Uncloaking a Lost Cause: Decolonizing ancestry estimation in the United States

Since the professionalization of US-based forensic anthropology in the 1970s, ancestry estimation has been included as a standard part of the biological profile, because practitioners have assumed it necessary to achieve identifications in medicolegal contexts. Simultaneously, forensic anthropologists have not fully considered the racist context of the criminal justice system in the United States related to the treatment of Black, Indigenous, and People of Color; nor have we considered that ancestry estimation might actually hinder identification efforts because of entrenched racial biases. Despite ongoing criticisms from mainstream biological anthropology that ancestry estimation perpetuates race science, forensic anthropologists have continued the practice. Recent years have seen the prolific development of retooled typological approaches with 21st century statistical prowess to include methods for estimating ancestry from cranial morphoscopic traits, despite no evidence that these traits reflect microevolutionary processes or are suitable genetic proxies for population structure; and such approaches have failed to critically evaluate the societal consequences for perpetuating the biological race concept. Around the country, these methods are enculturated in every aspect of the discipline ranging from university classrooms, to the board-certification examination marking the culmination of training, to standard operating procedures adopted by forensic anthropology laboratories. Here, we use critical race theory to interrogate the approaches utilized to estimate ancestry to include a critique of the continued use of morphoscopic traits, and we assert that the practice of ancestry estimation contributes to white supremacy. Based on the lack of scientific support that these traits reflect evolutionary history, and the inability to disentangle skeletal-based ancestry estimates from supporting the biological validity of race, we urge all forensic anthropologists to abolish the practice of ancestry estimation.     

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Capture‐recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture‐recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large‐scale simulation experiment based on the scheme discussed by Pledger.     

Parameter estimation for discretely observed linear birth‐and‐death processes

Birth‐and‐death processes are widely used to model the development of biological populations. Although they are relatively simple models, their parameters can be challenging to estimate, as the likelihood can become numerically unstable when data arise from the most common sampling schemes, such as annual population censuses. A further difficulty arises when the discrete observations are not equi‐spaced, for example, when census data are unavailable for some years. We present two approaches to estimating the birth, death, and growth rates of a discretely observed linear birth‐and‐death process: via an embedded Galton‐Watson process and by maximizing a saddlepoint approximation to the likelihood. We study asymptotic properties of the estimators, compare them on numerical examples, and apply the methodology to data on monitored populations.     

A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease

As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Toward this end, we propose to develop new statistical methods with improved sensitivity and accuracy to identify disease‐related genes specialized for psychiatric diseases. The qualitative psychiatric diagnoses such as case control often suffer from high rates of misdiagnosis and oversimplify the disease phenotypes. Our proposed method utilizes endophenotypes, the quantitative traits hypothesized to underlie disease syndromes, to better characterize the heterogeneous phenotypes of psychiatric diseases. We employ the structural equation modeling using the liability‐index model to link multiple genetically regulated expressions from PrediXcan and the manifest variables including endophenotypes and case‐control status. The proposed method can be considered as a general method for multivariate regression, which is particularly helpful for psychiatric diseases. We derive penalized retrospective likelihood estimators to deal with the typical small sample size issue. Simulation results demonstrate the advantages of the proposed method and the real data analysis of Alzheimer's disease illustrates the practical utility of the techniques. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database.     

晋西黄土区典型乔灌木短期水分利用效率对环境因子的响应

为明确晋西黄土区植物的水分利用规律及对半干旱区的适应策略, 提高黄土地区植被建设效益, 该研究对该地区典型乔灌木短期水分利用效率随环境因子的变化进行了探究。以典型乔木油松(Pinus tabuliformis)、刺槐(Robinia pseudoacacia)及其林下灌木黄刺玫(Rosa xanthina)、杠柳(Periploca sepium)为研究对象, 测定叶片可溶性糖稳定碳同位素比值(δ¹³C_leaf)与枝条渗出液稳定碳同位素比值(δ¹³C_branch), 使用δ¹³C_leaf推导计算7-10月叶片尺度下植物短期水分利用效率(WUE_leaf)变化趋势, 使用δ¹³C_branch明确植物光合作用后分馏情况, 确定半干旱区植物在生长季的水分变化规律对环境因子变化的响应。结果表明: (1) 7-10月4种植物δ¹³C_leaf总体呈现降低趋势, δ¹³C_branch呈现先升高后降低趋势。δ¹³C_leaf在种间和生活型中均存在差异。具体表现为: 灌木>乔木, 常绿乔木(油松) >落叶乔木(刺槐)。研究过程中未发现明显的碳同位素在光合作用后发生分馏的情况。(2) 4种植物WUE_leaf在7-8月保持稳定, 9-10月逐渐升高。21.5 ℃、0.9 kPa、52.4%分别为WUE_leaf随温度(T_a)、饱和水汽压差(VPD)、相对湿度(RH)变化的突变点, 突变点之后4种植物WUE_leaf均表现出稳定的变化趋势, 不再随T_a、VPD、RH升高而降低。(3) WUE_leaf与T_a、RH、VPD之间存在显著负相关关系, T_a通过非气孔因素, 即酶的作用改变光合速率, 引起WUE_leaf变化。RH、VPD等水分因子则通过改变气孔开度, 影响蒸腾, 进而改变WUE_leaf。随着土壤含水量(SWC)的升高, WUE_leaf呈现先升高后降低的趋势。油松林和刺槐林在SWC分别达到15%-18%、13%-14%时, WUE_leaf达到最高值。经过混合线性模型(LMM)分析得到, 油松和刺槐WUE_leaf主导环境因子分别为RH和VPD, 黄刺玫和杠柳WUE_leaf主导环境因子均为T_a。该研究得到了黄土地区典型乔灌木生长季水分利用效率变化的规律和主要环境影响因子, 明确了黄土地区植物对气候因子变化的适应机制。     

LEC–BiFC: a new method for rapid assay of protein interaction

Abstract

Protein–protein interactions play fundamental roles in most biological processes. Bimolecular fluorescence complementation (BiFC) is a promising method for its simplicity and direct visualization of protein–protein interactions in cells. This method, however, is limited by background fluorescence that appears without specific interaction between the proteins. We report here a point mutation (V150L) in one Venus BiFC fragment that efficiently decreases background fluorescence of BiFC assay. Furthermore, by combining this modified BiFC and linear expression cassette (LEC), we develop a simple and rapid method (LEC–BiFC) for protein interaction analysis that is demonstrated by a case study of the interaction between Bcl–X_L and Bak BH3 peptide. The total analysis procedure can be completed in two days for screening tens of mutants. LEC–BiFC can be applied easily in any lab equipped with a fluorescence microscope.     

Bone Load Estimation for the Proximal Femur Using Single Energy Quantitative CT Data

A density-based load estimation method was applied to determine femoral load patterns. Two-dimensional finite element models were constructed using single energy quantitative computed tomography (QCT) data from two femora. basic load cases included parabolic pressure joint loads and constant tractions on the greater trochanter. An optimization procedure adjusted magnitudes of the basic load cases, such that the applied mechanical stimulus approached the ideal stimulus throughout each model. Dominant estimated load directions were generally consistent with published experimental data for gait. Other estimated loads suggested that loads at extreme joint orientations may be important to maintenance of bone structure. Remodeling simulations with the estimated loads produced density distributions qualitatively similar to the QCT data sets. Average nodal density errors between QCT data and predictions were 0·24 g/cm³ and 0·28 g/cm³. The results indicate that density-based load estimation could improve understanding of loading patterns on bones.