Detection of long‐term, linear trends is affected by a number of factors, including the size of trend to be detected, the time span of available data, and the magnitude of variability and autocorrelation of the noise in the data. The number of years of data necessary to detect a trend is strongly dependent on, and increases with, the magnitude of variance (σ_N²) and autocorrelation coefficient (ϕ) of the noise. For a typical range of values of σ_N² and ϕ the number of years of data needed to detect a trend of 5%/decade can vary from ∼10 to >20 years, implying that in choosing sites to detect trends some locations are likely to be more efficient and cost‐effective than others. Additionally, some environmental variables allow for an earlier detection of trends than other variables because of their low variability and autocorrelation. The detection of trends can be confounded when sudden changes occur in the data, such as when an instrument is changed or a volcano erupts. Sudden level shifts in data sets, whether due to artificial sources, such as changes in instrumentation or site location, or natural sources, such as volcanic eruptions or local changes to the environment, can strongly impact the number of years necessary to detect a given trend, increasing the number of years by as much as 50% or more. This paper provides formulae for estimating the number of years necessary to detect trends, along with the estimates of the impact of interventions on trend detection. The uncertainty associated with these estimates is also explored. The results presented are relevant for a variety of practical decisions in managing a monitoring station, such as whether to move an instrument, change monitoring protocols in the middle of a long‐term monitoring program, or try to reduce uncertainty in the measurements by improved calibration techniques. The results are also useful for establishing reasonable expectations for trend detection and can be helpful in selecting sites and environmental variables for the detection of trends. An important implication of these results is that it will take several decades of high‐quality data to detect the trends likely to occur in nature.

Objective In North America, tuberculosis and nontuberculous mycobacterial (NTM) disease rates associated with antitumour necrosis factor α (anti-TNFα) therapy are unknown. Methods At Kaiser Permanente Northern California, the authors searched automated pharmacy records to identify inflammatory disease patients who received anti-TNF therapy during 2000–2008 and used validated electronic search algorithms to identify NTM and tuberculosis cases occurring during anti-TNF drug exposure. Results Of 8418 anti-TNF users identified, 60% had rheumatoid arthritis (RA). Among anti-TNF users, 18 developed NTM and 16 tuberculosis after drug start. Anti-TNF associated rates of NTM and tuberculosis were 74 (95% CI: 37 to 111) and 49 (95% CI: 18 to 79) per 100 000 person-years, respectively. Rates (per 100, 000 person-years) for NTM and tuberculosis respectively for etanercept were 35 (95% CI: 1 to 69) and 17 (95% CI: 0 to 41); infliximab, 116 (95% CI: 30 to 203) and 83 (95% CI: 10 to 156); and adalimumab, 122 (95% CI: 3 to 241) and 91 (95% CI: 19 to 267). Background rates for NTM and tuberculosis in unexposed RA-patients were 19.2 (14.2 to 25.0) and 8.7 (5.3 to 13.2), and in the general population were 4.1 (95% CI 3.9 to 4.4) and 2.8 (95% CI 2.6 to 3.0) per 100, 000 person-years. Among anti-TNF users, compared with uninfected individuals, NTM case-patients were older (median age 68 vs 50 years, p<0.01) and more likely to have RA (100% vs 60%, p<0.01); whereas, tuberculosis case-patients were more likely to have diabetes (37% vs 16%, p=0.02) or chronic renal disease (25% vs 6%, p=0.02). Conclusions Among anti-TNF users in USA, mycobacterial disease rates are elevated, and NTM is associated with RA.

In this Resource study, the authors used high-resolution mass spectrometry to elucidate the precise proteomic complement of the inner ear hair bundle. Many of the proteins that are enriched in the hair bundles are encoded by known deafness-associated genes. Hair bundles of the inner ear have a specialized structure and protein composition that underlies their sensitivity to mechanical stimulation. Using mass spectrometry, we identified and quantified >1,100 proteins, present from a few to 400,000 copies per stereocilium, from purified chick bundles; 336 of these were significantly enriched in bundles. Bundle proteins that we detected have been shown to regulate cytoskeleton structure and dynamics, energy metabolism, phospholipid synthesis and cell signaling. Three-dimensional imaging using electron tomography allowed us to count the number of actin-actin cross-linkers and actin-membrane connectors; these values compared well to those obtained from mass spectrometry. Network analysis revealed several hub proteins, including RDX (radixin) and SLC9A3R2 (NHERF2), which interact with many bundle proteins and may perform functions essential for bundle structure and function. The quantitative mass spectrometry of bundle proteins reported here establishes a framework for future characterization of dynamic processes that shape bundle structure and function.

We present the results of two independent analyses of trends in the vertical distribution of ozone. For most of the ozonesonde stations we use data that were recently reevaluated and reprocessed to improve their quality and internal consistency. The two analyses give similar results for trends in ozone. We attribute differences in results primarily to differences in data selection criteria, rather than in statistical trend models. We find significant decreases in stratospheric ozone at all stations in middle and high latitudes of the Northern Hemisphere from 1970 to 1996, with the largest decreases located between 12 and 21 km, and trends of −3 to −10%/decade near 17 km. The decreases are largest at the Canadian and the most northerly Japanese station and are smallest at the European stations and at Wallops Island. The mean midlatitude trend is largest, −7%/decade, from 12 to 18 km for 1970–1996, and the decrease in ozone is significant from 10.5 to 25 km. For 1980–1996, the decrease is more negative by 1–2%/decade, with a maximum trend of −9%/decade in the lowermost stratosphere. The trends vary seasonally from about 10 to 17 km, with largest ozone decreases in winter and spring. Trends in tropospheric ozone are highly variable and depend on the region. There are decreases or zero trends at the Canadian stations for 1970–1996, and decreases of −2 to −8%/decade for the mid‐troposphere for 1980–1996; the three European stations show increases for 1970–1996, but trends are close to zero for two stations for 1980–1996 and positive for one; there are increases in ozone for the three Japanese stations for 1970–1996, but trends are either positive or zero for 1980–1996. The United States stations show zero or slightly negative trends in tropospheric ozone after 1980. It is not possible to define reliably a mean tropospheric ozone trend for northern midlatitudes, given the small number of stations and the large variability in trends. The integrated column trends derived from the sonde data are consistent with trends derived from both surface‐based and satellite measurements of the ozone column.