Biological and spatial structure of an early classic period cemetery at Charco Redondo,Oaxaca

This article presents an analysis of biological and spatial patterning of an Early Classic (A.D. 250–500) Chatino cemetery at the archaeological site of Charco Redondo, located in the lower Río Verde Valley, Oaxaca, Mexico. The Early Classic was a time of political instability positioned between two phases of state‐level centralization within the coastal valley. The communal cemetery at Charco Redondo adds significantly to the inventory of excavated graves from this time period and provides novel data on mortuary practices during a critical phase in the development of state level polities in the region. Cluster analysis of mortuary data is combined with intracemetery biodistance approaches to reconstruct how the Charco Redondo cemetery was organized with respect to biological relationships. Cluster analysis of mortuary data identified three groupings of burials. Multidimensional scaling of Euclidean distances and Gower coefficients based on 45 odontometric and 13 dental morphological variables suggests a strong relationship between grave characteristics and locations and phenotypic variation. In other words, the cemetery at Charco Redondo appears biologically kin‐structured. The communal nature of the cemetery conflicts with the assumed “household” burial model for this time period. We propose the observed combination of features represents a transitional practice in which aspects of community, kin, and individual identity were signaled simultaneously within the funerary environment during a time of political transition in the Valley. This article highlights the utility of intracemetery biodistance analyses for examining dimensions of kinship, “house,” and community throughout Mesoamerica where overarching models often mask regional variability. Am J Phys Anthropol 152:217–229, 2013. © 2013 Wiley Periodicals, Inc.     

Statistical assessment of population variability: a methodological approach

We have implemented a multivariate statistical methodology to assess the degree and pattern of cranial variability in skeletal samples. Specifically, the method is designed to test whether variability in a skeletal sample exceeds "normal within-group variability" defined in the present instance as variability present among crania from a cemetery associated with a village. It involves comparing a covariance matrix derived from a sample of unknown composition to one representing "normal within-group variability." The method has been applied to two Plains Indian craniometric samples. The Leavenworth site (39CO9) represents the remnants of previously autonomous Arikara Indian villages devastated by epidemic diseases in the late 1700s. The Bad River 2 Phase is an archaeological designation grouping together closely related sites in the Bad-Cheyenne region of South Dakota dating from 1740-1795 AD. We were able to show substantial heterogeneity among crania from Leavenworth. District burial areas at Leavenworth account for some of the heterogeneity, supporting the notion that they represent an attempt to maintain former social distinctions. We were unable to differentiate among sites within the Bad River 2 Phase, suggesting that it is a valid biological unit.     

Binding matrix: a novel approach for binding site recognition

Recognition of protein-DNA binding sites in genomic sequences is a crucial step for discovering biological functions of genomic sequences. Explosive growth in availability of sequence information has resulted in a demand for binding site detection methods with high specificity. The motivation of the work presented here is to address this demand by a systematic approach based on Maximum Likelihood Estimation. A general framework is developed in which a large class of binding site detection methods can be described in a uniform and consistent way. Protein-DNA binding is determined by binding energy, which is an approximately linear function within the space of sequence words. All matrix based binding word detectors can be regarded as different linear classifiers which attempt to estimate the linear separation implied by the binding energy function. The standard approaches of consensus sequences and profile matrices are described using this framework. A maximum likelihood approach for determining this linear separation leads to a novel matrix type, called the binding matrix. The binding matrix is the most specific matrix based classifier which is consistent with the input set of known binding words. It achieves significant improvements in specificity compared to other matrices. This is demonstrated using 95 sets of experimentally determined binding words provided by the TRANSFAC database.     

