The Jiao Tong University Spectroscopic Telescope Project

The Jiao Tong University Spectroscopic Telescope(JUST)is a 4.4-meter f/6.0 segmented-mirror telescope dedicated to spectroscopic observations.The JUST primary mirror is composed of 18 hexagonal segments,each with a diameter of 1.1 m.JUST provides two Nasmyth platforms for placing science instruments.One Nasmyth focus fits a field of view of 10′and the other has an extended field of view of 1.2°with correction optics.A tertiary mirror is used to switch between the two Nasmyth foci.JUST will be installed at a site at Lenghu in Qinghai Province,China,and will conduct spectroscopic observations with three types of instruments to explore the dark universe,trace the dynamic universe,and search for exoplanets:(1)a multi-fiber(2000 fibers)medium-resolution spectrometer(R=4000-5000)to spectroscopically map galaxies and large-scale structure;(2)an integral field unit(IFU)array of 500 optical fibers and/or a long-slit spectrograph dedicated to fast follow-ups of transient sources for multi-messenger astronomy;(3)a high-resolution spectrometer(R~100000)designed to identify Jupiter analogs and Earth-like planets,with the capability to characterize the atmospheres of hot exoplanets.

mRNA疗法在肿瘤免疫治疗中的应用

mRNA疗法以mRNA为制剂治疗疾病,是一种新兴的基因疗法,既可通过功能性蛋白的表达治疗基因缺陷性疾病或组织修复,又可通过抗原或抗体或受体的表达应用于免疫治疗,具有极大的应用价值。在肿瘤免疫治疗中,编码肿瘤相关抗原、特异性抗原、抗体或受体的mRNA进入细胞质后翻译成蛋白质,进而诱导特定免疫反应,实现疾病的预防与治疗。随着免疫治疗技术和mRNA的技术发展,针对恶性肿瘤和传染性疾病等的mRNA免疫治疗已步入临床应用阶段。本文将就mRNA的合成、纯化及修饰,基于mRNA的肿瘤免疫疗法、临床试验结果及开发新药所遇到的关键性机遇与挑战进行综述。

脱发相关差异表达基因的生物信息学分析

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。

面向数据权利、数据定价和隐私计算的数据驱动学习

近年来,数据已成为数字经济中最重要的生产要素之一。与传统生产要素不同,数据的数字化性质使其难以合同和交易。因此,建立一个高效和标准的数据交易市场体系将有利于降低成本,提高行业各方的生产力。尽管许多研究致力于数据法规和其他数据交易问题,如隐私和定价,但很少有工作对机器学习和数据科学领域的这些研究进行全面回顾。为了提供对这个主题的完整和最新的理解,本文涵盖了数据交易过程中的三个关键问题:数据权利、数据定价和隐私计算。通过厘清这些主题之间的关系,本文提供了一个数据生态系统的全貌,其中数据由个人、研究机构和政府等数据主体生成,而数据处理者出于创新或运营目的获取数据,并通过适当的定价机制根据数据主体各自的所有权分配收益。为了使人工智能(AI)能够长期有益于人类社会的发展,人工智能算法需要通过数据保护法规(即隐私保护法规)进行评估,以帮助构建日常生活中值得信赖的人工智能系统。

Numerical studies on the radiation uniformity of Z-pinch dynamic hohlraum

Radiation uniformity is important for Z-pinch dynamic hohlraum driven fusion. In order to understand the radiation uniformity of Z-pinchdynamic hohlraum, the code MULTI-2D with a new developed magnetic field package is employed to investigate the related physical processeson Julong-I facility with drive current about 7e8 MA. Numerical simulations suggest that Z-pinch dynamic hohlraum with radiation temperaturemore than 100 eV can be created on Julong-I facility. Although some X-rays can escape out of the hohlraum from Z-pinch plasma and electrodes, the radiation field near the foam center is quite uniform after a transition time. For the load parameters used in this paper, the transitiontime for the thermal wave transports from r = 1 mm to r = 0 mm is about 2.0 ns. Implosion of a testing pellet driven by cylindrical dynamichohlraum shows that symmetrical implosion is hard to achieve due to the relatively slow propagation speed of thermal wave and the compressionof cylindrical shock in the foam. With the help of quasi-spherical implosion, the hohlraum radiation uniformity and corresponding pelletimplosion symmetry can be significantly improved thanks to the shape modulation of thermal wave front and shock wave front.

