scholarly journals Hürthle Cells Predict Hypothyroidism in Interferon-γ Transgenic Mice of Different Genetic Backgrounds

Endocrinology ◽  
2012 ◽  
Vol 153 (8) ◽  
pp. 4059-4066 ◽  
Author(s):  
Shintaro Iwama ◽  
Alessandra De Remigis ◽  
Justin A. Bishop ◽  
Hiroaki J. Kimura ◽  
Patrizio Caturegli
Keyword(s):  
Transgenic Mice ◽  
Regression Model ◽  
Thyroid Function ◽  
Multiple Linear Regression Model ◽  
Hashimoto Thyroiditis ◽  
Interferon Γ ◽  
Brown Adipocytes ◽  
Major Histocompatibility Complex Haplotype ◽  
Hurthle Cells ◽  
Hürthle Cells

Hürthle cells have long been described in Hashimoto thyroiditis but remain of undetermined significance. We have previously shown that Hürthle cells and hypothyroidism develop in C57BL/6J mice expressing interferon-γ (IFNγ) in the thyroid. To assess the influence of genetic backgrounds on Hürthle cell development, we crossed C57BL/6J IFNγ transgenic mice to 14 strains and analyzed thyroid histopathology and function in a cohort of 389 mice (225 transgenic and 164 wild type) using a multiple linear regression model that also included strain, sex, genotype, and major histocompatibility complex haplotype. We then queried the Johns Hopkins surgical pathology electronic archive for “Hashimoto” and/or “thyroiditis” keywords, reviewed the reports, and reexamined the Hashimoto slides. Hürthle cells were markedly affected by the genetic background: they were prominent and associated with hypothyroidism in the C57BL/6J, C57BL/6ByJ, C57BL/10J, C57BLKS/J, C57L/J, C58/J, and BPN/3J IFNγ transgenic strains, whereas they are mild or absent in the BPH/2J, BPL/1J, LP/J, CBA/J, Balb/cJ, DBA/1J, and NOD/ShiLtJ strains. Hürthle cells were the strongest predictor of hypothyroidism after adjusting for all the other covariates in the regression model. Interestingly, transgenic mice of the BPL/1J, DBA/1J, and NOD/ShiLtJ strains developed a marked accumulation of intrathyroidal brown adipocytes that was significantly associated with improved thyroid function. Hürthle cells were mentioned in 23% of the Hashimoto reports but increased to 79% upon our slide review. This study reports a novel association of Hürhtle cells and brown adipocytes on thyroid function that should prompt a reconsideration of their significance and role in pathogenesis of autoimmune thyroiditis.

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.

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.

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

scholarly journals Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1273
Author(s):  
James Todd ◽  
Richard Johnson
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Block Design ◽  
Color Space ◽  
Multiple Linear Regression Model ◽  
Cane Yield ◽  
Unmanned Aerial Systems ◽  
Family Selection ◽  
Ratoon Crop ◽  
Color Indices

Remote sensing techniques and the use of Unmanned Aerial Systems (UAS) have simplified the estimation of yield and plant health in many crops. Family selection in sugarcane breeding programs relies on weighed plots at harvest, which is a labor-intensive process. In this study, we utilized UAS-based remote sensing imagery of plant-cane and first ratoon crops to estimate family yields for a second ratoon crop. Multiple families from the commercial breeding program were planted in a randomized complete block design by family. Standard red, green, and blue imagery was acquired with a commercially available UAS equipped with a Red–Green–Blue (RGB) camera. Color indices using the CIELab color space model were estimated from the imagery for each plot. The cane was mechanically harvested with a sugarcane combine harvester and plot weights were obtained (kg) with a field wagon equipped with load cells. Stepwise regression, correlations, and variance inflation factors were used to identify the best multiple linear regression model to estimate the second ratoon cane yield (kg). A multiple regression model, which included family, and five different color indices produced a significant R2 of 0.88. This indicates that it is possible to make family selection predictions of cane weight without collecting plot weights. The adoption of this technology has the potential to decrease labor requirements and increase breeding efficiency.

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