scholarly journals How the Infestation Level of Varroa destructor Affects the Distribution Pattern of Multi-Infested Cells in Worker Brood of Apis mellifera

2020 ◽  
Vol 7 (3) ◽  
pp. 136 ◽  
Author(s):  
Ignazio Floris ◽  
Michelina Pusceddu ◽  
Alberto Satta
Keyword(s):  
Varroa Destructor ◽  
Prediction Models ◽  
Polynomial Regression ◽  
Management Strategies ◽  
Reproduction Rate ◽  
Regression Equations ◽  
Infestation Level ◽  
Mite Reproduction ◽  
Worker Brood ◽  
The Relationship

The mite Varroa destructor, the main ectoparasite of honey bees, is a threat to apiculture worldwide. Understanding the ecological interactions between Varroa and honeybees is fundamental for reducing mite impact in apiaries. This work assesses bee colonies with various Varroa infestation levels in apiaries to determine: (1) the relationship between multi-infested brood cells and brood infestation level, (2) the damage caused by Varroa to parasitized honey bee pupae, and (3) mite reproduction rate at various infestation levels. Data were collected from 19 worker brood combs, each from a different colony, ranging from 160 to 1725 (mean = 706) sealed cells per comb. Mite distribution was aggregated, ranging from about 2% to 74% infested cells per comb. The percentage of cells invaded by one, two, three, four, or more than four foundress mites, as a function of infestation level, was estimated by five highly significant (p < 0.0001) second-degree polynomial regression equations. The correction factors found could increase the precision of prediction models. Varroa fertility and adult bee longevity decreased as multi-infestation levels increased, and the implications of this relationship are discussed. Finally, these findings could improve sampling methods and the timing of mite treatments in apiaries, thus favoring sustainable management strategies.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Low fertility, fecundity and numbers of mated female offspring explain the lower reproductive success of the parasitic mite Varroa destructor in African honeybees

Parasitology ◽  
2018 ◽  
Vol 145 (12) ◽  
pp. 1633-1639 ◽  
Author(s):  
Beatrice T. Nganso ◽  
Ayuka T. Fombong ◽  
Abdullahi A. Yusuf ◽  
Christian W. W. Pirk ◽  
Charles Stuhl ◽  
...  
Keyword(s):  
Apis Mellifera ◽  
Reproductive Success ◽  
Varroa Destructor ◽  
Female Offspring ◽  
Low Fertility ◽  
Mite Infestation ◽  
Unmated Female ◽  
Mite Reproduction ◽  
Worker Brood ◽  
Honeybee Subspecies

AbstractAlthough Varroa destructor is the most serious ecto-parasite to the honeybee, Apis mellifera L., some honeybee populations such as Apis mellifera scutellata in Kenya can survive mite infestations without treatment. Previously, we reported that grooming behaviour could be a potential tolerant mechanism expressed by this honeybee subspecies towards mite infestation. However, both hygienic and grooming behaviours could not explain the lower mite-infestation levels recorded in these colonies. Here, we investigated the involvement of other potential resistant mechanisms including suppression of mite reproduction in worker brood cells of A. m. scutellata to explain the low mite numbers in their colonies. High infertility rates (26–27%) and percentages of unmated female offspring (39–58%) as well as low fecundity (1.7–2.2, average offspring produced) were identified as key parameters that seem to interact with one another during different seasons to suppress mite reproduction in A. m. scutellata colonies. We also identified offspring mortality in both sexes and absence of male offspring as key factors accounting for the low numbers of mated daughter mites produced in A. m. scutellata colonies. These results suggest that reduced mite reproductive success could explain the slow mite population growth in A. m. scutellata colonies.

scholarly journals Use of Modern Regression Analysis in the Dielectric Properties of Foods

Foods ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1472
Author(s):  
Yu-Kai Weng ◽  
Jiunyuan Chen ◽  
Ching-Wei Cheng ◽  
Chiachung Chen
Keyword(s):  
Regression Analysis ◽  
Dielectric Properties ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Treatment Method ◽  
Regression Equations ◽  
Constant Variance ◽  
Dependent Variables ◽  
Qualitative Variables ◽  
The Relationship

