Influenza H7N9 and H9N2 viruses: coexistence in poultry linked to human H7N9 infection and genome characteristics.

UNLABELLED: Avian influenza virus A of the novel H7N9 reassortant subtype was recently found to cause severe human respiratory infections in China. Live poultry markets were suspected locations of the human H7N9 infection sources, based on the cases' exposure histories and sequence similarities between viral isolates. To explore the role of live poultry markets in the origin of the novel H7N9 virus, we systematically examined poultry and environmental specimens from local markets and farms in Hangzhou, using real-time reverse transcription-PCR (RT-PCR) as well as high-throughput next-generation sequencing (NGS). RT-PCR identified specimens positive for the H7 and N9 genomic segments in all of the 12 poultry markets epidemiologically linked to 10 human H7N9 cases. Chickens, ducks, and environmental specimens from the markets contained heavily mixed subtypes, including H7, N9, H9, and N2 and sometimes H5 and N1. The idea of the coexistence of H7N9 and H9N2 subtypes in chickens was further supported by metagenomic sequencing. In contrast, human H7N9 infection cases (n = 31) were all negative for H9N2 virus according to real-time RT-PCR. The six internal segments were indistinguishable for the H7N9 and H9N2 viruses. The H9, N2, and internal-segment sequences were very close to the sequence of the H9N2 virus circulating in chickens in China recently. Our results provide direct evidence that H9N2 strains coexisted with the novel human-pathogenic H7N9 influenza virus in epidemiologically linked live poultry markets. Avian influenza A virus of the H9N2 subtype likely made a recent contribution to the evolution of the H7N9 virus and continues to do so. IMPORTANCE: Our results suggest that avian influenza A virus of the H9N2 subtype likely made a recent contribution to the evolution of the H7N9 virus, a novel reassortant avian influenza virus A subtype, and continues to do so. The finding helps shed light on how the H7N9 virus emerged, spread, and transmitted to humans. It is of considerable interest for assessing the risk of the possible emergence of novel reassortant viruses with enhanced transmissibility to humans.

Coal-Rock Dynamic Disaster Prevention Mechanism Based on the Dual Loads of Dynamic Barrier and Static Pressure Relief by Hydraulic Slotting.

According to the characteristics of coal-rock dynamic disasters and hydraulic slotting, the mechanism of dynamic load barrier and static load pressure relief in hydraulic slotting is proposed. The stress distribution in a coal mining face and the slotted area of a section coal pillar is analyzed by numerical simulation. The results show that the slot formed by hydraulic slotting can effectively alleviate the stress concentration and transfer the high-stress area to a deeper coal seam. When slotting and blocking the dynamic load propagation path in a coal seam, the wave intensity of the stress wave transmitted into the slot is greatly reduced, so the risk of a coal-rock dynamic disaster is reduced. A field application of hydraulic slotting prevention technology was carried out in the Hujiahe coal mine. An investigation of microseismic events and an evaluation of the rock noise system show that the average event energy within 100 m mining mileage decreased by 18%, the microseismic energy per unit footage decreased by 37%, the times of strong mine pressure behavior evaluated in the working face decreased by 17%, and the number of risks decreased by 89%. In conclusion, hydraulic slotting technology can effectively reduce the risk of coal-rock dynamic disasters in mining faces and provides a more effective technical means for coal-rock dynamic disaster prevention.

Multifactor designs able to examine the interactions.

Multifactor designs that are able to examine the interactions include factorial design, factorial design with a block factor, repeated measurement design; orthogonal design, split-block design, etc. Among all the above design types that are able to examine the interactions, the factorial design is the most commonly used. It is also called the full-factor experimental design, which means that the levels of all the experimental factors involved in the research are completely combined, and k independent repeated experiments are conducted under each experimental condition. The factorial design with a block factor can also examine the influence of a block factor formed by one or more important non experimental factors based on the factorial design. This article introduces the factorial design and the factorial design with a block factor by examples.

Three-factor designs unable to examine the interactions (part 2).

