DENetwork: Unveiling Regulatory and Signaling Networks Behind Differentially-Expressed Genes (preprint)

Differential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial insights into biological differences between sample groups. However, the conventional focus on differentially-expressed (DE) genes often omits non-DE regulators, which are an integral part of such differences. Moreover, DE genes frequently serve as passive indicators of transcriptomic variations rather than active influencers, limiting their utility as intervention targets. To address these shortcomings, we have developed DENetwork. This innovative approach deciphers the intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Unique in its integration of both DE and critical non-DE genes in a graphical model, DENetwork enhances the capabilities of traditional differential gene analysis tools, such as DESeq2. Our application of DENetwork to an array of simulated and real datasets showcases its potential to encapsulate biological differences, as demonstrated by the relevance and statistical significance of enriched gene functional terms. DENetwork offers a robust platform for systematically characterizing the biological mechanisms that underpin phenotypic differences, thereby augmenting our understanding of biological variations and facilitating the formulation of effective intervention strategies.

An Introduction to the "Midwest Academy of Management – Leading, Managing and Learning in View of Today's Grand Challenges" Special Issue: JMI

This special issue was developed in collaboration with the 2021 Midwest Academy of Management (MWAOM) Conference in Davenport, Iowa. It is the MWAOMs inaugural special issue with the Journal of Managerial Issuer. The 2021 MWAOM conference theme was Leading, Managing, and Learning in View of Todays Grand Challenges. The last few years have presented many grand challenges, from COVID-19, a national and global racial consciousness awakening, as well as political divide and economic disruptions.

Influence of characteristics and incentive types of webcast on users' attitudes

The novel coronavirus pneumonia brings new opportunities for the webcast. The characteristics of webcast and incentive types are two important potential variables that may affect user attitudes. Therefore, from this perspective, we test the mechanism of the two potential variables on the attitude of webcast users under the framework of ECM-ISC. Structural equation model analysis shows that: the interest of webcast has a significant positive impact on the cognitive attitude and emotional attitude of users;the explicit stimulation of webcast has a significant positive impact on the cognitive attitude and emotional attitude of users;the implicit incentive of webcast has a significant positive impact. The results show significant positive effects on users' cognitive attitude, emotional attitude, and behavior attitude;the interaction of live network characteristics and incentive types has a significant positive impact on users' cognitive attitude and emotional attitude.

Study on β-Chitosan against the binding of SARS-CoV-2S-RBD/ACE2 (preprint)

It has been known that SARS-CoV-2 which is considered similar to SARS-CoV invades human respiratory epithelial cells through interaction with the human angiotensin converting enzyme II (ACE2). In this work, SARS-CoV-2S-RBD and its cell receptor ACE2 were used to investigate the blocking effect and mechanism of {beta}-chitosan to the binding of them. Besides, inhibitory effect of {beta}-chitosan on inflammation induced by SARS-CoV-2S-RBD was also studied. Firstly, Native-PAGE results showed that {beta}-chitosan could bind with ACE2 or SARS-CoV-2S-RBD and the conjugate of {beta}-chitosan and ACE2 could no longer bind with SARS-CoV-2S-RBD. HPLC analyses suggested that was found the conjugate of {beta}-chitosan and SARS-CoV-2S-RBD displayed high binding affinity under the condition of high pressure (40 MPa) compared with that of ACE2 and SARS-CoV-2S-RBD. Furthermore, immunofluorescence detections on Vero E6 cells and hACE2 mice showed that {beta}-chitosan had a significant prevention and treatment effect on SARS-CoV-2S-RBD binding. Meanwhile, SARS-CoV-2S-RBD binding could activate the inflammation signaling pathways of cells and mice, however, {beta}-chitosan could dramatically suppress the inflammations activated by SARS-CoV-2S-RBD. Subsequently, Western blot analyses revealed that the expression levels of ACE2 in experimental groups treated with {beta}-chitosan significantly reduced. However, after the intervention of ADAM17 inhibitor (TAPI), the decreased ACE2 expressions affected by {beta}-chitosan up-regulated correspondingly. The results indicated that {beta}-chitosan has a similar antibody function, which can neutralize SARS-CoV-2S-RBD and effectively block the binding of SARS-CoV-2S-RBD with ACE2. ADAM17 activated by {beta}-chitosan can enhance the cleavage of ACE2 extracellular domain with a catalytic activity of Ang II degradation, and then the extracellular region was released into the extracellular environment. So, {beta}-chitosan could prevent the binding, internalization and degradation of ACE2 with SARS-CoV-2S-RBD and inhibit the activation of inflammatory signaling pathways at the same time. This work provides a valuable reference for the prevention and control of SARS-CoV-2 by {beta}-chitosan.

Assessment of the prevention and control effects on the outbreak of COVID-19 in Hunan, China: Based on a SEIAR Dynamic Model (preprint)

Background A new infectious disease, Coronavirus disease 2019 (COVID-19) has been first reported during December 2019 in Wuhan, China, cases have been exported to other cities and abroad rapidly. Hunan is the neighboring province of Wuhan, a series of preventive and control measures were taken to control the outbreak of COVID-19. It is critical to assess these measures on the epidemic progression for the benefit of global expectation.Method: A Susceptible-exposed-infections/asymptomatic-removed (SEIAR) model was established to evaluate the effect of preventive measures. Berkeley Madonna 8.3.18 was employed for the model simulation and prediction, and the curve-fitting problem was solved by Runge-Kutta fourth-order method.Results In this study, we found that Rt was 2.71 from January 21 to 27 and reduced to 0.21 after January 27, 2020. If measures have not been fully launched, patients in Hunan would reach the maximum (8.96 million) on March 25, 2020, and end in about 208 days; when measures have been fully launched, patients in Hunan would just reach the maximum (699) on February 9, 2020, and end in about 56 days, which was very closed to the actual situation.Conclusion The outbreak of COVID-19 in Hunan, China has been well controlled under current measures, full implementation of measures could reduce the peak value, short the time to peak and duration of the outbreak effectively, which could provide a reference for controlling of COVID-19 for other countries.