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.     

Kolavenic acid analog restores growth in HSET-overproducing fission yeast cells and multipolar mitosis in MDA-MB-231 human cells

Although cancer cells often harbor supernumerary centrosomes, they form pseudo-bipolar spindles via centrosome clustering, instead of lethal multipolar spindles, and thus avoid cell death. Kinesin-14 HSET/KIFC1 is a crucial protein involved in centrosome clustering. Accordingly, a compound that targets HSET could potentially inhibit cancer cell proliferation in a targeted manner. Here, we report three natural compounds derived from Solidago altissima that restored the growth of fission yeast cells exhibiting lethal HSET overproduction (positive screening), namely solidagonic acid (SA) (1), kolavenic acid analog (KAA: a stereo isomer at C-9 and C-10 of 6β-tigloyloxykolavenic acid) (2), and kolavenic acid (KA) (3). All three compounds suppressed fission yeast cell death and enabled reversion of the mitotic spindles from a monopolar to bipolar morphology. Compound 2, which exerted the strongest activity against HSET-overproducing yeast cells, also inhibited centrosome clustering in MDA-MB-231 human breast adenocarcinoma cells, which contained large numbers of supernumerary centrosomes. These natural compounds may be useful as bioprobes in studies of HSET function. Moreover, compound 2 is a prime contender in the development of novel agents for cancer treatment.     

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).     

A proposal of a fuzzy rule-based system for the analysis of health and health environments in Brazil

A challenging activity in the promotion of sustainable development is to synthesise indicators that can support decision-making. In developing countries such as Brazil, the use of information in support of the health and health-related environment of humans needs improvement. This paper presents a proposal for a system that can evaluate human health and urban environment sanitation in an integrated manner. The indicators selected for human health were child mortality rate, mortality rate due to acute diarrhoea for all ages and 1–4-year-old mortality rate due to acute respiratory tract infections. Environmental sanitation has been represented by indicators of sanitation coverage, such as sewerage. The paper describes the design of a fuzzy linguistic model that synthesises these indicators. The designed fuzzy rule-based system has proved to be a useful tool to integrate information and assist in the public planning of interrelated areas.     

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.     

Identification of Pak1 inhibitors using water thermodynamic analysis

Abstract

p21-activated kinases (Paks) play an integral component in various cellular diverse processes. The full activation of Pak is dependent upon several serine residues present in the N-terminal region, a threonine present at the activation loop, and finally the phosphorylation of these residues ensure the complete activation of Pak1. The present study deals with the identification of novel potent candidates of Pak1 using computational methods as anti-cancer compounds. A diverse energy based pharmacophore (e-pharmacophore) was developed using four co-crystal inhibitors of Pak1 having pharmacophore features of 5 (DRDRR), 6 (DRHADR), and 7 (RRARDRP and DRRDADH) hypotheses. These models were used for rigorous screening against e-molecule database. The obtained hits were filtered using ADME/T and molecular docking to identify the high affinity binders. These hits were subjected to hierarchical clustering using dendritic fingerprint inorder to identify structurally diverse molecules. The diverse hits were scored against generated water maps to obtain WM/MM ΔG binding energy. Furthermore, molecular dynamics simulation and density functional theory calculations were performed on the final hits to understand the stability of the complexes. Five structurally diverse novel Pak1 inhibitors (4835785, 32198676, 32407813, 76038049, and 32945545) were obtained from virtual screening, water thermodynamics and WM/MM ΔG binding energy. All hits revealed similar mode of binding pattern with the hinge region residues replacing the unstable water molecules in the binding site. The obtained novel hits could be used as a platform to design potent drugs that could be experimentally tested against cancer patients having increased Pak1 expression.     

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.