Three-factor designs that are unable to examine the interactions include crossover design and Latin square design, which can examine three factors: an experimental factor and two block factors. Although the two design types are not quite frequently used in practical research, an unexpected research effect will be achieved if they are correctly adopted on appropriate occasions. This article introduced the 3×3 crossover design and the Latin square design by examples.

Estimation of sample size and testing power (part 7).

Two-factor factorial design refers to the research involving two experimental factors and the number of the experimental groups equals to the product of the levels of the two experimental factors. In other words, it is the complete combination of the levels of the two experimental factors. The research subjects are randomly divided into the experimental groups. The two experimental factors are performed on the subjects at the same time, meaning that there is no order. The two experimental factors are equal during statistical analysis, that is to say, there is no primary or secondary distinction, nor nested relation. This article introduces estimation of sample size and testing power of quantitative data with two-factor factorial design.

How to choose an appropriate experimental design type (Part 1).

How to choose an appropriate experimental design type to arrange research factors and their levels is an important issue in experimental research. Choosing an appropriate design type is directly related to the accuracy and reliability of the research result. When confronting a practical issue, how can researchers choose the most appropriate design type to arrange the experiment based on research objective and specified situation? This article mainly introduces the related contents of the single-group design and the paired design through practical examples.

The balance principle in scientific research.

The principles of balance, randomization, control and repetition, which are closely related, constitute the four principles of scientific research. The balance principle is the kernel of the four principles which runs through the other three. However, in scientific research, the balance principle is always overlooked. If the balance principle is not well performed, the research conclusion is easy to be denied, which may lead to the failure of the whole research. Therefore, it is essential to have a good command of the balance principle in scientific research. This article stresses the definition and function of the balance principle, the strategies and detailed measures to improve balance in scientific research, and the analysis of the common mistakes involving the use of the balance principle in scientific research.

How to choose an appropriate experimental design type (Part 2).

How to choose an appropriate design type to arrange research factors and their levels is an important issue in scientific research. Choosing an appropriate design type is directly related to the accuracy, scientificness and credibility of a research result. When facing a practical issue, how can researchers choose the most appropriate experimental design type to arrange an experiment based on the research objective and the practical situation? This article mainly introduces the related contents of the design of one factor with two levels and the design of one factor with k (k≥3) levels by analyzing some examples.

Two-factor designs unable to examine the interactions (part 1).

Two-factor designs are quite commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of the experimental data under a certain condition (usually it is one of the experimental conditions which is formed by the complete combination of the levels of two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced non-complete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design. This article introduced the first three design types with examples.

Two-factor designs unable to examine the interactions (Part 2).

Two-factor designs are very commonly used in scientific research. If the two factors have interactions, research designs like the factorial design and the orthogonal design can be adopted; however, these designs usually require many experiments. If the two factors have no interaction or the interaction is not statistically significant on result in theory and in specialty, and the measuring error of experimental data under a certain condition (usually one of the experimental conditions that are formed by the complete combination of the levels of the two factors) is allowed in specialty, researchers can use random block design without repeated experiments, balanced incomplete random block design without repeated experiments, single factor design with a repeatedly measured factor, two-factor design without repeated experiments and two-factor nested design. This article introduces the last two design types by examples.

Three-factor designs unable to examine the interactions (part 1).

Three-factor designs that are unable to examine the interactions include crossover design and Latin square design, which can examine three factors, namely, an experimental factor and two block factors. Although the two design types are not quite frequently used in practical research, an unexpected research effect will be achieved if they are correctly adopted on appropriate occasions. Due to the limit of space, this article introduces two forms of crossover design.

Estimation of sample size and testing power (part 5).

Estimation of sample size and testing power is an important component of research design. This article introduced methods for sample size and testing power estimation of difference test for quantitative and qualitative data with the single-group design, the paired design or the crossover design. To be specific, this article introduced formulas for sample size and testing power estimation of difference test for quantitative and qualitative data with the above three designs, the realization based on the formulas and the POWER procedure of SAS software and elaborated it with examples, which will benefit researchers for implementing the repetition principle.

