COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of ...the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday.
CCR5 is the major coreceptor for human immunodeficiency virus (HIV). We investigated whether site-specific modification of the gene ("gene editing")--in this case, the infusion of autologous CD4 T ...cells in which the CCR5 gene was rendered permanently dysfunctional by a zinc-finger nuclease (ZFN)--is safe.
We enrolled 12 patients in an open-label, nonrandomized, uncontrolled study of a single dose of ZFN-modified autologous CD4 T cells. The patients had chronic aviremic HIV infection while they were receiving highly active antiretroviral therapy. Six of them underwent an interruption in antiretroviral treatment 4 weeks after the infusion of 10 billion autologous CD4 T cells, 11 to 28% of which were genetically modified with the ZFN. The primary outcome was safety as assessed by treatment-related adverse events. Secondary outcomes included measures of immune reconstitution and HIV resistance.
One serious adverse event was associated with infusion of the ZFN-modified autologous CD4 T cells and was attributed to a transfusion reaction. The median CD4 T-cell count was 1517 per cubic millimeter at week 1, a significant increase from the preinfusion count of 448 per cubic millimeter (P<0.001). The median concentration of CCR5-modified CD4 T cells at 1 week was 250 cells per cubic millimeter. This constituted 8.8% of circulating peripheral-blood mononuclear cells and 13.9% of circulating CD4 T cells. Modified cells had an estimated mean half-life of 48 weeks. During treatment interruption and the resultant viremia, the decline in circulating CCR5-modified cells (-1.81 cells per day) was significantly less than the decline in unmodified cells (-7.25 cells per day) (P=0.02). HIV RNA became undetectable in one of four patients who could be evaluated. The blood level of HIV DNA decreased in most patients.
CCR5-modified autologous CD4 T-cell infusions are safe within the limits of this study. (Funded by the National Institute of Allergy and Infectious Diseases and others; ClinicalTrials.gov number, NCT00842634.).
A patient with refractory multiple myeloma received an infusion of CTL019 cells, a cellular therapy consisting of autologous T cells transduced with an anti-CD19 chimeric antigen receptor, after ...myeloablative chemotherapy (melphalan, 140 mg per square meter of body-surface area) and autologous stem-cell transplantation. Four years earlier, autologous transplantation with a higher melphalan dose (200 mg per square meter) had induced only a partial, transient response. Autologous transplantation followed by treatment with CTL019 cells led to a complete response with no evidence of progression and no measurable serum or urine monoclonal protein at the most recent evaluation, 12 months after treatment. This response was achieved despite the absence of CD19 expression in 99.95% of the patient's neoplastic plasma cells. (Funded by Novartis and others; ClinicalTrials.gov number, NCT02135406.).
Extratropical influences on tropical sea surface temperature (SST) have implications for decadal predictability. We implement a cloud‐locking technique to highlight the critical role of clouds in ...shaping the tropical SST response to extratropical thermal forcing. With heating imposed over either the extratropical Northern Atlantic or Pacific, Hadley Cells respond similarly that the trades strengthen south of the rainband. The wind‐evaporation‐SST (WES) feedback leads to cooling over the southern subtropics, which is enhanced in the southeastern Pacific due to the positive feedback between SST and stratiform clouds. Cloud‐locking experiments show that zonal contrasts in SST and cloud feedbacks in the Pacific enhance the zonal surface winds, leading to increased evaporation and strengthens zonal SST difference. We propose that the meridional and zonal SST gradients are tightly linked via WES effects and the cloud‐radiative‐SST feedbacks, which are largely determined by the climatological rainband position and the spatial distribution of cloud properties.
Plain Language Summary
Tropical Pacific sea surface temperature (SST) plays a key role in climate variability around the globe. Understanding how tropical Pacific SST can be impacted by climate perturbation at mid‐to‐high latitudes is essential for improving climate prediction skills. In this study, how clouds modulate such interaction between tropics and extratropics is examined by utilizing global climate model simulations. With idealized heating being imposed in either the extratropical Northern Atlantic or Pacific, we found common response patterns in the tropical Pacific to the heating. Two factors shape the common response patterns. First, the anomalous southerly winds act to enhance or weaken the evaporative cooling, depending on the climatological rainband position and the associated trade wind directions. Secondly, the anomalous SST induced by changes in evaporation is either amplified or damped by cloud cover changes, since the relationships between SST and cloud amount depend on cloud types. A series of physical processes, which are largely established by the mean‐state climate pattern, act to link the zonal and meridional structure of the SST over the tropical Pacific.
Key Points
Tropical surface temperature responds similarly to idealized heating imposed over either North Atlantic or North Pacific as fast response
Clouds are essential in forming the tropical response pattern through their coupling with circulation and surface energy fluxes
The climatological rainband position in the tropics determines how clouds shape the tropical responses to extratropical forcing
After initiating antiretroviral therapy (ART), a rapid decline in HIV viral load is followed by a long period of undetectable viremia. Viral outgrowth assay suggests the reservoir continues to ...decline slowly. Here, we use full-length sequencing to longitudinally study the proviral landscape of four subjects on ART to investigate the selective pressures influencing the dynamics of the treatment-resistant HIV reservoir. We find intact and defective proviruses that contain genetic elements favoring efficient protein expression decrease over time. Moreover, proviruses that lack these genetic elements, yet contain strong donor splice sequences, increase relatively to other defective proviruses, especially among clones. Our work suggests that HIV expression occurs to a significant extent during ART and results in HIV clearance, but this is obscured by the expansion of proviral clones. Paradoxically, clonal expansion may also be enhanced by HIV expression that leads to splicing between HIV donor splice sites and downstream human exons.
