Factors affecting the transmission of dengue fever in Haikou city in 2019.

In this study, due to multiple cases of dengue fever in two locations in Haikou, Hainan, several factors affecting the transmission of dengue fever in Haikou in 2019 were analyzed. It was found that dengue fever spread from two sites: a construction site, which was an epidemic site in Haikou, and the university, where only four confirmed cases were reported. Comparative analysis revealed that the important factors affecting the spread of dengue fever in Haikou were environmental hygiene status, knowledge popularization of dengue fever, educational background, medical insurance coverage and free treatment policy knowledge and active response by the government.

Utilizing X-ray fluorescence (XRF) method to evaluate the content of metal elements in soil and their effects on the total phenolic and flavonoid contents of some medicinal plants.

Soil factors, especially metal elements in the soil, play a significant role in forming and accumulating secondary metabolites, which determine the medicinal properties of medicinal herbs. In this study, the concentrations of some metal elements (K, Mn, Fe, Cu, Zn, and Cr) in Cam Mountain and Tinh Bien Town, An Giang Province, Vietnam, were determined using the XRF method. We simultaneously determined the total phenolic and flavonoid content of some medicinal herbs collected from the collected soil sample areas, thereby assessing the influence of these elements on the formation of secondary metabolites in medicinal plants. The results showed that K, Mn, and Cr were mainly concentrated in the topsoil and transition layers; Fe and Cu elements tended to concentrate in the transition layer and the subsoil when surveying the soil profile. K, Mn, Cu, and Cr concentrations were more focused in Tinh Bien area, while Fe and Zn had higher concentrations at Cam Mountain. Additionally, results from evaluating the relationship between the content of the elements in the soil and the content of two active compounds also showed the correlation regression model between Zn and flavonoid expression by level 4 at the 5% significance level. Thus, the nonlinear model is suitable for evaluating the relationship between the content of metal elements in the soil and the active compound in medicinal plants.

Self-Sensing Variable Stiffness Actuation of Shape Memory Coil by an Inferential Soft Sensor.

Self-sensing actuation of shape memory alloy (SMA) means to sense both mechanical and thermal properties/variables through the measurement of any internally changing electrical property such as resistance/inductance/capacitance/phase/frequency of an actuating material under actuation. The main contribution of this paper is to obtain the stiffness from the measurement of electrical resistance of a shape memory coil during variable stiffness actuation thereby, simulating its self-sensing characteristics by developing a Support Vector Machine (SVM) regression and nonlinear regression model. Experimental evaluation of the stiffness of a passive biased shape memory coil (SMC) in antagonistic connection, for different electrical (like activation current, excitation frequency, and duty cycle) and mechanical input conditions (for example, the operating condition pre-stress) is done in terms of change in electrical resistance through the measurement of the instantaneous value. The stiffness is then calculated from force and displacement, while by this scheme it is sensed from the electrical resistance. To fulfill the deficiency of a dedicated physical stiffness sensor, self-sensing stiffness by a Soft Sensor (equivalently SVM) is a boon for variable stiffness actuation. A simple and well-proven voltage division method is used for indirect stiffness sensing; wherein, voltages across the shape memory coil and series resistance provide the electrical resistance. The predicted stiffness of SVM matches well with the experimental stiffness and this is validated by evaluating the performances such as root mean squared error (RMSE), the goodness of fit and correlation coefficient. This self-sensing variable stiffness actuation (SSVSA) provides several advantages in applications of SMA: sensor-less systems, miniaturized systems, simplified control systems and possible stiffness feedback control.

A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models.

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data.

Study on the threshold relationship between landscape pattern and water quality considering spatial scale effect-a case study of Dianchi Lake Basin in China.

