Empirical analysis of current status data for additive hazards model with auxiliary covariates

summary:In practice, it often occurs that some covariates of interest are not measured because of various reasons, but there may exist some auxiliary information available. In this case, an issue of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses statistical inference problems in the context of current status data arising from an additive hazards model with auxiliary covariates. An empirical log-likelihood ratio statistic for the regression parameter vector is defined and its limiting distribution is shown to be a standard chi-squared distribution. A profile empirical log-likelihood ratio statistic for a sub-vector of the parameters and its asymptotic distribution are also studied. To assess the finite sample performance of the proposed methods, simulation studies are implemented and simulation results show that the methods work well

Corporate Social Responsibility Reporting, Pyramidal Structure And Political Interference: Evidence From China

This paper attempts to investigate the relation between pyramidal structure and corporate social responsibility (CSR) reporting quality and the effect of political interference on the relation. Based on 1388 Chinese A-share listed firms during 2010-2012, this paper demonstrates that the separation between control and ownership rights is significantly and positively related to the CSR reporting quality in the state-owned firms (SOFs), while negatively related to the CSR reporting quality in the non-state-owned firms (NSOFs). Results also indicate that the pyramidal layer between the bottom firms and their top ultimate owners is negatively related to CSR reporting quality, particularly significant for the NSOFs. Our research enriches the corporate governance literature by giving insights into the mechanism of pyramidal structure in corporate reporting, and extends the understanding of political interference in the CSR field. This study has public policy implications for China as well as a number of other countries in the Asia–Pacific region.

Extreme rainfall and snowfall alter responses of soil respiration to nitrogen fertilization : a 3-year field experiment

Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 23 (2017): 3403-3417, doi:10.1111/gcb.13620.Extreme precipitation is predicted to be more frequent and intense accompanying global warming, and may have profound impacts on soil respiration (Rs) and its components, i.e., autotrophic (Ra) and heterotrophic (Rh) respiration. However, how natural extreme rainfall or snowfall events affect these fluxes are still lacking, especially under nitrogen (N) fertilization. In this study, extreme rainfall and snowfall events occurred during a 3-year field experiment, allowing us to examine their effects on the response of Rs, Rh and Ra to N supply. In normal rainfall years of 2011/2012 and 2012/2013, N fertilization significantly stimulated Rs by 23.9% and 10.9%, respectively. This stimulation was mainly due to the increase of Ra because of N-induced increase in plant biomass. In the record wet year of 2013/2014, however, Rs was independent on N supply because of the inhibition effect of the extreme rainfall event. Compared with those in other years, Rh and Ra were reduced by 36.8% and 59.1%, respectively, which were likely related to the anoxic stress on soil microbes and decreased photosynthates supply. Although N supply did not affect annual Rh, the response ratio (RR) of Rh flux to N fertilization decreased firstly during growing season, increased in nongrowing season and peaked during spring thaw in each year. Nongrowing season Rs and Rh contributed 5.5–16.4% to their annual fluxes, and were higher in 2012/2013 than other years due to the extreme snowfall inducing higher soil moisture during spring thaw. The RR of nongrowing season Rs and Rh decreased in years with extreme snowfall or rainfall compared to those in normal years. Overall, our results highlight the significant effects of extreme precipitation on responses of Rs and its components to N fertilization, which should be incorporated into models to improve the prediction of carbon-climate feedbacks.This research was funded by the Chinese Academy of Sciences (XDB15020100) and the National Natural Science Foundation of China (31561143011).2017-12-2

BOAT: An Experimental Platform for Researchers to Comparatively and Reproducibly Evaluate Bug Localization Techniques

BOAT available at http://www.vlis.zju.edu.cn/blp.</p

Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Objective: Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application. Methods: This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD. Conclusion: ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field

mTORC1 signalling and eIF4E/4E-BP1 translation initiation factor stoichiometry influence recombinant protein productivity from GS-CHOK1 cells

