Shell sclerochronology and stable isotopes of the bivalve Anomalocardia flexuosa (Linnaeus, 1767) from southern Brazil: : implications for environmental and archaeological studies

We conduct the first stable isotopic and sclerochronological calibration of the bivalve Anomalocardia flexuosa (Linnaeus, 1767) in relation to environmental variables in a subtropical coastal area of southern Brazil. We investigate incremental shell growth patterns and δ18O and δ13C values of modern specimens collected alive from the Laguna Lagoonal System (LLS). As shells of Anomalocardia flexuosa are also the main components of pre-Columbian archaeological shell mounds and middens distributed along the Brazilian coastline, late Holocene archaeological specimens from a local shell mound (Cabeçuda) were selected to compare their stable carbon and oxygen isotopes with those of modern specimens. Shell growth increments, δ18O and δ13C values respond to a complex of environmental conditions, involving, for example, the effects of temperature and salinity. The isotopic information extracted from archaeological specimens from Cabeçuda shell midden in the LLS indirectly indicates that environmental conditions during the late Holocene were different from present day. In particular, intra-shell δ18O and δ13C values of archaeological shells reveal a stronger marine influence at 3 ka cal BP, which is in contrast to the seasonal freshwater/seawater balance that currently prevails at the LLS

Formation of a copper(II)-tyrosyl complex at the active site of lytic polysaccharide monooxygenases following oxidation by H2O2

Hydrogen peroxide is a cosubstrate for the oxidative cleavage of saccharidic substrates by copper-containing lytic polysaccharide monooxygenases (LPMOs). The rate of reaction of LPMOs with hydrogen peroxide is high, but it is accompanied by rapid inactivation of the enzymes, presumably through protein oxidation. Herein, we use UV− vis, CD, XAS, EPR, VT/VH-MCD, and resonance Raman spectroscopies, augmented with mass spectrometry and DFT calculations, to show that the product of reaction of an AA9 LPMO with H2O2 at higher pHs is a singlet Cu(II)−tyrosyl radical species, which is inactive for the oxidation of saccharidic substrates. The Cu(II)−tyrosyl radical center entails the formation of significant Cu(II)−(●OTyr) overlap, which in turn requires that the plane of the d(x2−y2) SOMO of the Cu(II) is orientated toward the tyrosyl radical. We propose from the Marcus cross-relation that the active site tyrosine is part of a “hole-hopping” charge-transfer mechanism formed of a pathway of conserved tyrosine and tryptophan residues, which can protect the protein active site from inactivation during uncoupled turnover

An Integrated Approach to the Taxonomic Identification of Prehistoric Shell Ornaments

Shell beads appear to have been one of the earliest examples of personal adornments. Marine shells identified far from the shore evidence long-distance transport and imply networks of exchange and negotiation. However, worked beads lose taxonomic clues to identification, and this may be compounded by taphonomic alteration. Consequently, the significance of this key early artefact may be underestimated. We report the use of bulk amino acid composition of the stable intra-crystalline proteins preserved in shell biominerals and the application of pattern recognition methods to a large dataset (777 samples) to demonstrate that taxonomic identification can be achieved at genus level. Amino acid analyses are fast (<2 hours per sample) and micro-destructive (sample size <2 mg). Their integration with non-destructive techniques provides a valuable and affordable tool, which can be used by archaeologists and museum curators to gain insight into early exploitation of natural resources by humans. Here we combine amino acid analyses, macro- and microstructural observations (by light microscopy and scanning electron microscopy) and Raman spectroscopy to try to identify the raw material used for beads discovered at the Early Bronze Age site of Great Cornard (UK). Our results show that at least two shell taxa were used and we hypothesise that these were sourced locally

Genome-Wide Gene-Environment Study Identifies Glutamate Receptor Gene GRIN2A as a Parkinson's Disease Modifier Gene via Interaction with Coffee

Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P2df = 10−6, GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10−7) but not in light coffee-drinkers. The a priori Replication hypothesis that “Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0.59, PReplication = 10−3; ORPooled = 0.51, PPooled = 7×10−8. Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10−3), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10−13). Imputation revealed a block of SNPs that achieved P2df<5×10−8 in GWAIS, and OR = 0.41, P = 3×10−8 in heavy coffee-drinkers. This study is proof of concept that inclusion of environmental factors can help identify genes that are missed in GWAS. Both adenosine antagonists (caffeine-like) and glutamate antagonists (GRIN2A-related) are being tested in clinical trials for treatment of PD. GRIN2A may be a useful pharmacogenetic marker for subdividing individuals in clinical trials to determine which medications might work best for which patients

Auto-associative multivariate regression trees for cluster analysis

Multivariate Regression Trees, an intuitive and simple regression technique, intrinsically produce homogenous subsets of data. These characteristics imply that Multivariate Regression Trees have the potential to be utilised as an easily interpretable clustering method. The suitability of Multivariate Regression Trees as a clustering technique is investigated with two real datasets containing only explanatory variables. The preliminary results show that Multivariate Regression Trees as a clustering algorithm produce clusters of similar quality to the well-known K-means technique, and more recent approaches to Cluster Analysis including Mixture Models of Factor Analysers and Plaid Models. The study also evaluates the suitability of various criteria used to describe cluster solutions

Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles

Wavelet based analysis for mass spectrometry (MS) profiles of three groups of patients are analyzed for the purpose of developing a classification model. The first step in our model uses a DWT for feature extraction, using a linear combination of Symlets, Daubechies and Coiflets wavelet bases – collectively known as a super wavelet. Random Forests and Treeboost are then used to analyze the super wavelet coefficients to form the classification model. The method is illustrated using the publicly available prostate SELDI-TOF MS data from the American National Cancer Institute (NCI). The NCI data consists of 322 MS profiles with 15154 M / Z ratios, comprising of 69 malignant, 190 benign and 63 control patients, which we randomly divided into 70% training and 30% testing. From the Random Forest models, the super wavelet performed 2.7% to 5.7% better than other single wavelet types to give a 100% test set prediction rate for cancerous patients

A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies

As datasets are becoming larger, a solution to the problem of variable prediction, this problem is becoming harder. The problem is to define which subset of variables produces optimum predictions. The example studied aims to predict the chromatographic retention of 83 basic drugs on a Unisphere PBD column at pH 11.7 using 1272 molecular descriptors. The goal of this paper is to compare the relative performance of recently developed data mining methods, specifically classification and regression trees (CART), stochastic gradient boosting for tree-based models (Treeboost), and random forests (RF), with common statistical techniques in chemometrics; and genetic algorithms on multiple linear regression (GA-MLR), uninformative variable elimination partial least squares (UVE-PLS), and SIMPLS. The comparison will be performed primarily on predictive performance, but also on the variables found to be most important for the predictions. The results of this study indicated that, individually, GA-MLR (R2=0.93) outperformed all models. Further analysis found that a combination approach of GA-MLR and Treeboost (R2=0.98) further improved these results