It is of great significance to analyze the threshold relationship between landscape pattern and water quality for watershed water environment treatment. However, previous studies did not consider the influence of spatial scale on threshold. Therefore, this study proposed the idea of the relationship between landscape pattern and water quality threshold considering the spatial scale effect to solve the above problems. Firstly, the percentage of landscape composition area under 9 spatial scales (riparian buffer zone and sub-basin) of 20 rivers entering the lake in Dianchi Lake Basin was extracted to identify the optimal spatial scale of landscape pattern and water quality by redundant analysis (RDA). Then, a variety of nonlinear regression models such as power regression, exponential regression, quadratic regression, and segmented regression are used to quantitatively detect the thresholds of landscape pattern and water quality. The results show that (1) the spatial scale has a significant influence on the threshold relationship between landscape pattern and water quality, and the total interpretation rate of landscape pattern on water quality is the largest at the buffer scale of 1100 m riparian zone, which is an effective buffer for river governance. (2) Different spatial scales have different effects on the threshold relationship between landscape pattern and water quality. In the nonlinear regression model of landscape pattern and water quality in the buffer zone of 1100 m riparian zone, the significance and R2 of the equation are better than those of the sub-basin. (3) From the nonlinear relationship between landscape pattern and water quality, it is found that the landscape threshold can be quantitatively identified when the water quality changes abruptly or reaches the I ~ V water quality standard. Among them, the type-1 landscape threshold at the water quality mutation point can be used as the long-term goal of water quality protection in Dianchi Lake Basin, and the type-2 landscape threshold can be used as the short-term goal of water quality adjustment. The research results can provide a scientific basis for the governance of water environment and the rational planning of landscape pattern in Dianchi Lake Basin, and have practical significance for guiding the sustainable development of cities.

Predicting medical usage rate at mass gathering events in Belgium: development and validation of a nonlinear multivariable regression model.

BACKGROUND: Every year, volunteers of the Belgian Red Cross provide onsite medical care at more than 8000 mass gathering events and other manifestations. Today standardized planning tools for optimal preventive medical resource use during these events are lacking. This study aimed to develop and validate a prediction model of patient presentation rate (PPR) and transfer to hospital rate (TTHR) at mass gatherings in Belgium. METHODS: More than 200,000 medical interventions from 2006 to 2018 were pooled in a database. We used a subset of 28 different mass gatherings (194 unique events) to develop a nonlinear prediction model. Using regression trees, we identified potential predictors for PPR and TTHR at these mass gatherings. The additional effect of ambient temperature was studied by linear regression analysis. Finally, we validated the prediction models using two other subsets of the database. RESULTS: The regression tree for PPR consisted of 7 splits, with mass gathering category as the most important predictor variable. Other predictor variables were attendance, number of days, and age class. Ambient temperature was positively associated with PPR at outdoor events in summer. Calibration of the model revealed an R2 of 0.68 (95% confidence interval 0.60-0.75). For TTHR, the most determining predictor variables were mass gathering category and predicted PPR (R2 = 0.48). External validation indicated limited predictive value for other events (R2 = 0.02 for PPR; R2 = 0.03 for TTHR). CONCLUSIONS: Our nonlinear model performed well in predicting PPR at the events used to build the model on, but had poor predictive value for other mass gatherings. The mass gathering categories "outdoor music" and "sports event" warrant further splitting in subcategories, and variables such as attendance, temperature and resource deployment need to be better recorded in the future to optimize prediction of medical usage rates, and hence, of resources needed for onsite emergency medical care.

[Effects of weak low-frequency electromagnetic field on sleep structure during daytime sleep]. / Vliyanie ekspozitsii slabogo elektromagnitnogo polya sverkhnizkoi chastoty na strukturu dnevnogo sna.

OBJECTIVE: To test the hypothesis that weak electromagnetic fields of low frequencies (0.5-26 Hz) could affect daytime sleep features and structure. MATERIAL AND METHODS: Parameters of daytime sleep continuity were compared in the study with counterbalanced control/exposition (40 min exposure to electromagnetic field at 1 Hz/0.004 µT) scheme in 22 healthy volunteers. Nonlinear regression model was used to assess daytime sleep continuity. RESULTS: Exposure to a weak electromagnetic field of ultra-low frequency significantly improved the quality of sleep, assessed by the indicator of sleep continuity, namely, there were fewer transitions from the second and deeper stages of sleep to the first stage and to the state of wakefulness (p<0.0001). CONCLUSION: The results can be used to develop non-pharmacological methods of sleep correction, as well as to improve the quality of short-term sleep and its positive effect on well-being, cognitive function and working capacity.

