DeCoST: A New Approach in Drug Repurposing From Control System Theory

In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from “one: disease-gene-drug” to “multi: gene, dru” and from “lazy guilt-by-association” to “systematic model-based pattern matching,” mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear–quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.)

Peer-to-peer energy trading and smart contracting platform of community-based virtual power plant

Traditional centralized transactions require a control center for user demand matching, settlement and other processes. However, with the increase in the penetration rate of distributed energy in the community, the explosive increase in the number of transactions leads to a decrease in efficiency and it is difficult to guarantee user privacy and information security. The smart contract technology based on blockchain technology has the characteristics of decentralization, traceability and tamper resistance, and these key factors show unique advantages in distributed energy transactions. This paper explores Ethereum and smart contract technology, designs a peer-to-peer energy sharing mechanism with reward and punishment incentives and establishes a smart contract trading platform for smart community-based virtual power plant (CVPP). This paper verifies the functionality and effectiveness of smart contract. The results show that when the supply and demand ratio changes, the user can conduct energy transactions according to the contract without a third-party organization, which solves the problem of trust between the two parties and achieves the expected effect and runs successfully. In addition, the simulation results show that the peer-to-peer transaction based on smart contracts reduces the energy cost per household and increases the total benefit of CVPP

Controllable Synthesis of Metal-Organic Framework/Polyethersulfone Composites

Composite materials that contain metal-organic frameworks (MOFs) as a filler and a polymer matrix have attracted attention because they present a combination of high porosity and structural integrity. Phase compatibilities of the MOF and polymer play a vital role in the formation of the composites. In particular, the stiff polymer cannot easily adapt to penetrate into the surface pore of MOF and mainly depends on chemical attractions to form the MOF/polymer composites. We report the synthesis of MOF/polyethersulfone (Young’s modulus = ~2.6 GPa) via different fabrication methods, different MOF types and particle sizes, and different solvents. The formed network structures are robust, monolithic composites with 60% MOF loadings; also, the MOF surface area and porosity were fully preserved. The study explored the formation of the composite between MOF and a stiff polymer and encourages the design of more MOF/polymer composite materials across a wide range of applications

Designing, cloning and simulation studies of cancer/testis antigens based multi-epitope vaccine candidates against cutaneous melanoma: An immunoinformatics approach

Background: Melanoma is the most fatal kind of skin cancer. Among its various types, cutaneous melanoma is the most prevalent one. Melanoma cells are thought to be highly immunogenic due to the presence of distinct tumor-associated antigens (TAAs), which includes carcinoembryonic antigen (CEA), cancer/testis antigens (CTAs) and neo-antigens. The CTA family is a group of antigens that are only expressed in malignancies and testicular germ cells. Methods: We used integrative framework and systems-level analysis to predict potential vaccine candidates for cutaneous melanoma involving epitopes prediction, molecular modeling and molecular docking to cross-validate the binding affinity and interaction between potential vaccine agents and major histocompatibility molecules (MHCs) followed by molecular dynamics simulation, immune simulation and in silico cloning. Results: In this study, three cancer/testis antigens were targeted for immunotherapy of cutaneous melanoma. Among many CTAs that were studied for their expression in primary and malignant melanoma, NY-ESO-1, MAGE1 and SSX2 antigens are most prevalent in cutaneous melanoma. Cytotoxic and Helper epitopes were predicted, and the finest epitopes were shortlisted based on binding score. The vaccine construct was composed of the four epitope-rich domains of antigenic proteins, an appropriate adjuvant, His tag and linkers. This potential multi-epitope vaccine was further evaluated in terms of antigenicity, allergencity, toxicity and other physicochemical properties. Molecular interaction estimated through protein-protein docking unveiled good interactions characterized by favorable binding energies. Molecular dynamics simulation ensured the stability of docked complex and the predicted immune response through immune simulation revealed elevated levels of antibodies titer, cytokines, interleukins and immune cells (NK, DC and MA) population. Conclusion: The findings indicate that the potential vaccine candidates could be effective immunotherapeutic agents that modify the treatment strategies of cutaneous melanoma

Physiological and transcriptomic analyses reveal tea plant (Camellia sinensis L.) adapts to extreme freezing stress during winter by regulating cell wall structure

Abstract Tea plants grown in high-latitude areas are often damaged by extreme freezing temperatures in winter, leading to huge economic losses. Here, the physiological and gene expression characteristics of two tea cultivars (Xinyang No. 10 (XY10), a freezing-tolerant cultivar and Fudingdabaicha (FDDB), a freezing-sensitive cultivar) during overwintering in northern China were studied to better understand the regulation mechanisms of tea plants in response to natural freezing stress. Samples were collected at a chill (D1), freezing (D2) and recovery (D3) temperature in winter. TEM analysis of integrated leaf ultrastructure at D2 revealed lower malondialdehyde and relative electrical conductivity in XY10 than in FDDB, with serious cell structure damage in the latter, indicating XY10 was more resistant to freezing stress. Differential gene expression analysis among the different samples over winter time highlighted the following gene functions in cell wall metabolism (CesAs, COBLs, XTHs, PGs, PMEs), transcription factors (ERF1B and MYC2), and signal transduction (CDPKs and CMLs). The expression pattern of cellulose and pectin-related genes suggested higher accumulation of cellulosic and pectic materials in the cell wall of XY10, agreeing with the results of cell wall and its components. These results indicated that under the regulation of cell wall genes, the freezing-resistant tea cultivar can better maintain a well-knit cell wall structure with sufficient substances to survive natural freezing damage. This study demonstrated the crucial role of cell wall in tea plant resistance to natural freezing stress and provided important candidate genes for breeding of freezing-resistant tea cultivars

Drought risk analysis based on multivariate copula function in Henan Province, China

AbstractGlobal droughts have become more frequent in recent years, posing a serious threat to human production and life. In order to overcome the limitations of traditional studies in quantifying drought risk, this study uses runs theory to extract drought duration, severity and kurtosis as drought characteristic variables from standardized precipitation index (SPI) in Henan Province. Drought risk analysis models based on multivariate copula function are constructed to reveal the response relationships between drought characteristic variables. The results show that the smaller the SPI scale, the higher the sensitivity to identify drought processes. In addition, the multivariate copula function shows good fitting performance for the optimal joint distribution of drought characteristic variables, with R2 values exceeding 0.9. Drought joint recurrence period is positively correlated with drought characteristic variables, and the recurrence period within the duration of 4–6 months occur frequently, indicating a higher probability of experiencing short-term droughts and cross-seasonal droughts. When the drought duration, severity and kurtosis are greater than 2.5, 2200 and 1300, respectively, the drought joint recurrence period reaches 20 months. The research results have provided new methods for drought risk analysis and data support for formulating drought mitigation strategies in Henan Province