) was detected after the date when the new legislation came into place. It is also noteworthy that regardless of the change in legislation, new compounds continued to enter the market. Product ion spectral information on the third-generation SCs at different collision energies given in this paper will be of help for forensic and clinical laboratories and will facilitate the detection and identification of new SCs by laboratories of control. This information is very valuable for law enforcement and policymakers and will be of help in future prevention programs.

This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method.