The dielectric properties of food materials is used to describe the interaction of foods with electromagnetic energy for food technology and engineering. To quantify the relationship between dielectric properties and influencing factors, regression analysis is used in our study. Many linear or polynomial regression equations are proposed. However, the basic assumption of the regression analysis is that data with a normal distribution and constant variance are not checked. This study uses sixteen datasets from the literature to derive the equations for dielectric properties. The dependent variables are the dielectric constant and the loss factor. The independent variables are the frequency, temperature, and moisture content. The dependent variables and frequency terms are transformed for regression analysis. The effect of other qualitative factors, such as treatment method and the position of subjects on dielectric properties, are determined using categorical testing. Then, the regression equations can be used to determine which influencing factors are important and which are not. The method can be used for other datasets of dielectric properties to classify influencing factors, including quantitative and qualitative variables.

scholarly journals Effect of average diurnal barn airspace temperatures on prediction of their development during the day

2011 ◽  
Vol 59 (6) ◽  
pp. 161-166
Author(s):  
Gustav Chládek ◽  
Oto Hanuš ◽  
Daniel Falta
Keyword(s):  
Statistical Analysis ◽  
Polynomial Regression ◽  
Regression Equations ◽  
Degree Polynomial ◽  
Reliable Prediction ◽  
The Third ◽  
University Training ◽  
Diurnal Temperatures ◽  
The Relationship ◽  
Very High

A year-round (i.e. 365 days) experiment was performed at the Mendel University Training Farm in Žabčice, Czech Republic (GPS 49°0’51.967”N and 16°36’14.614”E, the altitude 179 m) with the aim to quantify the effect of the variation of average diurnal barn airspace temperatures on prediction of their changes during the day. Barn airspace temperatures were monitored daily in one-hour intervals and the recorded values were used for calculations of average diurnal temperatures. These were classified into 7 categories (i.e. below 0 °C; 0.1 to 5 °C; 5.1 to 10 °C; 10.1 to 15 °C; 15.1 to 20 °C; 20.1 to 25 °C and above 25 °C). Regarding this classification system, all differences between temperatures measured at identical hours but within various limits were statistically highly significant. The statistical analysis involved also the calculation of the third degree polynomial regression equations, which enabled to characterise the relationship between the temperature and the hour of measurement within the aforementioned categories of diurnal temperatures. Individual equations were markedly different and ranged from y = − 0.0019x3 + 0.0596x2 − 0.3797x − 1.2169 (for temperatures below 0 °C) to y = − 0.0108x3 + 0.3297x2 − 1.9367x + 24.3931 (for temperatures above 25 °C). Correlation coefficients (r) and coefficients of determination (R2) of these regression equations were generally very high and ranged from 0.872 to 0.976 and from 0.760 to 0.953, respectively. Regarding high values of both coefficients it can be concluded that the calculated equations enable a good and reliable prediction of the diurnal development of barn airspace temperatures.

scholarly journals Correction: Floris, I., et al. How the Infestation Level of Varroa destructor Affects the Distribution Pattern of Multi-Infested Cells in Worker Brood of Apis mellifera. Veterinary Science 2020, 7, 136

2020 ◽  
Vol 7 (4) ◽  
pp. 202
Author(s):  
Ignazio Floris ◽  
Michelina Pusceddu ◽  
Alberto Satta
Keyword(s):  
Apis Mellifera ◽  
Distribution Pattern ◽  
Varroa Destructor ◽  
Infestation Level ◽  
Veterinary Science ◽  
Journal Editorial ◽  
Worker Brood

We have recently been made aware by the reviewer and the Journal Editorial Offices of the following weaknesses in our recent paper [...]

Development and evaluation of trans-Amadi groundwater parameters: The integration of finite element techniques

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Finite Element ◽  
Polynomial Regression ◽  
Nitrate Concentration ◽  
Strong Relationship ◽  
Total Iron ◽  
Coefficient Of Determination ◽  
Port Harcourt ◽  
Polynomial Expression ◽  
The Relationship ◽  
Best Fit

Mathematical model was developed and evaluated to monitor and predict the groundwater characteristics of Trans-amadi region in Port Harcourt City. In this research three major components were considered such as chloride, total iron and nitrate concentration as well as the polynomial expression on the behavious on the concentration of each component was determined in terms of the equation of the best fit as well as the square root of the curve. The relationship between nitrate and distance traveled by Nitrate concentration by the model is given as Pc = 0.003x2 - 0.451x + 14.91with coefficient of determination, R² = 0.947, Chloride given as Pc = 0.000x2 - 0.071x + 2.343, R² = 0.951while that of Total Iron is given as Pc = 2E-05x2 - 0.003x + 0.110, R² = 0.930. All these show a strong relationship as established by Polynomial Regression Model. The finite element techniques are found useful in monitoring, predicting and simulating groundwater characteristics of Trans-amadi as well as the prediction on the variation on the parameters of groundwater with variation in time.