Experimental investigation of chimney-enhanced natural convection in hexagonal honeycombs

The natural convective heat transfer performance of an aluminum hexagonal honeycomb acting as a novel heat sink for LED cooling is experi- mentally investigated. The concept of adding an adiabatic square chimney ex- tension for heat transfer enhancement is proposed, and the effects of chimney shape, height, and diameter are quantified. The average Nuav of a heated hon- eycomb with straight chimney is significantly higher than that without chimney, and the enhancement increases with increasing chimney height. At a given chim- ney height, honeycombs with divergent chimneys perform better than those with convergent ones. For a fixed divergent angle, the Nuav number increases mono- tonically with increasing chimney height. In contrast, with the convergent angle fixed, there exists an optimal chimney height to achieve maximum heat transfer.

Thermal stretching in two-phase porous media: Physical basis for Maxwell model

An alternate yet general form of the classical effective thermal conductivity model （Maxwell model） for two-phase porous materials is presented, serving an explicit thermo-physicM basis. It is demonstrated that the reduced effective thermal conductivity of the porous media due to non-conducting pore inclusions is caused by the mechanism of thermal stretching, which is a combi- nation of reduced effective heat flow area and elongated heat transfer distance （thermal tortuosity）.

Analytical design of effective thermal conductivity for fluid-saturated prismatic cellular metal honeycombs

A comparative optimal design of fluid-saturated prismatic cellular metal honeycombs （PCMHs） having different cell shapes is presented for thermal management applications. Based on the periodic topology of each PCMH, a unit cell （UC） for thermal transport analysis was selected to calculate its effective thermal conductivity. Without introducing any empirical coefficient, we modified and extended the analytical model of parallel-series thermal-electric network to a wider porosity range （0.7 ~ 0.98） by considering the effects of two-dimensional local heat conduction in solid ligaments inside each UC. Good agreement was achieved between analytical predictions and numerical simulations based on the method of finite volume. The concept of ligament heat conduction efficiency （LTCE） was proposed to physically explain the mechanisms underlying the effects of ligament configuration on effective thermal conductivity （ETC）. Based upon the proposed theory, a construct strategy was developed for designing the ETC by altering the equivalent interaction angle with the direction of heat flow： relatively small average interaction angle for thermal conduction and relatively large one for thermal insulation.

Effective thermal conductivity of wire-woven bulk Kagome sandwich panels

Thermal transport in a highly porous metallic wire-woven bulk Kagome （WBK） is numerically and analytically modeled. Based on topology similarity and upon introducing an elongation parameter in thermal tortuosity, an idealized Kagome with non-twisted struts is employed. Special focus is placed upon quanti- fying the effect of topological anisotropy of WBK upon its effective conductivity. It is demonstrated that the effective conductivity reduces linearly as the poros- ity increases, and the extent of the reduction is significantly dependent on the orientation of WBK. The governing physical mechanism of anisotropic thermal transport in WBK is found to be the anisotropic thermal tortuosity caused by the intrinsic anisotropic topology of WBK.

Research and Analysis on Cooling Capacity of Temperature Adjustment Equipments in Agricultural Multi-span Greenhouse

The high summer indoor temperature in agricultural greenhouse is not conducive to crop growth,so it is necessary to cool the greenhouse. In this study,taking the agricultural multi-span greenhouse with strong temperature adjustment ability for example,we make an experimental analysis on the temperature changes inside and outside the greenhouse under nine different equipment combinations( fan,skylight,sunshade,sunshade + fan,cooling pad + fan,skylight + fan,sunshade + skylight,sunshade + fan + cooling pad,sunshade + skylight + fan),respectively,and conduct the overall assessment on the measures to cool the summer agricultural greenhouse,in order to provide a reference for selecting a cost-effective cooling method for greenhouse. Tests show that the three treatment modes of fan,sunshade and sunshade + fan almost have no cooling capacity; skylight,sunshade + skylight have cooling capacity,but the effect is slow; cooling pad + fan,sunshade + skylight +fan can reduce the indoor temperature,but the effect is not very good; skylight and fan have good cooling effect; sunshade + fan + cooling pad has obvious cooling effect,which can well meet the cooling needs in the summer greenhouse.

