scholarly journals Prediction of Metabolizable Energy Concentrations of Herbage in the Qinghai–Tibetan Plateau Using Tibetan Sheep Digestibility Data

Animals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 376
Author(s):  
Penghui Guo ◽  
Peng Gao ◽  
Fuhou Li ◽  
Shenghua Chang ◽  
Zhaofeng Wang ◽  
...  
Keyword(s):  
Prediction Error ◽  
Cold Season ◽  
Metabolizable Energy ◽  
Neutral Detergent Fiber ◽  
Tibet Plateau ◽  
Prediction Errors ◽  
Internal Validation ◽  
Diethyl Ether Extract ◽  
Tibetan Sheep ◽  
Mean Prediction Error

Due to its extremely harsh environment, including high altitude, hypoxia, long cold season, and strong ultraviolet radiation in the Qinghai–Tibet Plateau (QTP), herbage species and nutritional value of the pasture may differ considerably from elsewhere across the world. The aim of the present study was to develop biologically relevant equations for estimating the metabolizable energy (ME) value of fresh native herbages in the QTP using digestibility variables and chemical concentrations in the herbage offered to Tibetan sheep at the maintenance level. A total of 11 digestibility trials (6 sheep/trial) were performed in different grazing seasons from 2011 to 2016. The herbage was harvested daily in the morning and offered to sheep at the maintenance feeding level. Thirty-seven equations were developed for the prediction of herbage digestible energy (DE) and ME energy values. The mean prediction error for ME was the lowest when using herbage gross energy digestibility as a sole predictor. When using other digestibility variables (e.g., dry matter and organic matter) as primary predictors, addition of herbage nutrient concentration reduced the difference between predicted and actual values. When DE was used as the primary explanatory variable, mean prediction error was reduced with the addition of ash, nitrogen (N), diethyl ether extract (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF) concentrations. The internal validation of the present equations showed lower prediction errors when compared with those of existing equations for prediction of DE and ME concentrations in the herbage. Equations developed in the current study may thus allow for an improved and accurate prediction of metabolizable energy concentrations of herbage in practice, which is critical for the development of sustainable grazing systems in the QTP.

scholarly journals Rumen Microbiota of Tibetan Sheep (Ovis aries) Adaptation to Extremely Cold Season on the Qinghai-Tibetan Plateau

2021 ◽  
Vol 8 ◽  
Author(s):  
Qingshan Fan ◽  
Xiongxiong Cui ◽  
Zhaofeng Wang ◽  
Shenghua Chang ◽  
Metha Wanapat ◽  
...  
Keyword(s):  
Growth Performance ◽  
Nutritional Quality ◽  
Cold Season ◽  
Ovis Aries ◽  
Tibet Plateau ◽  
Rumen Microbiota ◽  
Next Generation Sequencing Technology ◽  
Tibetan Sheep ◽  
Serum Biochemical

The Qinghai-Tibet Plateau is characterized by low temperatures and hypoxia, and this feature is more obvious in the winter. However, it is not clear how Tibetan sheep adapt to extreme cold climates. To address this, we used physiological methods combined with next-generation sequencing technology to explore the differences in growth performance, forage nutrient digestion, serum biochemical indexes, and rumen microbial communities of Tibetan sheep (Ovis aries) between the summer and winter. In the summer, owing to the high nutritional quality of the forage, the Tibetan sheep showed enhanced forage degradation and fermentation though increased counts of important bacteria in the rumen, such as Bacteroidetes, Prevotella_1, Prevotellaceae_UCG-003, Ruminococcus_1, Saccharofermentans, and Ruminococcaceae_UCG-014, to improve the growth performance and increase serum immunity and antioxidant status. In the winter, owing to the low nutritional quality of the forage, the Tibetan sheep presented low values of forage degradation and fermentation indicators. The relative abundance of Firmicutes, the Firmicutes/Bacteroidetes ratio, microbial diversity, interactive activity between microorganisms, and metabolism were significantly increased, implying that the rumen microbiota could promote the decomposition of forage biomass and the maintenance of energy when forage nutritional value was insufficient in the winter. Our study helps in elucidating the mechanism by which Tibetan sheep adapt to the high-altitude harsh environments, from the perspective of the rumen microbiota.

scholarly journals The Rumen Microbiome and Its Metabolome Together With the Host Transcriptome Act to Regulate the Cold Season Adaptation Mechanism of Grazing Tibetan Sheep

2021 ◽  
Author(s):  
Xiu Liu ◽  
Yuzhu Sha ◽  
Weibing Lv ◽  
Xinyu Guo ◽  
Xiaoning Pu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Arachidonic Acid ◽  
Cytochrome P450 ◽  
Acid Metabolism ◽  
Cold Season ◽  
Arachidonic Acid Metabolism ◽  
Tibet Plateau ◽  
Rumen Epithelium ◽  
Tibetan Sheep ◽  
Metabolism Of Xenobiotics

