AMS Radiocarbon Dating of Tianma-Qucun Site in Shanxi, China

Tianma-Qucun is the biggest site of Western Zhou Dynasty discovered in Shanxi Province, China. It has been recognized as the early capital of Jin, a vassal state of Western Zhou. The territories were granted to the first Marquis of Jin with the title in the early days of Western Zhou. Bone sample series from the site were radiocarbon-dated by accelerator mass spectrometry (AMS) and calibrated with the Oxford calibration program OxCal 3.5. Bayesian analysis of the calibrated ages shows that the earliest residents of the Western Zhou came to Tianma-Qucun area in 1020-940 BC and the lower boundary of the Western Zhou is 796-754 BC, which corresponds well to the historical record 770 BC.Geochemistry & GeophysicsSCI(E)CPCI-S(ISTP)

Combined acoustic radiation force impulse, aminotransferase to platelet ratio index and Forns index assessment for hepatic fibrosis grading in hepatitis B

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Improvements and Applications of AMS Radiocarbon Measurement at Peking University

AMS radiocarbon measurements were started at Peking University in 1992 with a modified HICONEX 834 ion source. Some archaeological samples were measured at a sensitivity of 10(-14) with ca. 1.7% precision for modern samples. We have made many improvements in our first two years of operation: a high-intensity Cs sputtering ion source was installed; the graphite sample preparation technique was investigated; and the system stability has been improved. The blank sample background is currently ca. 0.006 MC and a precision within 1% can be reached for modern samples. Geological, archaeological, environmental and biomedical samples can be measured routinely. We present some typical applications.Geochemistry & GeophysicsSCI(E)

A Policy-Driven Large Scale Ecological Restoration: Quantifying Ecosystem Services Changes in the Loess Plateau of China

As one of the key tools for regulating human-ecosystem relations, environmental conservation policies can promote ecological rehabilitation across a variety of spatiotemporal scales. However, quantifying the ecological effects of such policies at the regional level is difficult. A case study was conducted at the regional level in the ecologically vulnerable region of the Loess Plateau, China, through the use of several methods including the Universal Soil Loss Equation (USLE), hydrological modeling and multivariate analysis. An assessment of the changes over the period of 2000–2008 in four key ecosystem services was undertaken to determine the effects of the Chinese government's ecological rehabilitation initiatives implemented in 1999. These ecosystem services included water regulation, soil conservation, carbon sequestration and grain production. Significant conversions of farmland to woodland and grassland were found to have resulted in enhanced soil conservation and carbon sequestration, but decreased regional water yield under a warming and drying climate trend. The total grain production increased in spite of a significant decline in farmland acreage. These trends have been attributed to the strong socioeconomic incentives embedded in the ecological rehabilitation policy. Although some positive policy results have been achieved over the last decade, large uncertainty remains regarding long-term policy effects on the sustainability of ecological rehabilitation performance and ecosystem service enhancement. To reduce such uncertainty, this study calls for an adaptive management approach to regional ecological rehabilitation policy to be adopted, with a focus on the dynamic interactions between people and their environments in a changing world

Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

<p>Abstract</p> <p>Background</p> <p>Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.</p> <p>Methods</p> <p>Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.</p> <p>Results</p> <p>A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.</p> <p>Conclusions</p> <p>The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.</p

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

The formation of actin waves during regeneration after axonal lesion is enhanced by BDNF

During development, axons of neurons in the mammalian central nervous system lose their ability to regenerate. To study the regeneration process, axons of mouse hippocampal neurons were partially damaged by an UVA laser dissector system. The possibility to deliver very low average power to the sample reduced the collateral thermal damage and allowed studying axonal regeneration of mouse neurons during early days in vitro. Force spectroscopy measurements were performed during and after axon ablation with a bead attached to the axonal membrane and held in an optical trap. With this approach, we quantified the adhesion of the axon to the substrate and the viscoelastic properties of the membrane during regeneration. The reorganization and regeneration of the axon was documented by long-term live imaging. Here we demonstrate that BDNF regulates neuronal adhesion and favors the formation of actin waves during regeneration after axonal lesion