41SM195A, The Browning Site

A surface collection of early 19 \u27 century historic sherds led to archaeological investigations in 2002 and 2003 at the Browning site (41SM195A) in eastern Smith County, Texas. My interest was whetted by mention in the original land abstract that the property had once been deeded to the Cherokee Indians. In all, a total of 6.5 cubic meters of archaeological deposits was excavated at the site, including 22 shovel tests and 10 1 x 1 m test units, and fine-screen and flotation samples were taken from a prehistoric midden deposit identified during the work. As a result, 1075 prehistoric and historic artifacts were recovered, along with new information about Woodland period archaeology in this part of East Texas. The initial shovel tests found, in addition to the historic component, a buried midden with evidence of Woodland period occupation. Based on the excavations, the midden covered approximately 500 square meters. The 19th century historic artifacts were found in the upper sediment zone, a brown sandy loam that was mostly gravel- free) covering the midden. The buried midden was a dark yellowish-brown gravelly loam that contained prehistoric pottery, animal bone, charred wood and nutshells, lithic materials, including lithic debris, flake tools, arrow and dart points, and ground stone tools. A calibrated radiocarbon date of A.D. 625 to 880, with a calibrated intercept of A.D. 685, was obtained on charred nutshell from 40-50 em bs in the midden zone. A series of Oxidizable Carbon Ratio (OCR) dates from the midden indicate that the midden began to from about A.D. 147, with dates of A.D. 357-815 from the main part of the midden, indicating when the Browning site was most intensively occupied in prehistoric times

Archeological Survey and Testing of Selected Prehistoric Sites along FM 481, Zavala County, Texas

Between April 1981 and December 1982, Texas Department of Transportation (TxDOT) personnel conducted archeological fieldwork along an approximately 13-km segment of FM 481 in northwest Zavala County. The work was part of an evaluation of the impacts of road improvements to a series of sites along the right-of-way. All of the sites but one (41ZV202) were found not to be eligible for listing on the National Register of Historic Places and not to warrant designations as State Archeological Landmarks. Additional work, not reported here, was later conducted at 41ZV202. As part of Work Authorization #57015PD004, the Environmental Affairs Division of TxDOT contracted with the Center for Archaeological Research (CAR) of The University of Texas at San Antonio to report on the fieldwork carried out at the sites during the early 1980s, identify data types warranting additional research, and conduct the appropriate analyses. The current document provides descriptions of the work undertaken along FM 481, assesses the analytical utility of the data types recovered, and reports the results of limited new research of selected data types. Note that all documentation of the project, including notes, photographs, and a sample of recovered artifacts are curated at the Center for Archaeological Research. The sample includes all projectile points, as well as other chipped and ground stone tools, and the debitage recovered for a 10% sample of proveniences

Application of collapsing methods for continuous traits to the Genetic Analysis Workshop 17 exome sequence data

Genetic Analysis Workshop 17 used real sequence data from the 1000 Genomes Project and simulated phenotypes influenced by a large number of rare variants. Our aim is to evaluate the performance of various collapsing methods that were developed for analysis of multiple rare variants. We apply collapsing methods to continuous phenotypes Q1 and Q2 for all 200 replicates of the unrelated individuals data. Within each gene, we collapse (1) all SNPs, (2) all SNPs with minor allele frequency (MAF) < 0.05, and (3) nonsynonymous SNPs with MAF < 0.05. We consider two tests when collapsing variants: using the proportion of variants and using the presence/absence of any variant. We also compare our results to a single-marker analysis using PLINK. For phenotype Q1, the proportion test for collapsing rare nonsynonymous SNPs often performed the best. Two genes (FLT1 and KDR) had statistically significant results. A single-marker analysis using PLINK also provided statistically significant results for some SNPs within these two genes. For phenotype Q2, collapsing rare nonsynonymous SNPs performed the best, with almost no difference between proportion and presence tests. However, neither collapsing methods nor a single-marker analysis provided statistically significant results at the true genes for Q2. We also found that a large number of noncausal genes had high correlations with causal genes for Q1 and Q2, which may account for inflated false positives

Enrichment analysis of genetic association in genes and pathways by aggregating signals from both rare and common variants

New high-throughput sequencing technologies have brought forth opportunities for unbiased analysis of thousands of rare genomic variants in genome-wide association studies of complex diseases. Because it is hard to detect single rare variants with appreciable effect sizes at the population level, existing methods mostly aggregate effects of multiple markers by collapsing the rare variants in genes (or genomic regions). We hypothesize that a higher level of aggregation can further improve association signal strength. Using the Genetic Analysis Workshop 17 simulated data, we test a two-step strategy that first applies a collapsing method in a gene-level analysis and then aggregates the gene-level test results by performing an enrichment analysis in gene sets. We find that the gene set approach which combines signals across multiple genes outperforms testing individual genes separately and that the power of the gene set enrichment test is further improved by proper adjustment of statistics to account for gene-wise differences