Dental relatedness corresponding to mortuary patterning at Huaca Loro, Peru

Within and between tombs at the 1,000-year-old site of Huaca Loro on north coastal Peru, interment characteristics vary to an intriguing degree. Following and elaborating upon prior intracemetery studies, biological relatedness among associated burial groupings was assessed using 23 dental characters (assuming familial allele segregation) for 29 individuals. Biological patterning was based on multivariate distance between individuals using all traits, rather than the previously widespread reliance upon univariate comparison of each trait separately within samples. This multivariate approach did seem more informative. Statistically significant variation of biological similarities and dissimilarities corresponded to spatial groupings and also to various specific archaeological indications of the cohesiveness, or lack thereof, of interment pattern. The partition of biological distances among tombs at Huaca Loro supports the archaeological evidence that the tombs represent a planned elite cemetery.     

我国公墓发展现状的地区性差异研究

殡葬行业是与人们息息相关的特殊领域,但由于多方面的原因,我国各地区公墓建设起步较晚且发展水平不均衡.通过实地调查、资料收集、数据分析,了解了我国公墓发展现状,并探索与公墓发展地区性差异关系紧密的因素,初步分析了人口密度与公墓发展现状的关系.其中,2010年各省人口数与总墓穴数相关系数为0.366,各省人口密度与墓穴人均占有量、入墓率的相关系数分别为0.899、0.854,初步分析了造成这一结果的原因.这为公墓总体规划和生态化建设提供依据,并为相关部门制定殡葬政策提供了参考.     

A unique human-fox burial from a pre-Natufian cemetery in the Levant (Jordan)

New human burials from northern Jordan provide important insights into the appearance of cemeteries and the nature of human-animal relationships within mortuary contexts during the Epipalaeolithic period (c. 23,000-11,600 cal BP) in the Levant, reinforcing a socio-ideological relationship that goes beyond predator-prey. Previous work suggests that archaeological features indicative of social complexity occur suddenly during the latest Epipalaeolithic phase, the Natufian (c. 14,500-11,600 cal BP). These features include sedentism, cemeteries, architecture, food production, including animal domestication, and burials with elaborate mortuary treatments. Our findings from the pre-Natufian (Middle Epipalaeolithic) cemetery of 'Uyun al-Hammam demonstrate that joint human-animal mortuary practices appear earlier in the Epipalaeolithic. We describe the earliest human-fox burial in the Near East, where the remains of dogs have been found associated with human burials at a number of Natufian sites. This is the first time that a fox has been documented in association with human interments pre-dating the Natufian and with a particular suite of grave goods. Analysis of the human and animal bones and their associated artefacts provides critical data on the nature and timing of these newly-developing relationships between people and animals prior to the appearance of domesticated dogs in the Natufian.     

Condition Number Estimation of Preconditioned Matrices

The present paper introduces a condition number estimation method for preconditioned matrices. The newly developed method provides reasonable results, while the conventional method which is based on the Lanczos connection gives meaningless results. The Lanczos connection based method provides the condition numbers of coefficient matrices of systems of linear equations with information obtained through the preconditioned conjugate gradient method. Estimating the condition number of preconditioned matrices is sometimes important when describing the effectiveness of new preconditionerers or selecting adequate preconditioners. Operating a preconditioner on a coefficient matrix is the simplest method of estimation. However, this is not possible for large-scale computing, especially if computation is performed on distributed memory parallel computers. This is because, the preconditioned matrices become dense, even if the original matrices are sparse. Although the Lanczos connection method can be used to calculate the condition number of preconditioned matrices, it is not considered to be applicable to large-scale problems because of its weakness with respect to numerical errors. Therefore, we have developed a robust and parallelizable method based on Hager’s method. The feasibility studies are curried out for the diagonal scaling preconditioner and the SSOR preconditioner with a diagonal matrix, a tri-daigonal matrix and Pei’s matrix. As a result, the Lanczos connection method contains around 10% error in the results even with a simple problem. On the other hand, the new method contains negligible errors. In addition, the newly developed method returns reasonable solutions when the Lanczos connection method fails with Pei’s matrix, and matrices generated with the finite element method.     

