Penalized estimation of sparse concentration matrices based on prior knowledge with applications to placenta elemental data

Identifying patterns of association or dependency among high-dimensional biological datasets with sparse precision matrices remains a challenge. In this paper, we introduce a weighted sparse Gaussian graphical model that can incorporate prior knowledge to infer the structure of the network of trace element concentrations, including essential elements as well as toxic metals and metaloids measured in the human placentas. We present the weighted L1 penalized regularization procedure for estimating the sparse precision matrix in the setting of Gaussian graphical models. First, we use simulation models to demonstrate that the proposed method yields a better estimate of the precision matrix than the procedures that fail to account for the prior knowledge of the network structure. Then, we apply this method to estimate sparse element concentration matrices of placental biopsies from the New Hampshire Birth Cohort Study. The chemical architecture for elements is complex; thus, the method proposed herein was applied to infer the dependency structures of the elements using prior knowledge of their biological roles.     

The role of a conserved threonine residue in the leader peptide of lasso peptide precursors

The conserved threonine (Thr) residue in the penultimate position of the leader peptide of lasso peptides microcin J25 and capistruin can be effectively replaced by several amino acids close in size and shape to Thr. These findings suggest a model for lasso peptide biosynthesis in which the Thr sidechain is a recognition element for the lasso peptide maturation machinery.     

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10–79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.     

Prediction of drug metabolites using neural machine translation

Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzyme families and therefore lack generalization, and additionally may involve manual work from experts limiting scalability. We present a rule-free, end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer learning on a deep learning transformer model for sequence translation, originally trained on chemical reaction data, to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites. Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites through phase I and phase II drug metabolism as well as other enzymes. Compared to existing rule-based approaches, our method has equivalent performance on the major enzyme families while it additionally finds metabolites through less common enzymes. Our results indicate that the proposed approach can provide a comprehensive study of drug metabolism that does not restrict to the major enzyme families and does not require the extraction of transformation rules.

The structure of the drug, represented with a SMILES sequence, is being translated into the structures of possible metabolites that can be formed in the human body.     

Multiple prompt gamma-ray analysis and construction of its beam line

By combining neutron activation analysis with multiple gamma-ray detection (gamma-gamma coincidence), we have proved better sensitivity and resolution for the trace element analysis than the ordinary single gamma-ray detection method. We now try to apply the multiple gamma-ray detection method to the prompt gamma ray analysis (PGA). We have established a new cold neutron beam line for PGA in Japan Research Reactor, JRR-3M, at Tokai establishment of Japan Atomic Energy Research Institute (JAERI). It consists of a beam shutter, a beam attenuator, a gamma-ray detector array, a sample changer, and a beam stopper. We construct a high-efficiency gamma-ray detector array specially designed for this purpose. Its performance has been evaluated with the Monte Carlo simulation code, GEANT 4.5.0.     

儿童癫痫与微量元素关系初探

测定42例癫痫病患儿血中必需微量元素及有害元素，对其含量变化与临床症状间的关系进行分析研究，表明人体内微量元素失衡对本病的发生发展起重要作用．     

激光诱导击穿光谱(LIBS)结合机器学习算法快速测定石油焦中微量元素

石油焦中微量元素对其作为预焙阳极的性能起着决定性的作用。首先,通过基于LIBS光谱构建用于石油焦中铁(Fe)和铜(Cu)定量分析的PLS校正模型。然后,考察了不同光谱预处理(归一化、多元散射校正、标准正态变换、一阶导数和二阶导数)以及变量选择算法(粒子群优化算法和变量重要性投影)对PLS校正模型预测性能的影响。建立了一种基于激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)结合偏最小二乘(Partial least squares, PLS)的石油焦中微量元素定量分析方法。结果显示,与其他PLS校正模型相比,基于二阶导数和变量重要性投影的PLS模型对Fe的预测性能最优,最优的交叉验证相关系数(R-squared cross validation,R²cv)为0.966 7,均方根误差(Root mean squared error cross validation, RMSEcv)为10.282 1 mg/kg,预测集的相关系数(R-squared prediction,R²p)为0.86...     