Heat and Humidity Transport Analysis Inside a Special Underground Building

The calculation of heat and humidity load serves as the cornerstone of Heating,Ventilation,and Air Conditioning(HVAC)design.Nevertheless,as the heat and humidity load characteristics of underground structures differ substantially from those of above-ground structures,it is a challenge to derive their accurate calculation procedure through engineering experience.Therefore,it is particularly important to carry out quantitative research on heat and humidity load.This study used Design Builder software to study the influence of the design state point of air conditioning in underground buildings on energy consumption.The study showed that compared with the single design temperature of 18℃,setting the temperature of 16℃ in winter and 22℃ in summer could reduce energy consumption by about 59%.And the hourly heat load,cooling load and humidity load in one year are simulated and calculated so as to quantitatively analyze the characteristics of the load.This provides a database for selecting suitable HVAC equipment.It is further emphasized that dehumidification is the key to HVAC design of underground structures,which provides a reference for similar engineering designs.

区块链与科学数据治理

数据已经成为继土地、劳动力、资本、技术之后的第五大生产要素.作为大数据的一个分支,科学大数据正在成为科学发现的新型驱动力[1].与其他数据相比,科学数据具有价值密度高、多学科交叉、供需分布广、伦理规范要求严格等特点,是国家创新发展的基础性战略资源.欧美等发达国家和地区对科学数据的管理和利用均出台了相应的法规.

High-performance formaldehyde prediction for indoor air quality assessment using time series deep learning

Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)in green intelligent building design.This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton,silk,and polyester fabric with heat flux in Harbin,Beijing,Xi’an,Shanghai,Guangzhou,and Kunming,China.The time-by-time indoor dry-bulb temperature(T),relative humidity(RH),and Cf,obtained from verified simulations,were collated and used as input data for the long short-term memory(LSTM)of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics(adsorption and release of formaldehyde).The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations.The LSTM-based model also predicted Cf with mean absolute percentage error(MAPE),symmetric mean absolute percentage error(SMAPE),mean absolute error(MAE),mean square error(MSE),and root mean square error(RMSE)that fell within 10%,10%,0.5,0.5,and 0.8,respectively.In addition,the characteristics of the input dataset,model parameters,the prediction accuracy of different indoor fabrics,and the uncertainty of the data set are analyzed.The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input,and the prediction accuracy of LSTM is better than recurrent neural network(RNN).The method’s feasibility was established,and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.

具有随机分布的全/半开孔隙的多孔泡沫吸声分形模型

具有随机孔隙的多孔泡沫在声音管理应用中得到越来越多的利用,其吸声系数通常作为性能评估和设计优化的关键声学参数.根据不同的加工方法,泡沫中的孔隙可以是全开或半开的,并且通常表现为分形分布特征.为了便于工程应用,有必要对这些泡沫的吸声系数进行分析预测.然而,分析预测泡沫的吸声系数是一个挑战.因此,本研究提出了半开孔和全开孔的简化代表结构来微观分析泡沫内部的流动特性,并应用分形理论来描述随机分布的孔隙.引入一个叫“开孔度”的参数来表征孔隙的开放程度,可以解析地获得代表结构的粘性和热特征长度.随后,在经典吸声系数Johnson-Champoux-Allard模型的基础上,建立了一种将代表结构与分形理论统一起来的解析模型,从而可以将吸声系数作为具有全开/半开孔泡沫的关键形态参数的函数进行预测.与已有的实验测量和数值模拟结果相比,本文提出的分析模型可以较好地预测了半开孔和全开孔泡沫的关键流动特性和吸声系数.在0到4500 Hz的频率范围内,半开孔泡沫相对于具有相同孔隙率的全开孔泡沫可以更好地衰减声波.在孔隙率为0.95的条件下,半开孔泡沫的总体吸声系数分别提高了21.2%、57.7%和75.8%,此时开孔度由0.75到0.50降低了0.25.

Establishing HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method

We combined data from the Sloan Digital Sky Survey(SDSS) and the Arecibo Legacy Fast ALFA Survey(ALFALFA) to establish the HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method that is free of the Malmquist bias. To enable abundance matching, a cross-match between the SDSS DR7 galaxy group sample and the ALFALFA HI sources provides a catalog of 16520 HI-galaxy pairs within 14270 galaxy groups(halos). By applying the observational completeness reductions for both optical and HI observations, we used the remaining 8180 ALFALFA matched sources to construct the model constraints. Taking into account the dependence of HI mass on both the galaxy and group properties, we establish two sets of scaling relations: one with a combination of stellar mass,(g-r) color and halo mass, and the other with stellar mass,specific star-formation rate(sSFR), and halo mass. We demonstrate that our models can reproduce the HI mass component as both stellar mass and halo mass. Additional tests showed that the conditional HI mass distributions as a function of the cosmic web type and the satellite fractions were well recovered.

