Science in Risk Assessment and Policy (SciRAP): An Online Resource for Evaluating and Reporting In Vivo (Eco)Toxicity Studies

(Eco)toxicity studies conducted according to internationally standardized test guidelines are often considered reliable by default and preferred as key evidence in regulatory risk assessment. At the same time regulatory agencies emphasize the use of all relevant (eco)toxicity data in the risk assessment process, including non-standard studies. However, there is a need to facilitate the use of such studies in regulatory risk assessment. Therefore, we propose a framework that facilitates a systematic and transparent evaluation of the reliability and relevance of (eco)toxicity in vivo studies for health and environmental risk assessment. The framework includes specific criteria to guide study evaluation, as well as a color-coding tool developed to aid the application of these criteria. In addition we provide guidance intended for researchers on how to report non-standard studies to ensure that they meet regulatory requirements. The intention of the evaluating and reporting criteria is to increase the usability of all relevant data that may fill information gaps in chemical risk assessments. The framework is publically available online, free of charge, at the Science in Risk Assessment and Policy (SciRAP) website: www.scirap.org. The aim of this article is to present the framework and resources available at the SciRAP website.     

Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.     

Correlation between spatial distributions of pollen data, archaeological records and physical parameters from north-western France: a GIS and numerical analysis approach

New methodologies using Geographical Information Systems (GIS) and statistical tools were developed to provide new elements to the interpretation of fossil pollen records on the large spatial scale of north-western France. The originality of these methods lies in the analysis of the spatial distribution of the archaeobotanical data in order to identify correlations with other spatial parameters such as geological, climatic, pedological, topographical and archaeological characteristics. 218 pollen analyses from north-western France and a series of thematic maps (geological, archaeological, climatic, etc.) were used. The application of numerical analyses makes it possible to describe the spatial distribution of pollen data at a regional scale, and to identify spatial correlations between pollen data and other environmental parameters, and between archaeobotanical groups, archaeological and abiotic parameters simultaneously. Two examples are presented and discussed: (A) The spatial distributions of the predominance of hazel over oak between 6700 and 5700 cal b.p. and of modern precipitation are shown to be positively correlated, i.e. hazel is dominant in the most humid areas of the region. (B) The pollen data from the Bronze Age show associations of (1) pollen groups ascribed to meadows, shrubland, and forests with cooler temperatures, higher altitudes and northern latitudes, and (2) pollen groups ascribed to moor environments and anthropogenic vegetation with warmer temperatures, southern latitudes and lower altitudes. The latter implies that the agricultural landscapes of the Bronze Age were mainly confined to southern latitudes and low altitudes of the region, while the areas characterised by high altitudes and low temperatures were characterised by extensive activities such as grazing by cattle. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Present status of sentinel lymph node biopsy in cervical cancer

Cervical cancer is the fourth most common cancer in women, and seventh overall. This disease represents a medical, economic and social burden. In early FIGO stage patients (IA, IB1 and IIA1), nodal involvement is the most important prognostic factor. Imaging evaluation of nodal metastasis is of limited value. In order to determine lymph node involvement, allow loco-regional control of the disease, define the need for adjuvant radiotherapy and improve survival, standard surgery for early disease is radical hysterectomy with systematic pelvic lymphadenectomy. However, this surgical treatment has risks and complications: longer operative time, larger blood loss, neurovascular or ureteral injury, lower-limb lymphedema, symptomatic lymphocysts, hydronephrosis. A method that allows to define the presence of regional metastasis with less morbidity and equal or greater precision is particularly relevant. The use of the sentinel lymph node biopsy is intended to reach that purpose. The present study reviews recent literature on the role of sentinel lymph node biopsy in cervical cancer, analyzing its indications and contraindications, injection and detection techniques, tracers used, surgical and pathological approaches and its applicability in up-to-date clinical practice.     

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte-Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features

Protein methylation, one of the most important post-translational modifications, typically takes place on arginine or lysine residue. The reversible modification involves a series of basic cellular processes. Identification of methyl proteins with their sites will facilitate the understanding of the molecular mechanism of methylation. Besides the experimental methods, computational predictions of methylated sites are much more desirable for their convenience and fast speed. Here, we propose a method dedicated to predicting methylated sites of proteins. Feature selection was made on sequence conservation, physicochemical/biochemical properties, and structural disorder by applying maximum relevance minimum redundancy and incremental feature selection methods. The prediction models were built according to nearest the neighbor algorithm and evaluated by the jackknife cross-validation. We built 11 and 9 predictors for methylarginine and methyllysine, respectively, and integrated them to predict methylated sites. As a result, the average prediction accuracies are 74.25%, 77.02% for methylarginine and methyllysine training sets, respectively. Feature analysis suggested evolutionary information, and physicochemical/biochemical properties play important roles in the recognition of methylated sites. These findings may provide valuable information for exploiting the mechanisms of methylation. Our method may serve as a useful tool for biologists to find the potential methylated sites of proteins.     

Prediction and analysis of protein palmitoylation sites

Palmitoylation is a universal and important lipid modification, involving a series of basic cellular processes, such as membrane trafficking, protein stability and protein aggregation. With the avalanche of new protein sequences generated in the post genomic era, it is highly desirable to develop computational methods for rapidly and effectively identifying the potential palmitoylation sites of uncharacterized proteins so as to timely provide useful information for revealing the mechanism of protein palmitoylation. By using the Incremental Feature Selection approach based on amino acid factors, conservation, disorder feature, and specific features of palmitoylation site, a new predictor named IFS-Palm was developed in this regard. The overall success rate thus achieved by jackknife test on a newly constructed benchmark dataset was 90.65%. It was shown via an in-depth analysis that palmitoylation was intimately correlated with the feature of the upstream residue directly adjacent to cysteine site as well as the conservation of amino acid cysteine. Meanwhile, the protein disorder region might also play an import role in the post-translational modification. These findings may provide useful insights for revealing the mechanisms of palmitoylation.     

基于文献计量的城市空间结构与生态环境关联性研究

以Web of Science核心合集中1999-2020年期间发表的城市空间结构与生态环境关联性的文献为研究样本,运用文献计量的方法,主要借助VOSviewer和Citespace软件,厘清该领域研究的主要特征与进展。研究结果表明：城市空间结构与生态环境关联性研究的文献总量上呈现波动上升的趋势,尤其在近五年的波动骤增态势十分显著,成为地理学、城乡规划学、环境与生态学等学科交叉融合研究的重要方向之一;研究内容主要聚焦于城市空间结构与大气环境的关联性机理,具体涉及温室气体排放、空气污染和城市热岛效应,同时水环境、土壤环境以及生物多样性等方面也越来越受到重视而逐步成为研究热点。未来可围绕构建两者关联性框架和评估体系、面向生态环境问题的城市空间结构优化设计、两者发展变化的模拟和预测等核心问题展开研究。     

Forest cover dynamics analysis and prediction modeling using logistic regression model

Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β = −26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2 km² of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC = 87%).