National Bureau of Standards, Gaithers- burg, MD): I would like to ask Heinrich Rohrer if he could- he had just very briefly showed us something that looked like magnetic information in one of his pictures-give us a few more seconds of discussion of what that was and how it works.DR. ROHRER: That was a tunneling experiment, not an atomic-force experiment, and the tip, as I said, was a film of cobalt/chromium.This experiment is a combination of force mapping and tunneling; you do it at the same time.The magnetic forces act on the film from which you tunnel.When the tip comes close to a domain wall or something like that, it is deflected in one way or another.We do not know the structure of the tip in this case, but we simply correct the deflections by moving the tip closer or further away until we have the originally set tunnel current.This is the combination of, let's say, force mapping with the tunneling microscope.The tip itself plays the role of the soft level in the atomic force microscope.If you are just interested in rough features, I think that this is a very convenient way of magnetic imaging.It is a very simple experiment with a very simple instrument.CYNTHIA FRIEND (Harvard University, Cambridge, MA): This I would like to direct to Dr. Rohrer and Dr. Hansma.You discussed diffusion of oxygen atoms on nickel, but I wonder if you could comment more generally if you see diffusion induced on surfaces of atoms and possibly small molecules, and if you can bracket a range in which you think you could possibly image molecules like smaller adsorbates.DR. ROHRER: You mean image when they diffuse under the tip or . . .?DR.FRIEND: In other words, do they diffuse?Let's say you wanted to look at CO on some transition metal, for example.Can you image it without inducing diffusion, and are there
Background —Moderate alcohol consumption is associated with reduced risk for coronary heart disease (CHD) in generally healthy populations. We assessed prospectively the association between moderate alcohol intake and CHD risk in women with type 2 diabetes mellitus, a group at high risk for cardiovascular disease. Methods and Results —We studied women in the Nurses’ Health Study who reported a diagnosis of diabetes mellitus at ≥30 years of age. During 39 092 person-years of follow-up from 1980 to 1994, there were 295 CHD events documented among this population, including 194 cases of nonfatal myocardial infarction and 101 cases of fatal CHD. Odds ratios derived from logistic regression were used to estimate relative risks (RRs) for CHD as a function of usual alcohol intake, with adjustment for potential confounders. Compared with diabetic women reporting no alcohol intake, the age-adjusted RR for nonfatal or fatal CHD among diabetic women reporting usual intake of 0.1 to 4.9 g (<0.5 drinks) of alcohol daily was 0.74 (95% CI 0.56 to 0.98), and among those reporting usual intake ≥5 g/d, it was 0.48 (95% CI 0.32 to 0.72) ( P for trend <0.0001). Inverse associations between alcohol intake and CHD risk remained significant in multivariate analysis adjusting for several other coronary risk factors (0.1 to 4.9 g/d: RR 0.72 [95% CI 0.54 to 0.96]; ≥5 g/d: RR 0.45 [0.29 to 0.68]). Conclusions —Although potential risks of alcohol consumption must be considered, these data suggest that moderate alcohol consumption is associated with reduced CHD risk in women with diabetes and should not be routinely discouraged.
Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.
The target population of the present study consisted of a 1-year (July 1985-June 1986) birth cohort from northern Finland. The prevalence of even slight hearing impairments (any threshold from 0.5 to 4 kHz > or = 25 db) at the age of 7 years was investigated among those 8,713 children still living in the area. The subjects for clinical audiometry were obtained in two ways. First, the standard clinical practice brought about 541 children, either with non-confirming results from their child welfare clinic screenings, suspected by their parents or already diagnosed as hearing impaired at a hospital. Secondly, in addition to this group, a random sample of 1,009 children, out of the 8,172 children not suspected, were also invited for audiometry. Of the clinical material, 101 children out of the 438 investigated were found to have impaired hearing according to the above criteria, and another 27 children out of the 789 investigated were obtained from the random sample. The estimated over-all prevalence of hearing impairments, even with the slight ones included, turned out to be 3.9% (95% confidence interval, CI, 2.7-5.7). Only 32% of the hearing impairments could be obtained according to the standard clinical policy! In conclusion, one cannot rely on clinical data when calculating prevalence figures for mild hearing impairments.
