Influence of crate size, oviposition time, number of adults and cannibalism on the reproduction of Tenebrio molitor

2019 ◽  
Vol 5 (4) ◽  
pp. 247-255 ◽  
Author(s):  
D. Deruytter ◽  
C.L. Coudron ◽  
S. Teerlinck
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Factorial Design ◽  
Surface Area ◽  
Predictive Power ◽  
A Priori ◽  
Density Effect ◽  
Final Number ◽  
Minimal Loss ◽  
Full Factorial

In an industrialised mealworm farm it is important to maximise the production and to know the number of mealworms in each container as early as possible in a fast and reliable way. Two experiments were performed. The first experiment assessed the influence of the number of beetles, crate surface area and oviposition time on the number produced mealworms. A full factorial design was used with 11 beetle densities (between 2.3-300 mg beetles/cm2), 5 oviposition times (from 1-14 days) and 4 different crate sizes (between 250-2,000 cm2). In the second experiment, the influence of cannibalism on the number of produced mealworms was assessed via an alternative oviposition method that prohibited cannibalism. Multiple linear regression was used to model the results. The results indicate that the number of beetles, oviposition time and surface area could predict the number of produced mealworms well. An increase in one of the three parameters increased the number of produced mealworms without reaching an optimum. Furthermore, the number of beetles and the oviposition time can be combined in one parameter, beetledays with minimal loss of predictive power of the model. Nevertheless, the number of produced mealworms per female did decline rapidly with increasing oviposition time and density. The latter is, at least in part, due to cannibalism, as the second experiment indicates that the density effect is almost eliminated when the beetles are unable to reach their eggs. In conclusion, this study indicates that it is possible to construct a formula that can be used to a priori determine the final number of produced mealworms based on the number of beetles, surface area and oviposition time and that cannibalism can greatly reduce the number of produced mealworms. Reducing cannibalism can greatly increase the efficiency and therefore production of a mealworm farm.

scholarly journals Prediction of the retention of β-diketonato complexes in TLC systems on silica gel by quantitative structure-retention relationships

2010 ◽  
Vol 75 (4) ◽  
pp. 513-521 ◽  
Author(s):  
Rada Baosic ◽  
Ana Radojevic ◽  
Zivoslav Tesic
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Surface Area ◽  
Molecular Orbital ◽  
Silica Gel ◽  
Molecular Descriptors ◽  
Quantitative Structure ◽  
Hydrophilic Lipophilic Balance ◽  
Structure Retention ◽  
Retention Parameters

Quantitative structure-retention relationships for a series of 30 mixed ?-diketonato complexes of cobalt(III), chromium(III) and ruthenium(III) were derived by multiple linear regression analyses using molecular descriptors obtained by quantum chemical calculations. The retention parameters were obtained by thin layer chromatography on silica gel using mono and two-component solvent systems. The molecular descriptors included in the multiple linear regression analysis were molecular weight, molecular volume, surface area, hydrophilic-lipophilic balance, percent hydrophilic surface area, dipole moment, polarizability, refractivity, energy of the highest occupied molecular orbital and energy of the lowest unoccupied molecular orbital. High agreement between the experimental and predicted retention parameters was obtained when polarizability and the hydrophilic-lipophilic balance were used as the molecular descriptors. Comparison of the models with those established on polyacrylonitrile showed that the structure of the sorbent is responsible for the chromatographic behaviour of the same compounds. The presented models can be used for the prediction of the retention of new solutes in screening chromatographic systems.

A Genetic Predictive Model for HbF in Sickle Cell Disease

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 319-319
Author(s):  
Kate Gardner ◽  
Tony Fulford ◽  
Helen Rooks ◽  
Nick Silver ◽  
Jo Howard ◽  
...  
Keyword(s):  
Sickle Cell Disease ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sickle Cell ◽  
Predictive Power ◽  
Clinical Severity ◽  
Predictive Score ◽  
Cell Transplant ◽  
Cell Disease ◽  
Hematopoietic Stem

