Identification of novel bat coronaviruses sheds light on the evolutionary origins of SARS-CoV-2 and related viruses.

Despite the discovery of animal coronaviruses related to SARS-CoV-2, the evolutionary origins of this virus are elusive. We describe a meta-transcriptomic study of 411 bat samples collected from a small geographical region in Yunnan province, China, between May 2019 and November 2020. We identified 24 full-length coronavirus genomes, including four novel SARS-CoV-2-related and three SARS-CoV-related viruses. Rhinolophus pusillus virus RpYN06 was the closest relative of SARS-CoV-2 in most of the genome, although it possessed a more divergent spike gene. The other three SARS-CoV-2-related coronaviruses carried a genetically distinct spike gene that could weakly bind to the hACE2 receptor in vitro. Ecological modeling predicted the co-existence of up to 23 Rhinolophus bat species, with the largest contiguous hotspots extending from South Laos and Vietnam to southern China. Our study highlights the remarkable diversity of bat coronaviruses at the local scale, including close relatives of both SARS-CoV-2 and SARS-CoV.

Emerging Pandemic Diseases: How We Got to COVID-19.

Infectious diseases prevalent in humans and animals are caused by pathogens that once emerged from other animal hosts. In addition to these established infections, new infectious diseases periodically emerge. In extreme cases they may cause pandemics such as COVID-19; in other cases, dead-end infections or smaller epidemics result. Established diseases may also re-emerge, for example by extending geographically or by becoming more transmissible or more pathogenic. Disease emergence reflects dynamic balances and imbalances, within complex globally distributed ecosystems comprising humans, animals, pathogens, and the environment. Understanding these variables is a necessary step in controlling future devastating disease emergences.

Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic.

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

Ebola Virus Glycoprotein with Increased Infectivity Dominated the 2013-2016 Epidemic.

The magnitude of the 2013-2016 Ebola virus disease (EVD) epidemic enabled an unprecedented number of viral mutations to occur over successive human-to-human transmission events, increasing the probability that adaptation to the human host occurred during the outbreak. We investigated one nonsynonymous mutation, Ebola virus (EBOV) glycoprotein (GP) mutant A82V, for its effect on viral infectivity. This mutation, located at the NPC1-binding site on EBOV GP, occurred early in the 2013-2016 outbreak and rose to high frequency. We found that GP-A82V had heightened ability to infect primate cells, including human dendritic cells. The increased infectivity was restricted to cells that have primate-specific NPC1 sequences at the EBOV interface, suggesting that this mutation was indeed an adaptation to the human host. GP-A82V was associated with increased mortality, consistent with the hypothesis that the heightened intrinsic infectivity of GP-A82V contributed to disease severity during the EVD epidemic.

Global patterns in excess body weight and the associated cancer burden.

The prevalence of excess body weight and the associated cancer burden have been rising over the past several decades globally. Between 1975 and 2016, the prevalence of excess body weight in adults-defined as a body mass index (BMI) ≥ 25 kg/m2 -increased from nearly 21% in men and 24% in women to approximately 40% in both sexes. Notably, the prevalence of obesity (BMI ≥ 30 kg/m2 ) quadrupled in men, from 3% to 12%, and more than doubled in women, from 7% to 16%. This change, combined with population growth, resulted in a more than 6-fold increase in the number of obese adults, from 100 to 671 million. The largest absolute increase in obesity occurred among men and boys in high-income Western countries and among women and girls in Central Asia, the Middle East, and North Africa. The simultaneous rise in excess body weight in almost all countries is thought to be driven largely by changes in the global food system, which promotes energy-dense, nutrient-poor foods, alongside reduced opportunities for physical activity. In 2012, excess body weight accounted for approximately 3.9% of all cancers (544,300 cases) with proportion varying from less than 1% in low-income countries to 7% or 8% in some high-income Western countries and in Middle Eastern and Northern African countries. The attributable burden by sex was higher for women (368,500 cases) than for men (175,800 cases). Given the pandemic proportion of excess body weight in high-income countries and the increasing prevalence in low- and middle-income countries, the global cancer burden attributable to this condition is likely to increase in the future. There is emerging consensus on opportunities for obesity control through the multisectoral coordinated implementation of core policy actions to promote an environment conducive to a healthy diet and active living. The rapid increase in both the prevalence of excess body weight and the associated cancer burden highlights the need for a rejuvenated focus on identifying, implementing, and evaluating interventions to prevent and control excess body weight.

