【latex版】水贴

The incidence of thyroid cancer has risen over the past decade, along with a rise in obesity. We studied the role of anthropometric risk factors for differentiated thyroid cancer at the time of diagnosis and at age 20 years in a case-control study conducted in eastern France between 2005 and 2010. The study included 761 adults diagnosed with differentiated thyroid cancer before 35 years of age between 2002 and 2006. They were matched with 825 controls from the general population. Odds ratios were calculated using conditional logistic regression models and were reported for all participants, those with papillary cancer only, and women only. The risk of thyroid cancer was higher for participants with a high body surface area (BSA), great height, or excess weight and for women with a high body fat percentage. Conversely, no significant association was found between body mass index and the risk of thyroid cancer. In the present study, we provide further evidence of the role of BSA and excess weight in the risk of thyroid cancer. These epidemiologic observations should be confirmed by further exploration of the biological mechanisms responsible for the associations of obesity and BSA with thyroid cancer.

Few studies have assessed the associations between residential proximity to power plants and adverse birth outcomes including preterm delivery (PTD), very preterm delivery (VPTD), and term low birth weight (LBW). We geocoded 423,719 singleton Florida births born from 2004 to 2005 and all active power plants and determined residential proximity to the nearest power plant for each birth. Prenatal exposure to particulate matter less than 2.5 μm in diameter for women living near different types of power plants was also determined by using National Environmental Public Health Tracking Network data. Logistic regression models were used to test the hypothesized associations. Women who lived closer to coal and solid waste power plants were exposed to higher levels of particulate matter less than 2.5 μm in diameter compared with other types. We observed a 1.8% (95% confidence interval (CI): 1.3, 2.3) increased odds for PTD, 2.2% (95% CI: 1.0, 3.4) for VPTD, and 1.1% (95% CI: 0.2, 2.0) for term LBW for each 5 km closer to any power plant. When stratifying by different fuel type, we found that only solid waste had an association with term LBW, whereas oil, gas, and solid waste all had an association with PTD and VPTD. Results were consistent when exposure was categorized by number of power plants. Our study found evidence of increasing odds of adverse birth outcomes among infants born to pregnant women living closer to power plants. More research is warranted to better understand the causal relationship.

Perceived discrimination is positively related to cardiovascular disease (CVD) risk factors; its relationship with incident CVD is unknown. Using data from the Multi-Ethnic Study of Atherosclerosis, a population-based multiethnic cohort study of 6,508 adults aged 45–84 years who were initially free of clinical CVD, we examined lifetime discrimination (experiences of unfair treatment in 6 life domains) and everyday discrimination (frequency of day-to-day occurrences of perceived unfair treatment) in relation to incident CVD. During a median 10.1 years of follow-up (2000–2011), 604 incident events occurred. Persons reporting lifetime discrimination in ≥2 domains (versus none) had increased CVD risk, after adjustment for race/ethnicity and sociodemographic factors, behaviors, and traditional CVD risk factors (hazard ratio (HR) = 1.36, 95% confidence interval (CI): 1.09, 1.70) and after control for chronic stress and depressive symptoms (HR = 1.28, 95% CI: 1.01, 1.60). Reported discrimination in 1 domain was unrelated to CVD (HR = 1.05, 95% CI: 0.86, 1.30). There were no differences by race/ethnicity, age, or sex. In contrast, everyday discrimination interacted with sex (P = 0.03). Stratified models showed increased risk only among men (for each 1–standard deviation increase in score, adjusted HR = 1.14, 95% CI: 1.03, 1.27); controlling for chronic stress and depressive symptoms slightly reduced this association (HR = 1.11, 95% CI: 0.99, 1.25). This study suggests that perceived discrimination is adversely related to CVD risk in middle-aged and older adults.

X is an array (a matrix if the dimension of the array is 2);
MARGIN is a variable defining how the function is applied: when MARGIN=1, it applies over rows, whereas with MARGIN=2, it works over columns. Noticeably, with the construct MARGIN=c(1,2) it applies to both rows and columns;
FUN is the function we want to apply and can be any R function, including a User Defined Function (more on functions in a separate post).

Now, beginners may have difficulties in visualizing what is actually happening, so a few pictures will help figuring it out. Let’s construct a 5 x 6 matrix and imagine we want to sum the values of each column: we can write something like

李书记信了佛，释方丈仍坚信党

南京成立8000人信息员队伍 含快递员煎饼摊摊主

沪指尾盘跳水跌2.2% 创业板重挫5%

力推改写全球互联网规则

浙江温州一位佛教界的达人也被举报了