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| 1 | BMAL1 knockout macaque monkeys display reduced sleep and psychiatric disorders显示文摘Circadian disruption is a risk factor for metabolic, psychiatric and age-related disorders, and non-human primate models could help to develop therapeutic treatments. Here, we report the generation of BMAL1 knockout cynomolgus monkeys for circadian-related disorders by CRISPR/Cas9 editing of monkey embryos. These monkeys showed higher nocturnal locomotion and reduced sleep, which was further exacerbated by a constant light regimen. Physiological circadian disruption was reflected by the markedly dampened and arrhythmic blood hormonal levels. Furthermore, BMAL1-deficient monkeys exhibited anxiety and depression, consistent with their stably elevated blood cortisol, and defective sensory processing in auditory oddball tests found in schizophrenia patients. Ablation of BMAL1 up-regulated transcriptional programs toward inflammatory and stress responses, with transcription networks associated with human sleep deprivation, major depressive disorders, and aging. Thus, BMAL1 knockout monkeys are potentially useful for studying the physiological consequences of circadian disturbance, and for developing therapies for circadian and psychiatric disorders. | Peiyuan Qiu Jian Jiang Zhen Liu Yijun Cai Tao Huang Yan Wang Qiming Liu Yanhong Nie Fang Liu Jiumu Cheng Qing Li Yun-Chi Tang Mu-ming Poo Qiang Sun Hung-Chun Chang | 2019 | National Science Review2019，6，1： | 21 |
| 2 | Intersection delay estimation from floating car data via principal curves: a case study on Beijing's road network显示文摘在大城市里可靠地在道路网络上估计即时旅行时间是一项紧迫的任务,尽管漂浮的汽车数据广泛地被用来反映真实交通。当前漂浮的汽车数据主要被用来在道路片断上估计即时交通条件,并且为拐弯延期评价做了很少。然而,道路交叉上的拐弯延期在现代城市里在道路网络上显著地作出贡献到全面旅行时间。在这篇论文,我们在场计算拐弯的一个技术框架与漂流汽车数据在道路网络上推迟。首先,与 GPS 收集的独创漂浮汽车数据装备了 taxies 基于 Hadoop 和 MongoDB 与一个分布式的系统被清洗并且匹配到一张街地图。第二,精制轨道数据集合在 96 次间隔之中被散布(从 0:00 ~ 23:59 ) 。所有与轨道片断轨道过去了的交叉被连接,并且组成了一件实验样品,当动脉的街上的交叉特殊被选择形成另一件实验样品时。第三,一个主要基于曲线的算法被介绍在给定的交叉估计拐弯延期。说服的算法统计上是不仅适合真实交通调节,而且对数据稀疏和失踪的数据问题感觉迟钝,它当前与收集技术的广泛地使用的漂浮的汽车数据是几乎不可避免的。我们采用了在 2011 在北京城市里从三月收集到 6 月的漂浮的汽车数据,它包含在数据体积为大约 600 GB 从大约 20000 辆装备 GPS 的出租车和说明产生的超过 260 万条轨道。结果显示出主要曲线我们介绍了的基于的算法在传统的方法上占主导地位,例如吝啬、中部的基于的途径,并且保持更高的评价精确性(大约 10%a [欧元] “ 15% 在 RMSE 更高) ,以及反映交通拥挤的变化趋势。与在交叉的旅行延期的评价结果,我们在三种次情形分析了拐弯延期的时间空间的分发(0:00a [欧元] “ 0:15, 8:15a [欧元] “ 8:30 和 12:00a [欧元] “ 12:15 ) 。它显示在一个在北京的单个旅行期间,道路网络上的一般水准 60% 旅行时间在交叉上被浪费,并且这种状况在白天是甚至更坏的。尽管 400 主要交叉仅仅拿 2.7% 所有交叉,他们占据大约 18% 旅行时间[出版摘要] | Xiliang LIU Feng LU Hengcai ZHANG Peiyuan QIU | 2013 | Frontiers of Earth Science2013，7，2： | 10 |
| 3 | A holistic approach to aligning geospatial data with multidimensional similarity measuring显示文摘Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information. | Li Yu Peiyuan Qiu Xiliang Liu Feng Lu Bo Wan | 2018 | International Journal of Digital Earth2018，11，8： | 4 |
| 4 | Detecting geo-relation phrases from web texts for triplet extraction of geographic knowledge:a context-enhanced method显示文摘As an effective organization form of geographic information,a geographic knowledge graph(GeoKG)facilitates numerous geography-related analyses and services.The completeness of triplets regarding geographic knowledge determines the quality of GeoKG,thus drawing considerable attention in the related domains.Mass unstructured geographic knowledge scattered in web texts has been regarded as a potential source for enriching the triplets in GeoKGs.The crux of triplet extraction from web texts lies in the detection of key phrases indicating the correct geo-relations between geo-entities.However,the current methods for key-phrase detection are ineffective because the sparseness of the terms in the web texts describing geo-relations results in an insufficient training corpus.In this study,an unsupervised context-enhanced method is proposed to detect geo-relation key phrases from web texts for extracting triplets.External semantic knowledge is introduced to relieve the influence of the sparseness of the georelation description terms in web texts.Specifically,the contexts of geo-entities are fused