scholarly journals 283 Ordered logistic regression analyze the effect of elements in serum and seminal plasma on abnormal sperm rate in boars

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 117-117
Author(s):  
Yinghui wu ◽  
chao Wang ◽  
Jian Peng
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Seminal Plasma ◽  
Logistic Regression Model ◽  
Sperm Morphology ◽  
Abnormal Sperm ◽  
Ordered Logistic Regression ◽  
Fe Content ◽  
Icp Ms ◽  
Influence Degree

Abstract Poor sperm morphology decrease sow litter size and the economic profitability of breeding herds. Our previous results revealed elements in serum and seminal plasma, such as copper, iron, and lead affects abnormal sperm rate (ABR) in boars. In this study, sperm morphology and elements in serum and seminal plasma of 385 boars were analyzed using CASA and ICP-MS, respectively from June to August in 2016. Multivariate ordered logistic regression model, which includes variables of boar breed, age, serum and seminal plasma elements was used to identify the influence degree of elements on ABR. The degree of ABR was classified grade 0: < 10%, grade 1: 10–20%, and grade 2: > 20%. Results showed ABR was influenced by boar breed, serum Cu and Fe, and seminal Pb contents (P < 0.0001). Yorkshire boars (OR: 0.321; CI: 0.187 to 0.551) and Landrace boars (OR: 0.224; CI: 0.135 to 0.371) had lower ABR than Duroc boars. Boars with serum Cu ≤2.0 mg/L had lower ABR than those with serum Cu ≥2.5 mg/L (OR: 0.483; CI: 0.281 to 0.830). ABR of boars with serum Fe ≤ 1.0mg/L was greater than that of boars with serum Fe 1.5 mg/L (OR: 2.213; CI: 1.188 to 4.120). In addition, boars with seminal plasma Pb 0 μg/L had lower ABR than those with seminal Pb ≥ 10 μg/L (OR: 0.362; CI: 0.174 to 0.757). In conclusion, Duroc boars had more risk of ABR compared with Yorkshire and Landrace boars. The decrease of seminal plasma Pb and serum Cu, and increased of serum Fe content can decrease ABR in boars.

Factors Associated with Increased Pain in Primary Dysmenorrhea: Analysis Using a Multivariate Ordered Logistic Regression Model

2017 ◽  
Vol 30 (2) ◽  
pp. 199-202 ◽  
Author(s):  
María I. Tomás-Rodríguez ◽  
Antonio Palazón-Bru ◽  
Damian R.J. Martínez-St John ◽  
Felipe Navarro-Cremades ◽  
José V. Toledo-Marhuenda ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Primary Dysmenorrhea ◽  
Factors Associated ◽  
Ordered Logistic Regression

A Predictive Ordered Logistic Regression Model as a Tool for Quality Review of Control Risk Assessments

2014 ◽  
Vol 28 (2) ◽  
pp. 209-229 ◽  
Author(s):  
Hussein Issa ◽  
Alexander Kogan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Risk Assessments ◽  
Internal Auditors ◽  
External Auditors ◽  
Quality Review ◽  
Audit Efficiency ◽  
Ordered Logistic Regression ◽  
Control Risk

ABSTRACT External auditors and management increasingly rely on control risk assessments conducted by internal auditors. Consequently, it is crucial to ensure the quality of such assessments and identify irregular instances that deviate from the normal pattern of assessments. Moreover, processing and prioritizing a large number of outlying internal auditors' assessments can help their superiors as well as external auditors overcome the human limitations of dealing with information overload and direct their investigations toward the more suspicious cases, consequently improving overall audit efficiency. In this paper, we use historic data consisting of control risk assessments procured from the internal audit department of a multinational consumer products company. It is used to infer an ordered logistic regression model to provide a quality review of internal auditors' and business owners' assessments of internal controls. We identify anomalous cases where the assessment does not conform to the expected value and develop a methodology to prioritize these outliers. The results indicate that the proposed model can serve as a quality review tool, thus improving audit efficiency, as well as a learning tool that non-experts can employ to gain expert-like knowledge. Additionally, the proposed ranking metrics proved effective in helping the auditors focus their efforts on the more problematic audits.