The Effects of Constant and Fluctuating Temperatures on Development of Diaphorina citri (Hemiptera: Liviidae), the Asian Citrus Psyllid.

The effects of six average daily temperatures, 15, 20, 25, 30, 32, and 35°C, that were either constant or fluctuating over 24 h on development times of California-sourced Diaphorina citri Kuwayama nymphs were examined. Thermal performance curves for immature stages of D. citri were characterized using one linear and six nonlinear models (i.e., Ratkowsky, Lobry-Rosso-Flandrois, Lactin-2, Brière-2, Beta, and Performance-2). Daily thermal fluctuations had significant effects on development times of D. citri nymphs, which differed across experimental temperatures. Diaphorina citri nymphs reared at constant temperatures completed development faster than those reared under fluctuating profiles with equivalent temperature means. Linear model estimates of degree-days required for completion of cumulative development of D. citri were 25% lower for constant temperatures when compared with fluctuating temperature regimens. Nonlinear model estimations of optimum developmental temperature and upper theoretical temperature bounds for development were similar for individuals reared under constant and fluctuating temperatures. Nevertheless, the estimated values of lower theoretical temperature limits above which development occurred were lower under fluctuating than constant temperatures. A meta-analysis of published D. citri temperature-dependent development literature, synthesizing datasets of five globally distributed populations (Brazil, California, China, Florida, and Japan) reared under different constant temperatures on six different host plants (i.e., Citrus limonia, C. sinensis cv Natal, C. sinensis cv. Pêra, C. reticulata, Fortunella margarita, and Murraya paniculata), together with the results of this study (C. volkameriana), revealed convergence in estimates of developmental parameters. These results have implications for predicting D. citri invasion and establishment risk and subsequent population performance across various climactic gradients and geographic regions.

Nonlinear Varying Coefficient Models with Applications to Studying Photosynthesis.

Motivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. One-step local linear estimators are developed for the nonlinear varying coefficient models and their asymptotic normality is established leading to point-wise asymptotic confidence bands for the coefficient functions. Two-step local linear estimators are also proposed for cases where the varying coefficient functions admit different degrees of smoothness; bootstrap confidence intervals are utilized for inference based on the two-step estimators. We further propose a generalized F test to study whether the coefficient functions vary over a covariate. We illustrate the proposed methodology via an application to an ecology data set and study the finite sample performance by Monte Carlo simulation studies.

Robust Analysis of High Throughput Screening (HTS) Assay Data.

Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well-known that the variance structure plays an important role in the analysis of linear and nonlinear regression models it is therefore important to have practically useful and easy to interpret methodology which is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article we describe preliminary test estimation (PTE) based methodology which is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE based method whereas the other method is very liberal. In contrast, the proposed PTE based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a data set obtained from the National Toxicology Program (NTP). Additional information, simulation results, data and computer code are available online as supplementary materials.

Robust nonlinear regression in applications.

Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose groups. This is because response to high doses often varies among experimental units (e.g., animals). Consequently, this may result in outliers (i.e., very low values) in that group. Unlike the linear models, in nonlinear models the outliers not only impact the point estimates of the model parameters but can also severely impact the estimate of the information matrix. Note that, the information matrix in a nonlinear model is a function of the model parameters. This is not the case in linear models. In addition to outliers, heteroscedasticity is a major concern when dealing with nonlinear models. Ignoring heteroscedasticity may lead to inaccurate coverage probabilities and Type I error rates. Robustness to outliers/influential observations and to heteroscedasticity is even more important when dealing with thousands of nonlinear regression models in quantitative high throughput screening assays. Recently, these issues have been studied very extensively in the literature (references are provided in this paper), where the proposed estimator is robust to outliers/influential observations as well as to heteroscedasticity. The focus of this paper is to provide the theoretical underpinnings of robust procedures developed recently.