计算机模式识别法对关节变形强直性脊柱炎微量元素谱的研究

测定了关节变形强直性脊柱炎患者及健康人头发样本中的十五种微量元素含量,运用计算机模式识别技术研究两类样本的微量元素谱差异,选择Ca、Pb、Zn、Ti、Ni、Mg和Sr元素为特征参量,用马氏距离法对两类样本进行回判和预报,其准确率均高于92%,高维空间分类成功,表明两类人的微量元素谱确实存在明显差异。     

PASS-based prediction of metabolites detection in biological systems

ABSTRACT

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield (‘major’, ‘minor’, ”trace” and ”negligible”) depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways (http://www.way2drug.com/mg).     

罗布泊地貌地质遗迹中某些元素的分析

用中子活化分析法测试了新疆罗布泊地区地质样品中的某些痕量、微量及常量元素含量,特别是对样品中放射性元素作了检测。结果表明,该地区的地质遗迹样品中,痕量、微量及常量元素的含量与中国大陆地壳中的含量相当,无明显的放射性异常;把这些样品当作建筑材料的原材料来使用,是安全的;通过对这些样品的检测分析,为该地区的化探普查提供了可靠的依据。     

Groundwater Quality at the Huaibei Coalfield,China

The environmental and health impacts of trace elements are of great concern because of their toxicity, persistence, and bioavailability. Twenty-six groundwater samples were collected from the coal mining areas to evaluate water quality and potential environmental impact caused by mining activities. The physical/chemical properties and trace element concentrations (As, Cd, Cr, Cu, Pb, and Zn) were determined. The physical/chemical parameters and the concentrations of trace elements varied among the shallow and confined groundwater, which may indicate the absence of hydraulic interactions between the aquifers. The concentrations of trace elements in the mining areas are higher than at background monitoring sites, which suggests that the mining activities may lead to environmental and health impacts. Based on a health risk assessment, the low hazard quotients and cancer risk values in confined groundwater samples indicate that the chronic and cancer adverse effects due to trace elements are negligible. This study provides a comprehensive assessment of groundwater trace element impacts from coal mining in China and is useful for environmental management.     

ETAAS method for the determination of Cd, Cr, Cu, Mn and Se in blood fractions and whole blood

An electrothermal atomic absorption method (ETAAS) for the direct determination of trace elements (Cd, Cr, Cu, Mn, Se) both in blood fractions (erythrocytes, plasma and lymphocytes) and whole blood was developed. Zeeman background correction and graphite tubes with L'vov platforms were used. Samples were diluted with HNO3/Triton X-100 and pipetted directly into the graphite tube. Ashing, pretreatment and atomization steps were optimized carefully for the different fractions and elements applying different matrix modifiers for each element. For the lymphocyte fraction a multi-fold injection technique was applied. Low detection limits of the ETAAS method (Cd 0.13 microgram/L, Cr 0.11 microgram/L, Cu 0.52 microgram/L, Mn 0.13 microgram/L, Se 0.7 microgram/L of whole blood) combined with small quantities of sample necessary for analysis allow determination of trace elements in this matrix. Verification of possible differences in the trace element status of humans was performed with statistical significance (P < 0.05). In addition, a contribution to the determination of normal values of essential elements was achieved. The method was applied for determination of trace elements in human blood and blood fractions of two groups (n = 50) different in health status.     

Determination of CGP 6140 and its N-oxide metabolite,CGP 13 231, in plasma and urine by columns or cartridge switching HPLC

Summary Two methods were developed for the determination of CGP 6140 and its N-oxide metabolite. The first method was based on trace enrichment, rinsing on a first column (LiChroprep RP-2) andbackflush on the analytical column (Spherisorb ODS 1). The second method was based on trace enrichment onto a cartridge andforwardflush on the analytical column using the Advanced Automated Sample Processor (AASP). CGP 6140 and its N-oxide metabolite are spectrophotometrically detected at 405nm. The reproducibility and accuracy of the methods were convenient. The limits of quantitation (50nmol/l in plasma and 250 nmol/l in urine) were sufficient to follow the concentrations of CGP 6140 and its N-oxide metabolite after a 100mg single oral dose of CGP 6140 to volunteers.     