Detection of significant disease risks using a spatial conditional autoregressive model

SUMMARY: The conditional autoregressive (CAR) model is widely used to describe the geographical distribution of a specific disease risk in lattice mapping. Successful developments based on frequentist and Bayesian procedures have been extensively applied to obtain two-stage disease risk predictions at the subregional level. Bayesian procedures are preferred for making inferences, as the posterior standard errors (SE) of the two-stage prediction account for the variability in the variance component estimates; however, some recent work based on frequentist procedures and the use of bootstrap adjustments for the SE has been undertaken. In this article we investigate the suitability of an analytical adjustment for disease risk inference that provides accurate interval predictions by using the penalized quasilikelihood (PQL) technique to obtain model parameter estimates. The method is a first-order approximation of the naive SE based on a Taylor expansion and is interpreted as a conditional measure of variability providing conditional calibrated prediction intervals, given the data. We conduct a simulation study to demonstrate how the method can be used to estimate the specific subregion risk by interval. We evaluate the proposed methodology by analyzing the commonly used example data set of lip cancer incidence in the 56 counties of Scotland for the period 1975-1980. This evaluation reveals a close similarity between the solutions provided by the method proposed here and those of its fully Bayesian counterpart.     

An improved general amino acid replacement matrix

Amino acid replacement matrices are an essential basis of protein phylogenetics. They are used to compute substitution probabilities along phylogeny branches and thus the likelihood of the data. They are also essential in protein alignment. A number of replacement matrices and methods to estimate these matrices from protein alignments have been proposed since the seminal work of Dayhoff et al. (1972). An important advance was achieved by Whelan and Goldman (2001) and their WAG matrix, thanks to an efficient maximum likelihood estimation approach that accounts for the phylogenies of sequences within each training alignment. We further refine this method by incorporating the variability of evolutionary rates across sites in the matrix estimation and using a much larger and diverse database than BRKALN, which was used to estimate WAG. To estimate our new matrix (called LG after the authors), we use an adaptation of the XRATE software and 3,912 alignments from Pfam, comprising approximately 50,000 sequences and approximately 6.5 million residues overall. To evaluate the LG performance, we use an independent sample consisting of 59 alignments from TreeBase and randomly divide Pfam alignments into 3,412 training and 500 test alignments. The comparison with WAG and JTT shows a clear likelihood improvement. With TreeBase, we find that 1) the average Akaike information criterion gain per site is 0.25 and 0.42, when compared with WAG and JTT, respectively; 2) LG is significantly better than WAG for 38 alignments (among 59), and significantly worse with 2 alignments only; and 3) tree topologies inferred with LG, WAG, and JTT frequently differ, indicating that using LG impacts not only the likelihood value but also the output tree. Results with the test alignments from Pfam are analogous. LG and a PHYML implementation can be downloaded from http://atgc.lirmm.fr/LG.     

Nested collagen matrices: a new model to study migration of human fibroblast populations in three dimensions

Fibroblast-3D collagen matrix culture provides a model system to analyze cell physiology under conditions that more closely resemble tissue than conventional 2D cell culture. Previous work has focused primarily on remodeling and contraction of collagen matrices by fibroblasts, and there has been little research on migration of cell populations within the matrix. Here, we introduce a nested collagen matrix model to analyze migration of fibroblasts in 3D collagen matrices. Nested collagen matrices were prepared by embedding contracted cell-containing matrices (also called dermal equivalents) inside cell-free matrices; migration occurred from the former to the latter. Control experiments with human dermal fragments in place of dermal equivalents confirmed the reliability of the model. Human fibroblast migration in nested collagen matrices occurred after a lag phase of 8-16 h, and cells migrating out of the inner matrices were bipolar with leading dendritic extensions. Migration was myosin II, Rho kinase and metalloproteinase-dependent but did not require plasma fibronectin. Platelet-derived growth factor but not lysophosphatidic acid or serum stimulated cell migration, although all three of these physiological agonists promote matrix remodeling and contraction. The nested collagen matrix model is a relatively easy, rapid and quantitative method to measure migration of cell populations. Our studies using this model demonstrate important differences between regulation of fibroblast migration and remodeling in collagen matrices.     