Lung development is a multistage process from conception to the postnatal period, disruption of which by air pollutants can trigger later respiratory morbidity.
We sought to evaluate the effects of ...weekly average fine particulate matter (particulate matter with an aerodynamic diameter less than 2.5 μm PM2.5) exposure during pregnancy and infancy on asthma and identify vulnerable times to help elucidate possible mechanisms of the effects of PM2.5 on asthma symptoms.
A birth cohort study including 184,604 children born during 2004-2011 in Taichung City was retrieved from the Taiwan Maternal and Child Health Database and followed until 2014. A daily satellite-based hybrid model was applied to estimate PM2.5 exposure for each subject. A Cox proportional hazard model combined with a distributed lag nonlinear model was used to evaluate the associations of asthma with PM2.5 exposure during pregnancy and infancy.
The birth cohort contained 34,336 asthmatic patients, and the mean age of children given a diagnosis of asthma was 3.39 ± 1.78 years. Increased exposure to PM2.5 during gestational weeks 6 to 22 and 9 to 46 weeks after birth were significantly associated with an increased incidence of asthma. The exposure-response relationship indicated that the hazard ratio (HR) of asthma increased steeply at PM2.5 exposure of greater than 93 μg/m3 during pregnancy. Additionally, the HRs remained significant with postnatal exposure to PM2.5 between 26 and 72 μg/m3 (range, 1.01-1.07 μg/m3), followed by a sharp increase in HRs at PM2.5 exposure of greater than 73 μg/m3.
Both prenatal and postnatal exposures to PM2.5 were associated with later development of asthma. The vulnerable time windows might be within early gestation and midgestation and infancy.
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To estimate the effects of BRCA1 and BRCA2 mutations on ovarian cancer and breast cancer survival.
We searched PubMed and EMBASE for studies that evaluated the associations between BRCA mutations and ...ovarian or breast cancer survival. Meta-analysis was conducted to generate combined HRs with 95% confidence intervals (CI) for overall survival (OS) and progression-free survival (PFS).
From 1,201 unique citations, we identified 27 articles that compared prognosis between BRCA mutation carriers and noncarriers in patients with ovarian or breast cancer. Fourteen studies examined ovarian cancer survival and 13 studies examined breast cancer survival. For ovarian cancer, meta-analysis demonstrated that both BRCA1 and BRCA2 mutation carriers had better OS (HR, 0.76; 95% CI, 0.70-0.83 for BRCA1 mutation carriers; HR, 0.58; 95% CI, 0.50-0.66 for BRCA2 mutation carriers) and PFS (HR, 0.65; 95% CI, 0.52-0.81 for BRCA1 mutation carriers; HR, 0.61; 95% CI, 0.47-0.80 for BRCA2 mutation carriers) than noncarriers, regardless of tumor stage, grade, or histologic subtype. Among patients with breast cancer, BRCA1 mutation carriers had worse OS (HR, 1.50; 95% CI, 1.11-2.04) than noncarriers but were not significantly different from noncarriers in PFS. BRCA2 mutation was not associated with breast cancer prognosis.
Our analyses suggest that BRCA mutations are robust predictors of outcomes in both ovarian and breast cancers and these mutations should be taken into account when devising appropriate therapeutic strategies.
COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of ...the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday.
Observational studies and Mendelian randomization experiments have been used to identify many causal factors for complex traits in humans. Given a set of causal factors, it is important to understand ...the extent to which these causal factors explain some, all, or none of the genetic heritability, as measured by single-nucleotide polymorphisms (SNPs) that are associated with the trait. Using the mediation model framework with SNPs as the exposure, a trait of interest as the outcome, and the known causal factors as the mediators, we hypothesize that any unexplained association between the SNPs and the outcome trait is mediated by an additional unobserved, hidden causal factor.
We propose a method to infer the effect size of this hidden mediating causal factor on the outcome trait by utilizing the estimated associations between a continuous outcome trait, the known causal factors, and the SNPs. The proposed method consists of three steps and, in the end, implements Markov chain Monte Carlo to obtain a posterior distribution for the effect size of the hidden mediator. We evaluate our proposed method via extensive simulations and show that when model assumptions hold, our method estimates the effect size of the hidden mediator well and controls type I error rate if the hidden mediator does not exist. In addition, we apply the method to the UK Biobank data and estimate parameters for a potential hidden mediator for waist-hip ratio beyond body mass index (BMI), and find that the hidden mediator has a large effect size relatively to the effect size of the known mediator BMI.
We develop a framework to infer the effect of potential, hidden mediators influencing complex traits. This framework can be used to place boundaries on unexplained risk factors contributing to complex traits.
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