Simultaneous sample preconcentration and matrix removal using field-flow fractionation coupled to inductively coupled plasma mass spectrometry

An on-channel sample preconcentration-matrix removal arrangement, based on coupling field-flow fractionation (FFF) to inductively coupled plasma mass spectrometry (ICP-MS), has been constructed for on-line sample pretreatment ICP-MS trace element determination. A commercial FFF system is modified to incorporate an on-channel preconcentration procedure allowing injection of up to 50 ml of sample, which could be preconcentrated by 50–1400 fold. A high molecular weight complexing agent added to the sample forms strong complexes with the measured trace analytes but not with the sample matrix. When the sample-complexing agent mixture is introduced to the FFF unit, the uncomplexed matrix element is removed by permeation through a membrane that separates the FFF sample compartment. The trace analytes remain in the FFF channel, because their high molecular weight complexes do not permeate through the membrane. Preconcentration and matrix elimination occur simultaneously. The matrix-free, preconcentrated sample is introduced directly to the ICP-MS nebulizer. The method was tested using 10-ml sample aliquots that contain As, Cd, Cu, Mo, Pb, Re, Sn, Te, Tl, Y, Zn and Zr analytes and 5000 mg l⁻¹ Ca or Na matrices and ethylene imine polymer complexing agent. Copper and Re isotopic ratio values in reference standards also were determined after preconcentration and matrix element removal.     

Certification of a second-generation biological reference material (freeze-dried human serum) for trace element determinations

The certification of a second-generation biological reference material (freeze-dried human serum) for trace element determinations is described. The material was prepared under rigorously controlled conditions to avoid extraneous additions. Analytical data were obtained by the authors as well as by numerous other intra- and extra-mural investigators, solicited on the basis of established experience in determining selected elements. For 14 trace elements (aluminium, chromium, manganese, iron, cobalt, copper, zinc, arsenic, selenium, bromine, rubidium, molybdenum, cadmium and caesium) certified values (in ng g^?1 or μg g^?1 dry weight) are listed; for an additional element (nickel) a best estimate (in ng g^? dry weight) is added. Trace element concentrations in the material, which is available to the scientific community, closely approximate those in normal, lyophilized blood plasma or serum samples. The material thus provides the means to check the accuracy and precision of analytical procedures for quantifying low-level trace elements in the best possible conditions and to detect errors that can easily be overlooked when reference materials with higher levels of trace elements are used. In addition, and in contrast to already existing biological reference materials with high levels of trace elements, it offers the possibility of identifying unsuspected errors at the sample preparation stage.     

Quantitative analysis of trace levels of surface contamination by X‐ray photoelectron spectroscopy. Part I: Statistical uncertainty near the detection limit

We discuss the problem of quantifying common sources of statistical uncertainties for analyses of trace levels of surface contamination by using X‐ray photoelectron spectroscopy. We examine the propagation of error for peak‐area measurements by using common forms of linear and polynomial background subtraction including the correlation of points used to determine both background and peak areas. This correlation has been neglected in previous analyses, but we show that it contributes significantly to the peak‐area uncertainty near the detection limit. We introduce the concept of relative background subtraction variance (RBSV) that quantifies the uncertainty introduced by the method of background determination relative to the uncertainty of the background area itself. The uncertainties of the peak area and atomic concentration and of the detection limit are expressed using the RBSV, which separates the contributions from the acquisition parameters, the background‐determination method, and the properties of the measured spectrum. These results are then combined to find acquisition strategies that minimize the total measurement time needed to achieve a desired detection limit or atomic‐percentage uncertainty for a particular trace element. Minimization of data‐acquisition time is important for samples that are sensitive to X‐ray dose and also for laboratories that need to optimize throughput.     

胎儿畸形与微量元素

出生缺陷是影响我国人口素质与经济可持续发展的重大公共卫生问题和社会问题。从出生缺陷高发区的环境地球化学特征、畸形儿的体内微量元素异常、胎儿畸形的病因与干预、胎儿畸形的产前诊断与防治等四个方面阐述了微量元素在胎儿畸形发生、监督、诊断和预防中的意义。