Many protein-based biotherapeutics are produced in cultured Chinese hamster ovary (CHO) cell lines. Recent reports have demonstrated that translation of recombinant mRNAs and global control of the translation machinery via mammalian target of rapamycin (mTOR) signalling are important determinants of the amount and quality of recombinant protein such cells can produce. mTOR complex 1 (mTORC1) is a master regulator of cell growth/division, ribosome biogenesis and protein synthesis, but the relationship between mTORC1 signalling, cell growth and proliferation and recombinant protein yields from mammalian cells, and whether this master regulating signalling pathway can be manipulated to enhance cell biomass and recombinant protein production (rPP) are not well explored. We have investigated mTORC1 signalling and activity throughout batch culture of a panel of sister recombinant glutamine synthetase-CHO cell lines expressing different amounts of a model monoclonal IgG4, to evaluate the links between mTORC1 signalling and cell proliferation, autophagy, recombinant protein expression, global protein synthesis and mRNA translation initiation. We find that the expression of the mTORC1 substrate 4E-binding protein 1 (4E-BP1) fluctuates throughout the course of cell culture and, as expected, that the 4E-BP1 phosphorylation profiles change across the culture. Importantly, we find that the eIF4E/4E-BP1 stoichiometry positively correlates with cell productivity. Furthermore, eIF4E amounts appear to be co-regulated with 4E-BP1 amounts. This may reflect a sensing of either change at the mRNA level as opposed to the protein level or the fact that the phosphorylation status, as well as the amount of 4E-BP1 present, is important in the co-regulation of eIF4E and 4E-BP1

High endemicity of alveolar echinococcosis in Yili Prefecture, Xinjiang Autonomous Region, the People’s Republic of China: infection status in different ethnic communities and in small mammals

Alveolar echinococcosis (AE) is a neglected zoonosis caused by the larval stage of the fox/dog tapeworm Echinococcus multilocularis. In this study, we collected data on 286 AE cases reported from Yili Prefecture, Xinjiang Autonomous Region, the People’s Republic of China from 1989 to 2015 with an annual incidence (AI) of 0.41/100,000. Among the patients, 73.08% were diagnosed in the last 11 years. The incidence (0.51–1.22 cases/100,000 residents) was higher in the high-altitude mountainous areas than those in low level areas (0.19–0.29/100,000 residents). In term of ethnic group, the AI of AE in Mongolian (2.06/100,000 residents) and Kazak (0.93/100,000) groups had higher incidence than the other ethnic groups, indicating sheep-farming activity is a risk for infection given that sheep farming is mainly practiced by these two groups in the prefecture. A total of 1411 small mammals were captured with 9.14% infected with E. multilocularis metacestodes. Microtus obscurus was the dominant species captured in the mountainous pasture areas with 15.01% infection rate, whereas Mus musculus and Apodemus sylvaticus were the dominant small mammals in the low altitude areas. Only 0.40% of A. sylvaticus were infected with E. multilocularis. These findings show that Yili Prefecture is a highly endemic area for AE and that the high-altitude pasture areas favorable for M. obscurus may play an important role in its transmission in this region

Subtle genetic changes enhance virulence of methicillin resistant and sensitive Staphylococcus aureus

<p>Abstract</p> <p>Background</p> <p>Community acquired (CA) methicillin-resistant <it>Staphylococcus aureus </it>(MRSA) increasingly causes disease worldwide. USA300 has emerged as the predominant clone causing superficial and invasive infections in children and adults in the USA. Epidemiological studies suggest that USA300 is more virulent than other CA-MRSA. The genetic determinants that render virulence and dominance to USA300 remain unclear.</p> <p>Results</p> <p>We sequenced the genomes of two pediatric USA300 isolates: one CA-MRSA and one CA-methicillin susceptible (MSSA), isolated at Texas Children's Hospital in Houston. DNA sequencing was performed by Sanger dideoxy whole genome shotgun (WGS) and 454 Life Sciences pyrosequencing strategies. The sequence of the USA300 MRSA strain was rigorously annotated. In USA300-MRSA 2658 chromosomal open reading frames were predicted and 3.1 and 27 kilobase (kb) plasmids were identified. USA300-MSSA contained a 20 kb plasmid with some homology to the 27 kb plasmid found in USA300-MRSA. Two regions found in US300-MRSA were absent in USA300-MSSA. One of these carried the arginine deiminase operon that appears to have been acquired from <it>S. epidermidis</it>. The USA300 sequence was aligned with other sequenced <it>S. aureus </it>genomes and regions unique to USA300 MRSA were identified.</p> <p>Conclusion</p> <p>USA300-MRSA is highly similar to other MRSA strains based on whole genome alignments and gene content, indicating that the differences in pathogenesis are due to subtle changes rather than to large-scale acquisition of virulence factor genes. The USA300 Houston isolate differs from another sequenced USA300 strain isolate, derived from a patient in San Francisco, in plasmid content and a number of sequence polymorphisms. Such differences will provide new insights into the evolution of pathogens.</p