Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time-consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high-capacity analysis. We implemented whole-slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We compared the results of the developed method against independent manual counting by human observers and validated the CNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH)-immunoreactive neurons were counted using unbiased stereology. The developed CNN algorithm and fully cloud-embedded Aiforia™ platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.

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We analyze a 140-year series of smallpox deaths in the Aland Islands, Finland. Vaccination, introduced in 1805, dramatically reduced the annual number of smallpox deaths. It also influenced the age distribution of smallpox deaths, changing smallpox from a childhood disease before 1805 to one which affected both adults and children after 1805. This appears to be due to the fact that Alanders were usually vaccinated only once during childhood and often lost their immunity during adulthood. Spectral analysis of the prevaccination time series of smallpox deaths demonstrates a strong seven-year periodicity, reflecting the amount of time necessary to build up a cohort of nonimmune individuals. After the introduction of vaccination, the periodicity changes to eight years. The probability that a parish in Aland was affected by a smallpox epidemic is shown to be highly correlated with migration patterns and parish population sizes.

We have compiled data on the frequency of first-cousin marriages in Finland using royal dispensation records for the time period 1810-1872 and national population statistics for the time period 1878-1920. For the earlier period, 0.315% of Finland's marriages were contracted between first cousins (2,331 of 739,387). During the second time period, 0.174% of Finland's marriages took place between first cousins (1,325 of 761,976). These figures, which yield average kinship coefficients of 0.00020 and 0.00011, respectively, show that the level of inbreeding in Finland due to first-cousin marriage has been quite low. An analysis of individual parishes shows that first-cousin marriages are, on average, substantially less frequent than predicted by a random-mating model. In order to evaluate determinants of first-cousin marriage, several predictive variables have been examined: parish ethnic composition (proportion of Swedish and Finnish speakers), husband's occupation (graded into 6 socioeconomic levels), geographic distance between spouses' premarital residences, population density, parish endogamy, and urban vs. rural residence. Various logistic and linear regression models were analyzed in which consanguinity was the dependent variable. The best predictors of consanguinity were ethnic composition and occupation. The other variables were not in general significant predictors. These results show that many of the "mate availability" factors that would be predicted theoretically to account for consanguinity variation (population density, geographic isolation, urban vs. rural residence) do not. Instead, the best predictors of consanguinity at the first-cousin level are cultural factors such as ethnicity and occupation. Evaluation of cultural variables can provide a greatly enriched interpretation of complex biosocial phenomena such as inbreeding.