Optimization and performance analytics of global aircraft-based wastewater surveillance networks.

Aircraft wastewater surveillance has been proposed as a novel approach to monitor the global spread of pathogens. Here we develop a computational framework to provide actionable information for designing and estimating the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potentials and find that networks of 10 to 20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSNs effectiveness while minimizing resource use. Our findings highlight that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSNs capabilities, stressing the importance of resource optimization. We show through retrospective analyses that WWSNs can significantly shorten the detection time for emerging pathogens. The presented approach offers a realistic analytic framework for the analysis of WWSNs at airports.

Effectiveness of brainwave entrainment on pre-operative fear and anxiety in pediatric dental patients: a randomized controlled trial.

PURPOSE: The study aims to evaluate the effectiveness of brainwave entrainment on pre-operative fear and anxiety in pediatric dental patients. METHODS: The study protocol received approval from the Institutional Ethical Committee under reference number 3010/IEC/2021. Pediatric patients (252) aged from 7 to 12 years, who reported to the dental department were randomized pre-operatively and presented either with brainwave entrainment (experimental), delivered using a "David delight plus device" or a standard behavior management protocol (control). Baseline and post-assessment of anxiety and fear levels were done using the Visual Facial Anxiety Scale and Frankl's behavior rating scale with Wright's modification. Vitals such as blood pressure and pulse rate were also measured. RESULTS: The study sample (n = 252) comprised 118 females and 134 males. The non-significant differences for values of (VFAS1, FRS1, HR1, and BP1) indicated similar baseline characteristics. In the brainwave entrainment group, the p values of the Mann-Whitney U test and Wilcoxon Signed Ranks test (p < 0.01) between the two-timepoints indicated a statistical difference for the values of (VFAS1, FBRS1, HR1, BP1) and (VFAS2, FBRS2, HR2, BP2). CONCLUSIONS: Brainwave entrainment effectively reduces pre-operative fear and anxiety in pediatric dental patients. Therefore, they can be a non-pharmacological and non-invasive behavior management aid. TRIAL REGISTRATION: Clinical Trial Registry of India database CTRI/2023/03/051066.

Association of electronic-cigarette, number of cigarettes, and marijuana use with high-risk Human Papillomavirus (HPV) among men and women: A cross-sectional analysis of a nationally representative sample.

BACKGROUND: Smoking is associated with an increased risk of HPV infection. However, the use of e-cigarettes and marijuana, number of cigarettes, and serum cotinine concentrations in relation with HPV (6, 11, 16, 18) and high-risk HPV (16 or 18) infections in underserved and understudied populations remain poorly understood. METHODS: Data included 687 males and 664 females among whom 489 were White, 375 were Black and 342 were Hispanics from the NHANES 2013-2016 with HPV and high-risk HPV infections. Smoking history included current and past smokers, number of cigarettes, use of e-cigarettes, marijuana, and serum cotinine levels. Weighted multivariable-adjusted logistic regression models were conducted. RESULTS: High-risk HPV infection was associated with current smoking history plus ≥ 20 cigarettes/day (OR=1.92, 95 % CI=1.09, 3.37) in the overall population. E-cigarettes use (5 days) was positively associated with high-risk HPV infection (OR=2.43, 95 % CI=1.13, 5.22) in the overall population, with similar findings with e-cigarette (past 30 days) among women and Whites. CONCLUSION: High number of cigarettes, e-cigarette usage and marijuana were associated with HPV and high-risk HPV infections in the overall population. Most of these associations remained significant when stratified by gender and race/ethnicity. Increasing use of e-cigarettes and marijuana in these population warrants further investigation for the prevention of HPV infection and related cancers.

Expression of a kinase inactive SLK is embryonic lethal and impairs cell migration in fibroblasts.

Kinases are known to have kinase activity independent functions. To gain further insights into potential kinase-independent functions of SLK/STK2, we have developed a kinase-dead allele, SLKK63R using in vivo CRISPR/Cas technology. Our studies show that blastocysts homozygote for SLKK63R do not develop into viable mice. However, heterozygotes are viable and fertile with no overt phenotypes. Analyses of mouse embryonic fibroblasts show that expression of SLKK63R results in a 50% decrease in kinase activity in heterozygotes. In contrast to previous studies, our data show that SLK does not form homodimers and that the kinase defective allele does not act in a dominant negative fashion. Expression of SLKK63R leads to altered Rac1 and RhoA activity, increased stress fiber formation and delayed focal adhesion turnover. Our data support a previously observed role for SLK in cell migration and suggest that at least 50% kinase activity is sufficient for embryonic development.

