scholarly journals Are Hyoid Bone and Tongue the Risk Factors Contributing to Postoperative Relapse for Mandibular Prognathism?

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Yu-Chuan Tseng ◽  
Steven Lai ◽  
Huey-Er Lee ◽  
Ker-Kong Chen ◽  
Chun-Ming Chen
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Hyoid Bone ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Mandibular Prognathism ◽  
Mandibular Setback ◽  
Lateral Cephalograms ◽  
Intraoral Vertical Ramus Osteotomy ◽  
One Year

Objective. The purpose of this study was to investigate postoperative stability and the correlation between hyoid, tongue, and mandible position following surgery for mandibular prognathism.Materials and Methods. Thirty-seven patients, treated for mandibular prognathism using intraoral vertical ramus osteotomy (IVRO), were evaluated cephalometrically. A set of four standardized lateral cephalograms were obtained from each subject preoperatively (T1), immediately postoperatively (T2), six weeks to three months postoperatively (T3), and more than one year postoperatively (T4). The Studentt-tests, the Pearson correlation coefficient, and the multiple linear regression were used for statistical analysis.Results. Immediately after surgery, menton (Me) setback was 12.8 mm, hyoid (H) setback was 4.9 mm, and vallecula epiglottica (V) setback was 5.8 mm. The postoperative stability significantly correlated (r=-0.512,p<0.01) with the amount of setback. The hyoid bone and tongue did not have significant effects on postoperative stability. Multiple linear regression model (R2=0.2658,p<0.05) showed predictability: Horizontal Relapse Me (T4-T2) = −6.406 − 0.488Me (T2-T1) + 0.069H (T2-T1) − 0.0619V (T2-T1).Conclusion. Mandibular setback surgery may push the hyoid and tongue significantly backward, but this did not correlate with mandibular relapse. Postoperative stability significantly correlated with the amount of mandibular setback.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

Personal Adaptive Method to Assess Mental Tension during Daily Life Using Heart Rate Variability

2012 ◽  
Vol 51 (01) ◽  
pp. 39-44 ◽  
Author(s):  
K. Matsuoka ◽  
K. Yoshino
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Test Data ◽  
Daily Life ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Training Data ◽  
Data Set

SummaryObjectives: The aim of this study is to present a method of assessing psychological tension that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life.Methods: HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mental tension level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s2) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mental tension score (Δtension) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Δtension from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the twofold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set.Results: The subjects’ mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. The subjects’ mean coefficient of determination was 0.28 with the training data set and 0.11 with the test data set.Conclusion: We proposed a method of assessing psychological tension that is optimized to every individual based on HRV data measured over a long period of daily life.

scholarly journals Changes in Tongue Area, Pharyngeal Area, and Pharyngeal Airway Velocity after Correction of Mandibular Prognathism

2021 ◽  
Vol 10 (19) ◽  
pp. 4560
Author(s):  
Chun-Ming Chen ◽  
Ting-Ying Yu ◽  
Szu-Ting Chou ◽  
Jung-Hsuan Cheng ◽  
Shih-Chieh Chen ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Statistical Significance ◽  
Vertical Movement ◽  
Slight Reduction ◽  
Airflow Velocity ◽  
Mandibular Prognathism ◽  
Pharyngeal Airway ◽  
Mandibular Setback ◽  
One Year ◽  
Pharyngeal Area

This study aimed to investigate the correlation between the amount of mandibular setback, and the related changes of the tongue area, pharyngeal area, and pharyngeal airflow velocity. Twenty-five patients treated for mandibular prognathism, and serial cephalograms were obtained (T1: preoperation, T2: more than one year postoperation). The postoperative area of the tongue, pharyngeal airway space, and pharyngeal airflow velocity were investigated. Statistical analysis was performed with the Student t-test and Pearson correlation. The amount of mandible setback was significant after surgery (12.8 mm; p < 0.001). The pharyngeal area was significantly reduced 115.5 mm2 (p = 0.046). There was a slight reduction of the tongue area (43.2 mm2; p = 0.305) and an increase of pharyngeal airflow velocity (0.3 m/s; p = 0.133). The Pearson correlation coefficient test showed no statistical significance among the amount of horizontal setback and vertical movement of the mandible, such as the reductions in the tongue area, the pharyngeal airway space, and the increase in pharyngeal airflow velocity. Larger amounts of mandibular setback caused a significant reduction of pharyngeal airway area, but without significant changes of the tongue area and pharyngeal airflow velocity.