Abstract Fetal hemoglobin (HbF) is strongly associated with clinical severity in the β-hemoglobinopathies, including sickle cell disease (SCD). In recent years, the three major HbF genetic loci (at BCL11A on chromosome 2p, HMIP-2 on chromosome 6q and Xmn1-HBG on chromosome 11p) have been more clearly characterized and mechanisms of the likely causal variants better defined. In this study, we have combined this new biological understanding with statistical methods to create a genetic "predictor" for HbF in SCD. We chose 7 variants to represent the 3 HbF quantitative trait loci (QTL) to investigate their utility in predicting HbF levels, and, in turn, clinical severity of SCD. For BCL11A, we used 2 markers: rs1427407 (62kb downstream of BCL11A) localizes to an erythroid-specific enhancer (Bauer et al. Science 2013) and rs6545816 tags a second signal 58kb downstream of BCL11A. The genetic architecture of HMIP-2 as a QTL comprises two elements, A and B (Menzel et al. Ann Hum Genet 2014). We have represented HMIP-2A withthe 3bp deletion rs66650371, shown as a causal variant (Stadhouders et al. JCI 2014) plus the ethnicity marker rs9376090. HMIP-2B is less well-characterized, we selected: rs9494142 (near the MYB enhancer) and rs9494145. For the β-globin locus, we used the long-established Xmn1 marker (rs7482144) in the proximal promoter 158kb upstream of HBG2. This is likely not the variant itself, but in tight linkage disequilibrium with the causal element. Of 892 initial patients (516 females, 376 males), we excluded 17 children aged under 5 because of the non-linear relationship between age and HbF at a young age (we confirmed this finding in our cohort). This left: 658 with HbSS, 206 with HbSC, 8 with HbSβ0 thalassemia, and 20 with HbSβ+ thalassemia. We then genotyped 666 patients with HbSS/HbSβ0 thalassemia for the 7 genetic variants. For each patient, we selected 'validated' HbF levels i.e. HbF not influenced by transfusion, drugs (especially hydroxyurea) or pregnancy. HbF levels were log-transformed (Ln). We then used multiple linear regression models to identify variants which were independently associated with Ln-HbF levels. Using only age and sex as covariates revealed predictive power r2~10% which was orthogonal to (i.e. additive) the predictive power of the variants, and so we did not include them in subsequent analysis. Also, by adding α-globin status to the model where known (N=272), the r2 remained unchanged and is not significant for α-globin status. We then normalized the 7 variants to take account of the mean allele count (a strongly predictive but rare variant may not explain much of the total population variance). We performed multiple linear regression to rationalize the 7 variants, and found 4 markers (rs6545816, rs1427407, rs66650371 and rs7482144) independently contributing HbF-boosting alleles (see table). Combining these 4 variants into a genetic risk model, as per the table, allows us to predict 21.8% of variability (r2) of HbF in our HbSS / HbSβ0 thalassemia patients. We validated the 4-variant risk score first with a 5-fold cross-validation within the cohort which demonstrated a mean r2=22% for the 5 folds. We then replicated the findings in the cohort of HbSC patients (N=206) and found the 4-variant model to predict HbF with variability r2=27.5% (i.e. towards r2=44% seen in non-anemic individuals). Thus, our 4-variant model provides a robust approach to genetic prediction of HbF in SCD. The predictive power appears to be larger for HbSC compared to HbSS (r2=27.5% vs 21.8%) which may be related to stress erythropoiesis in HbSS patients releasing immature erythrocytes as a non-genetic factor modifying HbF levels. This process is a first step towards creating a global genetic predictive score in SCD: stratifying patients with SCD early in life would enable us to offer curative therapy (i.e. hematopoietic stem cell transplant) to those identified as genetically severe. Disclosures No relevant conflicts of interest to declare.

Association of metabolic syndrome and IgA nephropathy

2010 ◽  
Vol 63 (8) ◽  
pp. 697-701 ◽  
Author(s):  
Liping Wang ◽  
Yong Zhang ◽  
Shanyuan Chen ◽  
Jian Chen ◽  
Yongze Zhuang ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Linear Regression ◽  
Renal Disease ◽  
Multiple Linear Regression ◽  
Iga Nephropathy ◽  
Surface Area ◽  
Body Surface ◽  
Regression Models ◽  
Linear Regression Models ◽  
Multiple Linear Regression Models