Bayesian hierarchical error model for analysis of gene expression data

MOTIVATION: Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be estimated separately for evaluating the statistical significance of differential expression patterns. Widely used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data. RESULTS: We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo to estimate a large number of parameters from a single-likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray datasets-primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data. AVAILABILITY: The software for the HEM is available from the authors upon request.     

Modeling vital rates improves estimation of population projection matrices

Population projection matrices are commonly used by ecologists and managers to analyze the dynamics of stage-structured populations. Building projection matrices from data requires estimating transition rates among stages, a task that often entails estimating many parameters with few data. Consequently, large sampling variability in the estimated transition rates increases the uncertainty in the estimated matrix and quantities derived from it, such as the population multiplication rate and sensitivities of matrix elements. Here, we propose a strategy to avoid overparameterized matrix models. This strategy involves fitting models to the vital rates that determine matrix elements, evaluating both these models and ones that estimate matrix elements individually with model selection via information criteria, and averaging competing models with multimodel averaging. We illustrate this idea with data from a population of Silene acaulis (Caryophyllaceae), and conduct a simulation to investigate the statistical properties of the matrices estimated in this way. The simulation shows that compared with estimating matrix elements individually, building population projection matrices by fitting and averaging models of vital-rate estimates can reduce the statistical error in the population projection matrix and quantities derived from it.     

Stature in an early mid-19th century poorhouse population: Highland Park, Rochester, New York

If the early 19th century United States was a developing country, then it may be expected that the lowest economic stratum would show some biological consequences of poverty. This report examines that question by estimating stature on 90 male and 64 female adult skeletons from an unmarked cemetery dating between 1826 and 1863. The Highland Park burial ground was adjacent to institutions which interned unfortunates of Rochester, Monroe County, in western New York. The best estimate of male stature (N = 84) is 172.6 cm. A review of other 18th and 19th century height data shows this value not to be relatively stunted, but rather exactly on the predicted mean of its time. If this was a poor population, stunting was not a consequence of poverty. Females (N = 59) showed a best height estimate of 160.0 cm. Less comparative data are available, but this too seems average for the time. A brief discussion of secular trends, changing income inequity and fluctuating stature from the 18th through 20th centuries puts this finding in context.     

Importance of correlations among matrix entries in stochastic models in relation to number of transition matrices

Stochastic matrix models are used to predict population viability and the risk of extinction. Different stochastic methods require different amounts of estimation effort and may lead to divergent estimates. We used 16 transition matrices collected from ten populations of the perennial herb Primula veris to compare population estimates produced by different stochastic methods, such as selection of matrices, selection of vital rates, selection of matrix elements, and Tuljapurkar's approximation. Specifically, we tested the reliability of the methods using different numbers of transition matrices, and examined the importance of correlations among matrix entries. When correlations among matrix entries were included in the models, selection of vital rates produced the lowest and Tuljapurkar's approximation produced the highest estimates of mean population growth rates. Selection of matrices and matrix elements often produced nearly similar population estimates. Simulations based on incompletely estimated correlations among matrix entries considerably differed from those based on all correlations estimated, particularly when correlations were strong. The magnitude of correlations among matrix entries depended on the number of matrices, which made it difficult to generalize correlations within a species. Given that selection of vital rates or matrix elements is used, correlations among matrix entries should usually be included in the model, and they should preferably be estimated from the present data rather than according to other information of the species.     

Selective quantitative bioanalysis of proteins in biological fluids by on-line immunoaffinity chromatography-protein digestion-liquid chromatography-mass spectrometry