Estimation of sample size and testing power (Part 4).

Sample size estimation is necessary for any experimental or survey research. An appropriate estimation of sample size based on known information and statistical knowledge is of great significance. This article introduces methods of sample size estimation of difference test for data with the design of one factor with two levels, including sample size estimation formulas and realization based on the formulas and the POWER procedure of SAS software for quantitative data and qualitative data with the design of one factor with two levels. In addition, this article presents examples for analysis, which will play a leading role for researchers to implement the repetition principle during the research design phase.

Estimation of sample size and testing power (part 6).

The design of one factor with k levels (k ≥ 3) refers to the research that only involves one experimental factor with k levels (k ≥ 3), and there is no arrangement for other important non-experimental factors. This paper introduces the estimation of sample size and testing power for quantitative data and qualitative data having a binary response variable with the design of one factor with k levels (k ≥ 3).

Data for characterization of the pore wetting process of equal-sized granular coals.

In this paper, all measurement and calculation data and their preparation process are presented in detail, which supplements the information published in this co-submission are related to the article "Characterization of the Pore Wetting Process of Equal-Sized Granular Coals based on LF-NMR" [1]. This includes the preparation and component analysis of samples, surface contact angle measurement, analysis of original T2 spectrum and wetting pore size distribution (W-PSD) conversion calculation process. Hence the reader can use the data for their validations and analysis. LF-NMR experiments were conducted for the granular coal pore wetting characterization at the large-diameter MacroMR12-150H-I imaging and analysis system, of Suzhou Niumai Corporation in Jiangsu Province, China. Combined with contact angle measurement, which used the JY-PHb contact angle test instrument, we analyzed the pore wetting process in porous media and its characterization method.

The principle of randomization in scientific research.

Scientific research design includes specialty design and statistics design which can be subdivided into experimental design, clinical trial design and survey design. Usually, statistics textbooks introduce the core aspects of experimental design as the three key elements, the four principles and the design types, which run through the whole scientific research design and determine the overall success of the research. This article discusses the principle of randomization, which is one of the four principles, and focuses on the following two issues--the definition and function of randomization and the real life examples which go against the randomization principle, thereby demonstrating that strict adherence to the randomization principle leads to meaningful and valuable scientific research.

General issues and precautions in the design for clinical trials of investigational new drugs.

The general problems existing in the clinical trials of investigational new drugs involve some key aspects such as the guiding principles, research designs, quality controls and statistical analyses. This paper explores the eight general issues in the clinical trials of investigational new drugs and presents precautionary measures with high operability. Research on the clinical trials of investigational new drugs is a complex project, which should be carried out strictly according to the policies, laws, criteria and operating rules set by related agencies. The neglect of research designs and data analyses will lead clinical trials to failure.

The control principle in scientific research.

The control principle is one of the four basic principles of research design. Without a control group, the conclusion of research will be unconvincing; furthermore, if the control group is not set properly, the conclusion will be unreliable. Generally, there is more than one control group in a multi-factor design. Problems like incomplete control and excessive control should be avoided. This article introduces the meaning and function of the control principle, common forms of control, common errors that researchers tend to make as well as analysis and differentiation of these errors.

The repetition principle in scientific research.

The repetition principle is important in scientific research, because the observational indexes are random variables, which require a certain amount of samples to reveal their changing regularity. The repetition principle stabilizes the mean and the standard variation, so that statistics of the sample can well represent the parameters of the population. Thus, the statistical inference will be reliable. This article discussed the repetition principle from the perspective of common sense and specialty with examples.

Methods and analysis of realizing randomized grouping.

Randomization is one of the four basic principles of research design. The meaning of randomization includes two aspects: one is to randomly select samples from the population, which is known as random sampling; the other is to randomly group all the samples, which is called randomized grouping. Randomized grouping can be subdivided into three categories: completely, stratified and dynamically randomized grouping. This article mainly introduces the steps of complete randomization, the definition of dynamic randomization and the realization of random sampling and grouping by SAS software.