scholarly journals Mindfulness and Its Predictors in Women With Polycystic Ovary Syndrome

2020 ◽  
Vol 24 (3) ◽  
pp. 246-257
Author(s):  
Roghayeh Arbabi Moghaddam ◽  
◽  
Seyedeh Batool Hasanpoor-Azghady ◽  
Leila Amiri Farahani ◽  
Shima Haghani ◽  
...  
Keyword(s):  
Linear Regression ◽  
Polycystic Ovary Syndrome ◽  
Multiple Linear Regression ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Multiple Linear Regression Analysis ◽  
Reproductive Age ◽  
Ovary Syndrome

Background: Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in women of reproductive age which can cause many problems such as hyperandrogenic symptoms and fertility problems. Objective: The present study aimed to determine the relationship of mindfulness with hyperandrogenic symptoms and demographic and fertility factors in women with PCOS. Methods: This descriptive correlational study was conducted on 181 women with PCOS referred to Firoozabadi and Firoozgar hospitals in Tehran, Iran who were selected using a continuous sampling method and based on inclusion criteria from June 2018 to August 2019. Data were collected using a demographic/fertility form, the modified Ferriman-Gallwey Scale, Ludwig Hair Loss Scale, and Mindfulness Attention Awareness Scale (MAAS). Data were analyzed using independent t-test, one-way ANOVA, Kruskal-Wallis test, Pearson correlation test, and multiple linear regression analysis. Findings: The mean MAAS score of women was 68.61±9.88 and was significantly correlated with age (P=0.01), wife’s education (P=0.001), wife’s occupation (P=0.005), economic status (P=0.02), husband satisfaction with wife’s body and appearance (P=0.02), body mass index (P=0.01), and duration of marriage (P<0.001). According to the multiple linear regression model, the duration of marriage could predict 22% of the variance in overall MAAS score. Conclusion: Mindfulness is associated with some demographic variables, among which the marriage is its predictor. It is recommended to pay attention to the reported variables in preparation of counseling or educational programs, along with other treatments, for women with PCOS.

scholarly journals Correlation between the Pharyngeal Airway Space and Head Posture after Surgery for Mandibular Prognathism

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chun-Ming Chen ◽  
Steven Lai ◽  
Ker-Kong Chen ◽  
Huey-Er Lee
Keyword(s):  
Mandibular Prognathism ◽  
Head Posture ◽  
Oropharyngeal Airway ◽  
Pharyngeal Airway ◽  
Mandibular Setback ◽  
Postoperative Changes ◽  
Lateral Cephalograms ◽  
Intraoral Vertical Ramus Osteotomy ◽  
Mandibular Setback Surgery ◽  
Vertical Ramus Osteotomy

Purpose. The aim of this study was to determine the correlation between the pharyngeal airway space and head posture after mandibular setback surgery for mandibular prognathism.Materials and Methods. Serial lateral cephalograms of 37 patients with mandibular prognathism who underwent intraoral vertical ramus osteotomy (IVRO) were evaluated before (T1) and immediately (T2), between 6 weeks and 3 months (T3), and more than 1 year (T4) after surgery. Pairedt-tests and Pearson’s correlation analysis were used to evaluate the postoperative changes in all cephalometric parameters, including the mandible, hyoid, head posture (craniocervical angle), and pharyngeal airway space.Results. The mandible and hyoid were set back by 12.8 mm and 4.9 mm, respectively, at T2. Furthermore, the hyoid showed significant inferior movement of 10.7 mm, with an 8 mm increase in the tongue depth. The upper oropharyngeal airway (UOP) shortened by 4.1 mm, the lower oropharyngeal airway (LOP) by 1.7 mm, and the laryngopharyngeal airway by 2 mm. The craniocervical angle showed a significant increase of 2.8°. UOP and LOP showed a significant correlation with the craniocervical angle at T2 and T4.Conclusions. Our findings conclude that the oropharyngeal airway space is significantly decreased and correlated with a change in the head posture after mandibular setback surgery.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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