scholarly journals Forecasting Helicoverpa armigera (Lepidoptera: Noctuidae) larval phenology in pigeonpea and chickpea crops using growing degree days

2021 ◽  
Vol 22 (3) ◽  
pp. 320-331
Author(s):  
B. KIRAN GANDHI ◽  
S.K. SINGH ◽  
KRISHNA KUMAR ◽  
S. VENNILA ◽  
Y. SRUJANA ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Helicoverpa Armigera ◽  
Logistic Regression Model ◽  
Management Practices ◽  
Insect Pest ◽  
Economic Losses ◽  
Economic Damage ◽  
Incidence Data ◽  
Helicoverpa Armígera

Gram pod borer, Helicoverpa armigera is a serious insect pest of pigeonpea and chickpea crops, responsible for huge economic losses. Timely forecasting and subsequent sensible management practices of H. armigera would save the crops from economic damage. In the present study, H. armigera larval incidence data was recorded from sixteen pigeonpea and chickpea growing locations (Maharashtra, India) for three seasons (2015, 2016 and 2017). Observed accumulated GDD (from 40 SMW to 7 SMW) revealed, H. armigera completed one generation in 29 days to develop 4 generations across the locations and seasons. After accumulating 86GDD (40 SMW) and 62 GDD (43 SMW), larval ‘biofix’ (initial incidence of larvae) was started in pigeonpea and chickpea, respectively. Logistic regression model estimated accumulated GDD required by H. armigera larvae to reach ETL in pigeonpea (629 GDD) and chickpea (378 GDD), which was same as observed accumulated GDD. Statistical criteria viz., Adjusted r2, AIC and BIC projected logistic regression model as a better performer in most cases. The geographically unique models developed based on biofix and accumulated GDD in this study can be used for timely advisories and sustainable management of H. armigera in pigeonpea and chickpea crops after field validation.

scholarly journals The Prevalence of Integument Injuries and Associated Risk Factors Among Canadian Turkeys

2022 ◽  
Vol 8 ◽  
Author(s):  
Emily M. Leishman ◽  
Nienke van Staaveren ◽  
Vern R. Osborne ◽  
Benjamin J. Wood ◽  
Christine F. Baes ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Management Practices ◽  
Human Interaction ◽  
Future Research ◽  
Feather Pecking ◽  
Final Logistic Regression Model ◽  
Wide Range

Injurious pecking can cause a wide range of damage and is an important welfare and economic issue in turkey production. Aggressive pecking typically targets the head/neck (HN) area, and feather pecking typically targets the back/tail (BT) area; injuries in these separate areas could be used as a proxy for the level of aggressive and feather pecking in a flock. The objective of this study was to identify risk factors for integument injuries in Canadian turkey flocks. A survey containing a questionnaire about housing and management practices and a scoring guide was distributed to 500 turkey farmers across Canada. The farmer scored pecking injuries in two different body areas (HN and BT) on a 0–2 scale on a subset of birds within each flock. Multivariable logistic regression modeling was used to identify factors associated with the presence of HN and BT injuries. The prevalence of birds with integument injuries ranged widely between the flock subsets (HN = 0–40%, BT = 0–97%), however the mean prevalence was low (HN = 6%, BT = 10%). The presence of injuries for logistic regression was defined as flocks with an injury prevalence greater than the median level of injury prevalence in the dataset (3.3% HN and 6.6% BT). The final logistic regression model for HN injuries contained five variables: flock sex, flock age, number of daily inspections, number of different people during inspections, and picking up birds during inspections (N = 62, pR2 = 0.23, α = 0.05). The final logistic regression model for BT injuries contained six variables: flock sex, flock age, litter depth, litter condition, inspection duration, and use of hospital pens for sick/injured birds (N = 59, pR2 = 0.29, α = 0.05). Flock age, and to a lesser extent, sex was associated with both types of injuries. From a management perspective, aggressive pecking injuries appear to be influenced by variables related to human interaction, namely during inspections. On the other hand, the presence of feather pecking injuries, was associated with litter condition and other management factors like separating sick birds. Future research on injurious pecking in turkeys should focus on these aspects of housing and management to better describe the relationship between the identified variables and the prevalence and severity of these conditions.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

scholarly journals 5-Hydroxymethylcytosine profiles of cfDNA are highly predictive of R-CHOP treatment response in diffuse large B cell lymphoma patients

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hang-Yu Chen ◽  
Wei-Long Zhang ◽  
Lei Zhang ◽  
Ping Yang ◽  
Fang Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
B Cell ◽  
Treatment Response ◽  
Logistic Regression Model ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Invasive Approach ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background Although R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard chemotherapy regimen for diffuse large B cell lymphoma (DLBCL) patients, not all patients are responsive to the scheme, and there is no effective method to predict treatment response. Methods We utilized 5hmC-Seal to generate genome-wide 5hmC profiles in plasma cell-free DNA (cfDNA) from 86 DLBCL patients before they received R-CHOP chemotherapy. To investigate the correlation between 5hmC modifications and curative effectiveness, we separated patients into training (n = 56) and validation (n = 30) cohorts and developed a 5hmC-based logistic regression model from the training cohort to predict the treatment response in the validation cohort. Results In this study, we identified thirteen 5hmC markers associated with treatment response. The prediction performance of the logistic regression model, achieving 0.82 sensitivity and 0.75 specificity (AUC = 0.78), was superior to existing clinical indicators, such as LDH and stage. Conclusions Our findings suggest that the 5hmC modifications in cfDNA at the time before R-CHOP treatment are associated with treatment response and that 5hmC-Seal may potentially serve as a clinical-applicable, minimally invasive approach to predict R-CHOP treatment response for DLBCL patients.

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