DNA-based methods for human identification principally rely upon genotyping of short tandem repeat (STR) loci. Electrophoretic-based techniques for variable-length classification of STRs are universally utilized, but are limited in that they have relatively low throughput and do not yield nucleotide sequence information. High-throughput sequencing technology may provide a more powerful instrument for human identification, but is not currently validated for forensic casework. Here, we present a systematic method to perform high-throughput genotyping analysis of the Combined DNA Index System (CODIS) STR loci using short-read (150 bp) massively parallel sequencing technology. Open source reference alignment tools were optimized to evaluate PCR-amplified STR loci using a custom designed STR genome reference. Evaluation of this approach demonstrated that the 13 CODIS STR loci and amelogenin (AMEL) locus could be accurately called from individual and mixture samples. Sensitivity analysis showed that as few as 18,500 reads, aligned to an in silico referenced genome, were required to genotype an individual (>99% confidence) for the CODIS loci. The power of this technology was further demonstrated by identification of variant alleles containing single nucleotide polymorphisms (SNPs) and the development of quantitative measurements (reads) for resolving mixed samples.

Summary Considerable amounts of data are being generated during the development and operation of unconventional reservoirs. Statistical methods that can provide data-driven insights into production performance are gaining in popularity. Unfortunately, the application of advanced statistical algorithms remains somewhat of a mystery to petroleum engineers and geoscientists. The objective of this paper is to provide some clarity to this issue, focusing on how to build robust predictive models and how to develop decision rules that help identify factors separating good wells from poor performers. The data for this study come from wells completed in the Wolfcamp Shale Formation in the Permian Basin. Data categories used in the study included well location and assorted metrics capturing various aspects of well architecture, well completion, stimulation, and production. Predictive models for the production metric of interest are built using simple regression and other advanced methods such as random forests (RFs), support-vector regression (SVR), gradient-boosting machine (GBM), and multidimensional Kriging. The data-fitting process involves splitting the data into a training set and a test set, building a regression model on the training set and validating it with the test set. Repeated application of a “cross-validation” procedure yields valuable information regarding the robustness of each regression-modeling approach. Furthermore, decision rules that can identify extreme behavior in production wells (i.e., top x% of the wells vs. bottom x%, as ranked by the production metric) are generated using the classification and regression-tree algorithm. The resulting decision tree (DT) provides useful insights regarding what variables (or combinations of variables) can drive production performance into such extreme categories. The main contributions of this paper are to provide guidelines on how to build robust predictive models, and to demonstrate the utility of DTs for identifying factors responsible for good vs. poor wells.

High circular tombs (HCTs) in southern Arabia provide valuable information for anthropologists who seek fundamental understanding of the transition of ancient peoples from a nomadic pastoral lifestyle, to agro-pastoralism, and eventually to the formation of ancient states. In particular, knowing the geographical distribution of HCTs across the region informs theories on patterns of territoriality and environmental and social factors that are implicated in the emergence of ancient civilizations.The small size of the HCTs, vast search regions, and rugged terrain make mapping them in the field difficult and costly. In this article, a detection algorithm is described and quantitatively evaluated and establishes the feasibility of automatically detecting these tombs in satellite imagery. By narrowing the search to a smaller set of candidate locations, wide area discovery and mapping can be performed much more effectively.

No abstract

Considerable amounts of data are being generated during the development and operation of unconventional reservoirs. Statistical methods that can provide data-driven insights into production performance are gaining in popularity. Unfortunately, the application of advanced statistical algorithms remains somewhat of a mystery to petroleum engineers and geoscientists. The objective of this paper is to provide some clarity to this issue, focusing on: (a) how to build robust predictive models, and (b) how to develop decision rules that help identify factors separating good wells from poor performers. The data for this study come from wells completed in the Wolfcamp shale formation in the Permian Basin. Data categories used in the study included well location and assorted metrics capturing various aspects of well architecture, well completion, stimulation, and production.Predictive models for the production metric of interest are built using simple regression and other advanced methods such Random Forests, Support Vector Regression, Gradient Boosting Machine and Multidimensional Kriging. The data fitting process involves splitting the data into a training set and a test set, building a regression model on the training set and validating it with the test set. Repeated application of a "cross-validation" procedure yields valuable information regarding the robustness of each regression modeling approach. Furthermore, decision rules that can identify extreme behavior in production wells (i.e., top x% of the wells versus bottom x% as ranked by the production metric) are generated using the classification and regression tree algorithm. The resulting decision tree provides useful insights as to what variables (or combinations of variables) can drive production performance into such extreme categories.The main contributions of this paper are to provide guidelines on how to build robust predictive models, and to demonstrate the utility of decision trees for identifying factors responsible for good versus poor wells.