High-speed information processing based on optical microcombs and its performance optimization

In recent decades, photonic technologies offer a solution by enabling ultrahigh speeds for advanced information processing. Among them, microwave photonic signal processors provide high reconfigurability in achieving diverse processing functions. Optical microcombs show appealing advantages by providing numerous wavelength channels from a compact device. This thesis focuses on the realization and performance improvement of microwave photonic signal processors based on optical microcombs. The influence of theoretical and experimental factors is demonstrated and the performance between discrete and integrated systems is compared. This work guides the design of microwave photonic signal processors and opens avenues to new applications.</p

Facilitating the Oxygen Evolution Reaction of Lithium Peroxide via Molecular Adsorption

A major obstacle to developing practical Li–O2 battery is the inefficient oxygen evolution reaction (OER) of lithium peroxide Li2O2, as the high energy barrier associated with the OER process causes low round-trip efficiency. In a real battery system, many kinds of additives and impurities exist in the electrolyte, and therefore, molecular adsorptions on the Li2O2 surface are unavoidable; nevertheless, their influences on the OER of Li2O2 are rarely understood. Herein, density functional theory (DFT) calculations are employed to simulate the OER of Li2O2 surface adsorbed with a single H2O molecule. Simulation results indicate that the H2O molecule can spontaneously adsorb on the Li2O2 surface during the whole OER process. Consequently, the energy barriers of OER on Li2O2 surface are decreased and thus the intrinsic overpotential of the Li–O2 battery is reduced. Our findings suggest that the OER process in a Li–O2 battery can be modified by controlling the electrolyte additives, providing a new strategy for the development of improved Li–O2 batteries

Biodegradable “Core–Shell” Rubber Nanoparticles and Their Toughening of Poly(lactides)

Poly­(lactide) (PLA) nanocomposites were fabricated by solution blending of commercial poly­(l-lactide) (PLLA) and biodegradable core–shell particles, in which the core–shell particles were synthesized via octa polyhedral oligomeric silsesquioxane (octaPOSS)-initiated ring-opening copolymerization of a mixture of ε-caprolactone and l-lactide to form poly­(ε-caprolactone-co-lactide) (PCLLA) as rubbery core, followed by polymerization of d-lactide to form poly­(d-lactide) (PDLA) as outer shell. The outer PDLA layer could facilitate strong interactions between core–shell rubber particles and PLLA matrix via formation of stereocomplex. The randomness of PCLLA and the subsequent grafting of PDLA were monitored using nuclear magnetic resonance (NMR). The rubbery characteristic of PCLLA was confirmed by differential scanning calorimetry (DSC) which showed a Tg of ∼−7 °C. Stereocomplexation between PLLA and POSS-rubber-D was confirmed using Fourier transform infrared spectroscopy (FT-IR), DSC, and X-ray diffraction (XRD). The resulting biodegradable nanocomposites exhibit a 10-fold increase in elongation at break while maintaining other mechanical properties such as Young’s modulus and tensile strength. XRD, light scattering, scanning electron microscope (SEM), and thermogravimetric analysis (TGA) studies suggested that strong stereocomplex matrix/rubber interactions, good particle dispersion, rubber-initiated crazing, and low rubber content are the possible mechanisms behind such significant enhancements

Fortran code of the Projected Shell Model: feasible shell model calculations for heavy nuclei

Abstract A Fortran program is presented which conforms with the framework of the Projected Shell Model. The theory is a genuine shell model configuration mixing approach but requires only a very small configuration space. This feature enables us to interpret numerical results in simple physical terms. The present code allows detailed spectroscopic calculations for low- and high-spin states in axially deformed nuclei. It is written using a very efficient algorithm published earlier and runs extremely f... Title of program: PSM_EE, PSM_EO, PSM_OE, PSM_OO Catalogue Id: ADGJ_v1_0 Nature of problem These Fortran codes confrom with the framework of the Projected Shell Model. The theory is a genuine shell model configuration mixing approach but requires only a very small configuration space. This feature enables us to interpret numerical results in simple physical terms. The present code allows detailed spectroscopic calculations for low- and high-spin states in axially deformed nuclei. Versions of this program held in the CPC repository in Mendeley Data ADGJ_v1_0; PSM_EE, PSM_EO, PSM_OE, PSM_OO; 10.1016/S0010-4655(97)00064-7 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019

Table2_A Comparative Study of Systems Pharmacology and Gene Chip Technology for Predicting Targets of a Traditional Chinese Medicine Formula in Primary Liver Cancer Treatment.xlsx

Background: The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established.Objective: To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer.Methods: Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula ZhenzhuXiaojiTang (ZZXJT). A third square alignment was performed with molecular docking.Results: Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions.Conclusion:In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar.</p

Table4_A Comparative Study of Systems Pharmacology and Gene Chip Technology for Predicting Targets of a Traditional Chinese Medicine Formula in Primary Liver Cancer Treatment.XLSX

Background: The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established.Objective: To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer.Methods: Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula ZhenzhuXiaojiTang (ZZXJT). A third square alignment was performed with molecular docking.Results: Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions.Conclusion:In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar.</p

Table1_A Comparative Study of Systems Pharmacology and Gene Chip Technology for Predicting Targets of a Traditional Chinese Medicine Formula in Primary Liver Cancer Treatment.xlsx

Background: The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established.Objective: To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer.Methods: Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula ZhenzhuXiaojiTang (ZZXJT). A third square alignment was performed with molecular docking.Results: Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions.Conclusion:In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar.</p

Table3_A Comparative Study of Systems Pharmacology and Gene Chip Technology for Predicting Targets of a Traditional Chinese Medicine Formula in Primary Liver Cancer Treatment.xlsx

Background: The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established.Objective: To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer.Methods: Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula ZhenzhuXiaojiTang (ZZXJT). A third square alignment was performed with molecular docking.Results: Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions.Conclusion:In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar.</p

HIF1α/miR-199a/ADM feedback loop modulates the proliferation of human dermal microvascular endothelial cells (HDMECs) under hypoxic condition

Hypoxia-inducible factor 1α (HIF1α) plays a protective role in the hypoxia-induced cellular injury. In the present study, we attempted to investigate the role and mechanism of HIF1αin human dermal microvascular endothelial cells (hDMECs), a common-used cell model for researches on the hypoxia-induced injury during skin wounds healing. As revealed by ChIP and online tools prediction and confirmed by luciferase reporter and ChIP assays, HIF1A can bind to the promoter regions of ADM and miR-199a, while miR-199a directly binds to the 3ʹUTR of HIF1A and ADM. Hypoxia stress induces HIF1α and ADM expression while inhibits miR-199a expression. Under hypoxic condition, HIF1α knockdown increases the nucleus translocation of p65 and the release of TNF-α and IL-8, inhibits the proliferation and migration, while promotes the cellular permeability in HDMECs upon hypoxic stress, while ADM overexpression and miR-199a inhibition exerted an opposite effect on HDMECs. ADM overexpression or miR-199a inhibition could partially reverse the effect of HIF1A knockdown under hypoxia. In summary, we demonstrate a feedback loop consists of HIF1α, miR-199a, and ADM which protect HDMECs from hypoxia-induced cellular injury by modulating the inflammation response, cell proliferation, migration and permeability in HDMECs.</p