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Early and differential diagnosis of Alzheimer's disease (AD) is a problem that puzzled many doctors. Reliable markers in easy-assembling samples are of considerable clinical diagnostic value. In this work, laser Raman spectroscopy (LRS) was developed a new method that potentially allows early and differential diagnosis of AD from the platelet sample. Raman spectra of platelets isolated from different ages of AD transgenic mice and non-transgenic controls were collected and analyzed. Multilayer perceptron networks (MLP) classification method was used to classify spectra and establish the diagnostic models. For differential diagnosis, spectra of platelets isolated from AD, Parkinson's disease (PD) and vascular dementia (VD) mice were also discriminated. Two notable spectral differences at 740 and 1654 cm −1 were revealed in the mean spectrum of platelets isolated from AD transgenic mice and the controls. MLP displayed a powerful ability in the classifying of early, advanced AD and the control group, and in differential diagnosis of PD and advanced AD, as well as VD and advanced AD. The results suggest that platelet detecting by LRS coupled with MLP analysis appears to be an easy and accurate method for early and differential diagnosis of AD. This technique could be rapidly promoted from laboratory to the hospital.

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Geographically weighted regression (GWR) is an important local method to explore spatial non-stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7-year period in northern China, a typical midlatitude, high-risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non-spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7-year average case is R 2 5 0.243 and AICc 5 837.99, while significant improvement has been made by the GWR calibration with R 2 5 0.800 and AICc 5 618.54. K E Y W O R D Sgeographically weighted regression, local determinants examination, malaria incidence, remote sensing monitoring data, spatial analysis models 1 | I NTR OD U CTI ON