Identifying mutated driver pathways in cancer by integrating multi-omics data

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.     

Rate constants of the reactions of O(3P) atoms with Br2 and NO2 over the temperature range 220-950 K

The kinetics of the reactions of Br₂ and NO₂ with ground state oxygen atoms have been studied over a wide temperature range, T = 220-950 K, using a low-pressure flow tube reactor coupled with a quadrupole mass spectrometer: O + NO₂ → NO + O₂ (1) and O + Br₂ → Br + BrO (2). The rate constant of reaction (1) was determined under pseudo–first-order conditions, either monitoring the kinetics of O-atom or NO₂ consumption in excess of NO₂ or of the oxygen atoms, respectively: k₁ = (6.1 ± 0.4) × 10⁻¹² exp((155 ± 18)/T) cm³ molecule⁻¹ s⁻¹ (where the uncertainties represent precision at the 2σ level, the estimated total uncertainty on k₁ being 15% at all temperatures). The temperature dependence of k₁, found to be in excellent agreement with multiple previous low-temperature data, was extended to 950 K. The rate constant of reaction (2) determined under pseudo–first-order conditions, monitoring the kinetics of Br₂ consumption in excess of O-atoms, showed upward curvature at low and high temperatures of the study and was fitted with the following three-parameter expression: k₂ = 9.85 × 10⁻¹⁶ T^1.41 exp(543/T) cm³ molecule⁻¹ s⁻¹ at T = (220-950) K, which is recommended from the present study with an independent of temperature conservative uncertainty of 15% on k₂.     

Anisotropy Effects and Observational Data on the Constraints of Evolution Dark Energy Models

We investigate cosmological dark energy models where the accelerated expansion of the universe is driven by a field with an anisotropic universe. The constraints on the parameters are obtained by maximum likelihood analysis using observational of 194 Type Ia supernovae(SNIa) and the most recent joint light-curve analysis(JLA) sample. In particular we reconstruct the dark energy equation of state parameter w(z) and the deceleration parameter q(z). We find that the best fit dynamical w(z) obtained from the 194 SNIa dataset does not cross the phantom divide line w(z) =-1 and remains above and close to w(z)≈-0.92 line for the whole redshift range 0 ≤ z ≤ 1.75 showing no evidence for phantom behavior. By applying the anisotropy effect on the ΛCDM model, the joint analysis indicates that ?_(σ0)= 0.0163 ± 0.03,with 194 SNIa, ?_(σ0)=-0.0032 ± 0.032 with 238 the SiFTO sample of JLA and ?_(σ0)= 0.011 ± 0.0117 with 1048 the SALT2 sample of Pantheon at 1σ′confidence interval. The analysis shows that by considering the anisotropy, it leads to more best fit parameters in all models with JLA SNe datasets. Furthermore, we use two statistical tests such as the usual χ_(min)~2/dof and p-test to compare two dark energy models with ΛCDM model. Finally we show that the presence of anisotropy is confirmed in mentioned models via SNIa dataset.     

Statistical inference for Vasicek-type model driven by Hermite processes

Let $Z$ denote a Hermite process of order $q\ge 1$ and self-similarity parameter $H\in (\frac{1}{2},1)$. This process is $H$-self-similar, has stationary increments and exhibits long-range dependence. When $q=1$, it corresponds to the fractional Brownian motion, whereas it is not Gaussian as soon as $q?2$. In this paper, we deal with a Vasicek-type model driven by $Z$, of the form $d{X}_t=a(b?X_t)dt+d{Z}_t$. Here, $a>0$ and $b\in \mathbb{R}$ are considered as unknown drift parameters. We provide estimators for $a$ and $b$ based on continuous-time observations. For all possible values of $H$ and $q$, we prove strong consistency and we analyze the asymptotic fluctuations.     

QUANTIS: Data quality assessment tool by clustering analysis

Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy-to-model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t-SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.