A Dual-Fluorescent Composite of Graphene Oxide and Poly(3-Hexylthiophene) Enables the Ratiometric Detection of Amines

A composite prepared by grafting a conjugated polymer, poly(3-hexylthiophene) (P3HT), to the surface of graphene oxide was shown to result in a dual-fluorescent material with tunable photoluminescent properties. Capitalizing on these unique features, a new class of graphene-based sensors that enables the ratiometric fluorescence detection of amine-based pollutants was developed. Moreover, through a detailed spectroscopic study, the origin of the optical properties of the aforementioned composite was studied and was found to be due to electronic decoupling of the conjugated polymer from the GO. The methodology described herein effectively overcomes a long-standing challenge that has prevented graphene based composites from finding utility in sensing and related applications.Meng, Dongli, Shaojun Yang, Dianming Sun, Yi Zeng, Jinhua Sun, Yi Li, Shouke Yan, Yong Huang, Christopher W. Bielawski, and Jianxin Geng. "A dual-fluorescent composite of graphene oxide and poly (3-hexylthiophene) enables the ratiometric detection of amines." Chemical Science 5, no. 8 (Apr., 2014): 3130-3134.Chemistr

Duality between the deconfined quantum-critical point and the bosonic topological transition

Recently significant progress has been made in $(2+1)$-dimensional conformal field theories without supersymmetry. In particular, it was realized that different Lagrangians may be related by hidden dualities, i.e., seemingly different field theories may actually be identical in the infrared limit. Among all the proposed dualities, one has attracted particular interest in the field of strongly-correlated quantum-matter systems: the one relating the easy-plane noncompact CP$^1$ model (NCCP$^1$) and noncompact quantum electrodynamics (QED) with two flavors ($N = 2$) of massless two-component Dirac fermions. The easy-plane NCCP$^1$ model is the field theory of the putative deconfined quantum-critical point separating a planar (XY) antiferromagnet and a dimerized (valence-bond solid) ground state, while $N=2$ noncompact QED is the theory for the transition between a bosonic symmetry-protected topological phase and a trivial Mott insulator. In this work we present strong numerical support for the proposed duality. We realize the $N=2$ noncompact QED at a critical point of an interacting fermion model on the bilayer honeycomb lattice and study it using determinant quantum Monte Carlo (QMC) simulations. Using stochastic series expansion QMC, we study a planar version of the $S=1/2$ $J$-$Q$ spin Hamiltonian (a quantum XY-model with additional multi-spin couplings) and show that it hosts a continuous transition between the XY magnet and the valence-bond solid. The duality between the two systems, following from a mapping of their phase diagrams extending from their respective critical points, is supported by the good agreement between the critical exponents according to the proposed duality relationships.Comment: 14 pages, 9 figure

In-Sample Policy Iteration for Offline Reinforcement Learning

Offline reinforcement learning (RL) seeks to derive an effective control policy from previously collected data. To circumvent errors due to inadequate data coverage, behavior-regularized methods optimize the control policy while concurrently minimizing deviation from the data collection policy. Nevertheless, these methods often exhibit subpar practical performance, particularly when the offline dataset is collected by sub-optimal policies. In this paper, we propose a novel algorithm employing in-sample policy iteration that substantially enhances behavior-regularized methods in offline RL. The core insight is that by continuously refining the policy used for behavior regularization, in-sample policy iteration gradually improves itself while implicitly avoids querying out-of-sample actions to avert catastrophic learning failures. Our theoretical analysis verifies its ability to learn the in-sample optimal policy, exclusively utilizing actions well-covered by the dataset. Moreover, we propose competitive policy improvement, a technique applying two competitive policies, both of which are trained by iteratively improving over the best competitor. We show that this simple yet potent technique significantly enhances learning efficiency when function approximation is applied. Lastly, experimental results on the D4RL benchmark indicate that our algorithm outperforms previous state-of-the-art methods in most tasks

SIRT3 Protects Rotenone-induced Injury in SH-SY5Y Cells by Promoting Autophagy through the LKB1-AMPK-mTOR Pathway.