Resolving the enigma of Iquitos and Manaus: A modeling analysis of multiple COVID-19 epidemic waves in two Amazonian cities.

The two nearby Amazonian cities of Iquitos and Manaus endured explosive COVID-19 epidemics and may well have suffered the world's highest infection and death rates over 2020, the first year of the pandemic. State-of-the-art epidemiological and modeling studies estimated that the populations of both cities came close to attaining herd immunity (>70% infected) at the termination of the first wave and were thus protected. This makes it difficult to explain the more deadly second wave of COVID-19 that struck again in Manaus just months later, simultaneous with the appearance of a new P.1 variant of concern, creating a catastrophe for the unprepared population. It was suggested that the second wave was driven by reinfections, but the episode has become controversial and an enigma in the history of the pandemic. We present a data-driven model of epidemic dynamics in Iquitos, which we also use to explain and model events in Manaus. By reverse engineering the multiple epidemic waves over 2 y in these two cities, the partially observed Markov process model inferred that the first wave left Manaus with a highly susceptible and vulnerable population (≈40% infected) open to invasion by P.1, in contrast to Iquitos (≈72% infected). The model reconstructed the full epidemic outbreak dynamics from mortality data by fitting a flexible time-varying reproductive number [Formula: see text] while estimating reinfection and impulsive immune evasion. The approach is currently highly relevant given the lack of tools available to assess these factors as new SARS-CoV-2 virus variants appear with different degrees of immune evasion.

Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions.

Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant's importation time, its infectiousness advantage and, its cross-infection on the novel variant's detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention's effectiveness due to the variants' competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant's basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions' regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population.

The nucleocapsid protein facilitates p53 ubiquitination-dependent proteasomal degradation via recruiting host ubiquitin ligase COP1 in PEDV infection.

Porcine epidemic diarrhea virus (PEDV) is a highly contagious enteric pathogen of the coronavirus family and caused severe economic losses to the global swine industry. Previous studies have established that p53 is a host restriction factor for PEDV infection, and p53 degradation occurs in PEDV-infected cells. However, the underlying molecular mechanisms through which PEDV viral proteins regulate p53 degradation remain unclear. In this study, we found that PEDV infection or expression of the nucleocapsid protein downregulates p53 through a post-translational mechanism: increasing the ubiquitination of p53 and preventing its nuclear translocation. We also show that the PEDV N protein functions by recruiting the E3 ubiquitin ligase COP1 and suppressing COP1 self-ubiquitination and protein degradation, thereby augmenting COP1-mediated degradation of p53. Additionally, COP1 knockdown compromises N-mediated p53 degradation. Functional mapping using truncation analysis showed that the N-terminal domains of N protein were responsible for interacting with COP1 and critical for COP1 stability and p53 degradation. The results presented here suggest the COP1-dependent mechanism for PEDV N protein to abolish p53 activity. This study significantly increases our understanding of PEDV in antagonizing the host antiviral factor p53 and will help initiate novel antiviral strategies against PEDV.

Temporal Dynamics, Discovery, and Emergence of Human-Transmissible RNA Viruses.

Transmissibility, the ability to spread within host populations, is a prerequisite for a pathogen to have epidemic or pandemic potential. Here, we estimate the phylogenies of human infectivity and transmissibility using 1,408 genome sequences from 743 distinct RNA virus species/types in 59 genera. By repeating this analysis using data sets censored by virus discovery date, we explore how temporal changes in the known diversity of RNA viruses-especially recent increases in recognized nonhuman viruses-have altered these phylogenies. Over time, we find significant increases in the proportion of RNA virus genera estimated to have a nonhuman-infective ancestral state, in the fraction of distinct human virus lineages that are purely human-transmissible or strictly zoonotic (compared to mixed lineages), and in the number of human viruses with nearest relatives known not to infect humans. Our results are consistent with viruses that are capable of spreading in human populations commonly emerging from a nonhuman reservoir. This is more likely in lineages that already contain human-transmissible viruses but is rare in lineages that contain only strictly zoonotic viruses.

