Improved Predictive Methods for Airblast Shielding by Barrier Walls

2006 ◽  
Author(s):  
Denis D. Rickman ◽  
Donald W. Murrell ◽  
Byron J. Armstrong
Keyword(s):  
Predictive Methods

scholarly journals Compositional Determinants of Prion Formation in Yeast

2009 ◽  
Vol 30 (1) ◽  
pp. 319-332 ◽  
Author(s):  
James A. Toombs ◽  
Blake R. McCarty ◽  
Eric D. Ross
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Amyloid Formation ◽  
Yeast Prion ◽  
Hydrophobic Residues ◽  
Predictive Methods ◽  
Infectious Proteins ◽  
Β Sheet ◽  
Eukaryotic Genomes

ABSTRACT Numerous prions (infectious proteins) have been identified in yeast that result from the conversion of soluble proteins into β-sheet-rich amyloid-like protein aggregates. Yeast prion formation is driven primarily by amino acid composition. However, yeast prion domains are generally lacking in the bulky hydrophobic residues most strongly associated with amyloid formation and are instead enriched in glutamines and asparagines. Glutamine/asparagine-rich domains are thought to be involved in both disease-related and beneficial amyloid formation. These domains are overrepresented in eukaryotic genomes, but predictive methods have not yet been developed to efficiently distinguish between prion and nonprion glutamine/asparagine-rich domains. We have developed a novel in vivo assay to quantitatively assess how composition affects prion formation. Using our results, we have defined the compositional features that promote prion formation, allowing us to accurately distinguish between glutamine/asparagine-rich domains that can form prion-like aggregates and those that cannot. Additionally, our results explain why traditional amyloid prediction algorithms fail to accurately predict amyloid formation by the glutamine/asparagine-rich yeast prion domains.

The Document on Rain (Yushu 雨書): Weather and Prognostication in Early China

2021 ◽  
Vol 2 (2) ◽  
pp. 191-223
Author(s):  
Rebecca Robinson
Keyword(s):  
Early China ◽  
Content Type ◽  
Predictive Methods ◽  
Scholarly Attention

Abstract The Document on Rain (Yushu 雨書) is a short manuscript that forms part of the Beijing University collection of Han slips. This text, divided into two sections, has thus far garnered little scholarly attention. However, it presents to us an unusual example of a daybook (rishu 日書)-type manuscript, one which is primarily concerned with the weather. The Document on Rain, while sharing many characteristics of excavated daybooks, is unusual in its treatment of humans. Rather than providing advice on whether or not one should undertake activity on a certain day or engaging in the discourse about whether or not humans can manipulate the weather, the Document on Rain represents an understanding of the weather as a phenomenon that cannot be manipulated by humans, but one which can, perhaps, be understood. The Document on Rain integrates practices of prognostication based on calendrical and sexagenary cycles with theories about rain and its relationship to the symbolic characteristics of the twenty-eight lodges (ershiba xiu 二十八宿). This article analyses some of the predictive methods in the text and situates it within a longer tradition of meteoromantic practices.

scholarly journals Performance Analysis of Rule Based Algorithms Applied to a Cardiovascular Dataset

2013 ◽  
Vol 12 (2) ◽  
pp. 3277-3285
Author(s):  
Dev Mukherji ◽  
Nikita Padalia
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Alternative Methods ◽  
Support Vector ◽  
Rule Based ◽  
Prevention And Treatment ◽  
Predictive Methods ◽  
Single Data

Cardiovascular disease is one of the dominant concerns of society, affecting millions of people each year. Early and accurate diagnosis of risk of heart disease is one of major areas of medical research, aimed to aid in its prevention and treatment. Most of the approaches used to predict the occurrence of heart disease use single data mining techniques. However, performances of predictive methods have recently increased upon research into hybrid and alternative methods. This paper analyses the performance of logistic regression, support vector machine, and decision trees along with rule-based hybrids of the three in an attempt to create a more accurate predictive model.

Sign in / Sign up

Export Citation Format

Share Document