The influence of basic plan parameters on calculated small field output factors – A multicenter study

PurposeThe influence of basic plan parameters such as slice thickness, grid resolution, algorithm type and field size on calculated small field output factors (OFs) was evaluated in a multicentric study.Methods and materialsThree computational homogeneous water phantoms with slice thicknesses (ST) 1, 2 and 3 mm were shared among twenty-one centers to calculate OFs for 1x1, 2x2 and 3x3 cm² field sizes (FSs) (normalized to 10x10 cm² FS), with their own treatment planning system (TPS) and the energy clinically used for stereotactic body radiation therapy delivery. OFs were calculated for each combination of grid resolution (GR) (1, 2 and 3 mm) and ST and finally compared with the OFs measured for the TPS commissioning. A multivariate analysis was performed to test the effect of basic plan parameters on calculated OFs.ResultsA total of 509 data points were collected. Calculated OFs are slightly higher than measured ones. The multivariate analysis showed that Center, GR, algorithm type, and FS are predictive variables of the difference between calculated and measured OFs (p < 0.001). As FS decreases, the spread in the difference between calculated and measured OFs became larger when increasing the GR. Monte Carlo and Analytical Anisotropic Algorithms, presented a dependence on GR (p < 0.01), while Collapsed Cone Convolution and Acuros did not. The effect of the ST was found to be negligible.ConclusionsModern TPSs slightly overestimate the calculated small field OFs compared with measured ones. Grid resolution, algorithm, center number and field size influence the calculation of small field OFs.     

甘肃省生境质量变化的图谱特征

生境质量是影响地区生态系统服务价值和保护地球生态系统中生物多样性的重要因素。以地处三大自然区交汇带的甘肃省为研究对象,在定量估算地区2000—2018年生境质量水平基础上,基于图谱变化分析理论和InVEST模型,探索地区生境质量时空分异格局及其图谱转移状态和变化强度。结果表明: 2000—2018年,甘肃省生境质量总体维持中等水平并略有提升,在空间上自北向南呈逐级递增的阶梯式变化特征,在数量上则高低并存;从图谱转移视角分析,甘肃省生境质量格局较为稳定,未发生状态转移的图谱单元占主导,而在发生了生境质量状态转移的图谱单元中,“较高较低”、“较高高”、“较高低”这6类状态间的互换转移最显著,空间分布也较为集聚; “北剧南和”是甘肃省生境质量变化强度的主要格局,自北向南依次为“强变区”、“复合区”、“温缓区”和“平和区”4类变化强度区。     

Cluster analysis of pressure pain threshold maps from the trapezius muscle

The aim of this study was to investigate and present a new mapping method to describe muscle pain sensitivity based on the assessment of pressure pain threshold (PPT) over the trapezius muscle. PPT data were recorded from 36 points in 20 healthy males using a standardised grid. Points were clustered using the K-means algorithm with a fixed initialisation procedure. The total number of clusters was determined on the basis of (1) R ² evaluation of the clustering outcome compared against a desired 95% reduction in variance criterion and (2) the number of empty clusters. A minimum of three clusters were found which fulfilled the criteria. The proposed method enables the identification of a relation between the muscle subdivisions and pressure pain sensitivity within the trapezius. Further, the cluster analysis will enable the study of differences in pain sensitivity distributions between patients and controls and quantify the effect of intervention (physical or pharmacological treatments).     

Pattern classification of kinematic and kinetic running data to distinguish gender,shod/barefoot and injury groups with feature ranking

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified.

The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task.

Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.     

Automated analysis of courtship suppression learning and memory in Drosophila melanogaster

Study of the fruit fly, Drosophila melanogaster, has yielded important insights into the underlying molecular mechanisms of learning and memory. Courtship conditioning is a well-established behavioral assay used to study Drosophila learning and memory. Here, we describe the development of software to analyze courtship suppression assay data that correctly identifies normal or abnormal learning and memory traits of individual flies. Development of this automated analysis software will significantly enhance our ability to use this assay in large-scale genetic screens and disease modeling. The software increases the consistency, objectivity, and types of data generated.     

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

ABSTRACT

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.     

Machine learning techniques for protein function prediction

Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Random forests-based differential analysis of gene sets for gene expression data

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients.     

3D HUMAN MOTION RETRIEVAL BASED ON HUMAN HIERARCHICAL INDEX STRUCTURE

With the development and wide application of motion capture technology, the captured motion data sets are becoming larger and larger. For this reason, an efficient retrieval method for the motion database is very important. The retrieval method needs an appropriate indexing scheme and an effective similarity measure that can organize the existing motion data well. In this paper, we represent a human motion hierarchical index structure and adopt a nonlinear method to segment motion sequences. Based on this, we extract motion patterns and then we employ a fast similarity measure algorithm for motion pattern similarity computation to efficiently retrieve motion sequences. The experiment results show that the approach proposed in our paper is effective and efficient.