Dosimetric impact of statistical uncertainty on Monte Carlo dose calculation algorithm in volumetric modulated arc therapy using Monaco TPS for three different clinical cases

AimTo study the dosimetric impact of statistical uncertainty (SU) per plan on Monte Carlo (MC) calculation in Monaco? treatment planning system (TPS) during volumetric modulated arc therapy (VMAT) for three different clinical cases.BackgroundDuring MC calculation SU is an important factor to decide dose calculation accuracy and calculation time. It is necessary to evaluate optimal acceptance of SU for quality plan with reduced calculation time.Materials and methodsThree different clinical cases as the lung, larynx, and prostate treated using VMAT technique were chosen. Plans were generated with Monaco? V5.11 TPS with 2% statistical uncertainty. By keeping all other parameters constant, plans were recalculated by varying SU, 0.5%, 1%, 2%, 3%, 4%, and 5%. For plan evaluation, conformity index (CI), homogeneity index (HI), dose coverage to PTV, organ at risk (OAR) dose, normal tissue receiving dose ≥5 Gy and ≥10 Gy, integral dose (NTID), calculation time, gamma pass rate, calculation reproducibility and energy dependency were analyzed.ResultsCI and HI improve as SU increases from 0.5% to 5%. No significant dose difference was observed in dose coverage to PTV, OAR doses, normal tissue receiving dose ≥5 Gy and ≥10 Gy and NTID. Increase of SU showed decrease in calculation time, gamma pass rate and increase in PTV max dose. No dose difference was seen in calculation reproducibility and dependent on energy.ConclusionFor VMAT plans, SU can be accepted from 1% to 3% per plan with reduced calculation time without compromising plan quality and deliverability by accepting variations in point dose within the target.     

Assigning alleles to different loci in amplifications of duplicated loci

Duplicated loci, for example those associated with major histocompatibility complex (MHC) genes, often have similar DNA sequences that can be coamplified with a pair of primers. This results in genotyping difficulties and inaccurate analyses. Here, we present a method to assign alleles to different loci in amplifications of duplicated loci. This method simultaneously considers several factors that may each affect correct allele assignment. These are the sharing of identical alleles among loci, null alleles, copy number variation, negative amplification, heterozygote excess or heterozygote deficiency, and linkage disequilibrium. The possible multilocus genotypes are extracted from the alleles for each individual and weighted to estimate the allele frequencies. The likelihood of an allele configuration is calculated and is optimized with a heuristic algorithm. Monte‐Carlo simulations and three empirical MHC data sets are used as examples to evaluate the efficacy of our method under different conditions. Our new software, mhc‐typer V1.1, is freely available at https://github.com/huangkang1987/mhc-typer .     

Structural insights into the catalytic mechanism of 5-methylthioribose 1-phosphate isomerase

5-Methylthioribose 1-phosphate isomerase (M1Pi) is a crucial enzyme involved in the universally conserved methionine salvage pathway (MSP) where it is known to catalyze the conversion of 5-methylthioribose 1-phosphate (MTR-1-P) to 5-methylthioribulose 1-phosphate (MTRu-1-P) via a mechanism which remains unspecified till date. Furthermore, although M1Pi has a discrete function, it surprisingly shares high structural similarity with two functionally non-related proteins such as ribose-1,5-bisphosphate isomerase (R15Pi) and the regulatory subunits of eukaryotic translation initiation factor 2B (eIF2B). To identify the distinct structural features that lead to divergent functional obligations of M1Pi as well as to understand the mechanism of enzyme catalysis, the crystal structure of M1Pi from a hyperthermophilic archaeon Pyrococcus horikoshii OT3 was determined. A meticulous structural investigation of the dimeric M1Pi revealed the presence of an N-terminal extension and a hydrophobic patch absent in R15Pi and the regulatory α-subunit of eIF2B. Furthermore, unlike R15Pi in which a kink formation is observed in one of the helices, the domain movement of M1Pi is distinguished by a forward shift in a loop covering the active-site pocket. All these structural attributes contribute towards a hydrophobic microenvironment in the vicinity of the active site of the enzyme making it favorable for the reaction mechanism to commence. Thus, a hydrophobic active-site microenvironment in addition to the availability of optimal amino-acid residues surrounding the catalytic residues in M1Pi led us to propose its probable reaction mechanism via a cis-phosphoenolate intermediate formation.     

Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a smart approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using gradient boosting machines and Gaussian processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a twofold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50% and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.     

Selection of relevant features for EEG signal classification of schizophrenic patients

In this paper, EEG signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of determining the more informative channels and finally distinguishing the two groups. For each case, 22 channels of EEG were recorded. A two-stage feature selection algorithm is designed, such that, the more informative channels are first selected to enhance the discriminative information. Two methods, bidirectional search and plus-L minus-R (LRS) techniques are employed to select these informative channels. The interesting point is that most of selected channels are located in the temporal lobes (containing the limbic system) that confirm the neuro-phychological differences in these areas between the schizophrenic and normal participants. After channel selection, genetic algorithm (GA) is employed to select the best features from the selected channels. In this case, in addition to elimination of the less informative channels, the redundant and less discriminant features are also eliminated. A computationally fast algorithm with excellent classification results is obtained. Implementation of this efficient approach involves several features including autoregressive (AR) model parameters, band power, fractal dimension and wavelet energy. To test the performance of the final subset of features, classifiers including linear discriminant analysis (LDA) and support vector machine (SVM) are employed to classify the reduced feature set of the two groups. Using the bidirectional search for channel selection, a classification accuracy of 84.62% and 99.38% is obtained for LDA and SVM, respectively. Using the LRS technique for channel selection, a classification accuracy of 88.23% and 99.54% is also obtained for LDA and SVM, respectively. Finally, the results are compared and contrasted with two well-known methods namely, the single-stage feature selection (evolutionary feature selection) and principal component analysis (PCA)-based feature selection. The results show improved accuracy of classification in relatively low computational time with the two-stage feature selection.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

A varying‐coefficient generalized odds rate model with time‐varying exposure: An application to fitness and cardiovascular disease mortality

Varying‐coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time‐varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying‐coefficient generalized odds rate model that allows flexible estimation of age‐modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed‐effects model, and its associated age‐varying coefficient function is represented by cubic B‐splines. An expectation‐maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian‐Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E‐step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study.