The impact of corporate governance on the total factor productivity of pharmaceutical enterprises: a study based on the fsQCA method.

The pharmaceutical industry is an important industry for the national economy and the people's livelihood, which is not only beneficial to the people's livelihood, but also has huge commercial value. How to promote the development of Chinese pharmaceutical industry is an urgent problem to be solved. In this study, 47 listed pharmaceutical companies are taken as cases, and Qualitative Comparative Analysis of Fuzzy Sets (fsQCA) is used to analyze the influence of five antecedent conditions on the total factor productivity of pharmaceutical enterprises from the perspective of corporate governance, and to explore the composition to Total Factor Productivity (TFP) improvement. The results are as follows. First, single corporate governance factor does not constitute the necessary condition to improve the TFP of pharmaceutical enterprises. Second, there are three configurations of high TFP of pharmaceutical enterprises, among these, two configurations belong to regulatory constraints type and one configuration belongs to the active board type. There is only one configurations to low TFP of pharmaceutical enterprises: the passive board. Based on the perspective of configuration, this paper discusses how corporate governance drives TFP improvement in pharmaceutical enterprises, which can provide systematic thinking and practical guidance for each company to promote its TFP improvement according to its own corporate structure.

Bayesian-based calibration for water quality model parameters.

To improve the efficiency and accuracy of water quality model parameter calibration and avoid local optima and the phenomenon in which different parameters have the same effect, this paper proposed a novel Bayesian-based water quality model parameter calibration method. Using Bayesian inference, the parameter calibration problem was converted into a posterior probability function sampling problem, which was sampled using the Markov Chain Monte Carlo algorithm. The convergence speed of the calibration was further improved by setting the optimized initial sampling value. The influences of the initial sampling value, Markov chain length, and proposal distribution form on the calibration effect were evaluated using four specific cases. The results indicate that (1) the mean relative error (MRE) of the parameter calibration results of this method is less than 10%, with the calibration MRE of Dx and Dy being 5.3% and 8.3%, respectively; (2) when the parameter sensitivity is low, the calibration effect of this method is relatively poor, with a calibration MRE of 46% for k; (3) the parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC, choosing a reasonable Markov chain length and a suitable proposal distribution form. PRACTITIONER POINTS: Bayesian-based water quality model parameter calibration method is proposed and posterior probability distribution was sampled using the MCMC algorithm. Parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC. As a result, efficient and accurate parameter calibration of water quality models was achieved. This method is widely applicable to various models, and the calibration speed depends on the calculation speed of the model.

Contamination Assessment for Cancer Next-Generation Sequencing.

CONTEXT.­: The presence of allogeneic contamination impacts clinical reporting in cancer next-generation sequencing specimens. Although consensus guidelines recommend the identification of contaminating DNA as a part of quality control, implementation of contamination assessment methods in clinical molecular diagnostic laboratories has not been reported in the literature. OBJECTIVE.­: To develop and implement a method to assess allogeneic contamination in clinical cancer next-generation sequencing specimens. DESIGN.­: We describe a method to detect contamination based on the evaluation of single-nucleotide polymorphic sites from tumor-only specimens. We validate this method and apply it to a large cohort of cancer sequencing specimens. RESULTS.­: Identification of specimen contamination was validated via in silico and in vitro mixtures, and reference range and reproducibility were established in a panel of normal specimens. The algorithm accurately detects an episode of systemic contamination due to reagent impurity. We prospectively applied this algorithm across 7571 clinical cancer specimens from a targeted next-generation sequencing panel, in which 262 specimens (3.5%) were predicted to be affected by greater than 5% contamination. CONCLUSIONS.­: Allogeneic contamination can be inferred from intrinsic cancer next-generation sequencing data without paired normal sequencing. The adoption of this approach can be useful as a quality control measure for laboratories performing clinical next-generation sequencing.

Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.

OBJECTIVE: To differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model. METHODS: Sixty-six patients from two centres (PA, n = 31; GBM, n = 35) were randomly divided into training and validation data sets (about 2:1). Quantitative radiomic features of the tumours were extracted from contrast-enhanced MR images. A subset of features was selected by feature stability and Boruta algorithm. The selected features were used to build a decision tree model. Predictive accuracy, sensitivity and specificity were used to assess model performance. The classification outcome of the model was combined with tumour location, age and gender features, and multivariable logistic regression analysis and permutation test using the entire data set were performed to further evaluate the decision tree model. RESULTS: A total of 271 radiomic features were successfully extracted for each tumour. Twelve features were selected as input variables to build the decision tree model. Two features S(1, -1) Entropy and S(2, -2) SumAverg were finally included in the model. The model showed an accuracy, sensitivity and specificity of 0.87, 0.90 and 0.83 for the training data set and 0.86, 0.80 and 0.91 for the validation data set. The classification outcome of the model related to the actual tumour types and did not rely on the other three features (p < 0.001). CONCLUSIONS: A decision tree model with two features derived from the contrast-enhanced MR images performed well in differentiating PA from GBM. KEY POINTS: • MRI findings of PA and GBM are sometimes very similar. • Radiomics provides much more quantitative information about tumours. • Radiomic features can help to distinguish PA from GBM.

Asynchrony of Base-Pair Breaking and Nucleotide Releasing of Helicases in DNA Unwinding.

Helicases harness the energy of nucleotide triphosphate hydrolysis to unwind double-stranded DNA (dsDNA) in discrete steps. In spite of intensive studies, the mechanism of stepping is still poorly understood. Here, we applied single-molecule fluorescent resonant energy transfer to characterize the stepping of two nonring helicases, Escherichia coli RecQ ( E. coli RecQ) and Saccharomyces cerevisiae Pif1 (ScPif1). Our data showed that when forked dsDNA with free overhangs are used as substrates, both E. coli RecQ and ScPif1 unwind the dsDNA in nonuniform steps that distribute over broad ranges. When tension is exerted on the overhangs, the overall profile of the step-size distribution of ScPif1 is narrowed, whereas that of E. coli RecQ remains unchanged. Moreover, the measured step sizes of the both helicases concentrate on integral multiples of a half base pair. We propose a universal stepping mechanism, in which a helicase breaks one base pair at a time and sequesters the nascent nucleotides and then releases them after a random number of base-pair breaking events. The mechanism can interpret the observed unwinding patterns quantitatively and provides a general view of the helicase activity.