Four human cases of localized cutaneous leishmaniasis caused by Leishmania naiffi are reported. Two of the cases were infected in French Guiana, one in French Guiana or Martinique, and the other in Ecuador or Peru. The geographical distribution of L. naiffi is clearly larger than that initially reported. Three zymodemes were represented by the four isolates, confirming that there is intraspecific polymorphism in L. naiffi.

The amplification of target DNA by highly specific probes using the polymerase chain reaction (PCR) provides a highly sensitive and specific method for the detection of malaria infection. The use the of PCR in settings with varying endemicity within one survey area has not been investigated intensively. Therefore, a cross-sectional study was conducted in the districts of Kabarole and Bundibugyo in western Uganda using material from three villages with different epidemiologic situations regarding malaria and DNA primers for a PCR that had shown satisfactory sensitivity and specificity in previous trials. The sensitivity of the PCR varied significantly (P < 0.001) in the three survey villages (between 63.2% and 83.9% for the primer pair K1-14-1 and between 37.9% and 69.9% for the primer pair MSP-1) and was highly linked to geographic differences and social exchanges of the inhabitants with other areas of the district. According to the results of this investigation, it is advisable not to use a single primer pair in epidemiologic field studies for the detection of falciparum malaria. The use of combined primer pairs and the frequent confirmation of the results by microscopy are recommended.

SummaryIt has been proposed that polymorphisms of the Merozoite Surface Protein 1 and 2 (MSP1 and MSP2) and the Glutamate Rich Protein (GLURP) genes can be considered as genetic markers for the genotyping of field populations of Plasmodium falciparum. During a field study on in vivo drug resistance against chloroquine, sulphadoxine/pyrimethamine (S/P) and cotrimoxazole in West Uganda, sensitive and resistant isolates were collected from patients by fingerprick for genotyping. 59 (72.8%) of the 81 P. falciparum samples isolated at day 0 showed multiclonal infection with 2-7 clones. Among the isolates we investigated, presence of the allelic family MAD20 of MSP1 at day 0 was significantly (P ϭ 0.0041) associated with decreased resistance to antimalarials. Use of this method in a field study on in vivo drug resistance demonstrates another potential application of genotyping as a tool for epidemiological investigations.

Dialectometric intensity estimation as introduced in Rumpf et al. ) and Pickl and Rumpf (2011) is a method for the unsupervised generation of maps visualizing geolinguistic data on the level of linguistic variables. It also extracts spatial information for subsequent statistical analysis. However, as intensity estimation involves geographically conditioned smoothing, this method can lead to undesirable results. Geolinguistically relevant structures such as rivers, political borders or enclaves, for instance, are not taken into account and thus their manifestations in the distributions of linguistic variants are blurred. A possible solution to this problem, as suggested and put to the test in this paper, is to use linguistic distances rather than geographical (Euclidean) distances in the estimation. This methodological adjustment leads to maps which render geolinguistic distributions more faithfully, especially in areas that are deemed critical for the interpretation of the resulting maps and for subsequent statistical analyses of the results.

When geographic language variation is examined on a broad level, it is common practice to aggregate large quantities of data to identify dominating structures. This practice, however, may result in a neglection of valuable geolinguistic data. In this talk, new perspectives on non-aggregative methods of dealing with large corpora of dialect maps are presented that aim at preserving the distinct features of individual maps. In order to achieve this, methods derived from spatial statistics, stochastic image analysis and pattern recognition are applied and adapted to the analysis of dialect data. Firstly, we introduce a new method of clustering individual maps. We employ fuzzy clustering algorithms which provide a new angle on clustering techniques in linguistics by allowing the researcher to detect and measure gradual similarities between individual maps rather than form "hard" clusters as in conventional hierarchical clustering. This is achieved by automated comparison of statistical properties of the maps. The method can be used for grouping maps based on their spatial similarities while at the same time allowing for the investigation of semantic/phonetic/ontic etc. relationships between such spatially related maps. Secondly, we employ means of stochastic image analysis and pattern recognition in order to automatically and objectively detect geometric structures in dialect map corpora, a usually tedious and error-prone task when done manually. A quantitative method for the detection of ellipsoid patterns and their centres in dialect maps is outlined, along with the identification of parameters necessary for adapting the technique to any dataset. We argue that these methods offer promising enhancements for quantitative research techniques on geographical language variation.