Abstract BackgroundTibetan sheep are important ruminants on the Qinghai-Tibet Plateau. They can maintain a normal life and reproduce in harsh environments under extreme cold and low oxygen. However, the molecular and metabolic mechanisms underlying the adaptability of Tibetan sheep during the cold season are still unclear. Hence, we conducted a comprehensive analysis of rumen epithelial morphology, epithelial transcriptomics, microbiology and metabolomics in a Tibetan sheep model to understand the interaction between the rumen host and microbiota and their metabolites and to explore the potential regulatory mechanism of Tibetan sheep adaptability to the cold season of the plateau. ResultsMorphological analysis showed that the ruminal muscle layer thickness and nipple width of Tibetan sheep increased significantly during the cold season ( P <0.05), and the thickness of the stratum corneum, stratum granulosa and stratum spinous of the rumen epithelium increased significantly ( P <0.05). Transcriptomics analysis showed that the differential genes were primarily enriched in the PPAR signaling pathway (ko03320), legionellosis (ko05134), phagosome (ko04145), arginine and proline metabolism (ko00330), and metabolism of xenobiotics by cytochrome P450 (ko00980). Unique differential metabolites were identified in cold season, such as cynaroside A, sanguisorbin B and tryptophyl-valine, which were mainly enriched in arachidonic acid metabolism, arachidonic acid metabolism and linolenic acid metabolism pathways, and had certain correlation with microorganisms. Integrated transcriptome-metabolome-microbiome analysis showed that epithelial gene- GSTM3 expression was upregulated in the metabolism of xenobiotics by the cytochrome P450 pathway during the cold season, leading to the downregulation of some harmful metabolites; TLR5 gene expression was upregulated and CD14 gene expression was downregulated in the legionellosis pathway during the cold season. A large number of metabolites, such as glucosidic acid and vitamin A, were produced in the steroid hormone biosynthesis and retinol metabolism pathways. ConclusionThis study comprehensively described the interaction mechanism between the rumen host and microbes and their metabolites in grazing Tibetan sheep during the cold season. Under the stimulation of the cold plateau environment, the morphological structure of the rumen epithelium of Tibetan sheep undergoes adaptive changes. Rumen epithelial genes, microbiota and metabolites act together in some key pathways related to cold season adaptation.

scholarly journals Gender differences in refraction prediction error of five formulas for cataract surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yibing Zhang ◽  
Tingyang Li ◽  
Aparna Reddy ◽  
Nambi Nallasamy
Keyword(s):  
Gender Differences ◽  
Cataract Surgery ◽  
Prediction Error ◽  
Statistical Difference ◽  
Independent Predictor ◽  
Study Data ◽  
Prediction Errors ◽  
Optical Biometry ◽  
Iol Power ◽  
Lens Power

Abstract Objectives To evaluate gender differences in optical biometry measurements and lens power calculations. Methods Eight thousand four hundred thirty-one eyes of five thousand five hundred nineteen patients who underwent cataract surgery at University of Michigan’s Kellogg Eye Center were included in this retrospective study. Data including age, gender, optical biometry, postoperative refraction, implanted intraocular lens (IOL) power, and IOL formula refraction predictions were gathered and/or calculated utilizing the Sight Outcomes Research Collaborative (SOURCE) database and analyzed. Results There was a statistical difference between every optical biometry measure between genders. Despite lens constant optimization, mean signed prediction errors (SPEs) of modern IOL formulas differed significantly between genders, with predictions skewed more hyperopic for males and myopic for females for all 5 of the modern IOL formulas tested. Optimization of lens constants by gender significantly decreased prediction error for 2 of the 5 modern IOL formulas tested. Conclusions Gender was found to be an independent predictor of refraction prediction error for all 5 formulas studied. Optimization of lens constants by gender can decrease refraction prediction error for certain modern IOL formulas.

scholarly journals Does Every Calculation Formula Fit for All Types of Intraocular Lenses? Optimization of Constants for Tecnis ZA9003 and ZCB00 Is Necessary

Medicina ◽  
2021 ◽  
Vol 57 (4) ◽  
pp. 319
Author(s):  
Ivajlo Popov ◽  
Veronika Popova ◽  
Juraj Sekac ◽  
Vladimir Krasnik
Keyword(s):  
Prediction Error ◽  
Poor Performance ◽  
Intraocular Lenses ◽  
Power Calculation ◽  
Universal Formula ◽  
Aspheric Iol ◽  
Different Types ◽  
The Mean ◽  
Clinically Significant ◽  
Mean Prediction Error

