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Reprogramming of somatic cells to induced pluripotent stem cells, by overexpressing certain factors referred to as the reprogramming factors, can revolutionize regenerative medicine. To provide a coherent description of induced pluripotency from the gene regulation perspective, we use 35 microarray datasets to construct a reprogramming gene regulatory network. Comprising 276 nodes and 4471 links, the resulting network is, to the best of our knowledge, the largest gene regulatory network constructed for human fibroblast reprogramming and it is the only one built using a large number of experimental datasets. To build the network, a model that relates the expression profiles of the initial (fibroblast) and final (induced pluripotent stem cell) states is proposed and the model parameters (link strengths) are fitted using the experimental data. Twenty nine additional experimental datasets are collectively used to test the model/network, and good agreement between experimental and predicted gene expression profiles is found. We show that the model in conjunction with the constructed network can make useful predictions. For example, we demonstrate that our approach can incorporate the effect of reprogramming factor stoichiometry and that its predictions are consistent with the experimentally observed trends in reprogramming efficiency when the stoichiometric ratios vary. Using our model/network, we also suggest new (not used in training of the model) candidate sets of reprogramming factors, many of which have already been experimentally verified. These results suggest our model/network can potentially be used in devising new recipes for induced pluripotency with higher efficiencies. Additionally, we classify the links of the network into three classes of different importance, prioritizing them for experimental verification. We show that many of the links in the top ranked class are experimentally known to be important in reprogramming. Finally, comparing with other methods, we show that using our model is advantageous. [ABSTRACT FROM AUTHOR]

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. [ABSTRACT FROM AUTHOR]

Mitochondria play a critical role in the generation of metabolic energy and are crucial for eukaryotic cell survival and proliferation. In most sexual eukaryotes, mitochondrial DNA (mtDNA) is inherited from only one parent in non-Mendelian inheritance in contrast to the inheritance of nuclear DNA. The model organism Saccharomyces cerevisiae is commonly used to study mitochondrial biology. It has two mating types: MATa and MATα. Previous studies have suggested that the mtDNA inheritance patterns in hybrid diploid cells depend on the genetic background of parental strains. However, the underlying mechanisms remain unclear. To elucidate the mechanisms, we examined the effects of environmental factors on the mtDNA inheritance patterns in hybrids obtained by crossing S. cerevisiae with its close relative S. paradoxus. The results demonstrated that environmental factors can influence mtDNA transmission in hybrid diploids, and that the inheritance patterns are strain dependent. The fitness competition assay results showed that the fitness differences can explain the mtDNA inheritance patterns under specific conditions. However, in this study, we found that fitness differences cannot fully be explained by mitochondrial activity in hybrids under stress conditions. [ABSTRACT FROM AUTHOR]

Meta-analysis methods that combine -values into a single unified -value are frequently employed to improve confidence in hypothesis testing. An assumption made by most meta-analysis methods is that the -values to be combined are independent, which may not always be true. To investigate the accuracy of the unified -value from combining correlated -values, we have evaluated a family of statistical methods that combine: independent, weighted independent, correlated, and weighted correlated -values. Statistical accuracy evaluation by combining simulated correlated -values showed that correlation among -values can have a significant effect on the accuracy of the combined -value obtained. Among the statistical methods evaluated those that weight -values compute more accurate combined -values than those that do not. Also, statistical methods that utilize the correlation information have the best performance, producing significantly more accurate combined -values. In our study we have demonstrated that statistical methods that combine -values based on the assumption of independence can produce inaccurate -values when combining correlated -values, even when the -values are only weakly correlated. Therefore, to prevent from drawing false conclusions during hypothesis testing, our study advises caution be used when interpreting the -value obtained from combining -values of unknown correlation. However, when the correlation information is available, the weighting-capable statistical method, first introduced by Brown and recently modified by Hou, seems to perform the best amongst the methods investigated. [ABSTRACT FROM AUTHOR]

Given the expanding availability of scientific data and tools to analyze them, combining different assessments of the same piece of information has become increasingly important for social, biological, and even physical sciences. This task demands, to begin with, a method-independent standard, such as the P-value, that can be used to assess the reliability of a piece of information. Good's formula and Fisher's method combine independent P-values with respectively unequal and equal weights. Both approaches may be regarded as limiting instances of a general case of combining P-values from m groups; P-values within each group are weighted equally, while weight varies by group. When some of the weights become nearly degenerate, as cautioned by Good, numeric instability occurs in computation of the combined P-values. We deal explicitly with this difficulty by deriving a controlled expansion, in powers of differences in inverse weights, that provides both accurate statistics and stable numerics. We illustrate the utility of this systematic approach with a few examples. In addition, we also provide here an alternative derivation for the probability distribution function of the general case and show how the analytic formula obtained reduces to both Good's and Fisher's methods as special cases. A C++ program, which computes the combined P-values with equal numerical stability regardless of whether weights are (nearly) degenerate or not, is available for download at our group website http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/ downloads/CoinedPValues.html. [ABSTRACT FROM AUTHOR]