scholarly journals Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe

2020 ◽  
Vol 12 (13) ◽  
pp. 5317 ◽  
Author(s):  
Caterina De Lucia ◽  
Pasquale Pazienza ◽  
Mark Bartlett
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Investment Decisions ◽  
Current Debate ◽  
Financial Indicators ◽  
Public Enterprises ◽  
Responsible Investment ◽  
Ordered Logistic Regression

The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings. In particular, the changing behaviors towards sustainability issues of the global community and the availability of environmental, social and governance (ESG) indicators are attracting investors to socially responsible investment decisions. Furthermore, whereas the strategic importance of ESG metrics has been particularly studied for private enterprises, little attention have received public companies. To address this gap, the present work has three aims—1. To predict the accuracy of main financial indicators such as the expected Return of Equity (ROE) and Return of Assets (ROA) of public enterprises in Europe based on ESG indicators and other economic metrics; 2. To identify whether ESG initiatives affect the financial performance of public European enterprises; and 3. To discuss how ESG factors, based on the findings of aims #1 and #2, can contribute to the advancements of the current debate on Corporate Social Responsibility (CSR) policies and practices in public enterprises in Europe. To fulfil the above aims, we use a combined approach of machine learning (ML) techniques and inferential (i.e., ordered logistic regression) model. The former predicts the accuracy of ROE and ROA on several ESG and other economic metrics and fulfils aim #1. The latter is used to test whether any causal relationships between ESG investment decisions and ROA and ROE exist and, whether these relationships exist, to assess their magnitude. The inferential analysis fulfils aim #2. Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial indicators. In addition, the existing relationship appears more evident when companies invest in environmental innovation, employment productivity and diversity and equal opportunity policies. As a result, to fulfil aim #3 useful policy insights are advised on these issues to strengthen CSR strategies and sustainable development practices in European public enterprises.

scholarly journals Logistic Regression Analysis Factors Affecting Sperm Motility and Abnormal Sperm Morphology in Boars

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1004
Author(s):  
Yinghui Wu ◽  
Chao Wang ◽  
Jiajian Tan ◽  
Hong-kui Wei ◽  
Haiqing Sun ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sperm Motility ◽  
Seminal Plasma ◽  
Inductively Coupled Plasma ◽  
Sperm Morphology ◽  
Semen Quality ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Inductively Coupled ◽  
Abnormal Sperm

Logistic regression models, including variables of boar breed, age, serum, and seminal plasma elements, were used to identify the influencing factors of sperm motility and morphology in this study. Sperm motility degree was classified as grade 0: ≤85% and grade 1: >85%. Abnormal sperm morphology was classified as grade 0: ≤10%, grade 1: 10–20%, and grade 2: >20%. Element concentration of 385 boars was detected by inductively coupled plasma mass spectrometry. Results showed that boars with serum Cu ≥ 2.5 mg/L had lower sperm motility (odds ratio (OR): 0.496; 95% confidence interval (CI): 0.285–0.864) and higher abnormal sperm morphology (OR: 2.003; 95% CI: 1.189–3.376) than those with serum Cu ≤ 2.0 mg/L. Boars with serum Fe ≥ 1.5 mg/L had lower abnormal sperm morphology than those with serum Fe ≤ 1.0 mg/L (OR: 0.463; 95% CI: 0.255–0.842). The presence of Pb in seminal plasma increased abnormal sperm morphology. The probability of abnormal sperm morphology >20% from boars with seminal plasma Pb increased with a range of 5.78–15.30% than that from boars without seminal plasma Pb among three breeds. In conclusion, serum Cu excess, serum Fe deficiency, and seminal plasma Pb are risk factors for poor semen quality in boars.

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