A quantitative method for the determination of proteins in complex biological matrices has been developed based on the selectivity of antibodies for sample purification followed by proteolytic digestion and quantitative mass spectrometry. An immunosorbent of polyclonal anti-bovine serum albumin (BSA) antibodies immobilized on CNBR agarose is used in the on-line mode for selective sample pretreatment. Next, the purified sample is trypsin digested to obtain protein specific peptide markers. Subsequent analysis of the peptide mixture using a desalination procedure and a separation step coupled, on-line to an ion-trap mass spectrometer, reveals that this method enables selective determination of proteins in biological matrices like diluted human plasma. This approach enhances substantially the selectivity compared to common quantitative analysis executed with immunoassays and colorimetry, fluorimetry or luminescence detection. Hyphenation of the immunoaffinity chromatography with on-line digestion and chromatography-mass spectrometry is performed and a completely on-line quantification of the model protein BSA in bovine and human urine was established. A detection limit of 170 nmol/l and a quantification limit of 280 nmol/l is obtained using 50 microl of either standard or spiked biological matrix. The model system allows fully automated absolute quantitative mass spectrometric analysis of intact proteins in biological matrices without time-consuming labeling procedures.     

Elucidating inequality in Nubia: An examination of entheseal changes at Kerma (Sudan)

Located 10 km south of the Third Cataract of the Nile River, the ancient city of Kerma was once capital to the second largest state in Africa. The Eastern Cemetery at Kerma (～4 km east of city center) encompasses 80+ hectares and was used over a period of 1,500 years (3,200–1,500 BC). Excavated in the early 20th century by George Reisner, the cemetery contained an estimated 20,000–40,000 individuals. Reisner classified these burials into multiple categories, including chiefs and human sacrifices, based on burial position and grave goods. This study investigates the skeletal embodiment of social inequality by examining variation in entheseal severity between the Kerma burial classifications. Seventeen entheses were examined using the Hawkey and Merbs (1995) scoring method (n = 205 individuals); age, sex, and body size variables were considered by employing Mann–Whitney U tests and partial Spearman's correlations. This analysis suggests that significant differences in entheseal changes existed between select burial types. Specifically, “corridor sacrifices” had significantly higher rates of entheseal changes while “chiefs” and “subsidiary burials” had similar entheseal changes; furthermore, within these burial categories, males had higher entheseal scores despite body size controls. The elevated entheseal changes in the sacrificial burials may be due to an intensive agro‐pastoral lifestyle or other demanding forms of manual labor. In conclusion, the disparity of entheseal markers between burial subgroups at Kerma might reflect a degree of social inequality within this state level society. This bioarchaeological research informs our understanding of socially‐defined categories of persons as well as everyday life in Ancient Kerma. Am J Phys Anthropol 156:192–202, 2015. © 2014 Wiley Periodicals, Inc.     

Temporal aspects of biological distance: serial correlation and trend in a prehistoric skeletal lineage

A number of previous studies have examined biological variability within individual human skeletal samples, using internal chronology as an explanatory source for variation. In this paper time series and matrix comparison methods are used to examine temporal serial correlation and trend of biological characteristics within a skeletal sample. These methods follow appropriately upon the assumption that the processes of genetic drift and migration lead to temporal autocorrelation of the mean genotype within individual genetic lineages. In the current analysis, using skeletal material from the Pete Klunk Mound Group, temporal serial correlation is demonstrated between samples from different mounds. This indicates that a previous provisional archaeological ordering of the mounds is probably correct. Absence of temporal trend for any of the nonmetric traits examined here suggests a lack of directional evolutionary forces operating on these traits.     

Secator: a program for inferring protein subfamilies from phylogenetic trees

With the huge increase of protein data, an important problem is to estimate, within a large protein family, the number of sensible subsets for subsequent in-depth structural, functional, and evolutionary analyses. To tackle this problem, we developed a new program, Secator, which implements the principle of an ascending hierarchical method using a distance matrix based on a multiple alignment of protein sequences. Dissimilarity values assigned to the nodes of a deduced phylogenetic tree are partitioned by a new stopping rule introduced to automatically determine the significant dissimilarity values. The quality of the clusters obtained by Secator is verified by a separate Jackknife study. The method is demonstrated on 24 large protein families covering a wide spectrum of structural and sequence conservation and its usefulness and accuracy with real biological data is illustrated on two well-studied protein families (the Sm proteins and the nuclear receptors).