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| 1 | Cloning of four DREB genes from Tibetan Sophora moorcroftiana and analysis of their expression during abiotic stress显示文摘Sophora moorcroftiana is an endemic, droughtresistant shrub that grows in Tibet and has some degree of resistance to salt, cold, heat, and drought. In the present study, four dehydration responsive element-binding(DREB) genes(Sm DREB1, Sm DREB2, Sm DREB and Sm DREB1) were isolated from S. moorcroftiana for the first time and their expression and proline content under abiotic stress were analyzed. Proline accumulated in seedlings under drought, salt, cold, and heat stress treatments. The four genes were variously expressed in response to the four abiotic stresses. Sm DREB1 was induced by drought, cold, and heat stresses; Sm DREB2 and Sm DREB4 were both induced by salt, cold, and heat stresses, whereas Sm DREB3 was induced by drought and heat stresses. Thus, these four genes may participate in conferring tolerance to these four abiotic stresses and are candidate genes for genetic engineering in the future. | Weijie Yao Yaru Fu Yanfu Zhang Hui-e Li | 2016 | Journal of Forestry Research2016，27，3： | 2 |
| 2 | Progress in plant phenology modeling under global climate change显示文摘Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces.Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon,water and energy fluxes between the vegetation and near-surface atmosphere.Therefore,an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon,water and energy balance of terrestrial ecosystems.Phenological studies have developed rapidly under global change conditions,while the research of phenology modeling is largely lagged.Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles.Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues.In this review,we first summarized the drivers of plant phenology and overviewed the development of plant phenology models.Finally,we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions. | Yongshuo FU Xinxi LI Xuancheng ZHOU Xiaojun GENG Yahui GUO Yaru ZHANG | 2020 | Science China Earth Sciences2020，63，9： | 1 |
| 3 | Subcarrier and power allocation in OFDM-based relaying system 显示文摘 | FU Yaru ZHU Qi | 2012 | Journal of Information and Computer Science2012，7，21： | 1 |
| 4 | GhWDL3 is involved in the formation and development of fiber cell morphology in upland cotton(Gossypium hirsutum L.)显示文摘Background Cotton fiber is a model tissue for studying microtubule-associated proteins(MAPs).The Xklp2(TPX2)proteins that belong to the novel MAPs member mainly participate in the formation and development of microtubule(MT).However,there is a lack of studies concerning the systematic characterization of the TPX2 genes family in cotton.Therefore,the identification and portrayal of G.hirsutum TPX2 genes can provide key targets for molecular manipula-tion in the breeding of cotton fiber improvement.Result In this study,TPX2 family genes were classified into two distinct subclasses TPXLs and MAP genes WAVE DAMP-ENED2-LIKE(WDLs)and quite conservative in quantity.GhWDL3 was significantly up-regulated in 15 days post anthe-sis fibers of ZRI-015(an upland cotton with longer and stronger fiber).GhWDL3 promotes all stem hairs to become straight when overexpressed in Arabidopsis,which may indirectly regulate cotton fiber cell morphology during fiber development.Virus induced gene silencing(VIGS)results showed that GhWDL3 inhibited fiber cell elongation at fiber development periods through regulating the expression of cell wall related genes.Conclusion These results reveal that GhWDL3 regulated cotton fiber cell elongation and provide crucial information for the further investigation in the regulatory mechanisms/networks of cotton fiber length. | CHEN Baojun TIAN Zailong FU Guoyong ZHANG Ai SUN Yaru WANG Jingjing PAN Zhaoe LI Hongge HU Daowu XIA Yingying HE Shoupu DU Xiongming | 2024 | Journal of Cotton Research2024，7，1： | 0 |
| 5 | On Cost Minimization for Cache-Enabled D2D Networks with Recommendation显示文摘To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies. | Yu Hua Yaru Fu Qi Zhu | 2022 | China Communications2022，19，11： | 0 |
| 6 | A nanoagent for concurrent therapy of breast cancer bone metastasis and cancer-induced bone pain through SLC7A11 interruption and photodynamic therapy显示文摘Bone metastasis,a life-threatening complication of advanced breast cancer,is often accompanied by debilitating pain(cancer-induced bone pain,CIBP)that severely impairs life quality and survival.The concurrent treatment of bone metastases and CIBP remains a clinical challenge because the therapeutic options are limited.In this study,we construct a near-infrared light-activated nano-therapeutic system to meet this conundrum.In detail,sorafenib(SRF)and photosensitizer(chlorin e6,Ce6)are encapsulated into mesoporous hydroxyapatite nanoparticles(HANPs),which are further functionalized with hyaluronic acid(HA)to obtain HA-SRF/Ce6@HANPs system.The designed nanoplatform destroys tumor cells in vitro and in vivo via the synergism of SRF(interrupting the exchange of cystine/glutamate by inhibiting SLC7A11)and photodynamic therapy(PDT,inducing reactive oxygen species generation).The decrease in tumor burden and reduction of extracellular glutamate significantly attenuate CIBP in mice model with developing bone cancer.Moreover,the combination of HA-SRF/Ce6@HANPs and PDT inhibit osteoclasts activation,promote osteoblast differentiation and accelerate bone repair.Overall,the nanoagent with good biocompatibility may provide an effective therapy method for the concurrent treatment of breast cancer bone metastasis and CIBP. | Qi Fu Zhongming Lian Mengya Niu Yaru Huang Yanqiu Ai Long He Dandan Zhang Cuixia Zheng Jian-Jun Yang Lei Wang Dandan Tian | 2024 | Chinese Chemical Letters2024，35，2： | 0 |
| 7 | Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks with Imperfect Channel-State Information显示文摘This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),respectively.The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based grouping.It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently low.Besides,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference.Moreover,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves oppositely.Afterwards,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission rates.In the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm. | Zheng Shi Hong Wang Yaru Fu Guanghua Yang Shaodan Ma Xinrong Ye | 2021 | China Communications2021，18，10： | 0 |
| 8 | On recommendation-aware content caching for 6G:An artificial intelligence and optimization empowered paradigm显示文摘Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future. | Yaru Fu Khai Nguyen Doan Tony Q.S.Quek | 2020 | Digital Communications and Networks2020，6，3： | 0 |
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