(DarkAI)Mapping the large-scale density field of dark matter using artificial intelligence

Herein,we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos.We built a UNet-architecture neural network and trained it using the COmoving Lagrangian Acceleration fast simulation,which is an approximation of the N-body simulation with 5123 particles in a box size of 500 h^(-1)Mpc.Further,we tested the resulting UNet model not only with training-like test samples but also with standard N-body simulations,such as the Jiutian simulation with 61443particles in a box size of 1000 h^(-1)Mpc and the ELUCID simulation,which has a different cosmology.The real-space dark-matter density fields in the three simulations can be reconstructed reliably with only a small reduction of the cross-correlation power spectrum at 1%and 10%levels at k=0.1 and 0.3 h Mpc-1,respectively.The reconstruction clearly helps to correct for redshift-space distortions and is unaffected by the different cosmologies between the training(Planck2018)and test samples(WMAP5).Furthermore,we tested the application of the UNet-reconstructed density field to obtain the velocity&tidal field and found that this approach provides better results compared with the traditional approach based on the linear bias model,showing a 12.2%improvement in the correlation slope and a 21.1%reduction in the scatter between the predicted and true velocities.Thus,our method is highly efficient and has excellent extrapolation reliability beyond the training set.This provides an ideal solution for determining the three-dimensional underlying density field from the plentiful galaxy survey data.

Compression and acceleration processes of spherical shells in gold cones

Double-cone ignition[Zhang et al.,Phil.Trans.R.Soc.A 378,20200015(2020)]was proposed recently as a novel path for direct-drive inertial confinement fusion using high-power lasers.In this scheme,plasma jets with both high density and high velocity are required for collisions.Here we report preliminary experimental results obtained at the Shenguang-II upgrade laser facility,employing a CHCl shell in a gold cone irradiated with a two-ramp laser pulse.The CHCl shell was pre-compressed by the first laser ramp to a density of 3.75 g/cm^(3)along the isentropic path.Subsequently,the target was further compressed and accelerated by the second laser ramp in the cone.According to the simulations,the plasma jet reached a density of up to 15 g/cm^(3),while measurements indicated a velocity of 126.8±17.1 km/s.The good agreements between experimental data and simulations are documented.

定量与工程生物学在mRNA基因疗法中的应用

信使RNA(messenger RNA,mRNA)作为基因信息的瞬时载体,是一种多功能、灵活及安全的基因治疗手段,但由于mRNA分子的不稳定性和免疫原性,这种基因疗法最初并没有得到普及.在过去几十年里,定量与工程生物学学科交叉融合使得以mRNA为基础的基因治疗方法逐渐从概念验证阶段走向临床治疗阶段.本文从mRNA的功能结构、体外合成mRNA的设计和技术创新等方面阐述了定量与工程生物学在mRNA基因治疗中的应用.借助一些实验室和临床应用案例展示了定量与工程生物学方法指导mRNA药物设计,解决当代医疗问题.有效的工程设计方法将有助于针对各种适应证和遗传背景建立精准的mRNA基因治疗平台.目前,尽管基于mRNA的基因治疗方法在大多数适应证中仍处于预临床阶段,但各种工程与定量生物设计方法的积累可能会为新一代mRNA基因疗法拓宽道路.

Machine-learning guided optimization of laser pulses for direct-drive implosions

The optimization of laser pulse shapes is of great importance and a major challenge for laser direct-drive implosions.In this paper,we propose an efficient intelligent method to perform laser pulse optimization via hydrodynamic simulations guided by the genetic algorithm and random forest algorithm.Compared to manual optimizations,the machine-learning guided method is able to efficiently improve the areal density by a factor of 63%and reduce the in-fiight-aspect ratio by a factor of 30%at the same time.A relationship between the maximum areal density and ion temperature is also achieved by the analysis of the big simulation dataset.This design method has been successfully demonstrated by the2021 summer double-cone ignition experiments conducted at the SG-II upgrade laser facility and has great prospects for the design of other inertial fusion experiments.

Machine-learning guided optimization of laser pulses for direct-drive implosions

There was a mistake in the value of V in the righthand column of Table l.A correct version of the table is below.Also,the units of this value when stated in the text were incorrectly stated as g/cm²,where they should have been stated askm/s.