The Emergence of Non-Linear Evolutionary Trade-offs and the Maintenance of Genetic Polymorphisms.

Evolutionary models of quantitative traits often assume trade-offs between beneficial and detrimental traits, requiring modelers to specify a function linking costs to benefits. The choice of trade-off function is often consequential; functions that assume diminishing returns (accelerating costs) typically lead to single equilibrium genotypes, while decelerating costs often lead to evolutionary branching. Despite their importance, we still lack a strong theoretical foundation to base the choice of trade-off function. To address this gap, we explore how trade-off functions can emerge from the genetic architecture of a quantitative trait. We developed a multi-locus model of disease resistance, assuming each locus had random antagonistic pleiotropic effects on resistance and fecundity. We used this model to generate genotype landscapes and explored how additive versus epistatic genetic architectures influenced the shape of the trade-off function. Regardless of epistasis, our model consistently led to accelerating costs. We then used our genotype landscapes to build an evolutionary model of disease resistance. Unlike other models with accelerating costs, our approach often led to genetic polymorphisms at equilibrium. Our results suggest that accelerating costs are a strong null model for evolutionary trade-offs and that the eco-evolutionary conditions required for polymorphism may be more nuanced than previously believed.

Stochastic Electrochemical Measurement of a Biofouling Layer on Gold.

Adsorption of a biofouling layer on the surface of biosensors decreases the electrochemical activity and hence shortens the service life of biosensors, particularly implantable and wearable biosensors. Real-time quantification of the loss of activity is important for in situ assessment of performance while presenting an opportunity to compensate for the loss of activity and recalibrate the sensor to extend the service life. Here, we introduce an electrochemical noise measurement technique as a tool for the quantification of the formation of a biofouling layer on the surface of gold. The technique uniquely affords thermodynamic and kinetic information without applying an external bias (potential and/or current), hence allowing the system to be appraised in its innate state. The technique relies on the analysis of non-faradaic current and potential fluctuations that are intrinsically generated by the interaction of charged species at the electrode surface, i.e., gold. An analytical model is extended to explain the significance of parameters drawn from statistical analysis of the noise signal. This concept is then examined in buffered media in the presence of albumin, a common protein in the blood and a known source of a fouling layer in biological systems. Results indicate that the statistical analysis of the noise signal can quantify the loss of electrochemical activity, which is also corroborated by impedance spectroscopy as a complementary technique.

NLC-Based Sunscreen Formulations with Optimized Proportion of Encapsulated and Free Filters Exhibit Enhanced UVA and UVB Photoprotection.

The topical use of sunscreens is recommended for avoiding the damaging effects of UV radiation. However, improvements are still needed in the existing products to enhance their photoprotection effectiveness and safety. This involves minimizing the use of chemical UV filters while providing enhanced and prolonged photoprotection. This work investigated novel sunscreen formulations and their UV protection effects by encapsulating Uvinul® A, Tinosorb® S, and Uvinul® T150 into nanostructured lipid carriers (NLCs) based on bacuri butter and raspberry seed oil. First, the impact of critical formulation and process parameters on NLCs' particle size was evaluated using a 22 Face Centered Central Composite Design. Then, formulations were evaluated in terms of critical quality factors, in vitro skin permeation, and in vitro and in vivo photoprotection activities. The developed NLCs-containing formulations exhibited appropriate size (122-135 nm), PdI (<0.3), encapsulation efficiency (>90%), and drug content (>80%), which were preserved for at least 90 days under different stability conditions. Moreover, these NLCs-based formulations had equivalent skin permeation to emulsion-based controls, and the addition of NLCs into sunscreen cream bases in the optimum proportion of 20% (w/w) resulted in enhanced UVA and UVB photoprotection levels, despite a 10% reduction in the total filters content. Altogether, these results describe the application of nanoencapsulated organic UV filters in innovative sunscreen formulations to achieve superior photoprotection and cosmeceutical properties.

Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic.

The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.

Quantitative subcellular reconstruction reveals a lipid mediated inter-organelle biogenesis network.

The structures and functions of organelles in cells depend on each other but have not been systematically explored. We established stable knockout cell lines of peroxisomal, Golgi and endoplasmic reticulum genes identified in a whole-genome CRISPR knockout screen for inducers of mitochondrial biogenesis stress, showing that defects in peroxisome, Golgi and endoplasmic reticulum metabolism disrupt mitochondrial structure and function. Our quantitative total-organelle profiling approach for focussed ion beam scanning electron microscopy revealed in unprecedented detail that specific organelle dysfunctions precipitate multi-organelle biogenesis defects, impair mitochondrial morphology and reduce respiration. Multi-omics profiling showed a unified proteome response and global shifts in lipid and glycoprotein homeostasis that are elicited when organelle biogenesis is compromised, and that the resulting mitochondrial dysfunction can be rescued with precursors for ether-glycerophospholipid metabolic pathways. This work defines metabolic and morphological interactions between organelles and how their perturbation can cause disease.

Financial Performance is Associated With PPE Shortages in Chain-Affiliated Nursing Homes During the COVID-19 Pandemic: A Longitudinal Study.

Many nursing homes operated at thin profit margins prior to the COVID-19 pandemic. This study examines the role of nursing homes' financial performance and chain affiliation in shortages of personal protection equipment (PPE) during the first year of the COVID-19 pandemic. We constructed a longitudinal file of 79 868 nursing home-week observations from 10 872 unique facilities. We found that a positive profit margin was associated with a 21.0% lower probability of reporting PPE shortages in chain-affiliated nursing homes, but not in non-chain nursing homes. Having adequate financial resources may help nursing homes address future emergencies, especially those affiliated with a multi-facility chain.

The unintended consequences of inconsistent closure policies and mobility restrictions during epidemics.

Background: Controlling the spread of infectious diseases-even when safe, transmission-blocking vaccines are available-may require the effective use of non-pharmaceutical interventions (NPIs), e.g., mask wearing, testing, limits on group sizes, venue closure. During the SARS-CoV-2 pandemic, many countries implemented NPIs inconsistently in space and time. This inconsistency was especially pronounced for policies in the United States of America (US) related to venue closure. Methods: Here, we investigate the impact of inconsistent policies associated with venue closure using mathematical modeling and high-resolution human mobility, Google search, and county-level SARS-CoV-2 incidence data from the USA. Specifically, we look at high-resolution location data and perform a US-county-level analysis of nearly 8 million SARS-CoV-2 cases and 150 million location visits, including 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7662 grocery stores, and 13.5 million gym visitors across 5483 gyms. Results: Analyzing the interaction between venue closure and changing mobility using a mathematical model shows that, across a broad range of model parameters, inconsistent or partial closure can be worse in terms of disease transmission as compared to scenarios with no closures at all. Importantly, changes in mobility patterns due to epidemic control measures can lead to increase in the future number of cases. In the most severe cases, individuals traveling to neighboring jurisdictions with different closure policies can result in an outbreak that would otherwise have been contained. To motivate our mathematical models, we turn to mobility data and find that while stay-at-home orders and closures decreased contacts in most areas of the USA, some specific activities and venues saw an increase in attendance and an increase in the distance visitors traveled to attend. We support this finding using search query data, which clearly shows a shift in information seeking behavior concurrent with the changing mobility patterns. Conclusions: While coarse-grained observations are not sufficient to validate our models, taken together, they highlight the potential unintended consequences of inconsistent epidemic control policies related to venue closure and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic. Supplementary Information: The online version contains supplementary material available at 10.1186/s44263-023-00028-z.

