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"Lack of population structure in an important fishery species of mud sh" by Renae L. Kirby, Catheline Y.M. Froehlich et al.

From a conservation standpoint, species that are managed without consideration of their population sizes and connectivity have the potential to be over-exploited and/or incur population decline. The burrowing shrimp, Trypaea australiensis, is an important ecosystem engineer and fishery resource caught in large numbers for which population information is unknown for properly managing the species. Here, we determined the level of population structure of T. australiensis across three locations along the East Coast of New South Wales, Australia, using genome-wide single nucleotide polymorphisms (SNPs) obtained through double digest Restriction-site Associated DNA-sequencing (ddRAD-seq). Analysis of population structure, including pairwise Fst (−0.003 to −0.001), STRUCTURE (K = 2) and Discriminant Analysis of Principal Components (DAPC) showed no evidence of structure among locations. Our findings provide crucial preliminary population genetic data for a key cryptic species, that also s ....

East Coast , New South Wales , Restriction Site Associated , Discriminant Analysis , Principal Components , Cryptic Species ,

Strong isolation by distance and evidence of population microstructure reflect ongoing Plasmodium falciparum transmission in Zanzibar

Strong isolation by distance and evidence of population microstructure reflect ongoing Plasmodium falciparum transmission in Zanzibar
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United Kingdom , Bagamoyo District , Democratic Republic Of The Congo , Dar Es Salaam , Zanzibar Central South , Zanzibar Urban West , Qiagen Miniprep , Le Menach , Pemba Bisanzio , Mipwrangler Aydemir , Pemba Holzschuh , Abebe Fola , Muhimbili University Of Health , National Institutes Of Health , European Union , University Of North Carolina At Chapel Hill , Commonwealth Development Office , Centre For Global Infectious Disease Analysis , Pemba Panel , Community Jameel , United Kingdom Medical Research Council , No World Health Organization , Yang Fund , World Health Organization , Tv World Health Organization , Unguja Panel ,

"Spatial linear discriminant analysis approaches for remote-sensing cla" by Thomas Suesse, Alexander Brenning et al.

Linear Discriminant Analysis (LDA) is a popular and simple classification tool that often outperforms more sophisticated modern machine learning techniques in remote sensing. We introduce a novel LDA method that uses spatial autocorrelation of all pixels of an object to be classified but also of other objects of the training set that are spatially close to improve classification performance. To simplify spatial modelling and model fitting, the methodology is applied to the transformed feature vectors. We term this method conditional spatial LDA. Much alike universal Kriging in geostatistical interpolation, the combined use of feature data and conditioning on labelled training data in conditional spatial LDA was best able to exploit the available geospatial data. The method is illustrated on a crop classification case study from the Aconcagua agricultural region in central Chile. ....

Discriminant Analysis , Conditional Classification , Ependent Data , Linear Discriminant Analysis , Semantic Classification , Spatial Autocorrelation ,

Daily Deal: The Essential MATLAB & Simulink Training Course

As the name suggests, classification algorithms are what allow computers to well. classify new observations, like how your inbox decides which incoming emails are spam or how Siri recognizes your voice. The Essential MATLAB & Simulink Training Course will show you how to implement classification algorithms using MATLAB, one of the most powerful tools inside… ....

Daily Deal , Training Course , K Nearest Neighbor , Discriminant Analysis ,

Daily Deal: The Essential MATLAB Training Course

As the name suggests, classification algorithms are what allow computers to well. classify new observations, like how your inbox decides which incoming emails are spam or how Siri recognizes your voice. The Essential MATLAB Training Bundle will show you how to implement classification algorithms using MATLAB, one of the most powerful tools inside a data… ....

Daily Deal , K Nearest Neighbor , Discriminant Analysis ,