Selective disruption of high sensitivity heat activation but not capsaicin activation of TRPV1 channels by pore turret mutations.

The capsaicin receptor transient receptor potential vanilloid (TRPV)1 is a highly heat-sensitive ion channel. Although chemical activation and heat activation of TRPV1 elicit similar pungent, painful sensation, the molecular mechanism underlying synergistic activation remains mysterious. In particular, where the temperature sensor is located and whether heat and capsaicin share a common activation pathway are debated. To address these fundamental issues, we searched for channel mutations that selectively affected one form of activation. We found that deletion of the first 10 amino acids of the pore turret significantly reduced the heat response amplitude and shifted the heat activation threshold, whereas capsaicin activation remained unchanged. Removing larger portions of the turret disrupted channel function. Introducing an artificial sequence to replace the deleted region restored sensitive capsaicin activation in these nonfunctional channels. The heat activation, however, remained significantly impaired, with the current exhibiting diminishing heat sensitivity to a level indistinguishable from that of a voltage-gated potassium channel, Kv7.4. Our results demonstrate that heat and capsaicin activation of TRPV1 are structurally and mechanistically distinct processes, and the pore turret is an indispensible channel structure involved in the heat activation process but is not part of the capsaicin activation pathway. Synergistic effect of heat and capsaicin on TRPV1 activation may originate from convergence of the two pathways on a common activation gate

Simulation of emission spectra of transition-metal dichalcogenide monolayers with the multimode Brownian oscillator model

The multimode Brownian oscillator model is employed to simulate the emission spectra of transition metal dichalcogenide monolayers. Good agreement is obtained between measured and simulated photoluminescence spectra of WSe2, WS2, MoSe2 and MoS2 at various temperatures. The Huang-Rhys factor extracted from the model can be associated with that from the modified semi-empirical Varshni equation at high temperatures. Individual mechanisms leading to the unique temperature-dependent emission spectra of those TMDs are validated by the MBO fitting, while it is in turn confirmed that the MBO analysis is an effective method for studying the optical properties of TMD monolayers. Parameters extractd from the MBO fitting can be used to explore exciton-photon-phonon dynamics of TMDs in a more comprehensive model

Application of liposomes for antimicrobial photodynamic therapy

As more and more antibiotic-resistant organisms are emerging continuously, the development of new antibiotics falls behind the evolution of antibiotic-resistance. Thus there is an urgent need to search for alternative antibacterial drugs. Nowadays, antimicrobial photodynamic therapy (APDT) has emerged as an efficacious modality to treat various kinds of microbial infections. Meanwhile, liposomes are shown to be an attractive drug delivery system in the treatment of infections and may improve the APDT efficiency. Therefore, the aims of this study are to develop bacteria-targeting liposomes to further improve APDT, and to develop a high-throughput method for screening a large number of photosensitizer-loaded liposomal formulations. In publication 1 and 2, a generation II photosensitizer (PS), temoporfin, was incorporated into liposomes for APDT, afterwards two bacteria-targeting ligands, the antimicrobial peptide WLBU2 and the lectin Wheat Germ Agglutinin (WGA) were successfully coupled to the surface of temoporfin-loaded liposomes, respectively, using an aminogroup-reactive functional lipid: NHS-PEG2000-DSPE. The delivery of temoporfin to Methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (P. aeruginosa) was confirmed by fluorescence microscopy and flow cytometry, thus demonstrating that more temoporfin was delivered to bacteria by the modified liposomes than by unmodified liposomes. Consequently, both of the two bacteria-targeting liposomes eradicated all MRSA and enhanced the photodynamic inactivation of P. aeruginosa in the in vitro photodynamic inactivation test. In particular, WLBU2 seems to be a better bacteria-targeting ligand than WGA. These results demonstrate that the strategy of using bacteria-targeting liposomes is promising for improving the APDT efficiency against both gram-positive and gram-negative bacteria in the local infections. To speed up the screening process of liposomal formulations and develop a method suitable for large-scale production of liposomes, a novel strategy for the fast and convenient high-throughput screening of liposomal formulations was developed in Publication 3, utilizing the automation of the ethanol injection method. This strategy was illustrated by the preparation and screening of the liposomal formulation library of temoporfin. To optimize the formulations, different parameters were investigated, including lipid types, lipid concentration, the ratio of ethanol to aqueous solution, the ratio of drug to lipid and the addition of functional phospholipids. Numerous formulations (261 samples) were screened quickly in a high-throughput way. The factors affecting the properties of liposomes were investigated step-by-step, where liposomes were prepared and characterized automatically, making it easy and fast to optimize the liposomal formulations of temoporfin. The obtained optimized liposomes were unilamellar spheres with a diameter of about 50 nm, and were very stable for over 20 weeks. What’s more, this high-throughput method is also applicable for preparing bacteria-targeting liposomes of different compositions, showing many advantages over the conventional methods. All the results demonstrate that this high-throughput screening strategy is fast, automated, materially efficient, labor-saving, time-saving, economic, facile, and highly reproducible. This approach is promising for the development of new formulations to enhance APDT; due to the nature of the process, the approach is readily amenable to scale-up of production. In conclusion, bacteria-targeting liposomes are useful drug delivery system for APDT, and the high-throughput method will facilitate the search for more suitable liposomal formulations. These PS-loaded liposomal formulations have potential clinical applications for the treatment of microbial infections

LookinGood^{\pi}: Real-time Person-independent Neural Re-rendering for High-quality Human Performance Capture

