What training and evaluation programs will be required to maintain a high state of preparedness?

2021 ◽  
pp. 111-113
Author(s):  
Dominic Golding ◽  
Jeanne X. Kasperson ◽  
Roger E. Kasperson ◽  
Robert Goble ◽  
John E. Seley ◽  
...  
JAMA ◽  
1966 ◽  
Vol 196 (6) ◽  
pp. 496-498 ◽  
Author(s):  
J. Bishop

1997 ◽  
Vol 481 (1) ◽  
pp. 433-446 ◽  
Author(s):  
D. W. Hoard ◽  
Paula Szkody
Keyword(s):  

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.


2011 ◽  
Vol 143 (1) ◽  
pp. 23 ◽  
Author(s):  
Haritma Gaur ◽  
Alok C. Gupta ◽  
Paul J. Wiita

2021 ◽  
Vol 4 ◽  
Author(s):  
Vasileios Ioakeimidis ◽  
Nareg Khachatoorian ◽  
Corinna Haenschel ◽  
Thomas A. Papathomas ◽  
Attila Farkas ◽  
...  

Abstract The hollow-mask illusion is an optical illusion where a concave face is perceived as convex. It has been demonstrated that individuals with schizophrenia and anxiety are less susceptible to the illusion than controls. Previous research has shown that the P300 and P600 event-related potentials (ERPs) are affected in individuals with schizophrenia. Here, we examined whether individual differences in neuroticism and anxiety scores, traits that have been suggested to be risk factors for schizophrenia and anxiety disorders, affect ERPs of healthy participants while they view concave faces. Our results confirm that the participants were susceptible to the illusion, misperceiving concave faces as convex. We additionally demonstrate significant interactions of the concave condition with state anxiety in central and parietal electrodes for P300 and parietal areas for P600, but not with neuroticism and trait anxiety. The state anxiety interactions were driven by low-state anxiety participants showing lower amplitudes for concave faces compared to convex. The P300 and P600 amplitudes were smaller when a concave face activated a convex face memory representation, since the stimulus did not match the active representation. The opposite pattern was evident in high-state anxiety participants in regard to state anxiety interaction and the hollow-mask illusion, demonstrating larger P300 and P600 amplitudes to concave faces suggesting impaired late information processing in this group. This could be explained by impaired allocation of attentional resources in high-state anxiety leading to hyperarousal to concave faces that are unexpected mismatches to standard memory representations, as opposed to expected convex faces.


Energy ◽  
2022 ◽  
Vol 238 ◽  
pp. 121809
Author(s):  
Shanshan Guo ◽  
Ruixin Yang ◽  
Weixiang Shen ◽  
Yongsheng Liu ◽  
Shenggang Guo

1988 ◽  
Vol 231 (1) ◽  
pp. 69-84 ◽  
Author(s):  
C. S. R. Day ◽  
A. F. Tennant ◽  
A. C. Fabian
Keyword(s):  

2005 ◽  
Vol 29 (2) ◽  
pp. 131-135
Author(s):  
Ming-xuan Zhang ◽  
Jin-lu Qu
Keyword(s):  

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