BackgroundThe disorders associated with metabolic syndrome (MS) can lead to renal disease. IgA nephropathy is the most common form of glomerulonephritis, and many patients with this disorder progress to renal failure.AimsTo identify the effect of MS on IgA nephropathy by retrospectively comparing patients who had IgA nephropathy and MS with those who had IgA nephropathy alone.Methods30 patients with MS and IgA nephropathy (MS group), and 30 matched controls with IgA nephropathy alone (non-MS group) were enrolled. IgA nephropathy was diagnosed by renal biopsy; activity and severity was graded by two classification systems. MS was diagnosed by criteria of the Diabetes Society of the Chinese Medical Association.ResultsSimple and multiple linear regression models (which adjusted for age, gender and body surface area) showed that only hypertension significantly affected serum creatinine, an indicator of the clinical severity of renal disease. Simple and multiple linear regression models (which adjusted for age, gender and body surface area) also showed that hypertensive patients had higher Katafuchi scores, an indicator of the pathological severity of renal disease.ConclusionAmong the disorders associated with MS, hypertension is the most important factor for renal disease.

Modeling and parameter optimization of the papermaking processes by using regression tree model and full factorial design

TAPPI Journal ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 123-137
Author(s):  
JOSÉ L. RODRIGUEZ-ALVAREZ, ◽  
ROGELIO LOPEZ-HERRERA ◽  
IVÁN E. VILLALON-TURRUBIATES ◽  
GERARDO GRIJALVA-AVILA ◽  
JORGE L. GARCÍA ALCARAZ
Keyword(s):  
Linear Regression ◽  
Factorial Design ◽  
Regression Tree ◽  
Full Factorial Design ◽  
Operating Conditions ◽  
Tree Model ◽  
Predictive Capacity ◽  
Output Pressure ◽  
Full Factorial ◽  
Regression Tree Model

One of the major challenges in the pulp and paper industry is taking advantage of the large amount of data generated through its processes in order to develop models for optimization purposes, mainly in the paper-making, where the current practice for solving optimization problems is the error-proofing method. First, the multi-ple linear regression technique is applied to find the variables that affect the output pressure controlling the gap of the paper sheet between the rod sizer and spooner sections, which is the main cause of paper breaks. As a measure to determine the predictive capacity of the adjusted model, the coefficient of determination (R2) and s values for the output pressure were considered, while the variance inflation factor was used to identify and elimi-nate the collinearity problem. Considering the same amount of data available by using machine learning, the regres-sion tree was the best model based on the root mean square error (RSME) and R2. To find the optimal operating con-ditions using the regression tree model as source of output pressure measurement, a full factorial design was developed. Using an alpha level of 5%, findings show that linear regression and the regression tree model found only four independent variables as significant; thus, the regression tree model demonstrated a clear advantage over the linear regression model alone by improving operating conditions and demonstrating less variability in output pressure. Furthermore, in the present work, it was demonstrated that the adjusted models with good predictive capacity can be used to design noninvasive experiments and obtain.

Study on the Competitive Adsorption of Dimethoate with Multiple Pesticides and Heavy Metals Based on Fractional Factorial Design

2013 ◽  
Vol 864-867 ◽  
pp. 229-233
Author(s):  
Bing Chuan Cheng ◽  
Lin Liu ◽  
Xiao Yu Cai ◽  
Meng Wang ◽  
Yu Li
Keyword(s):  
Heavy Metals ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Factorial Design ◽  
Competitive Adsorption ◽  
Fractional Factorial Design ◽  
Second Order ◽  
Fractional Factorial ◽  
Surficial Sediments ◽  
Main Effects

In order to reveal the composite contaminations characteristic of dimethoate adsorption onto the surficial sediments, the competitive adsorption of dimethoate in pesticide (dimethoate, metalaxyl, atrazine, malathion, and prometryn)/heavy metals (copper, zinc, lead, cadmium and nickel) system is investigated. A 210-5 fractional factorial design method at resolution IV and a multiple linear regression adsorption model are used to identify the main effects and interactions of above ten pollutions. The adsorption amount of dimethoate surficial sediments is set as the dependent variable, and the main effects and second-order interactions of ten pollutions are set as independent variables. Thus, a multiple linear regression model of dimethoate adsorption is screened and established. The results of model show that the main effects of Cd, malathion and prometryn performed a significant antagonistic effect (α=0.05) on the adsorption of dimethoate onto the sediment (competitive adsorption effects), and the order is: prometryn (-0.0925) > Cd (-0.0878) > malathion (-0.0827); while heavy metal Zn performed a significant synergy effect on the adsorption of dimethoate. The second-order interaction effects of Zn*prometryne, Pb*atrazine and Pb*atrazine has a significant antagonistic impact on the adsorption of dimethoate in sediments, which is in a sequence of Zn*prometryne (-0.0967) > Pb*atrazine (-0.0945) > Cd*atrazine (-0.0922). Moreover, according to the rate of contributions of main effects and second-order interaction effects in composite contaminations system, we can also estimate and definite the pollution levels of target pollutant.

