Prediction of location of active sites in biologically active peptides

In a previous paper we demonstrated that the short-range compact regions in atrial natriuretic factor (-hANF) predicted by the average distance map (ADM) correspond to its active sites [Kikuchi,J. Protein Chem.11, 579–581 (1992)]. In the present paper we apply the same method to other bioactive peptides and peptidic enzyme inhibitors. We again observe that active sites in each peptide are contained in short-range compact regions predicted by the ADM for the peptide. This demonstrates that the ADM method predicts the possible location of active sites in biologically active peptides in general. The possibility of practical application of the present method to rational drug design is also discussed.     

Nucleotide Sequence Surrounding the Locus Marker D21S246 on Human Chromosome 21

Most ofthe human Not I linking clones identified to date areconsidered to be derived from CpG islands because ofthe recognitionsequence of this enzyme, and CpG islands have been reportedto be located around the 5' regions of genes. As a pilot study,we determined the complete nucleotide sequence (41,924 bp) ofa human cosmid clone (LL21NC02Q7A10) containing the marker D21S246originating from a Not I linking clone. As a result of sequenceanalysis, we successfully mapped and revealed the genomic genestructure for KIAA0002 previously reported as a cDNA clone.This gene consists of 15 exons and was shown to exist at theD21S246 locus on human chromosome 21q21.3–q22.1. Theseresults demonstrated that genomic marker-anchored DNA sequencingis a useful approach for the human genome project.     

Magnetic-field flux density and spectral characteristics of motor-driven personal appliances

Flux density and spectral measurements were carried out on magnetic fields generated by several types of motor-driven personal appliances used near the body. Among the units tested were several for which the average flux densities, as determined at the surfaces of the appliance, exceeded 0.4 mT. Time-rates-of-change (dB/dt) for several units exceeded 1000 T/s, and several units exhibited high-frequency components in the low-MHz range. Use of such appliances, although normally of short duration, can represent exposure to magnetic fields of relatively high flux density, which may also have high-frequency components. Compared to other household and commercial sources of magnetic fields, those generated by certain motor-driven personal appliances may represent a significant contribution to time-weighted average exposure and may represent an important source of local induced currents in the body. Furthermore, high-frequency transients that represent only a minor contribution to time-weighted average exposure may generate significant instantaneous induced currents. © 1994 Wiley-Liss, Inc.

1 This article is a US Government work and, as such, is in the public domain in the United States of America.

     

Discrimination of folding types of globular proteins based on average distance maps constructed from their sequences

It has been shown that probable portions which form contacts in a protein can be predicted by means of an average distance map (ADM) as well as regular structures (-helices and -turns) defined as short-range compact regions (Kikuchiet al., 1988a,c). In this paper, we analyze the occurrence of those portions and short-range compact regions on ADMs for various proteins regarding their folding types. We have found out that each folding type of proteins shows characteristic distribution of such parts on ADMS. We also discuss the possibility of the prediction of folding types of proteins by ADMs.     

前庭—交感反应的中枢通路分析

用锋电位触发叠加(STA)技术对大鼠前庭内侧核(NVM)、下段脑干网状结构(RF)和前庭小脑进行了探查,观察了这些结构中对摆动旋转起反应的单位(PPU)与内脏大神经(SN)记录的交感反应之间的关系。发现用NVM的PPU进行STA,在SN可得一阳性反应,其潜伏期为33.28±3.1ms;用下段脑干RF的PPU进行STA,SN的峰反应潜伏期为11.3±0.91ms;用前庭小脑的PPU进行STA,SN的峰反应潜伏期为21.86±1.73ms。本结果提示前庭交感反应的最近的脊髓上中继站是下段脑干RF旁正中区核团,其下行冲动可能是由网状脊髓束中慢速传导的纤维传导的。根据三个脑区PPU引起的SN-STA潜伏期的不同,在前庭交感反应中前庭小脑所处的地位可能在NVM和下段脑干RF核团之间。     

Latent group detection in functional partially linear regression models

In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.     

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping

“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.     

Model misspecification and bias for inverse probability weighting estimators of average causal effects

Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating process, namely, when all models are misspecified. We apply our approach to three prototypical estimators of the average causal effect, two IPW estimators, using a misspecified PS model, and an augmented IPW (AIPW) estimator, using misspecified models for the outcome regression (OR) and the PS. For the two IPW estimators, we show that normalization, in addition to having a smaller variance, also offers some protection against bias due to model misspecification. To analyze the question of when the use of two misspecified models is better than one we derive necessary and sufficient conditions for when the AIPW estimator has a smaller bias than a simple IPW estimator and when it has a smaller bias than an IPW estimator with normalized weights. If the misspecification of the outcome model is moderate, the comparisons of the biases of the IPW and AIPW estimators show that the AIPW estimator has a smaller bias than the IPW estimators. However, all biases include a scaling with the PS-model error and we suggest caution in modeling the PS whenever such a model is involved. For numerical and finite sample illustrations, we include three simulation studies and corresponding approximations of the large-sample biases. In a dataset from the National Health and Nutrition Examination Survey, we estimate the effect of smoking on blood lead levels.     

气候变化下青藏高原全缘叶绿绒蒿的潜在分布变迁

青藏高原物种丰富且属于气候变化敏感区,研究气候变化对青藏高原物种的潜在分布影响,对于该区域物种多样性保护具有重要意义。该研究以一级濒危藏药植物全缘叶绿绒蒿为研究对象,利用加权平均算法(weighted average algorithm, WAA)构建随机森林(RF)、灵活判别分析(FDA)及人工神经网络(ANN)的集成模型,同时对比分析了WAA模型和不同生态位模型的预测精度。最后利用WAA模型预测了全缘叶绿绒蒿在当前(1970~2000年平均)和未来(2041~2060年平均)气候情景下的潜在分布,其中未来气候考虑了2种“共享社会经济路径”(SSP2-45和SSP5-85)。结果显示:(1) WAA模型的预测表明,基于RF、FDA和ANN的集成模型的AUC值为0.926,在AUC值最高RF模型的基础上提高了3%,在FDA和ANN模型的AUC值的基础上均提高了5%。(2) WAA模型确定,全缘叶绿绒蒿的潜在分布对年降水量和最暖季降水量最为敏感,其次是最热月份最高气温,同时对最湿月份降水量以及等温性表现出较低的敏感性。(3)当前全缘叶绿绒蒿潜在分布区主要分布在甘肃西南部、青海东部至南部、四川西部和西北部、云南西北部和东北部、西藏东部。(4)未来气候变化下青藏高原全缘叶绿绒蒿潜在分布预测表明,在2050年SSP2-45情景下,全缘叶绿绒蒿的潜在分布区大小与当前潜在分布区大小基本相同,但整体向西北方向高海拔高纬度地区迁移;在SSP5-85情景下,全缘叶绿绒蒿的潜在分布区明显收缩,且向西北高纬度高海拔地区延伸的趋势更加明显。     

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased “hypothesis-free” analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.