374 research outputs found

    Are secular correlations between sunspots, geomagnetic activity, and global temperature significant?

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    Recent studies have led to speculation that solar-terrestrial interaction, measured by sunspot number and geomagnetic activity, has played an important role in global temperature change over the past century or so. We treat this possibility as an hypothesis for testing. We examine the statistical significance of cross-correlations between sunspot number, geomagnetic activity, and global surface temperature for the years 1868–2008, solar cycles 11–23. The data contain substantial autocorrelation and nonstationarity, properties that are incompatible with standard measures of cross-correlational significance, but which can be largely removed by averaging over solar cycles and first-difference detrending. Treated data show an expected statistically-significant correlation between sunspot number and geomagnetic activity, Pearson p < 10^(−4), but correlations between global temperature and sunspot number (geomagnetic activity) are not significant, p = 0.9954, (p = 0.8171). In other words, straightforward analysis does not support widely-cited suggestions that these data record a prominent role for solar-terrestrial interaction in global climate change. With respect to the sunspot-number, geomagnetic-activity, and global-temperature data, three alternative hypotheses remain difficult to reject: (1) the role of solar-terrestrial interaction in recent climate change is contained wholly in long-term trends and not in any shorter-term secular variation, or, (2) an anthropogenic signal is hiding correlation between solar-terrestrial variables and global temperature, or, (3) the null hypothesis, recent climate change has not been influenced by solar-terrestrial interaction

    Altered Lung Morphogenesis, Epithelial Cell Differentiation and Mechanics in Mice Deficient in the Wnt/β-Catenin Antagonist Chibby

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    The canonical Wnt/β-catenin pathway plays crucial roles in various aspects of lung morphogenesis and regeneration/repair. Here, we examined the lung phenotype and function in mice lacking the Wnt/β-catenin antagonist Chibby (Cby). In support of its inhibitory role in canonical Wnt signaling, expression of β-catenin target genes is elevated in the Cby−/− lung. Notably, Cby protein is prominently associated with the centrosome/basal body microtubule structures in embryonic lung epithelial progenitor cells, and later enriches as discrete foci at the base of motile cilia in airway ciliated cells. At birth, Cby−/− lungs are grossly normal but spontaneously develop alveolar airspace enlargement with reduced proliferation and abnormal differentiation of lung epithelial cells, resulting in altered pulmonary function. Consistent with the Cby expression pattern, airway ciliated cells exhibit a marked paucity of motile cilia with apparent failure of basal body docking. Moreover, we demonstrate that Cby is a direct downstream target for the master ciliogenesis transcription factor Foxj1. Collectively, our results demonstrate that Cby facilitates proper postnatal lung development and function

    Epidemiologic Risk Factors for In Situ and Invasive Breast Cancers Among Postmenopausal Women in the National Institutes of Health-AARP Diet and Health Study

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    Comparing risk factor associations between invasive breast cancers and possible precursors may further our understanding of factors related to initiation versus progression. Accordingly, among 190,325 postmenopausal participants in the National Institutes of Health-AARP Diet and Health Study (1995-2011), we compared the association between risk factors and incident ductal carcinoma in situ (DCIS; n = 1,453) with that of risk factors and invasive ductal carcinomas (n = 7,525); in addition, we compared the association between risk factors and lobular carcinoma in situ (LCIS; n = 186) with that of risk factors and invasive lobular carcinomas (n = 1,191). Hazard ratios and 95% confidence intervals were estimated from multivariable Cox proportional hazards regression models. We used case-only multivariable logistic regression to test for heterogeneity in associations. Younger age at menopause was associated with a higher risk of DCIS but lower risks of LCIS and invasive ductal carcinomas (P for heterogeneity < 0.01). Prior breast biopsy was more strongly associated with the risk of LCIS than the risk of DCIS (P for heterogeneity = 0.04). Increased risks associated with use of menopausal hormone therapy were stronger for LCIS than DCIS (P for heterogeneity = 0.03) and invasive lobular carcinomas (P for heterogeneity < 0.01). Associations were similar for race, age at menarche, age at first birth, family history, alcohol consumption, and smoking status, which suggests that most risk factor associations are similar for in situ and invasive cancers and may influence early stages of tumorigenesis. The differential associations observed for various factors may provide important clues for understanding the etiology of certain breast cancers

    Deep generative modeling for single-cell transcriptomics.

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    Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task

    The Smoking Paradox in the Development of Psoriatic Arthritis among Psoriasis Patients – A Population-Based Study

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    Objectives: Smoking is strongly associated with an increased risk of psoriatic arthritis (PsA) in the general population, but not among psoriasis patients. We sought to clarify the possible methodologic mechanisms behind this paradox. Methods: Using 1995-2015 data from The Health Improvement Network, we performed survival analysis to examine the association between smoking and incident PsA in the general population and among psoriasis patients. We clarified the paradox using mediation analysis and conducted bias sensitivity analyses to evaluate the potential impact of index event bias and quantify its magnitude from uncontrolled/unmeasured confounders. Results: Of 6.65 million subjects without PsA at baseline, 225,213 participants had psoriasis and 7,057 developed incident PsA. Smoking was associated with an increased risk of PsA in the general population (RR, 1.27; 95% CI, 1.19-1.36), but with a decreased risk among psoriasis patients (RR 0.91; 95% CI, 0.85-0.99). Mediation analysis showed that the effect of smoking on the risk of PsA was mediated almost entirely through its effect on psoriasis. Bias sensitivity analyses indicated that even when the relation of uncontrolled confounders to either smoking or PsA was modest (both RRs = ~1.50), it could reverse the biased estimate of effect of smoking among psoriasis patients (RR=0.9). Conclusions: In this large cohort representative of the UK general population, smoking was positively associated with PsA risk in the general population, but negatively associated among psoriasis patients. Conditioning on a causal intermediate variable (psoriasis) can reverse the association between smoking and PsA, explaining the smoking paradox for the risk of PsA among psoriasis patients
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