Background and Objectives: To evaluate the performance of intraocular lenses (IOLs) using power calculation formulas on different types of IOL. Materials and Methods: 120 eyes and four IOL types (BioLine Yellow Accurate Aspheric IOL (i-Medical), TECNIS ZCB00, TECNIS ZA9003 (Johnson & Johnson) (3-piece IOL) and Softec HD (Lenstec)) were analyzed. The performance of Haigis, Barret Universal II and SKR-II formulas were compared between IOL types. The mean prediction error (ME) and mean absolute prediction error (MAE) were analyzed. Results: The overall percentage of eyes predicted within ±0.25 diopters (D) was 40.8% for Barret; 39.2% Haigis and 31.7% for SRK-II. Barret and Haigis had a significantly lower MAE than SRK-II (p < 0.05). The results differed among IOL types. The largest portion of eyes predicted within ±0.25 D was with the Barret formula in ZCB00 (33.3%) and ZA9003 (43.3%). Haigis was the most accurate in Softec HD (50%) and SRK-II in Biolline Yellow IOL (50%). ZCB00 showed a clinically significant hypermetropic ME compared to other IOLs. Conclusions: In general, Barret formulas had the best performance as a universal formula. However, the formula should be chosen according to the type of IOL in order to obtain the best results. Constant optimizations are necessary for the Tecnis IOL ZCB00 and ZA9003, as all of the analyzed formulas achieved a clinically significant poor performance in this type of IOL. ZCB00 also showed a hypermetropic shift in ME in all the formulas.

Characterizing CH 4 , CO 2 and N 2 O emission from barn feeding Tibetan sheep in Tibetan alpine pastoral area in cold season

2017 ◽  
Vol 157 ◽  
pp. 84-90 ◽  
Author(s):  
Tianwei Xu ◽  
Na Zhao ◽  
Linyong Hu ◽  
Shixiao Xu ◽  
Hongjin Liu ◽  
...  
Keyword(s):  
Cold Season ◽  
Pastoral Area ◽  
Tibetan Sheep

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

scholarly journals A Homeostatic Model of Subjective Cognitive Decline

2018 ◽  
Vol 8 (12) ◽  
pp. 228 ◽  
Author(s):  
Akiko Mizuno ◽  
Maria Ly ◽  
Howard Aizenstein
Keyword(s):  
Cognitive Decline ◽  
Prediction Error ◽  
Primary Source ◽  
Subjective Cognitive Decline ◽  
Prediction Errors ◽  
Functional Neuroanatomy ◽  
System A ◽  
Prediction Error Minimization ◽  
Breakdown Model ◽  
Normal Cognition

Subjective Cognitive Decline (SCD) is possibly one of the earliest detectable signs of dementia, but we do not know which mental processes lead to elevated concern. In this narrative review, we will summarize the previous literature on the biomarkers and functional neuroanatomy of SCD. In order to extend upon the prevailing theory of SCD, compensatory hyperactivation, we will introduce a new model: the breakdown of homeostasis in the prediction error minimization system. A cognitive prediction error is a discrepancy between an implicit cognitive prediction and the corresponding outcome. Experiencing frequent prediction errors may be a primary source of elevated subjective concern. Our homeostasis breakdown model provides an explanation for the progression from both normal cognition to SCD and from SCD to advanced dementia stages.

Process Mean Shift Detection Using Prediction Error Analysis

1998 ◽  
Vol 120 (3) ◽  
pp. 489-495 ◽  
Author(s):  
S. J. Hu ◽  
Y. G. Liu
Keyword(s):  
Error Analysis ◽  
Prediction Error ◽  
Control Charts ◽  
Mean Shift ◽  
Measurement Data ◽  
Step Function ◽  
False Alarms ◽  
Prediction Errors ◽  
Function Type ◽  
Shift Detection

Autocorrelation in 100 percent measurement data results in false alarms when the traditional control charts, such as X and R charts, are applied in process monitoring. A popular approach proposed in the literature is based on prediction error analysis (PEA), i.e., using time series models to remove the autocorrelation, and then applying the control charts to the residuals, or prediction errors. This paper uses a step function type mean shift as an example to investigate the effect of prediction error analysis on the speed of mean shift detection. The use of PEA results in two changes in the 100 percent measurement data: (1) change in the variance, and (2) change in the magnitude of the mean shift. Both changes affect the speed of mean shift detection. These effects are model parameter dependent and are obtained quantitatively for AR(1) and ARMA(2,1) models. Simulations and examples from automobile body assembly processes are used to demonstrate these effects. It is shown that depending on the parameters of the AMRA models, the speed of detection could be increased or decreased significantly.

The Embodiment of Concepts

2018 ◽  
pp. 640-660
Author(s):  
Michiel Van Elk ◽  
Harold Bekkering
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Conceptual Representation ◽  
Recurrent Processing ◽  
Different Dimensions ◽  
Prior Models ◽  
Processing Framework

We characterize theories of conceptual representation as embodied, disembodied, or hybrid according to their stance on a number of different dimensions: the nature of concepts, the relation between language and concepts, the function of concepts, the acquisition of concepts, the representation of concepts, and the role of context. We propose to extend an embodied view of concepts, by taking into account the importance of multimodal associations and predictive processing. We argue that concepts are dynamically acquired and updated, based on recurrent processing of prediction error signals in a hierarchically structured network. Concepts are thus used as prior models to generate multimodal expectations, thereby reducing surprise and enabling greater precision in the perception of exemplars. This view places embodied theories of concepts in a novel predictive processing framework, by highlighting the importance of concepts for prediction, learning and shaping categories on the basis of prediction errors.

Sign in / Sign up

Export Citation Format

Share Document