The fetal lineage is susceptible to Zika virus infection within days of fertilization.

Adults contracting Zika virus (ZIKV) typically exhibit mild symptoms, yet ZIKV infection of pregnant individuals can cause miscarriage or birth defects in their offspring. Many studies have focused on maternal-to-fetal ZIKV transmission via blood and placenta. Notably, however, ZIKV is also transmitted sexually, raising the possibility that ZIKV could infect the embryo shortly after fertilization, long before the placenta is established. Here, we evaluate the consequences of ZIKV infection in mouse embryos during the first few days of embryogenesis. We show that divergent strains of ZIKV can infect the fetal lineage and can cause developmental arrest, raising concern for the developmental consequences of sexual ZIKV transmission. This article has an associated 'The people behind the papers' interview.

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

Pemetrexed alleviates piglet diarrhea by blocking the interaction between porcine epidemic diarrhea virus nucleocapsid protein and Ezrin.

Porcine epidemic diarrhea virus (PEDV) is an enteric coronavirus that causes high mortality in piglets, thus posing a serious threat to the world pig industry. Porcine epidemic diarrhea (PED) is related to the imbalance of sodium absorption by small intestinal epithelial cells; however, the etiology of sodium imbalanced diarrhea caused by PEDV remains unclear. Herein, we first proved that PEDV can cause a significant decrease in Na+/H+ exchanger 3 (NHE3) expression on the cell membrane, in a viral dose-dependent manner. Further study showed that the PEDV nucleocapsid (N) protein participates in the regulation of NHE3 activity through interacting with Ezrin. Flame atomic absorption spectroscopy results indicated a serious imbalance in Na+ concentration inside and outside cells following overexpression of PEDV N. Meanwhile, molecular docking technology identified that the small molecule drug Pemetrexed acts on the PEDV N-Ezrin interaction region. It was confirmed that Pemetrexed can alleviate the imbalanced Na+ concentration in IPEC-J2 cells and the diarrhea symptoms of Rongchang pigs caused by PEDV infection. Overall, our data suggest that the interaction between PEDV N and Ezrin reduces the level of phosphorylated Ezrin, resulting in a decrease in the amount of NHE3 protein on the cell membrane. This leads to an imbalance of intracellular and extracellular Na+, which causes diarrhea symptoms in piglets. Pemetrexed is effective in relieving diarrhea caused by PEDV. Our results provide a reference to screen for anti-PEDV targets and to develop drugs to prevent PED.IMPORTANCEPorcine epidemic diarrhea (PED) has caused significant economic losses to the pig industry since its initial outbreak, and the pathogenic mechanism of porcine epidemic diarrhea virus (PEDV) is still under investigation. Herein, we found that the PEDV nucleocapsid protein interacts with Ezrin to regulate Na+/H+ exchanger 3 activity. In addition, we screened out Pemetrexed, a small molecule drug, which can effectively alleviate pig diarrhea caused by PEDV. These results provide support for further exploration of the pathogenesis of PEDV and the development of drugs to prevent PED.

Deep profiling of potential substrate atlas of porcine epidemic diarrhea virus 3C-like protease.

