Long Non-Coding RNA in Non-Small Cell Lung Cancers

Non-small cell lung cancer (NSCLC) accounts for nearly 80% of diagnosed lung cancers. Due to the predominantly late diagnosis of NSCLC and drug resistance in the targeted therapy approaches, the 5-year overall survival rate is still less than 19%. Thus, novel diagnosis and treatment approaches are needed. Many efforts have been made to achieve great progress in understanding the genomic landscape of NSCLC and the molecular mechanisms involved in tumorigenesis. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein-coding potential. They are encoded across the genome and are involved in a wide range of cellular and biological processes. Dysregulation of lncRNAs is associated with a number of cancer-related processes, including epigenetic regulation, microRNA silencing, and DNA damage. Furthermore, lncRNAs have been reported to have the potential as biomarker for diagnosis and prognosis, as well as the therapy targets. Here in this chapter, we review some well-characterized lncRNAs associated with NSCLCs and the potential of lncRNAs as biomarkers in the diagnosis and prognosis of NSCLCs

Layer-Based Data Aggregation and Performance Analysis in Wireless Sensor Networks

Due to the similarity and correlation among sensed data in wireless sensor network, it is an important way to reduce the number of packets transmitted with data aggregation technology so as to prolong the network lifetime. However, data aggregation is still a challenging issue since quality-of-service, such as end-to-end delay, is generally considered as a severe criterion required in many applications. We focus on the minimum-latency data aggregation problem and proposed a new efficient scheme for it. The basic idea is that we first build an aggregation tree by ordering nodes into layers, and then we proposed a scheduling algorithm on the basis of the aggregation tree to determine the transmission time slots for all nodes in the network with collision avoiding. We have proved that the upper bound for data aggregation with our proposed scheme is bounded by (15R+Δ-15) for wireless sensor networks in two-dimensional space. Extensive simulation results have demonstrated that the proposed scheme has better practical performance compared with related works

Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.This work is supported by the National Science Foundation of China under Grand nos. 61370210 and 61103175, Fujian Provincial Natural Science Foundation of China under Grant nos. 2011J01345, 2013J01232, and 2013J01229, and the Development Foundation of Educational Committee of Fujian Province under Grand no. 2012JA12027. It has also been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary Projects.Cheng, H.; Su, Z.; Zhang, D.; Lloret, J.; Yu, Z. (2014). Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks. International Journal of Distributed Sensor Networks. 2014:1-14. https://doi.org/10.1155/2014/576573S1142014Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292-2330. doi:10.1016/j.comnet.2008.04.002Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Diallo, O., Rodrigues, J. J. P. C., Sene, M., & Lloret, J. (2015). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 26(2), 604-620. doi:10.1109/tpds.2013.207Oliveira, L. M. L., Rodrigues, J. J. P. C., Elias, A. G. F., & Zarpelão, B. B. (2014). Ubiquitous Monitoring Solution for Wireless Sensor Networks with Push Notifications and End-to-End Connectivity. Mobile Information Systems, 10(1), 19-35. doi:10.1155/2014/270568Diallo, O., Rodrigues, J. J. P. C., & Sene, M. (2012). Real-time data management on wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1013-1021. doi:10.1016/j.jnca.2011.12.006Boyinbode, O., Le, H., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2/3), 130. doi:10.1504/ijssc.2011.040339Aslam, N., Phillips, W., Robertson, W., & Sivakumar, S. (2011). A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks. Information Fusion, 12(3), 202-212. doi:10.1016/j.inffus.2009.12.005Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847-860. doi:10.1007/s11276-012-0438-zNaeimi, S., Ghafghazi, H., Chow, C.-O., & Ishii, H. (2012). A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks. Sensors, 12(6), 7350-7409. doi:10.3390/s120607350Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Rajagopalan, R., & Varshney, P. (2006). Data-aggregation techniques in sensor networks: a survey. IEEE Communications Surveys & Tutorials, 8(4), 48-63. doi:10.1109/comst.2006.283821Al-Karaki, J. N., Ul-Mustafa, R., & Kamal, A. E. (2009). Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms. Computer Networks, 53(7), 945-960. doi:10.1016/j.comnet.2008.12.001Tan, H. O., Korpeoglu, I., & Stojmenovic, I. (2011). Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 22(3), 489-500. doi:10.1109/tpds.2010.68Gao, Q., Zuo, Y., Zhang, J., & Peng, X.-H. (2010). Improving Energy Efficiency in a Wireless Sensor Network by Combining Cooperative MIMO With Data Aggregation. IEEE Transactions on Vehicular Technology, 59(8), 3956-3965. doi:10.1109/tvt.2010.2063719Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793-802. doi:10.1016/j.comcom.2010.10.003Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. doi:10.1109/sahcn.2011.5984932Xu, Y., & Choi, J. (2012). Spatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields. Automatica, 48(8), 1735-1740. doi:10.1016/j.automatica.2012.05.029Min, J.-K., & Chung, C.-W. (2010). EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations. Journal of Systems and Software, 83(2), 271-282. doi:10.1016/j.jss.2009.08.004Jianzhong Li, & Siyao Cheng. (2012). (ε, δ)-Approximate Aggregation Algorithms in Dynamic Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(3), 385-396. doi:10.1109/tpds.2011.193Hung, C.-C., Peng, W.-C., & Lee, W.-C. (2012). Energy-Aware Set-Covering Approaches for Approximate Data Collection in Wireless Sensor Networks. IEEE Transactions on Knowledge and Data Engineering, 24(11), 1993-2007. doi:10.1109/tkde.2011.224Liu, C., Wu, K., & Pei, J. (2007). An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation. IEEE Transactions on Parallel and Distributed Systems, 18(7), 1010-1023. doi:10.1109/tpds.2007.1046Xiaobo Zhang, Heping Wang, Nait-Abdesselam, F., & Khokhar, A. A. (2009). Distortion Analysis for Real-Time Data Collection of Spatially Temporally Correlated Data Fields in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 58(3), 1583-1594. doi:10.1109/tvt.2008.928906Karasabun, E., Korpeoglu, I., & Aykanat, C. (2013). Active node determination for correlated data gathering in wireless sensor networks. Computer Networks, 57(5), 1124-1138. doi:10.1016/j.comnet.2012.11.018Gupta, H., Navda, V., Das, S., & Chowdhary, V. (2008). Efficient gathering of correlated data in sensor networks. ACM Transactions on Sensor Networks, 4(1), 1-31. doi:10.1145/1325651.1325655Campobello, G., Leonardi, A., & Palazzo, S. (2012). Improving Energy Saving and Reliability in Wireless Sensor Networks Using a Simple CRT-Based Packet-Forwarding Solution. IEEE/ACM Transactions on Networking, 20(1), 191-205. doi:10.1109/tnet.2011.2158442Tseng, L.-C., Chien, F.-T., Zhang, D., Chang, R. Y., Chung, W.-H., & Huang, C. (2013). Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning. IEEE Communications Letters, 17(12), 2304-2307. doi:10.1109/lcomm.2013.102113.131876Rodrigues, J. J. P. C., & Neves, P. A. C. S. (2010). A survey on IP-based wireless sensor network solutions. International Journal of Communication Systems, n/a-n/a. doi:10.1002/dac.1099Aziz, A. A., Sekercioglu, Y. A., Fitzpatrick, P., & Ivanovich, M. (2013). A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 15(1), 121-144. doi:10.1109/surv.2012.031612.00124Mehlhorn, K. (1988). A faster approximation algorithm for the Steiner problem in graphs. Information Processing Letters, 27(3), 125-128. doi:10.1016/0020-0190(88)90066-xCheng, H., Liu, Q., & Jia, X. (2006). Heuristic algorithms for real-time data aggregation in wireless sensor networks. 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Evaluation of brain default network fMRI of Insomnia with Depression patients at Resting state

Abstract: Research Purpose: By conducting fMRI research on insomniacs with depression in resting state, this experiment reveals the abnormality in the patient's DMN and its neural pathogenesis, and different degrees of depression's impact on the neural networks causing weakened cognitive function. Consequently, it offers objective imageological basis for clinical cognitive impairment treatment and evaluation of such treatment. Method: a group of 40 cases are selected as the insomniac group, consisting of 20 as mild depression group and 20 as moderate depression group. And another 40 cases are selected as the HC group. All the testees take PSQI, HAMD, 3.0T routine MRI examination and fMRI, and cases with abnormal brain structures are excluded. Then on the basis of PCC as the seed point, comparisons are made between the insomniac group and HC group, between mild and moderate depression group in terms of their DMN differences. Result: Depressive Insomniac Group have stronger functional connection with PCC/pC: bilateral superior frontal gyri and bilateral middle cingulate gyri; the following regions have weaker functional connection: left occipital lobe lingual gyrus/ parahippocampal gyrus/ fusiform gyrus, right superior temporal gyrus/temporal pole, right middle temporal gyrus/middle occipital gyrus, and left occipital lobe/middle temporal gyrus. Compared with Mildly Depressive Group, the following encephalic regions of Moderately Depressive Insomniac Group have stronger functional connection with PCC/pC: right middle cingulate cortex and right frontal gyrus; the following regions have weaker functional connection: left parahippocampal gyrus. Conclusion: There is abnormity in the brain default mode network of insomniacs with depressive symptoms. The depression degree of insomniacs varies. There are differences in the brain default mode network. It is suggested that there is a positive correlation between the middle cingulate gyrus and insomnia and depression, this is also shown between the activated degree of the middle frontal gyrus and insomnia and depression. There is a negative correlation between the activated degree of the parahippocampal gyrus and insomnia and depression. This research also suggested that there is a cognitive disorder and a neutral network mechanism of emotion regulation disorder among depressive insomniacs

Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model

Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.This work is supported by the National Science Foundation of China under Grand No. 61370210 and the Development Foundation of Educational Committee of Fujian Province under Grand No. 2012JA12027.Cheng, H.; Su, Z.; Lloret, J.; Chen, G. (2014). Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model. Sensors. 14(11):20940-20962. https://doi.org/10.3390/s141120940S2094020962141

Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model

Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime

Access Scheduling on the Control Channels in TDMA Wireless Mesh Networks

Abstract. The access scheduling on the control channels in TDMA wireless mesh networks is studied in this paper. The problem is to assign time-slots for each node in the network to access the control channels so that it is guaranteed that each node can broadcast the control packet to any one-hop neighbor in one scheduling cycle. The objective is to minimize the total number of different time-slots in the scheduling cycle. The original contributions of this paper are that it has taken the large interference range problem into consideration for the first time and proposed two algorithms for the scheduling problem, namely, the Speak Once algorithm and the Speak Separately algorithm. We prove that the number of time-slots by the second algorithm is upper-bounded by min(n, 2K) in some special cases, where n is the node number and K is the maximum node degree. The fully distributed versions of these algorithms are given in this paper. Simulation results also show that the performance of the Speak Separately algorithm is rather better than that of the Speak Once algorithm

Node Selection Algorithms with Data Accuracy Guarantee in Service-Oriented Wireless Sensor Networks

The service-oriented architecture is considered as a new emerging trend for the future of wireless sensor networks in which different types of sensors can be deployed in the same area to support various service requirements. The accuracy of the sensed data is one of the key criterions because it is generally a noisy version of the physical phenomenon. In this paper, we study the node selection problem with data accuracy guarantee in service-oriented wireless sensor networks. We exploit the spatial correlation between the service data and aim at selecting minimum number of nodes to provide services with data accuracy guaranteed. Firstly, we have formulated this problem into an integer nonlinear programming problem to illustrate its NP-hard property. Secondarily, we have proposed two heuristic algorithms, namely, Separate Selection Algorithm (SSA) and Combined Selection Algorithm (CSA). The SSA is designed to select nodes for each service in a separate way, and the CSA is designed to select nodes according to their contribution increment. Finally, we compare the performance of the proposed algorithms with extended simulations. The results show that CSA has better performance compared with SSA

Characterization of <i>Pseudomonas</i> sp. En3, an Endophytic Bacterium from Poplar Leaf Endosphere with Plant Growth-Promoting Properties

Growth-promoting endophytic bacteria possess substantial potential for sustainable agriculture. Here, we isolated an endophytic bacterium, Pseudomonas sp. En3, from the leaf endosphere of Populus tomentosa and demonstrated its significant growth-promoting effects on both poplar and tomato seedlings. The phosphorus solubilization and nitrogen fixation abilities of strain En3 were confirmed via growth experiments on NBRIP and Ashby media, respectively. Salkowski staining and HPLC-MS/MS confirmed that En3 generated indole-3-acetic acid (IAA). The infiltration of En3 into leaf tissues of multiple plants did not induce discernible disease symptoms, and a successful replication of En3 was observed in both poplar and tobacco leaves. Combining Illumina and Nanopore sequencing data, we elucidated that En3 possesses a circular chromosome of 5.35 Mb, exhibiting an average G + C content of 60.45%. The multi-locus sequence analysis (MLSA) and genome average nucleotide identity (ANI) supported that En3 is a novel species of Pseudomonas and constitutes a distinct phylogenetic branch with P. rhizosphaerae and P. coleopterorum. En3 genome annotation analysis revealed the presence of genes associated with nitrogen fixation, phosphate solubilization, sulfur metabolism, siderophore biosynthesis, synthesis of IAA, and ethylene and salicylic acid modulation. The findings suggest that Pseudomonas sp. En3 exhibits significant potential as a biofertilizer for crop and tree cultivation