Impact of vaccination on the COVID-19 pandemic in U.S. states

Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies

Endogenous cross-region human mobility and pandemics

We study infectious diseases using a Susceptible-Infected-Recovered-Deceased model with endogenous cross-region human mobility. Individuals weigh the risk of infection against economic opportunities when moving across regions. The model predicts that the mobility rate of susceptible individuals declines with a higher infection rate at the destination. With cross-region mobility, a decrease in the transmission rate or an increase in the removal rate of the virus in any region reduces the global basic reproduction number (R0). Global R0 falls between the minimum and maximum of local R0s. A new method of Normalized Hat Algebra is developed to solve the model dynamics. Simulations indicate that a decrease in global R0 does not always imply a lower cumulative infection rate. Local and central governments may prefer different mobility control policies

ULK1 phosphorylates Exo70 to suppress breast cancer metastasis

乳腺癌是威胁女性生命健康的“头号杀手”，而远处转移是乳腺癌患者死亡的主要原因。因此，了解乳腺癌如何发动侵袭和转移，对于有效治疗乳腺癌、延长病人生存期具有重要意义。本研究中，该团队发现ULK1通过结合并磷酸化胞泌蛋白复合体关键亚基Exo70来抑制乳腺癌转移。ULK1对Exo70上Ser47，Ser59和Ser89位点的磷酸化，严重地削弱了Exo70的自身寡聚化和与其它胞外分泌复合体亚基的结合，进而减少了细胞运动伪足形成以及基质金属蛋白酶的分泌，从而抑制乳腺癌细胞的迁移和侵袭。该论文首次揭示了胞外分泌复合体重要成员Exo70在乳腺癌中受到ULK1和ERK1/2的双重磷酸化调控，从而使得乳腺癌细胞可以根据外环境来决定潜伏还是发动侵袭转移，为乳腺癌的治疗提供了新的理论基础。 本论文的通讯作者为占艳艳副教授、郭巍教授和胡天惠教授。医学院博士生毛丽媛、占艳艳副教授、吴斌博士和医学院博士生于强为共同第一作者。【Abstract】Increased expression of protein kinase ULK1 was reported to negatively correlate with breast cancer metastasis. Here we report that ULK1 suppresses the migration and invasion of human breast cancer cells. The suppressive effect is mediated through direct phosphorylation of Exo70, a key component of the exocyst complex. ULK1 phosphorylation inhibits Exo70 homo-oligomerization as well as its assembly to the exocyst complex, which are needed for cell protrusion formation and matrix metalloproteinases secretion during cell invasion. Reversely, upon growth factor stimulation, Exo70 is phosphorylated by ERK1/2, which in turn suppresses its phosphorylation by ULK1. Together, our study identifies Exo70 as a substrate of ULK1 that inhibits cancer metastasis, and demonstrates that two counteractive regulatory mechanisms are well orchestrated during tumor cell invasion.This work was supported by the grants from the National Natural Science Foundation of China (81572589, U1405228, 81472568, and 31770860), the Natural Science Foundation of Fujian grant (2017J06020, 2018J01400, 2017R1036-4, 2017R1036-6, 2016R1034-1, and 2016R1034-4), and the Xiamen Science and Technology grant (3502Z20159013) to Y.-y.Z. and T.H., and National Institute of Health R01 GM111128 to W.G.该论文的研究成果是在国家自然科学基金和福建省基金的资助下，与美国宾夕法尼亚大学和清华大学共同协作完成的

Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes

The fidelity of quantum states is an important concept in quantum information. Improving quantum fidelity is very important for both quantum communication and quantum computation. In this paper, we use a quantum neural network (QNN) to enhance the fidelity of [6, 2, 2] quantum convolutional codes. Towards the circuit of quantum convolutional codes, the target quantum state |0⟩ or |1⟩ is turned into entangled quantum states, which can defend against quantum noise more effectively. As the quantum neural network works better for quantum states with low dimension, we divide the quantum circuits into two parts. Then we apply the quantum neural network to each part of the circuit. The results of the simulation show that the network performs well in enhancing the fidelity of the quantum states. Through the quantum neural network, the fidelity of the first part is enhanced from 95.2% to 99.99%, and the fidelity of the second part is enhanced from 93.88% to 94.57%

A Performance–Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation

Teleportation is an important protocol in quantum communication. Realizing teleportation between arbitrary nodes in multi-hop quantum networks is of great value. Most of the existing multi-hop quantum networks are based on Bell states or Greeberger–Horne–Zeilinger (GHZ) states. Bell state is more susceptible to noise than GHZ states after purification, but generating a GHZ state consumes more basic states. In this paper, a new quantum multi-hop network scheme is proposed to improve the interference immunity of the network and avoid large consumption at the same time. Teleportation is realized in a network based on entanglement swapping, fusion, and purification. To ensure the robustness of the system, we also design the purification algorithm. The simulation results show the successful establishment of entanglement with high fidelity. Cirq is used to verify the network on the Noisy Intermediate-Scale Quantum (NISQ) platform. The robustness of the fusion scheme is better than the Bell states scheme, especially with the increasing number of nodes. This paper provides a solution to balance the performance and consumption in a multi-hop quantum network

A Multi-Objective Optimisation Mathematical Model with Constraints Conducive to the Healthy Rhythm for Lighting Control Strategy

Studies have shown that illuminance and correlated colour temperature (CCT) are strongly correlated with body responses such as circadian rhythm, alertness, and mood. It is worth noting that these responses show a complex and variable coupling, which needs to be solved using accurate mathematical models for the regulation of indoor light parameters. Therefore, in this study, by weighing the evaluations of visual comfort, alertness, valence, and arousal of mood, a multi-objective optimisation mathematical model was developed with constraints conducive to the healthy rhythm. The problem was solved with the multi-objective evolutionary algorithm based on the decomposition differential evolution (MOEA/D-DE) algorithm. Taking educational space as the analysis goal, a dual-parameter setting strategy for illuminance and CCT covering four modes was proposed: focused learning, comfortable learning, soothing learning, and resting state, which could provide a scientific basis for the regulation of the lighting control system. The alertness during class time reached 3.01 compared to 2.34 during break time, showing a good light facilitation effect. The proposed mathematical model and analysis method also have the potential for application in the lighting design and control in other spaces to meet the era of intelligent, highly flexible, and sustainable buildings

Hazard Assessment of Urban Waterlogging Disaster on Underground Substations in Shanghai

Based on the two methods of the index system and the scenario simulation, hazard assessment of urban waterlogging disaster on underground substations in Shanghai was carried out. The results show that: (1) the return period rainfall of underground substations in Shanghai gradually increases with the extension of the return period; in terms of spatial distribution, the return period precipitation of the stations along the river and within the inner ring is higher than that of the stations outside the inner ring. (2) The waterlogging thresholds of stations between the inner ring and the middle ring are highest. The waterlogging thresholds of stations within the inner ring and between the middle ring and the outer ring are higher than those in the suburban stations. (3) Under the 20-year return period extreme precipitation scenario, the urban waterlogging risks of underground substations in Shanghai are mainly at low and medium levels. Under the 50-year and longer return period extreme precipitation scenario, the risks of inner ring stations increase to medium-high level