Coronavirus (CoV) 3C-like protease (3CLpro) is essential for viral replication and is involved in immune escape by proteolyzing host proteins. Deep profiling the 3CLpro substrates in the host proteome extends our understanding of viral pathogenesis and facilitates antiviral drug discovery. Here, 3CLpro from porcine epidemic diarrhea virus (PEDV), an enteropathogenic CoV, was used as a model which to identify the potential 3CLpro cleavage motifs in all porcine proteins. We characterized the selectivity of PEDV 3CLpro at sites P5-P4'. We then compiled the 3CLpro substrate preferences into a position-specific scoring matrix and developed a 3CLpro profiling strategy to delineate the protein substrate landscape of CoV 3CLpro. We identified 1,398 potential targets in the porcine proteome containing at least one putative cleavage site and experimentally validated the reliability of the substrate degradome. The PEDV 3CLpro-targeted pathways are involved in mRNA processing, translation, and key effectors of autophagy and the immune system. We also demonstrated that PEDV 3CLpro suppresses the type 1 interferon (IFN-I) cascade via the proteolysis of multiple signaling adaptors in the retinoic acid-inducible gene I (RIG-I) signaling pathway. Our composite method is reproducible and accurate, with an unprecedented depth of coverage for substrate motifs. The 3CLpro substrate degradome establishes a comprehensive substrate atlas that will accelerate the investigation of CoV pathogenicity and the development of anti-CoV drugs.IMPORTANCECoronaviruses (CoVs) are major pathogens that infect humans and animals. The 3C-like protease (3CLpro) encoded by CoV not only cleaves the CoV polyproteins but also degrades host proteins and is considered an attractive target for the development of anti-CoV drugs. However, the comprehensive characterization of an atlas of CoV 3CLpro substrates is a long-standing challenge. Using porcine epidemic diarrhea virus (PEDV) 3CLpro as a model, we developed a method that accurately predicts the substrates of 3CLpro and comprehensively maps the substrate degradome of PEDV 3CLpro. Interestingly, we found that 3CLpro may simultaneously degrade multiple molecules responsible for a specific function. For instance, it cleaves at least four adaptors in the RIG-I signaling pathway to suppress type 1 interferon production. These findings highlight the complexity of the 3CLpro substrate degradome and provide new insights to facilitate the development of anti-CoV drugs.

Fundamental limitations on efficiently forecasting certain epidemic measures in network models.

The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.

Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019.

We developed mathematical models to analyze a large dengue virus (DENV) epidemic in Reunion Island in 2018-2019. Our models captured major drivers of uncertainty including the complex relationship between climate and DENV transmission, temperature trends, and underreporting. Early assessment correctly concluded that persistence of DENV transmission during the austral winter 2018 was likely and that the second epidemic wave would be larger than the first one. From November 2018, the detection probability was estimated at 10%-20% and, for this range of values, our projections were found to be remarkably accurate. Overall, we estimated that 8% and 18% of the population were infected during the first and second wave, respectively. Out of the 3 models considered, the best-fitting one was calibrated to laboratory entomological data, and accounted for temperature but not precipitation. This study showcases the contribution of modeling to strengthen risk assessments and planning of national and local authorities.

Risk of Subsequent Respiratory Virus Detection After Primary Virus Detection in a Community Household Study-King County, Washington, 2019-2021.

BACKGROUND: The epidemiology of respiratory viral infections is complex. How infection with one respiratory virus affects risk of subsequent infection with the same or another respiratory virus is not well described. METHODS: From October 2019 to June 2021, enrolled households completed active surveillance for acute respiratory illness (ARI), and participants with ARI self-collected nasal swab specimens; after April 2020, participants with ARI or laboratory-confirmed severe acute respiratory syndrome coronavirus 2 and their household members self-collected nasal swab specimens. Specimens were tested using multiplex reverse-transcription polymerase chain reaction for respiratory viruses. A Cox regression model with a time-dependent covariate examined risk of subsequent detections following a specific primary viral detection. RESULTS: Rhinovirus was the most frequently detected pathogen in study specimens (406 [9.5%]). Among 51 participants with multiple viral detections, rhinovirus to seasonal coronavirus (8 [14.8%]) was the most common viral detection pairing. Relative to no primary detection, there was a 1.03-2.06-fold increase in risk of subsequent virus detection in the 90 days after primary detection; risk varied by primary virus: human parainfluenza virus, rhinovirus, and respiratory syncytial virus were statistically significant. CONCLUSIONS: Primary virus detection was associated with higher risk of subsequent virus detection within the first 90 days after primary detection.

Using BCG Vaccine to Enhance Nonspecific Protection of Health Care Workers During the COVID-19 Pandemic: A Randomized Controlled Trial.

