Therapeutic approaches for sickle cell disease by targeting BCL11A

Sickle cell disease (SCD) is one of the most serious diseases in the world caused by gene mutat ions. People with SCD have sickle-shaped red blood cells. BCL11A can inhibit the formation of γ-protein which can lead to the reduction of HbF,the reduction of the ability of hemoglobin to carry oxygen. Thus, d ecreasing the quantity of BCL11A can be a potential target to treat SCD.One of the potentially effective tr eatment approaches is to edit BCL11A genes with CRISPR technology.CRISPR is a gene-editing technolo gy,which can precisely and efficiently locate the treatment targets.With CRISPR technology,the gene expr ession of BCL11A can be blocked,and γ-protein can be switched to form more HbF.Hemoglobin levels for transporting oxygen can return to normal.This review introduces and summarizes the mechanisms to treat SCD andtheir limitations.Previous research papers revealed that the treatments of SCD are one-dimension al and these treatments just can inhibit disease.Fundamental problems of SCD remain to be solved

Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation

We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov games with independent linear function approximation, where each agent has its own function approximation for the state-action value functions that are marginalized by other players' policies. We design new algorithms for learning the Markov coarse correlated equilibria (CCE) and Markov correlated equilibria (CE) with sample complexity bounds that only scale polynomially with each agent's own function class complexity, thus breaking the curse of multiagents. In contrast, existing works for Markov games with function approximation have sample complexity bounds scale with the size of the \emph{joint action space} when specialized to the canonical tabular Markov game setting, which is exponentially large in the number of agents. Our algorithms rely on two key technical innovations: (1) utilizing policy replay to tackle non-stationarity incurred by multiple agents and the use of function approximation; (2) separating learning Markov equilibria and exploration in the Markov games, which allows us to use the full-information no-regret learning oracle instead of the stronger bandit-feedback no-regret learning oracle used in the tabular setting. Furthermore, we propose an iterative-best-response type algorithm that can learn pure Markov Nash equilibria in independent linear Markov potential games. In the tabular case, by adapting the policy replay mechanism for independent linear Markov games, we propose an algorithm with $\widetilde{O}(\epsilon^{-2})$ sample complexity to learn Markov CCE, which improves the state-of-the-art result $\widetilde{O}(\epsilon^{-3})$ in Daskalakis et al. 2022, where $\epsilon$ is the desired accuracy, and also significantly improves other problem parameters.Comment: 51 pages. Update: Accepted for presentation at the Conference on Learning Theory (COLT) 202

Spatiotemporal Hologram

Spatiotemporal structured light has opened up new avenues for optics and photonics. Current spatiotemporal manipulation of light mostly relies on phase-only devices such as liquid crystal spatial light modulator to generate spatiotemporal optical fields with unique photonic properties. However, simultaneous manipulation of both amplitude and phase of the complex field for the spatiotemporal light is still lacking, limiting the diversity and richness of achievable photonic properties. In this work, a simple and versatile spatiotemporal holographic method that can arbitrarily sculpture the spatiotemporal light is presented. The capabilities of this simple yet powerful method are demonstrated through the generation of fundamental and higher-order spatiotemporal Bessel wavepacket, spatiotemporal crystal-like and quasi-crystal-like structures, and spatiotemporal flat-top wavepackets. Fully customizable spatiotemporal wavepackets will find broader application in investigating the dynamics of spatiotemporal fields and interactions between ultrafast spatiotemporal pulses and matters, unveiling previously hidden light-matter interactions and unlocking breakthroughs in photonics and beyond

Influence and Optimization of Packet Loss on the Internet-Based Geographically Distributed Test Platform for Fuel Cell Electric Vehicle Powertrain Systems

In view of recent developments in fuel cell electric vehicle powertrain systems, Internet-based geographically distributed test platforms for fuel cell electric vehicle powertrain systems become a development and validation trend. Due to the involvement of remote connection and the Internet, simulation with connected models can suffer great uncertainty because of packet loss. Such a test platform, including packet loss characteristics, was built using MATLAB/Simulink for use in this paper. The simulation analysis results show that packet loss affects the stability of the whole test system. The impact on vehicle speed is mainly concentrated in the later stage of simulation. Aiming at reducing the effect of packet loss caused by Internet, a robust model predictive compensator was designed. Under this compensator, the stability of the system is greatly improved compared to the system without a compensator

Instrumental support in the physical activity community - premilinary results

Currently, we witness the growth of ICT-mediated solutions for chronic diseases management, especially to assist and support patients in lifestyle changes in order to improve their health condition. Being physically active is one the recommended lifestyle changes for patients with chronic diseases. The challenge within those ICT-mediated solutions for physical activity support is to allow patients to manage themselves their physical activity level (PAL) and provide them with the needed social support. One of those solutions available is the use of Virtual Community (VC)

Was Kaposi’s sarcoma-associated herpesvirus introduced into China via the ancient Silk Road? An evolutionary perspective

Kaposi’s sarcoma-associated herpesvirus (KSHV) has become widely dispersed worldwide since it was first reported in 1994, but the seroprevalence of KSHV varies geographically. KSHV is relatively ubiquitous in Mediterranean areas and the Xinjiang Uygur Autonomous Region, China. The origin of KSHV has long been puzzling. In the present study, we collected and analysed 154 KSHV ORF-K1 sequences obtained from samples originating from Xinjiang, Italy, Greece, Iran and southern Siberia using Bayesian evolutionary analysis in BEAST to test the hypothesis that KSHV was introduced into Xinjiang via the ancient Silk Road. According to the phylogenetic analysis, 72 sequences were subtype A and 82 subtype C, with C2 (n = 56) being the predominant subtype. The times to the most recent common ancestors (tMRCAs) of KSHV were 29,872 years (95% highest probability density [HPD], 26,851–32,760 years) for all analysed sequences and 2037 years (95% HPD, 1843–2229 years) for Xinjiang sequences in particular. The tMRCA of Xinjiang KSHV was exactly matched with the time period of the ancient Silk Road approximately two thousand years ago. This route began in Chang’an, the capital of the Han dynasty of China, and crossed Central Asia, ending in the Roman Empire. The evolution rate of KSHV was slow, with 3.44 × 10−6 substitutions per site per year (95% HPD, 2.26 × 10−6 to 4.71 × 10−6), although 11 codons were discovered to be under positive selection pressure. The geographic distances from Italy to Iran and Xinjiang are more than 4000 and 7000 kilometres, respectively, but no explicit relationship between genetic distance and geographic distance was detected