Ovarian low-grade serous carcinoma (LGSC) has fewer mutations than ovarian high-grade serous carcinoma (HGSC) and a less aggressive clinical course. However, an overwhelming majority of LGSC patients ...do not respond to conventional chemotherapy resulting in a poor long-term prognosis comparable to women diagnosed with HGSC. KRAS and BRAF mutations are common in LGSC, leading to clinical trials targeting the MAPK pathway. We assessed the stability of targetable somatic mutations over space and/or time in LGSC, with a view to inform stratified treatment strategies and clinical trial design.
Eleven LGSC cases with primary and recurrent paired samples were identified (stage IIB-IV). Tumor DNA was isolated from 1-4 formalin-fixed paraffin-embedded tumor blocks from both the primary and recurrence (n = 37 tumor and n = 7 normal samples). Mutational analysis was performed using the Ion Torrent AmpliSeqTM Cancer Panel, with targeted validation using Fluidigm-MiSeq, Sanger sequencing and/or Raindance Raindrop digital PCR.
KRAS (3/11), BRAF (2/11) and/or NRAS (1/11) mutations were identified in five unique cases. A novel, non-synonymous mutation in SMAD4 was observed in one case. No somatic mutations were detected in the remaining six cases. In two cases with a single matched primary and recurrent sample, two KRAS hotspot mutations (G12V, G12R) were both stable over time. In three cases with multiple samplings from both the primary and recurrent surgery some mutations (NRAS Q61R, BRAF V600E, SMAD4 R361G) were stable across all samples, while others (KRAS G12V, BRAF G469V) were unstable.
Overall, the majority of cases with detectable somatic mutations showed mutational stability over space and time while one of five cases showed both temporal and spatial mutational instability in presumed drivers of disease. Investigation of additional cases is required to confirm whether mutational heterogeneity in a minority of LGSC is a general phenomenon that should be factored into the design of clinical trials and stratified treatment for this patient population.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Long Term Evolution (LTE) and LTE-A researchers are deploying variety of techniques such as Multiple Inputs Multiple Outputs (MIMO) to achieve the higher throughput, while paying little attention to ...power consumption. In this paper, meticulous base station power consumption model is proposed, while more significantly, the model is calibrated. Then the relations between system parameters and power consumption of each sub-components is established, this energy consumption model and LTE system level simulation platform are integrated as a system level power consumption simulation platform. Two different base station energy saving technology applications are verified. Result shows 1.7 times EE improvement on average day utilizing the two schemes, while maximum EE increase per hour is 4.3 times.
As an extension to hidden Markov models, the hidden semi-Markov models allow the probability distribution of staying in the same state to be a general distribution. Therefore, hidden semi-Markov ...models are good at modeling sequences with succession of homogenous zones by choosing appropriate state duration distributions. Hidden semi-Markov models are generative models. Most times they are trained by maximum likelihood estimation. To compensate model mis-specification and provide protection against outliers, hidden semi-Markov models can be trained discriminatively given a labeled training set at the expense of increased training complexity. As an alternative to discriminative training, in this paper, we consider model mis-specification and outliers by adopting robust methods. Specifically, we use Student's t mixture models as the emission distributions of hidden semi-Markov models. The proposed robust hidden semi-Markov models are used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and the glioblastoma multiforme data illustrate the reliability of the technique.
Ground target detection, recognition and tracking play critical roles in aerial video surveillance applications. A ground moving target detection and tracking system based on active vision is studied ...firstly. After compensating the global motion, the independent moving targets are detected by the salience of residual images. Then object trackers are initialized to track these detected moving targets, at the same time, by controlling the payload equipment to achieve the given surveillance tasks. The influence of the movement of UAV to swivel table is studied and the apparent motion function is developed. In order to achieve good tracking performance, a mixed H2/H-infinite robust flight controller is designed for the UAV and a controller containing the disturbance observer (DOB) is designed for payload platform. This video servo control system successfully compensates the influence of UAV movement to swivel table control. Real-time simulation results show the effectiveness of the designed system.
Machine learning algorithms are widely used for quality assessment of tandem mass spectra based on a number of features. However, it is still unclear which features are most relevant to the quality ...of tandem mass spectra. In this paper, a sparse logistical regression method is proposed for selecting the most relevant features from those features found in the literature. To investigate the performance of the proposed method, experiments are conducted on two datasets. The results show the sparse logistical regression model can effectively select a small number of highly relevant features for tandem mass spectrum quality assessment.
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model ...generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the MLIP models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts.
X and γ ray reference radiation field is a necessary facility for the calibration of X and γ radiation dosimeter. The dosimetric parameters of radiation field directly affects the reliability of the ...transmission of numerical values. To measure the dosimetric parameters of the radiation field rapidly, a new automatic measuring system of dosimetric parameters for radiation field has been investigated, which enables remote control of the positioning of the manipulator and the measurement of the ionization chamber. It features preset measurement paths, automated measurements, readout and recording data, and the ability to derive measurement results. The dose-rate distribution, scattered radiation characteristics and uniformity of 137Cs γ-ray reference radiation field, 60Co γ-ray reference radiation field, low-energy, medium-energy and high-energy X-ray reference radiation field were measured by an automated measurement system. The results show that the air kerma rates at 1-6 m away from the 137Cs multi-source irr
Model based clustering for tandem mass spectrum quality assessment Ding, Jiarui; Shi, Jinhong; Wu, Fang-Xiang
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,
01/2009, Volume:
2009
Conference Proceeding, Journal Article
Several computational methods have been proposed to assess the quality of tandem mass spectra. These methods range from supervised to unsupervised algorithms, discriminative to generative models. ...Unsupervised learning algorithms for tandem mass spectra are not probabilistic model based and they don't provide probabilities for spectra quality assessment. In this study, the distribution of high quality spectra and poor quality spectra are modeled by a mixture of Gaussian distributions. The Expectation Maximization (EM) algorithm is used to estimate the parameters of the Gaussian mixture model. A spectrum is assigned to the high quality or poor quality cluster according to its posterior probability. Experiments are conducted on two datasets: ISB and TOV. The results show about 57.64% and 66.38% of poor quality spectra can be removed without losing more than 10% of high quality spectra for the two spectral datasets, respectively. This indicates clustering as an exploratory data analysis tool is valuable for the quality assessment of tandem mass spectra without using a pre-labeled training dataset.
Abstract
Introduction
The tandem mass spectrometer is a powerful tool with which to generate peptide (tandem) mass spectrum data for the analysis of complex biological protein mixtures in ...genomic-related disease cell lines. However, the majority of experimental tandem mass spectra cannot be interpreted by any database search engines. One of the main reasons this happens is that majority of experimental spectra are of quality too poor to be interpretable. Interpreting these “un-interpretable” spectra is a waste of time. Therefore, it is worthwhile to determine the quality of mass spectra before any interpretation.
Objectives
This paper proposes an approach to classifying tandem spectra into two groups: one with high quality and one with poor quality.
Methods
The proposed approach has two steps. First, each spectrum is mapped to a feature vector which describes the quality of the spectrum. Then, a weighted K-means clustering method is applied in order to classify the tandem mass spectra.
Results and Conclusion
Computational experiments illustrate that one cluster contains the majority of the high-quality spectra, while the other contains the majority of the poor-quality spectra. This result indicates that if we just search the spectra in the high-quality cluster, we can save the time for searching the majority of poor-quality spectra while losing a minimal amount of high-quality spectra. The software created for this work is available upon request.
Full text
Available for:
FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, UL, UM, UPUK, VKSCE, ZAGLJ
PDF
