| 1. |
- Zhou, Huimin, et al.
(författare)
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Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands
- 2021
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 307
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Tidskriftsartikel (refereegranskat)abstract
- Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.
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| 2. |
- Yu, Wenjin, et al.
(författare)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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| 3. |
- Mi, Yushuai, et al.
(författare)
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Downregulation of homeobox gene Barx2 increases gastric cancer proliferation and metastasis and predicts poor patient outcomes
- 2016
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Ingår i: Oncotarget. - : IMPACT JOURNALS LLC. - 1949-2553. ; 7:37, s. 60593-60608
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Tidskriftsartikel (refereegranskat)abstract
- Barx2 is a Bar family homeodomain transcription factor shown to play a critical role in cell adhesion and cytoskeleton remodeling, key processes in carcinogenesis and metastasis. Using quantitative real-time PCR, Western blotting, and immunohistochemistry, we found that Barx2 is expressed at lower levels in human gastric cancer (GC) tissues than in adjacent normal mucosa. In a multivariate analysis, Barx2 expression emerged as an independent prognostic factor for disease-free and overall survival. Kaplan-Meier survival analysis showed a trend toward even shorter overall survival in the patient group with Barx2-negative tumors, independent of advanced UICC stage and tumor relapse. Using in vitro and in vivo assays, we demonstrated that under normal conditions Barx2 inhibited GC cell proliferation and invasiveness through inhibition of the Wnt/beta-catenin signaling pathway. These findings indicate that reduction or loss of Barx2 dis-inhibits GC cell proliferation and invasion, and that reduction in Barx2 could serve as an independent prognostic biomarker for poor outcome in GC patients.
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| 4. |
- She, Huimin, et al.
(författare)
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A Network-based System Architecture for Remote Medical Applications
- 2007
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Ingår i: Proceedings of the Asia-Pacific Advanced Network Meeting.
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Konferensbidrag (refereegranskat)abstract
- Nowadays, the evolution of wireless communication and networktechnologies enables remote medical services to be availableeverywhere in the world. In this paper, a network-based systemarchitecture adopting wireless personal area network (WPAN)protocol IEEE 802.15.4/Zigbee standard and 3G communicationnetworks for remote medical applications is proposed. In theproposed system, the number and type of medical sensors arescalable depending on individual needs. This feature allows thesystem to be flexibly applied in several medical applications.Furthermore, a differentiated service using priority scheduling anddata compression is introduced. This scheme can not only reducetransmission delay for critical physiological signals and enhancebandwidth utilization at the same time, but also decrease powerconsumption of the hand-held personal server which uses batteryas the energy source.
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| 5. |
- She, Huimin, et al.
(författare)
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Analysis of Traffic Splitting Mechanisms for 2D Mesh Sensor Networks
- 2008
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Ingår i: International Journal of Software Engineering and Its Applications. - 1738-9984. ; 2:3
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Tidskriftsartikel (refereegranskat)abstract
- For many applications of sensor networks, it is essential to ensure that messages aretransmitted to their destinations within delay bounds and the buffer size of each sensor nodeis as small as possible. In this paper, we firstly introduce the system model of a mesh sensornetwork. Based on this system model, the expressions for deriving the delay bound and bufferrequirement bound are presented using network calculus theory. In order to balance trafficload and improve resource utilization, three traffic splitting mechanisms are proposed. Andthe two bounds are derived in these traffic splitting mechanisms. To show how our methodapplies to real applications, we conduct a case study on a fresh food tracking application,which monitors the food freshness status in real-time during transportation. The numericalresults show that the delay bound and buffer requirement bound are reduced while applyingtraffic splitting mechanisms. Thus the performance of the whole sensor network is improvedwith less cost.
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| 6. |
- She, Huimin, et al.
(författare)
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Analytical Evaluation of Retransmission Schemes in Wireless Sensor Networks
- 2009
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Ingår i: 2009 IEEE VEHICULAR TECHNOLOGY CONFERENCE. - 9781424425167 ; , s. 38-42
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Konferensbidrag (refereegranskat)abstract
- Retransmission has been adopted as one of the most popular schemes for improving transmission reliability in wireless sensor networks. Many previous works have been done on reliable transmission issues in experimental ways, however, there still lack of analytical techniques to evaluate these solutions. Based on the traffic model, service model and energy model, we propose an analytical method to analyze the delay and energy metrics of two categories of retransmission schemes: hop-by-hop retransmission (HBH) and end-to-end retransmission (ETE). With the experiment results, the maximum packet transfer delay and energy efficiency of these two scheme are compared in several scenarios. Moreover, the analytical results of transfer delay are validated through simulations. Our experiments demonstrate that HBH has less energy consumption at the cost of lager transfer delay compared with ETE. With the same target success probability, ETE is superior on the delay metric for low bit-error-rate (BER) cases, while HBH is superior for high BER cases.