with category semantic knowledge and word semantic knowledge.Subsequently,an enhanced corpus is generated using frequency-based statistics.Finally,the geo-relation key phrases are detected from the enhanced contexts using the statistical lexical features from the enhanced corpus.Experiments are conducted with real web texts.In comparison with the well-known frequency-based methods,the proposed method improves the precision of detecting the key phrases of the geo-relation description by approximately 20%.Moreover,compared with the well-defined geo-relation properties in DBpedia,the proposed method provides quintuple key-phrases for indicating the geo-relations between geo-entities,which facilitate the generation of new triplets from web texts. | Peiyuan Qiu Li Yu Jialiang Gao Feng Lu | 2019 | Big Earth Data2019，3，3： | 1 |
| 5 | Respondent-driven sampling to recruit in-country migrant workers in China: A methodological assessment显示文摘 | PEIYUAN QIU YANG YANG XIAO g | 2012 | Scandinavian Journal of Public Health2012，40，： | 1 |
| 6 | Depression and associated factors in internal migrant workers in China 显示文摘 | Qiu Peiyuan Caine E Yang Yang | 2011 | J Affect Disord2011，134，13： | 1 |
| 7 | Synthesis of NaYF_(4):20% Yb^(3+),2% Er^(3+),2% Ce^(3+)@NaYF_(4) nanorods and their size dependent uptake efficiency under flow condition显示文摘Lanthanide doped fluorescent nanoparticles have gained considerable attention in biomedical applications.However,the low uptake efficiency of nanoparticles by cells has limited their applications.In this work,we demonstrate how the uptake efficiency is affected by the size of nanoparticles under flow conditions.Using the same size NaYF_(4):20%Yb^(3+),2%Er^(3+),2%Ce^(3+)(the contents of rare earths elements are in molar fraction)nanoparticles as core,NaYF_(4):20%Yb^(3+),2%Er^(3+),2%Ce^(3+)@NaYF_(4) core-shell structured nanorods(NRs)with different sizes of 60-224 nm were synthesized by thermal decomposition and hot injection method.Under excitation at 980 nm,a strong upconversion green emission(541 nm,^(2)H_(11/2)→^(4) I_(15/2) of Er^(3+))is observed for all samples.The emission intensity for each size nanorod was calibrated and is found to depend on the width of NRs.Under flow conditions,the nanorods with 96 nm show a maximum uptake efficiency by endothelial cells.This work demonstrates the importance of optimizing the size for improving the uptake efficiency of lanthanide-doped nanoparticles. | Dongmei Qiu Jie Hu Peiyuan Wang Decai Huang Yaling Lin Haina Tian Xiaodong Yi Qilin Zou Haomiao Zhu | 2022 | Journal of Rare Earths2022，40，10： | 0 |
| 8 | Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO-Catalyzed Primary Alcohol Oxidation显示文摘Though alcohol oxidations were considered as well-established reactions,selecting productive conditions or predicting reaction yields for unseen alcohols remained as major challenges.Herein,an auto machine learning(ML)model for TEMPO-catalyzed oxida-tion of primary alcohols to the corresponding carboxylic acids is disclosed.A dataset of 3444 data,consisting of 282 primary alco-hols and 45 conditions,were generated using high-throughput experimentation(HTE).With the HTE data and 105 descriptors,a multi-label prediction was performed with AutoGluon(an open-source auto machine learning framework)and KNIME(an open-source data analytics platform).For the independent test of 240 reactions(a full matrix of 20 unseen alcohols and 12 condi-tions),AutoGluon with multi-label prediction for yield prediction(AGMP)gave excellent performance.For external test of 1308 re-actions(consisting of 84 alcohols and 45 conditions),AGMP still afforded good results with R2 as 0.767 and MAE as 4.9%.The model also revealed that the newly generated descriptor(Y/N,classification of the reaction reactivity)was the most relevant descriptor for yield prediction,offering a new perspective to integrate HTE and ML in organic synthesis. | Jia Qiu Yougen Xu Shimin Su Yadong Gao Peiyuan Yu Zhixiong Ruan Kuangbiao Liao | 2023 | Chinese Journal of Chemistry2023，41，2： | 0 |
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