BACKGROUND: The BCG (Bacillus Calmette-Guérin) vaccine can induce nonspecific protection against unrelated infections. We aimed to test the effect of BCG on absenteeism and health of Danish health care workers (HCWs) during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A single-blinded randomized controlled trial included 1221 HCWs from 9 Danish hospitals. Participants were randomized 1:1 to standard dose BCG or placebo. Primary outcome was days of unplanned absenteeism. Main secondary outcomes were incidence of COVID-19, all-cause hospitalization, and infectious disease episodes. RESULTS: There was no significant effect of BCG on unplanned absenteeism. Mean number of days absent per 1000 workdays was 20 in the BCG group and 17 in the placebo group (risk ratio, 1.23; 95% credibility interval, 0.98-1.53). BCG had no effect on incidence of COVID-19 or all-cause hospitalization overall. In secondary analyses BCG revaccination was associated with higher COVID-19 incidence (hazard ratio [HR], 2.47; 95% confidence interval [CI], 1.07-5.71), but also reduced risk of hospitalization (HR, 0.28; 95% CI, .09-.86). The incidence of infectious disease episodes was similar between randomization groups (HR, 1.09; 95% CI, .96-1.24). CONCLUSIONS: In this relatively healthy cohort of HCWs, there was no overall effect of BCG on any of the study outcomes. CLINICAL TRIALS REGISTRATION: NCT0437329 and EU Clinical Trials Register (EudraCT number 2020-001888-90).

Interest in and Preference for Long-acting Injectable Antiretroviral Therapy in the Era of Approved Cabotegravir/Rilpivirine among Reproductive-aged Women in the U.S. South.

Among 103 reproductive-aged women with HIV in the U.S. South surveyed post-approval of long-acting injectable (LAI) cabotegravir/rilpivirine, nearly two-thirds reported willingness to try LAI antiretroviral therapy (ART). Most expressed preference for LAI over daily oral ART and had minimal concerns over potential LAI-ART use impacting reproductive health.

Genomic characterization and virulence gene profiling of Erysipelothrix rhusiopathiae isolated from widespread muskox mortalities in the Canadian Arctic Archipelago.

BACKGROUND: Muskoxen are important ecosystem components and provide food, economic opportunities, and cultural well-being for Indigenous communities in the Canadian Arctic. Between 2010 and 2021, Erysipelothrix rhusiopathiae was isolated from carcasses of muskoxen, caribou, a seal, and an Arctic fox during multiple large scale mortality events in the Canadian Arctic Archipelago. A single strain ('Arctic clone') of E. rhusiopathiae was associated with the mortalities on Banks, Victoria and Prince Patrick Islands, Northwest Territories and Nunavut, Canada (2010-2017). The objectives of this study were to (i) characterize the genomes of E. rhusiopathiae isolates obtained from more recent muskox mortalities in the Canadian Arctic in 2019 and 2021; (ii) identify and compare common virulence traits associated with the core genome and mobile genetic elements (i.e. pathogenicity islands and prophages) among Arctic clone versus other E. rhusiopathiae genomes; and iii) use pan-genome wide association studies (GWAS) to determine unique genetic contents of the Arctic clone that may encode virulence traits and that could be used for diagnostic purposes. RESULTS: Phylogenetic analyses revealed that the newly sequenced E. rhusiopathiae isolates from Ellesmere Island, Nunavut (2021) also belong to the Arctic clone. Of 17 virulence genes analysed among 28 Arctic clone isolates, four genes - adhesin, rhusiopathiae surface protein-A (rspA), choline binding protein-B (cbpB) and CDP-glycerol glycerophosphotransferase (tagF) - had amino acid sequence variants unique to this clone when compared to 31 other E. rhusiopathiae genomes. These genes encode proteins that facilitate E. rhusiopathiae to attach to the host endothelial cells and form biofilms. GWAS analyses using Scoary found several unique genes to be overrepresented in the Arctic clone. CONCLUSIONS: The Arctic clone of E. rhusiopathiae was associated with multiple muskox mortalities spanning over a decade and multiple Arctic islands with distances over 1000 km, highlighting the extent of its spatiotemporal spread. This clone possesses unique gene content, as well as amino acid variants in multiple virulence genes that are distinct from the other closely related E. rhusiopathiae isolates. This study establishes an essential foundation on which to investigate whether these differences are correlated with the apparent virulence of this specific clone through in vitro and in vivo studies.