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| 7. |
- She, Huimin, et al.
(författare)
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Deterministic Worst-case Performance Analysis for Wireless Sensor Networks
- 2008
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Ingår i: Proceedings of the International Wireless Communications and Mobile Computing Conference. - 9781424422029 ; , s. 1081-1086
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Konferensbidrag (refereegranskat)abstract
- Dimensioning wireless sensor networks requires formal methods to guarantee network performance and cost in any conditions. Based on network calculus, this paper presents a deterministic analysis method for evaluating the worst-case performance and buffer cost of sensor networks. To this end, we introduce three general traffic flow operators and derive their delay and buffer bounds. These operators are general because they can be used in combination to model any complex traffic flowing scenarios in sensor networks. Furthermore, our method integrates variable duty cycle to allow the sensor nodes to operate at lower rates thus saving power. Moreover, it incorporates traffic splitting mechanisms in order to balance network workload and nodes' buffers. To show how our method applies to real applications, we conduct a case study on a fresh food tracking application, which monitors the food freshness in realtime. The experimental results demonstrate that our method can be either used to perform network planning before deployment, or to conduct network reconfiguration after deployment.
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| 8. |
- She, Huimin, et al.
(författare)
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Modeling and Analysis of Rayleigh Fading Channels using Stochastic Network Calculus
- 2011
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Konferensbidrag (refereegranskat)abstract
- Deterministic network calculus (DNC) is not suitable for deriving performance guarantees for wireless networks due to their inherently random behaviors. In this paper, we develop a method for Quality of Service (QoS) analysis of wireless channels subject to Rayleigh fading based on stochastic network calculus. We provide closed-form stochastic service curve for the Rayleigh fading channel. With this service curve, we derive stochastic delay and backlog bounds. Simulation results verify that the bounds are reasonably tight. Moreover, through numerical experiments, we show the method is not only capable of deriving stochastic performance bounds, but also can provide guidelines for designing transmission strategies in wireless networks.
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| 9. |
- She, Huimin, et al.
(författare)
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Performance Analysis of Flow-Based Traffic Splitting Strategy on Cluster-Mesh Sensor Networks
- 2012
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Ingår i: International Journal of Distributed Sensor Networks. - : Hindawi Publishing Corporation. - 1550-1329 .- 1550-1477. ; , s. 232937-
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Tidskriftsartikel (refereegranskat)abstract
- Performance analysis is crucial for designing predictable and cost-efficient sensor networks. Based on the network calculus theory, we propose a flow-based traffic splitting strategy and its analytical method for worst-case performance analysis on cluster-mesh sensor networks. The traffic splitting strategy can be used to alleviate the problem of uneven network traffic load. The analytical method is able to derive close-form formulas for the worst-case performance in terms of the end-to-end least upper delay bounds for individual flows, the least upper backlog bounds, and power consumptions for individual nodes. Numerical results and simulations are conducted to show benefits of the splitting strategy as well as validate the analytical method. The numerical results show that the splitting strategy enables much better balance on network traffic load and power consumption. Moreover, the simulation results verify that the theoretic bounds are fairly tight.
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| 10. |
- She, Huimin, et al.
(författare)
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Stochastic Coverage in Event-Driven Sensor Networks
- 2011
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Ingår i: 2011 IEEE 22nd International Symposium On Personal Indoor And Mobile Radio Communications (PIMRC). - New York : IEEE. - 9781457713484 ; , s. 915-919
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Konferensbidrag (refereegranskat)abstract
- One of the primary tasks of sensor networks is to detect events in a field of interest (FoI). To quantify how well events are detected in such networks, coverage of events is a fundamental problem to be studied. However, traditional studies mostly focus on analyzing the coverage of the FoI, which is usually called are a coverage. In this paper, we propose an analytic method to evaluate the performance of event coverage in sensor networks with randomly deployed sensor nodes and stochastic event occurrences. We provide formulas to calculate the probabilities of event coverage and event missing. The numerical results show how these two probabilities change with the sensor and event densities. Moreover, simulations are conducted to validate the analytic method. This method can provide guidelines for determining the amount of sensor nodes to achieve a certain level of coverage in event-driven sensor networks.
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