Cancer is one of the most troublesome diseases and a leading cause of death worldwide. Recently, novel treatments have been continuously developed to improve the disadvantages of conventional therapies, such as prodigious expenses, unwanted side effects, and tumor recurrence. Here, we provide the first non-invasive treatment that has combined epigallocatechin gallate (EGCG), the most abundant catechin in green tea, with a low strength pulsed electric field (PEF) and a low energy ultrasound (US). It has been observed that the cell viability of human pancreatic cancer PANC-1 was decreased approximately to 20% of the control after this combination treatment for 72 h. Besides, the combined triple treatment significantly reduced the high tolerance of HepG2 cells to the EGCG-induced cytotoxicity and similarly exhibited compelling proliferation-inhibitory effects. We also found the combined triple treatment increased the intracellular reactive oxygen species (ROS) and acidic vesicles, and the EGCG-induced inhibition of Akt phosphorylation was dramatically intensified. In this study, the apoptosis inhibitor Z-VAD-FMK and the autophagy inhibitor 3-MA were, respectively, shown to attenuate the anticancer effects of the triple treatment. This indicates that the triple treatment-induced autophagy was switched from cytoprotective to cytotoxic, and hence, cooperatively caused cell death with the apoptosis. Since the EGCG is easily accessible from the green tea and mild for a long-term treatment, and the non-invasive physical stimulations could be modified to focus on a specific location, this combined triple treatment may serve as a promising strategy for anticancer therapy. [ABSTRACT FROM AUTHOR]

Daily urine protein (UP) loss is a cumbersome but important measurement to guide diagnosis and treatment of renal disease. Spot urine protein-creatinine ratio (UPCR) can been applied to estimate daily proteinuria. However, the correlations between spot and 24h proteinuria remain controversial. In this cross-sectional study, simultaneous collection of 24h and spot urines were performed from 1,039 (derivation cohort) and 204 CKD patients (validation cohort) of Chang Gung Memorial Hospital, from 2007 to 2017. The correlations between spot UPCR and 24h proteinuria were compared. The mean age of patients of derivation and validation cohort was 63 and 55 years and the mean estimated glomerular filtration rate was 62 ± 35 and 59 ± 36 mL/min/m2, respectively. The correlation coefficient was 0.819 between UPCR and 24hUP. Prediction equation was derived as: Log1024hUP (g) = 0.814 x Log10UPCR (mg/mg) + 0.110 x Gender– 0.004 x Age + 0.004 x Body weight (kg) + 0.002 x CKD stage coefficient– 0.018, where CKD stage coefficient: CKD stage G1 = 1, G2 = 2, G3a = 3.1, G3b = 3.2, G4 = 4, G5 = 5. Correlation coefficient between measured and predicted 24hUP among derivation group and validation group is 0.866 and 0.915, respectively. However, the agreement of spot and daily estimates was less pronounced with proteinuria > 3g than lower values in Bland-Altman analysis. Spot UPCR can accurately predict 24hUP in patients with daily proteinuria below 3g. The development of this equation may facilitate estimation of 24hUP in the clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: We estimated the volume of vestibular schwannomas by an ice cream cone formula using thin-sliced magnetic resonance images (MRI) and compared the estimation accuracy among different estimating formulas and between different models. Methods: The study was approved by a local institutional review board. A total of 100 patients with vestibular schwannomas examined by MRI between January 2011 and November 2015 were enrolled retrospectively. Informed consent was waived. Volumes of vestibular schwannomas were estimated by cuboidal, ellipsoidal, and spherical formulas based on a one-component model, and cuboidal, ellipsoidal, Linskey’s, and ice cream cone formulas based on a two-component model. The estimated volumes were compared to the volumes measured by planimetry. Intraobserver reproducibility and interobserver agreement was tested. Estimation error, including absolute percentage error (APE) and percentage error (PE), was calculated. Statistical analysis included intraclass correlation coefficient (ICC), linear regression analysis, one-way analysis of variance, and paired t-tests with P < 0.05 considered statistically significant. Results: Overall tumor size was 4.80 ± 6.8 mL (mean ±standard deviation). All ICCs were no less than 0.992, suggestive of high intraobserver reproducibility and high interobserver agreement. Cuboidal formulas significantly overestimated the tumor volume by a factor of 1.9 to 2.4 (P ≤ 0.001). The one-component ellipsoidal and spherical formulas overestimated the tumor volume with an APE of 20.3% and 29.2%, respectively. The two-component ice cream cone method, and ellipsoidal and Linskey’s formulas significantly reduced the APE to 11.0%, 10.1%, and 12.5%, respectively (all P < 0.001). Conclusion: The ice cream cone method and other two-component formulas including the ellipsoidal and Linskey’s formulas allow for estimation of vestibular schwannoma volume more accurately than all one-component formulas. [ABSTRACT FROM AUTHOR]

In biosynthesis of natural products, potential intermediates or analogs of a particular compound in the crude extracts are commonly overlooked in routine assays due to their low concentration, limited structural information, or because of their insignificant bio-activities. This may lead into an incomplete and even an incorrect biosynthetic pathway for the target molecule. Here we applied multiple compound mining approaches, including genome scanning and precursor ion scan-directed mass spectrometry, to identify potential pyrrolamide compounds in the fermentation culture of Streptomyces netropsis. Several novel congocidine and distamycin analogs were thus detected and characterized. A more reasonable route for the biosynthesis of pyrrolamides was proposed based on the structures of these newly discovered compounds, as well as the functional characterization of several key biosynthetic genes of pyrrolamides. Collectively, our results implied an unusual “iterative strategy” underlying the pyrrole polymerization in the biosynthesis of pyrrolamide antibiotics. [ABSTRACT FROM AUTHOR]