SIRT3 is a class III histone deacetylase that modulates energy metabolism, genomic stability and stress resistance. It has been implicated as a potential therapeutic target in a variety of neurodegenerative diseases, including Parkinson's disease (PD). Our previous study demonstrates that SIRT3 had a neuroprotective effect on a rotenone-induced PD cell model, however, the exact mechanism is unknown. In this study, we investigated the underlying mechanism. We established a SIRT3 stable overexpression cell line using lentivirus infection in SH-SY5Y cells. Then, a PD cell model was established using rotenone. Our data demonstrate that overexpression of SIRT3 increased the level of the autophagy markers LC3 II and Beclin 1. After addition of the autophagy inhibitor 3-MA, the protective effect of SIRT3 diminished: the cell viability decreased, while the apoptosis rate increased; α-synuclein accumulation enhanced; ROS production increased; antioxidants levels, including SOD and GSH, decreased; and MMP collapsed. These results reveal that SIRT3 has neuroprotective effects on a PD cell model by up-regulating autophagy. Furthermore, SIRT3 overexpression also promoted LKB1 phosphorylation, followed by activation of AMPK and decreased phosphorylation of mTOR. These results suggest that the LKB1-AMPK-mTOR pathway has a role in induction of autophagy. Together, our findings indicate a novel mechanism by which SIRT3 protects a rotenone-induced PD cell model through the regulation of autophagy, which, in part, is mediated by activation of the LKB1-AMPK-mTOR pathway

Relevant long-range interaction of the entanglement Hamiltonian emerges from a short-range system

Beyond the Li-Haldane conjecture, we find the entanglement Hamiltonian (EH) is actually not closely similar to the original Hamiltonian on the virtual edge. Unexpectedly, the EH has some relevant long-range interacting terms which hugely affect the physics. Without loss of generality, we study a spin-1/2 Heisenberg bilayer to obtain the entanglement information between the two layers. Although the entanglement spectrum carrying Goldstone mode seems like a Heisenberg model on a single layer which is consistent with Li-Haldane conjecture, we demonstrate there actually is a finite temperature phase transition for the EH. The results violate the Mermin-Wagner theorem which means there should be relevant long-range terms in the EH. It reveals that the Li-Haldane conjecture ignores necessary corrections for the EH which may lead totally different physics.Comment: 7+2 pages, 3+2 figure

Improving Offline-to-Online Reinforcement Learning with Q-Ensembles

Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the performance due to the lack of exploration. To overcome it, offline-to-online RL combines offline pre-training with online fine-tuning, which enables the agent to further refine its policy by interacting with the environment in real-time. Despite its benefits, existing offline-to-online RL methods suffer from performance degradation and slow improvement during the online phase. To tackle these challenges, we propose a novel framework called Ensemble-based Offline-to-Online (E2O) RL. By increasing the number of Q-networks, we seamlessly bridge offline pre-training and online fine-tuning without degrading performance. Moreover, to expedite online performance enhancement, we appropriately loosen the pessimism of Q-value estimation and incorporate ensemble-based exploration mechanisms into our framework. Experimental results demonstrate that E2O can substantially improve the training stability, learning efficiency, and final performance of existing offline RL methods during online fine-tuning on a range of locomotion and navigation tasks, significantly outperforming existing offline-to-online RL methods

Spindle oscillations are generated in the dorsal thalamus and modulated by the thalamic reticular nucleus

Spindle waves occur during the early stage of slow wave sleep and are thought to arise in the thalamic reticular nucleus (TRN), causing inhibitory postsynaptic potential spindle-like oscillations in the dorsal thalamus that are propagated to the cortex. We have found that thalamocortical neurons exhibit membrane oscillations that have spindle frequencies, consist of excitatory postsynaptic potentials, and co-occur with electroencephalographic spindles. TRN lesioning prolonged oscillations in the medial geniculate body (MGB) and auditory cortex (AC). Injection of GABA~A~ antagonist into the MGB decreased oscillation frequency, while injection of GABA~B~ antagonist increased spindle oscillations in the MGB and cortex. Thus, spindles originate in the dorsal thalamus and TRN inhibitory inputs modulate this process, with fast inhibition facilitating the internal frequency and slow inhibition limiting spindle occurrence