Enhancing the discovery of rare disease variants through hierarchical modeling

Advances in next-generation sequencing technology are enabling researchers to capture a comprehensive picture of genomic variation across large numbers of individuals with unprecedented levels of efficiency. The main analytic challenge in disease mapping is how to mine the data for rare causal variants among a sea of neutral variation. To achieve this goal, investigators have proposed a number of methods that exploit biological knowledge. In this paper, I propose applying a Bayesian stochastic search variable selection algorithm in this context. My multivariate method is inspired by the combined multivariate and collapsing method. In this proposed method, however, I allow an arbitrary number of different sources of biological knowledge to inform the model as prior distributions in a two-level hierarchical model. This allows rare variants with similar prior distributions to share evidence of association. Using the 1000 Genomes Project single-nucleotide polymorphism data provided by Genetic Analysis Workshop 17, I show that through biologically informative prior distributions, some power can be gained over noninformative prior distributions

A pathway-based association analysis model using common and rare variants

How various genetic effects in combination affect susceptibility to certain disease states continues to be a major area of methodological research. Various rare variant models have been proposed, in response to a common failure to either identify or validate biologically driven causal genetic variants in genome-wide association studies. Adopting the idea that multiple rare variants may effectively produce a combined effect equal to a single common variant effect through common linkage with this variant, we construct a pathway-based genetic association analysis model using both common and rare variants. This genetic model is applied to the disease status of unrelated individuals in replication 1 from Genetic Analysis Workshop 17. In this simulated example, we were able to identify several pathways that were potentially associated with the disease status and found that common variants showed stronger genetic effect than rare variants

Life on Jackson Creek, Smith County, Texas: Archeological Investigations of a 14th Century Caddo Domicile at the Leaning Rock Site (41SM325)

The 14th century Caddo Leaning Rock site was initially discovered in the Fall of 2004. It was located during reconnaissance to search out a location for the survey portion of the Texas Archeologica! Society\u27s Academy IO I held in Tyler in February 2005. This was not a formal survey with transect lines. nor one using regularly spaced shovel tests. but was rather more of a windshield \u27 type survey, consisting of driving across pasture lands looking at gopher mounds and checking fore, evidence of archeological deposits on likely looking landforms. !n this area. landform and soil type seem to be the major determining factors in locating Caddo sites. The sandy soils in the scattered gopher mounds appeared almost white. especially in droughty conditions that prevailed at the time. causing an area with darker mounds of soil to catch my attention. Pocket gophers (G. breviceps) can play havoc with buried archeological deposits but can also be useful in bringing buried soils along with archeological materials to the surface from their underground tunnel system. While this dark area could have been the result of past historic land clearing and burning activities. a closer inspection revealed burned bone. mussel she!L and Caddo sherds mixed in the dark brown soils in the scattered gopher mounds. The next step was to record the site with the State of Texas, obtaining the trinomial 41SM325. It is common practice to also gin: sites informal names and after recording several hundred sites, selecting a name becomes a challenge. One large sandstone slab, pan of the R-horizon that is exposed around the margins of Leaning Rock. was unearthed during prior landclearing activities and pushed up against a lonely pine tree on the northern margins of the site: consequently the nom de plume Leaning Rock

Radon backgrounds in the DEAP-1 liquid-argon-based Dark Matter detector

The DEAP-1 \SI{7}{kg} single phase liquid argon scintillation detector was operated underground at SNOLAB in order to test the techniques and measure the backgrounds inherent to single phase detection, in support of the \mbox{DEAP-3600} Dark Matter detector. Backgrounds in DEAP are controlled through material selection, construction techniques, pulse shape discrimination and event reconstruction. This report details the analysis of background events observed in three iterations of the DEAP-1 detector, and the measures taken to reduce them. The $^{222}$Rn decay rate in the liquid argon was measured to be between 16 and \SI{26}{\micro\becquerel\per\kilogram}. We found that the background spectrum near the region of interest for Dark Matter detection in the DEAP-1 detector can be described considering events from three sources: radon daughters decaying on the surface of the active volume, the expected rate of electromagnetic events misidentified as nuclear recoils due to inefficiencies in the pulse shape discrimination, and leakage of events from outside the fiducial volume due to imperfect position reconstruction. These backgrounds statistically account for all observed events, and they will be strongly reduced in the DEAP-3600 detector due to its higher light yield and simpler geometry

Improving Photoelectron Counting and Particle Identification in Scintillation Detectors with Bayesian Techniques

Many current and future dark matter and neutrino detectors are designed to measure scintillation light with a large array of photomultiplier tubes (PMTs). The energy resolution and particle identification capabilities of these detectors depend in part on the ability to accurately identify individual photoelectrons in PMT waveforms despite large variability in pulse amplitudes and pulse pileup. We describe a Bayesian technique that can identify the times of individual photoelectrons in a sampled PMT waveform without deconvolution, even when pileup is present. To demonstrate the technique, we apply it to the general problem of particle identification in single-phase liquid argon dark matter detectors. Using the output of the Bayesian photoelectron counting algorithm described in this paper, we construct several test statistics for rejection of backgrounds for dark matter searches in argon. Compared to simpler methods based on either observed charge or peak finding, the photoelectron counting technique improves both energy resolution and particle identification of low energy events in calibration data from the DEAP-1 detector and simulation of the larger MiniCLEAN dark matter detector.Comment: 16 pages, 16 figure