Two-stage Sampling on Additive Model for Quantitative Sensitive Question Survey and Its Application

Objective To explore scientific sampling methods and corresponding formulas for quantitative sensitive question survey on two-stage random sampling. To provide scientific data for the prevention and control of high risk AIDS population in Beijing. Methods Additive model for quantitative sensitive question survey, two-stage random sampling, properties of variance and mean were used. Results Formulas for the esti¬mation of the population proportions and its variance on additive model for quantitative sensitive question survey were deduced. The survey methods and formulas were employed successfully in the survey of the age of the first time when MSM having sex with men and the result was 21.9747. Conclusion The methods and corresponding formulas for two-stage sampling on additive model for quantitative sensitive question survey are feasible. Key words: Sensitive questions; Additive model for randomized response technique; Two-stage sampling; MS

Developing an unsupervised real-time anomaly detection scheme for time series with multi-seasonality

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency

Effects of tea garden soil on aroma components and related gene expression in tea leaves

In order to explore the effect of soil on the synthesis of aroma components in tea leaves, tea seedlings replanted in tea rhizosphere soil of different ages were used as research materials. Tea seedlings were replanted in soils aged 0, 4, 9, and 30 years, and after one year of growth, 34, 37, 29, and 26 substances were detected in the tea leaves, respectively, using gas chromatography-mass spectrometry (GC-MS). The relative contents of terpenoids and alcohols in the tea leaves dropped from 66.40% to 44.52% and 5.21% to 2.61%, respectively, as the age of the rhizosphere soil increased. Aldehydes, esters, and nitrogen compounds increased from 3.80% to 22.36%, 1.33% to 12.02%, and 3.13% to 19.96%, respectively, as the age of the rhizosphere soil increased. Gene differential expression measured by fluorescence quantitative PCR (qRT-PCR) showed that the number of nerolidol synthetase and linalool synthase genes in tea leaves increased significantly, and the terpineol synthetase, phellandrene synthase, myrcene synthetase, ocimene synthase, limonene synthetase, germacrene synthase, and farnesene synthase genes declined significantly with the increase in soil age. In summary, as the number of years tea had been planted in the soil increased, the soil significantly affected the expression of terpene synthase genes in tea leaves, and then the composition and content of aroma substances in tea leaves changed. The results provide a theoretical basis for the improvement of tea quality

Novel murine tumour models depend on strain and route of inoculation

This study describes variations in tumour growth patterns which occur when changes in the routes of inoculation and mouse strain are used to introduce tumours into established murine model systems that are known to vary in location and aggression. Intraperitoneal, subcutaneous, intravenous and hydrodynamic inoculations of B16F10 cells were compared among CD-1, C57BL/6 and Balb/c mice. Most surprisingly, allogeneic tumour growth in Balb/c mice after intravenous and hydrodynamic inoculation of B16F10 cells was faster than tumour growth in the syngeneic C57BL/6 mice. These and other variations in the tumour growth patterns described here can help provide the researcher with more experimental control when planning to use the optimal tumour model for any particular study

New pattern-based method for identifying recent HIV-1 infections from the viral env sequence

The study investigates a pattern-based method for measuring intra-patient viral genetic diversity for determination of recent infections and estimation of population incidence. Pattern-based diversities of recent infections are significantly lower than that of chronic ones. With larger window periods varying from 200 to 350 days, a higher accuracy (90%95%) not affected by advanced disease nor antiretroviral therapy (ART) treatment could be obtained. The pattern-based genetic method is supplementary to existing serology-based assays, both suitable for use in low and high epidemic regions respectively. The envelope (env) gene of HIV/SIV is the most variable of viral genes

Tracking the intracellular drug release from graphene oxide using surface-enhanced Raman spectroscopy

NSFC [21073224, 51361130033]; MOST [2013CB933703]; Innovation Project of CAS [KJCX2.YW.M12]; State Key Laboratory of Physical Chemistry of Solid Surfaces at Xiamen UniversityWe have developed a graphene oxide (GO)-based nanoplatform simultaneously loaded with a chemical drug and Ag nanoparticles (NPs), and employed it to study the drug release from GO in living cells by surface-enhanced Raman spectroscopy (SERS). In our strategy, doxorubicin (DOX), a typical model anticancer drug, was loaded onto chemically prepared GO by means of pi-pi stacking, while the Ag NPs were covalently modified onto GO. After incubation of the DOX- and Ag NPs-loaded GO with Ca Ski cells for several hours, DOX will detach from the GO in an acidic environment due to the pH-dependent p-p interaction between DOX and GO. Real-time measurement of SERS signals of DOX using the GO loaded with Ag NPs as a SERS-active substrate allows us to monitor the process of the drug release inside the living cell. The SERS results reveal that DOX is initially released from the GO surface inside the lysosomes, then escapes into the cytoplasm, and finally enters the nucleus, while GO, the nanocarrier, remains within the cytoplasm, without entering the nucleus

Evaluation of the comparative influence of magnesium sulfate single and combination with ritodrine hydrochloride in the prevention of preterm delivery

This project was designed to explore the effects of ritodrine hydrochloride combined with magnesium sulfate in the prevention of preterm delivery of patients with threatened premature birth. 128 cases of threatened premature birth were randomly divided into two groups according to the number table method. The control group was treated with magnesium sulfate, while the study group was treated with ritodrine hydrochloride combined with magnesium sulfate. The d(p>0.05). ata was analyzed using SPSS 18.0 and was subjected to Chi-square and t test. The onset time and prolonged gestation time of the study group were shorter than those of the control group (p0.05). The heart rate per minute of the study group was higher than that of the control group (p<0.05). There was no difference in blood pressure between the study group and the control group Nevertheless, the neurological function, pregnancy outcome and neonatal status of the group were better than those of the control group (p<0.05)