Expanded geographic distribution and dietary strategies of the earliest Oldowan hominins and Paranthropus.

The oldest Oldowan tool sites, from around 2.6 million years ago, have previously been confined to Ethiopia's Afar Triangle. We describe sites at Nyayanga, Kenya, dated to 3.032 to 2.581 million years ago and expand this distribution by over 1300 kilometers. Furthermore, we found two hippopotamid butchery sites associated with mosaic vegetation and a C4 grazer-dominated fauna. Tool flaking proficiency was comparable with that of younger Oldowan assemblages, but pounding activities were more common. Tool use-wear and bone damage indicate plant and animal tissue processing. Paranthropus sp. teeth, the first from southwestern Kenya, possessed carbon isotopic values indicative of a diet rich in C4 foods. We argue that the earliest Oldowan was more widespread than previously known, used to process diverse foods including megafauna, and associated with Paranthropus from its onset.

Catalyst-free decarboxylation of 4-hydroxycinnamic acids: efficient synthesis of 4-vinylphenols.

We report herein an efficient protocol for the synthesis of 4-vinylphenols by a catalyst-free decarboxylation of trans-4-hydroxycinnamic acids. A variety of 4-vinylphenols has been synthesized in moderate to excellent yields. This protocol also features no polymerization.

Analysis of array CGH data for cancer studies using fused quantile regression.

MOTIVATION: The identification of DNA copy number changes provides insights that may advance our understanding of initiation and progression of cancer. Array-based comparative genomic hybridization (array-CGH) has emerged as a technique allowing high-throughput genome-wide scanning for chromosomal aberrations. A number of statistical methods have been proposed for the analysis of array-CGH data. In this article, we consider a fused quantile regression model based on three motivations: (1) quantile regression may provide a more comprehensive picture for the ratio profile of copy numbers than the standard mean regression approach; (2) for simplicity, most available methods assume uniform spacing between neighboring clones, while incorporating the information of physical locations of clones may be helpful and (3) most current methods have a set of tuning parameters that must be carefully tuned, which introduces complexity to the implementation. RESULTS: We formulate the detection of regions of gains and losses in a fused regularized quantile regression framework, incorporating physical locations of clones. We derive an efficient algorithm that computes the entire solution path for the resulting optimization problem, and we propose a simple estimate for the complexity of the fitted model, which leads to convenient selection of the tuning parameter. Three published array-CGH datasets are used to demonstrate our approach. AVAILABILITY: R code are available at http://www.stat.lsa.umich.edu/~jizhu/code/cgh/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multivariate correlation estimator for inferring functional relationships from replicated genome-wide data.

UNLABELLED: Estimating pairwise correlation from replicated genome-scale (a.k.a. OMICS) data is fundamental to cluster functionally relevant biomolecules to a cellular pathway. The popular Pearson correlation coefficient estimates bivariate correlation by averaging over replicates. It is not completely satisfactory since it introduces strong bias while reducing variance. We propose a new multivariate correlation estimator that models all replicates as independent and identically distributed (i.i.d.) samples from the multivariate normal distribution. We derive the estimator by maximizing the likelihood function. For small sample data, we provide a resampling-based statistical inference procedure, and for moderate to large sample data, we provide an asymptotic statistical inference procedure based on the Likelihood Ratio Test (LRT). We demonstrate advantages of the new multivariate correlation estimator over Pearson bivariate correlation estimator using simulations and real-world data analysis examples. AVAILABILITY: The estimator and statistical inference procedures have been implemented in an R package 'CORREP' that is available from CRAN [http://cran.r-project.org] and Bioconductor [http://www.bioconductor.org/]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cost-efficient higher-order crossover designs in comparative bioavailability studies.

BACKGROUND AND OBJECTIVE: Cost is an extremely important factor to consider when planning drug clinical trials. Higher-order crossover designs have recently drawn considerable attention in comparative bioavailability studies because of their desirable statistical properties. In this paper, we compared the cost efficiency of five commonly used higher-order crossover designs under certain cost function for comparative bioavailability studies. METHODS: Multivariate normal data were simulated under scenarios of a wide range of variability and correlations (coefficient of variation = 10-40%; correlation coefficient rho = 0.2-0.8). Monte Carlo simulations and mixed-effects models were carried out to obtain empirical sample sizes for each design using Schuirmann's two-one sided test procedure, under an 80% power and a 5% significance level, based on the US FDA bioequivalence criteria (80-125%). The five crossover designs studied were the two-period four-sequence (D2 x 4), the three-period two-sequence (D3 x 2), the three-period four-sequence (D3 x 4), the four-period two-sequence (D4 x 2), and the four-period four-sequence (D4 x 4). Costs for each design were then determined by a cost function, which takes into account costs for recruiting and screening, costs associated with period, and the overheads incurred for multiple sequences. Comparison of the costs for the above-mentioned designs was made under different scenarios. RESULTS: There was no single design uniformly dominating the others in terms of cost efficiency for comparative bioavailability studies. The designs D3 x 2 and D4 x 4 (especially the former) have the best overall performance in terms of cost efficiency for comparative bioavailability studies. They dominated the other designs under most of the scenarios. The design D2 x 4 showed the worst performance among the five crossover designs. CONCLUSIONS: A D3 x 2, and D4 x 2 crossover designs are recommended to achieve cost efficiency with a given power. The D2 x 4 crossover design is not recommended in general for comparative bioavailability studies.

[Detection of serum tumor supplied group of factors and its clinical significance].

BACKGROUND & OBJECTIVE: Tumor supplied group of factors (TSGF) is the first kind of tumor marker in vitro which was primarily obtained the approval by Chinese government to land the market, there was no any reports about it in abroad before. In order to know its specificity and sensitivity, we designed this study to detect the TSGF in serum of the patients with malignancies and evaluate its clinical significance for diagnosis. METHODS: Serum TSGF in 523 cases with malignancies and 400 cases of normal control were detected with chemical colorimetry. The diagnostic accuracy was evaluated. RESULTS: Mean levels of serum TSGF in the patients without malignancies (group A), the patients with malignancies (group B), the patients after curative treatment for malignancies (group C) and the normal control (group D) were 66.57, 71.24, 60.78, and 53.56 u/ml, respectively. After the statistical analysis, differences between the group A and group B, group A and group D, group B and group C, group B and group D, group C and group D were found (P < 0.05). But there was no obvious difference between the group A and group C (P > 0.05). The sensitivity was 63.86%; the specificity was 90.89%; the truly negative rate was 87.01%; and the falsely negative rate was 13.00%. CONCLUSIONS: Serum TSGF increased in malignancies and may be helpful for screening and early detection of cancer.