Forecasting hospital-level COVID-19 admissions using real-time mobility data

For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. At the same time, anonymized phone-collected mobility data proved to correlate well with the number of cases for the first two waves of the pandemic (spring 2020, and fall-winter 2021). In this work, we show how mobility data could bolster hospital-specific COVID-19 admission forecasts for five hospitals in Massachusetts during the initial COVID-19 surge. The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. We conclude that mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

The COVID-19 pandemic amplified long-standing racial disparities in the United States criminal justice system

During the first year of the COVID-19 pandemic, the number of incarcerated people in the United States decreased by at least 16%--the largest, fastest reduction in prison population in American history. Using an original dataset curated from public sources on prison demographics across all 50 states and the District of Columbia, we show that incarcerated white people benefited disproportionately from this decrease in the U.S. prison population, and the fraction of incarcerated Black and Latino people sharply increased. This pattern deviates from a decade-long trend before 2020 and the onset of COVID-19, during which the proportion of incarcerated Black people was declining. Using case studies of select states, we explore and quantify mechanisms that could explain these disparities: temporary court closures that led to fewer prison admissions, changes in the frequency of police interactions, and state-level prison release policies that sought to de-densify congregate settings. These findings illuminate how systemic inequalities pervade juridicial and penal institutions and are key features of mass incarceration in America.

Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.

Higher education responses to COVID-19 in the United States: Evidence for the impacts of university policy

With a dataset of testing and case counts from over 1,400 institutions of higher education (IHEs) in the United States, we analyze the number of infections and deaths from SARS-CoV-2 in the counties surrounding these IHEs during the Fall 2020 semester (August to December, 2020). We used a matching procedure designed to create groups of counties that are aligned along age, race, income, population, and urban/rural categories--socio-demographic variables that have been shown to be correlated with COVID-19 outcomes. We find that counties with IHEs that remained primarily online experienced fewer cases and deaths during the Fall 2020 semester; whereas before and after the semester, these two groups had almost identical COVID-19 incidence. Additionally, we see fewer deaths in counties with IHEs that reported conducting any on-campus testing compared to those that reported none. We complement the statistical analysis with a case study of IHEs in Massachusetts--a rich data state in our dataset--which further highlights the importance of IHE-affiliated testing for the broader community. The results in this work suggest that campus testing can itself be thought of as a mitigation policy and that allocating additional resources to IHEs to support efforts to regularly test students and staff would be beneficial to mitigating the spread of COVID-19 in the general population.

The effect of eviction moratoriums on the transmission of SARS-CoV-2

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess municipal eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.

Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

IntroductionCloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, research is limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. MethodsSerial cross-sectional surveys were administered June 3 through July 27, 2020 via web platform. Surveys queried individuals likelihood to wear a face mask to the grocery store or with family and friends. Responses (N=378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. ResultsWe find an upward trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Segmented regression analysis of mask wearing found no statistical change following mandates, however the positive trend of increased mask wearing over time was preserved. ConclusionWidespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.

The unintended consequences of inconsistent pandemic control policies

Controlling the spread of COVID-19 - even after a licensed vaccine is available - requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc.. To date, such interventions have neither been uniformly nor systematically implemented in most countries. For example, even when under strict stay-at-home orders, numerous jurisdictions granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling search and mobility data to a simple mathematical model of SARS-COV2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the United States of America (US), some specific activities and venues often see an increase in attendance. Indeed, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period. Strikingly, our mathematical model reveals that, across a broad range of model parameters, partial measures can often be worse than no measures at all. In the most severe cases, individuals not complying with policies by traveling to neighboring jurisdictions can create epidemics when the outbreak would otherwise have been controlled. Taken together, our data analysis and modelling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

The intensity of COVID-19 outbreaks is modulated by SARS-CoV-2 free-living survival and environmental transmission

Variation in free-living, microparasite survival can have a meaningful impact on the ecological dynamics of established and emerging infectious diseases. Nevertheless, resolving the importance of environmental transmission in the ecology of epidemics remains a persistent challenge, requires accurate measuring the free-living survival of pathogens across reservoirs of various kinds, and quantifying the extent to which interaction between hosts and reservoirs generates new infections. These questions are especially salient for emerging pathogens, where sparse and noisy data can obfuscate the relative contribution of different infection routes. In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-host) and indirect (environmental) transmission and then fit this model to empirical data from 17 countries affected by an emerging virus (SARS-CoV-2). From an ecological perspective, our model highlights the potential for environmental transmission to drive complex, non-linear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting such models with environmental transmission to real outbreak data from SARS-CoV-2 transmission highlights that variation in environmental transmission is an underappreciated aspect of the ecology of infectious disease, and an incomplete understanding of its role has consequences for public health interventions.