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

scholarly journals Sodium Metasilicate Cemented Analogue Material and Its Mechanical Properties

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Songlin Yue ◽  
Yanyu Qiu ◽  
Pengxian Fan ◽  
Pin Zhang ◽  
Ning Zhang
Keyword(s):  
Complex Geometry ◽  
Sodium Metasilicate ◽  
Raw Material ◽  
Fine Aggregate ◽  
Regression Equations ◽  
Deformation And Failure ◽  
Orthogonal Experiments ◽  
Low Modulus ◽  
New Type ◽  
The Relationship

Analogue material with appropriate properties is of great importance to the reliability of geomechanical model test, which is one of the mostly used approaches in field of geotechnical research. In this paper, a new type of analogue material is developed, which is composed of coarse aggregate (quartz sand and/or barite sand), fine aggregate (barite powder), and cementitious material (anhydrous sodium silicate). The components of each raw material are the key influencing factors, which significantly affect the physical and mechanical parameters of analogue materials. In order to establish the relationship between parameters and factors, the material properties including density, Young’s modulus, uniaxial compressive strength, and tensile strength were investigated by a series of orthogonal experiments with hundreds of samples. By orthogonal regression analysis, the regression equations of each parameter were obtained based on experimental data, which can predict the properties of the developed analogue materials according to proportions. The experiments and applications indicate that sodium metasilicate cemented analogue material is a type of low-strength and low-modulus material with designable density, which is insensitive to humidity and temperature and satisfies mechanical scaling criteria for weak rock or soft geological materials. Moreover, the developed material can be easily cast into structures with complex geometry shapes and simulate the deformation and failure processes of prototype rocks.

scholarly journals Factors Influencing Creatine Kinase Response in Youth National Team Soccer Players

2021 ◽  
pp. 194173812199938
Author(s):  
Gabor Schuth ◽  
Gyorgy Szigeti ◽  
Gergely Dobreff ◽  
Peter Revisnyei ◽  
Alija Pasic ◽  
...  
Keyword(s):  
Creatine Kinase ◽  
Prediction Models ◽  
Soccer Players ◽  
Linear Regression Models ◽  
Level Of Evidence ◽  
Internal Load ◽  
Youth Soccer ◽  
National Team ◽  
Weekly Training ◽  
The Relationship

Background: Previous studies have examined the relationship between external training load and creatine kinase (CK) response after soccer matches in adults. This study aimed to build training- and match-specific CK prediction models for elite youth national team soccer players. Hypothesis: Training and match load will have different effects on the CK response of elite youth soccer players, and there will be position-specific differences in the most influential external and internal load parameters on the CK response. Study Design: Prospective cohort study. Level of Evidence: Level 4. Methods: Forty-one U16-U17 youth national team soccer players were measured over an 18-month period. Training and match load were monitored with global positioning system devices. Individual CK values were measured from whole blood every morning in training camps. The dataset consisted of 1563 data points. Clustered prediction models were used to examine the relationship between external/internal load and consecutive CK changes. Clusters were built based on the playing position and activity type. The performance of the linear regression models was described by the R2 and the root-mean-square error (RMSE, U/L for CK values). Results: The prediction models fitted similarly during games and training sessions ( R2 = 0.38-0.88 vs 0.6-0.77), but there were large differences based on playing positions. In contrast, the accuracy of the models was better during training sessions (RMSE = 81-135 vs 79-209 U/L). Position-specific differences were also found in the external and internal load parameters, which best explained the CK changes. Conclusion: The relationship between external/internal load parameters and CK changes are position specific and might depend on the type of session (training or match). Morning CK values also contributed to the next day’s CK values. Clinical Relevance: The relationship between position-specific external/internal load and CK changes can be used to individualize postmatch recovery strategies and weekly training periodization with a view to optimize match performance.

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