[Characteristics and Sources of PM2.5 Pollution During Winter in Handan City from 2016 to 2020].

To explore the characteristics and sources of PM2.5 pollution in winter of Handan City in the past five years, PM2.5 samples were collected in winter of 2016 to 2020, and eight types of water-soluble inorganic ions were analyzed. The principal component analysis(PCA) model was used to analyze the types of pollution sources, and the backward trajectory and potential source contribution factor(PSCF) were used to simulate the transport trajectory and pollution sources. The results showed that the PM2.5 concentration in winter of 2018 was the highest, increasing by 60.44%, 25.46%, 91.43%, and 21.53% compared with that in 2016, 2017, 2019, and 2020, respectively. In the winter of 2020, the concentration of water-soluble inorganic ions(WSIIs) decreased by 18.86% compared with that in 2016, and WSIIs/PM2.5 decreased to 26.69%. The PM2.5 concentration(110.20-209.65 µg·m-3) at night was higher than that in the daytime(95.21-193.00 µg·m-3). The concentration of NO3- and NH4+ increased more at night. On the contrary, the concentration and proportion of Cl-decreased annually. In the winter of 2020, the daytime concentrations of K+, Ca2+, Na+, and Mg2+ decreased by 69.72%, 97.10%, 90.91%, and 74.51% compared with that of 2018, and the night concentrations decreased by 66.67%, 95.38%, 91.67%, and 77.78%, respectively. In 2020, the concentrations of NO3-, SO42-, and NH4+ on polluted days were 4.90, 5.80, and 5.20 times those on non-polluted days, with the largest increase in five years. PCA results showed that the main sources of pollution were secondary sources, coal sources, biomass combustion sources, and road and building dust. The backward trajectory and PSCF analysis results showed that pollution transport continued to exist between south-central Mongolia and central Inner Mongolia in winter and was influenced by the transport between northern Henan and Handan and central Hebei and Handan in winter of 2016 and 2017, whereas the latter had a greater impact in winter of 2018-2020.

[Characteristics of Haze Pollution Episodes During Autumn and Winter in 2018 in Shijiazhuang].

The mass concentration and chemical composition of fine particles were continuously observed on-line from October 31 to December 3, 2018 at Hebei Key Laboratory of Haze Pollution Prevention and Control in Shijiazhuang. The characteristics of haze pollution in autumn and winter in Shijiazhuang were analyzed. The results showed that during the observation period, four haze pollution episodes occurred with PM2.5 as the primary pollutant, and the maximum daily concentration was 154, 228, 379, and 223 µg·m-3, respectively, reaching a heavy pollution level or above. The main components of PM2.5were water-soluble inorganic ions (WSⅡ) and carbon-containing aerosols, accounting for (60.7±15.6)% and (21.6±9.7)% of PM2.5 mass concentration, respectively. Compared with clean days, the mass concentration of WSⅡ and carbon aerosol during haze pollution increased by 4.4 times and 3.1 times, respectively, which was the main cause of haze pollution. NO3-, SO42-, and NH4+(SNA) were the main components of WSⅡ, accounting for (91.5±17.3)% of the total WSⅡ concentration, of which NO3- took up the highest proportion. The explosive growth of SNA during haze pollution was the main reason for the extremely high PM2.5concentration. Under non-high humidity conditions, the formation rates of unit mass substrates (NO3-, SO42-) were not significantly different, but the transformation of SO42- was significantly promoted after the liquid phase oxidation of SO2 was triggered under high humidity conditions. The atmosphere in Shijiazhuang is rich in NH3, and the molar ratio of n(NH4+) to n(NO3-+2×SO42-) in PM2.5 was greater than 1. The presence of a large amount of NH3 could promote the transformation of NO3- and SO42- and aggravate pollution. During the haze pollution period, the accumulation of primary pollutants from coal and motor vehicles was the main reason for the increase in carbon-containing aerosol. Compared with clean days, the formation of SOC was inhibited. Before the beginning of the warm season, the mobile form was the main pollution source of PM2.5, contributing 30.8% and 39.8% of PM2.5 mass concentration. With the increase of coal combustion emissions, the contribution of coal-fired sources gradually increased to 25.5%, becoming the primary pollution source.

Clinical progression and predictors of death in patients with severe fever with thrombocytopenia syndrome in China.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease of which the clinical progression and factors related to death are still unclear. OBJECTIVE: To identify the clinical progression of SFTS and explore predictors of fatal outcome throughout the disease progress. STUDY DESIGN: A prospective study was performed in a general hospital located in Xinyang city during 2011-2013. Confirmed SFTS patients were recruited and laboratory parameters that were commonly evaluated in clinical practice were collected. The clinical progression was determined based on analysis of dynamic profiles and Friedman's test. At each clinical stage, the laboratory features that could be used to predict fatal outcome of SFTS patients were identified by stepwise discriminant analysis. RESULTS: Totally 257 survivors and 54 deceased SFTS patients were recruited and the data of 11 clinical and laboratory parameters along their entire disease course were consecutively collected. Three clinical stages (day 1-5 post onset, day 6-11 post onset and day 12 to hospital discharge) were determined based on distinct clinical parameters evaluations. Multivariate discriminant analysis at each clinical stage disclosed the indicators of the fatal outcome as decreased platelet counts at early stage, older age and increased AST level at middle stage, and decreased lymphocyte percentage and increased LDH level at late stage. CONCLUSIONS: The significant indicators at three clinical stages could be used to assist identifying the patients with high risk of death. This knowledge might help to perform supportive treatment and avoid fatality.

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.

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.

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.

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.

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.

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.

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.

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).

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 3).

This article introduces the definition and sample size estimation of three special tests (namely, non-inferiority test, equivalence test and superiority test) for qualitative data with the design of one factor with two levels having a binary response variable. Non-inferiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is not clinically inferior to that of the positive control drug. Equivalence test refers to the research design of which the objective is to verify that the experimental drug and the control drug have clinically equivalent efficacy. Superiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is clinically superior to that of the control drug. By specific examples, this article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.

Estimation of sample size and testing power (part 2).

This article introduces definitions of three special tests, namely, non-inferiority test (to verify that the efficacy of the experimental drug is clinically not inferior to that of the positive control drug), equivalence test (to verify that the efficacy of the experimental drug is equivalent to that of the control drug) and superiority test (to verify that the efficacy of the experimental drug is superior to that of the control drug), and methods of sample size estimation under the three different conditions. By specific examples, the article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.

Estimation of sample size and testing power (Part 1).

This article introduces the general concepts and methods of sample size estimation and testing power analysis. It focuses on parametric methods of sample size estimation, including sample size estimation of estimating the population mean and the population probability. It also provides estimation formulas and introduces how to realize sample size estimation manually and by SAS software.

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.

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.