We propose LookinGood^{\pi}, a novel neural re-rendering approach that is aimed to (1) improve the rendering quality of the low-quality reconstructed results from human performance capture system in real-time; (2) improve the generalization ability of the neural rendering network on unseen people. Our key idea is to utilize the rendered image of reconstructed geometry as the guidance to assist the prediction of person-specific details from few reference images, thus enhancing the re-rendered result. In light of this, we design a two-branch network. A coarse branch is designed to fix some artifacts (i.e. holes, noise) and obtain a coarse version of the rendered input, while a detail branch is designed to predict "correct" details from the warped references. The guidance of the rendered image is realized by blending features from two branches effectively in the training of the detail branch, which improves both the warping accuracy and the details' fidelity. We demonstrate that our method outperforms state-of-the-art methods at producing high-fidelity images on unseen people

Photon-assisted Landau-Zener transitions in a periodically driven Rabi dimer coupled to a dissipative mode

We investigate multiple photon-assisted Landau-Zener (LZ) transitions in a hybrid circuit quantum electrodynamics device in which each of two interacting transmission-line resonators is coupled to a qubit, and the qubits are driven by periodic driving fields and also coupled to a common phonon mode. The quantum state of the entire composite system is modeled using the multi-$\rm D_2$ Ansatz in combination with the time-dependent Dirac-Frenkel variational principle. Applying a sinusoidal driving field to one of the qubits, this device is an ideal platform to study the photon-assisted LZ transitions by comparing the dynamics of the two qubits. A series of interfering photon-assisted LZ transitions take place if the photon frequency is much smaller than the driving amplitude. Once the two energy scales are comparable, independent LZ transitions arise and a transition pathway is revealed using an energy diagram. It is found that both adiabatic and nonadiabatic transitions are involved in the dynamics. Used to model environmental effects on the LZ transitions, the common phonon mode coupled to the qubits allows for more available states to facilitate the LZ transitions. An analytical formula is obtained to estimate the short-time phonon population and produces results in reasonable agreement with numerical calculations. Equipped with the knowledge of the photon-assisted LZ transitions in the system, we can precisely manipulate the qubit state and successfully generate the qubit dynamics with a square-wave pattern by applying driving fields to both qubits, opening up new venues to manipulate the states of qubits and photons in quantum information devices and quantum computer

Simple Hardware-Efficient PCFGs with Independent Left and Right Productions

Scaling dense PCFGs to thousands of nonterminals via a low-rank parameterization of the rule probability tensor has been shown to be beneficial for unsupervised parsing. However, PCFGs scaled this way still perform poorly as a language model, and even underperform similarly-sized HMMs. This work introduces \emph{SimplePCFG}, a simple PCFG formalism with independent left and right productions. Despite imposing a stronger independence assumption than the low-rank approach, we find that this formalism scales more effectively both as a language model and as an unsupervised parser. As an unsupervised parser, our simple PCFG obtains an average F1 of 65.1 on the English PTB, and as a language model, it obtains a perplexity of 119.0, outperforming similarly-sized low-rank PCFGs. We further introduce \emph{FlashInside}, a hardware IO-aware implementation of the inside algorithm for efficiently scaling simple PCFGs.Comment: Accepted to Findings of EMNLP, 202

A Deep‐Learning Approach to the Dynamics of Landau–Zenner Transitions

Traditional approaches to the dynamics of the open quantum systems with high precision are often resource intensive. How to improve computation accuracy and efficiency for target systems is an extremely difficult challenge. In this work, combining unsupervised and supervised learning algorithms, a deep-learning approach is introduced to simulate and predict Landau–Zenner dynamics. Data obtained from multiple Davydov (Formula presented.) Ansatz with a low multiplicity of four are used for training, while the data from the trial state with a high multiplicity of ten are adopted as target data to assess the accuracy of prediction. After proper training, our method can successfully predict and simulate Landau–Zenner dynamics using only random noise and two adjustable model parameters. Compared to the high-precision dynamics data from multiple Davydov (Formula presented.) Ansatz with a multiplicity of ten, the error rate falls below 0.6%.Ministry of Education (MOE)Accepted versionThe authors gratefully acknowledge the support of the Singapore Ministry of Education Academic Research Fund (Grant Nos. 2018-T1-002-175 and 2020-T1-002- 075)). K. Sun would also like to thank the Natural Science Foundation of Zhejiang Province (Grant No. LY18A040005) for partial support. L.L. Gao acknowledges the support of the Graduate Scientific Research Foundation of Hangzhou Dianzi University

Quantifying non-Markovianity for a chromophore-qubit pair in a super-Ohmic bath

An approach based on a non-Markovian time-convolutionless polaron master equation is used to probe the quantum dynamics of a chromophore-qubit in a super-Ohmic bath. Utilizing a measure of non-Markovianity based on dynamical fixed points, we study the effects of the environment temperature and the coupling strength on the non-Markovian behavior of the chromophore in a super-Ohmic bath. It is found that an increase in the temperature results in a reduction in the backflow information from the environment to the chromophore, and therefore, a suppression of non-Markovianity. In the weak coupling regime, increasing coupling strength will enhance the non- Markovianity, while the effect is reversed in the strong coupling regime.Comment: 10 pages, 9 figure

Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks

Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE (\hgnn{}), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). To alleviate error propagation,we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model. For higher-order modeling, we build a hypergraph, where nodes are entities (provided by the span pruner) and relations thereof, and hyperedges encode interactions between two different relations or between a relation and its associated subject and object entities. We then run a hypergraph neural network for higher-order inference by applying message passing over the built hypergraph. Experiments on three widely used benchmarks (\acef{}, \ace{} and \scierc{}) for ERE task show significant improvements over the previous state-of-the-art PL-marker.Comment: Accepted to Proceedings of EMNLP, 202