Project description:Parchment represents an invaluable cultural reservoir. Retrieving an additional layer of information from these abundant, dated livestock-skins via the use of ancient DNA (aDNA) sequencing has been mooted by a number of researchers. However, prior PCR-based work has indicated that this may be challenged by cross-individual and cross-species contamination, perhaps from the bulk parchment preparation process. Here we apply next generation sequencing to two parchments of seventeenth and eighteenth century northern English provenance. Following alignment to the published sheep, goat, cow and human genomes, it is clear that the only genome displaying substantial unique homology is sheep and this species identification is confirmed by collagen peptide mass spectrometry. Only 4% of sequence reads align preferentially to a different species indicating low contamination across species. Moreover, mitochondrial DNA sequences suggest an upper bound of contamination at 5%. Over 45% of reads aligned to the sheep genome, and even this limited sequencing exercise yield 9 and 7% of each sampled sheep genome post filtering, allowing the mapping of genetic affinity to modern British sheep breeds. We conclude that parchment represents an excellent substrate for genomic analyses of historical livestock.
Project description:Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, fewer metaparameters, and provides interpretable results. Here, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time, heralding the next generation of reservoir computing.
Project description:The purpose of this study is to determine the proportion of patients diagnosed with Lynch syndrome in colorectal cancer patients with the loss of staining by immunohistochemistry (IHC) of any of the mismatch repair (MMR) proteins. Besides, this study aims to test the specificity and the sensitivity of detecting microsatellite instability (MSI) by next-generation sequencing, and to find out the consistency between IHC and MSI in colorectal cancer patients in China. In addition, researchers want to analyze the clinical characteristics and germline mutation of Lynch syndrome in Chinese population.