scholarly journals A93 VOLUME OF EMR EXPOSURE IN TRAINING IS CORRELATED WITH POLYPECTOMY COGNITIVE COMPETENCE AMONGST RECENT GASTROENTEROLOGY GRADUATES

2021 ◽  
Vol 4 (Supplement_1) ◽  
pp. 66-67
Author(s):  
T Jeyalingam ◽  
C M Walsh ◽  
S C Grover ◽  
S Heitman ◽  
J Mosko
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Online Survey ◽  
Assessment Tool ◽  
A Priori ◽  
Cognitive Competence ◽  
Independent Practice ◽  
Incomplete Resection ◽  
Colonoscopy Quality ◽  
Quiz Score

Abstract Background Competence in performing polypectomy is increasingly appreciated as a colonoscopy quality metric, as incomplete resection can lead to post-colonoscopy colorectal cancer, particularly for polyps removed using piecemeal endoscopic mucosal resection (EMR). The relationship between training experiences and cognitive competence in polypectomy has not been previously described. Aims We aimed to examine associations between training and assessment experiences, self-reported comfort, and cognitive competence in polypectomy amongst recent graduates of Canadian gastroenterology training programs. Methods An online survey was distributed to recent GI graduates (≤5 years in independent practice). The survey comprised 4 sections: (1) demographics; (2) training and assessment experiences in colonoscopy, polypectomy, and EMR; (3) self-reported comfort in performing aspects of polypectomy outlined in the Direct Observation of Polypectomy Skills Assessment Tool; and (4) performance on a 22-item multiple choice quiz intended to assess cognitive competence in polypectomy (items and correct responses to which were determined a priori based on agreement of two experts). Data was analyzed using descriptive statistics and associations between predictors (demographics, training/assessment experiences, self-reported comfort) and outcomes (quiz score) were assessed using multiple linear regression. Results There were 28 survey respondents, comprising 13 (46%) who trained in advanced endoscopy, 5 (18%) in hepatology, 2 (7%) in motility, 1 (4%) in IBD, 1 (4%) in nutrition, and 6 (21%) with no advanced training. This cohort had a mean (SD) duration in independent practice of 29.0 (18.4) months. Their mean (SD) annual volume of colonoscopy, polypectomy, and EMR in independent practice was 530 (221), 182 (76), 28 (16), respectively and they had completed 525 (203) colonoscopies, 146 (92) polypectomies, and 23 (20) EMRs in their prior training. Their mean (SD) quiz score was 71.9% (13.2%). ANOVA revealed significant score differences based on fellowship history, with those trained in advanced endoscopy achieving the highest scores (81.1%, P=0.01). Multiple linear regression revealed that the number of EMRs completed during training was significantly correlated with quiz performance (B=0.60, P=0.03). Conclusions EMR experience during training appears to be associated with cognitive competence in polypectomy in independent practice. These results suggest increasing exposure to EMR in training may improve polypectomy quality amongst practicing endoscopists. Funding Agencies CAG

Teaching How to Derive Directly Interpretable Coding Schemes for Multiple Regression Analysis

1985 ◽  
Vol 10 (3) ◽  
pp. 223-238 ◽  
Author(s):  
Ronald C. Serlin ◽  
Joel R. Levin
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
General Procedure ◽  
A Priori ◽  
Analysis Of Covariance ◽  
Qualitative Variable ◽  
Coding Schemes ◽  
Treatment Interaction ◽  
Statistical Results

Multiple linear regression is a versatile model for encompassing analysis of variance, analysis of covariance, and aptitude-by-treatment interaction designs. The question of how to teach the coding of levels of a qualitative variable is addressed in this paper. Although a variety of coding schemes will produce invariant omnibus statistical results for a given set of data, one’s interpretation of treatment effects and treatment differences depends on the particular code values chosen. A general procedure is presented that allows the user to generate, on an a priori basis, code values that result in directly interpretable estimates of interest.

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