scholarly journals Application of Multivariate Modeling for Radiation Injury Assessment: A Proof of Concept

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
David L. Bolduc ◽  
Vilmar Villa ◽  
David J. Sandgren ◽  
G. David Ledney ◽  
William F. Blakely ◽  
...  
Keyword(s):  
Predictive Power ◽  
Radiation Injury ◽  
Estimation Algorithm ◽  
Absolute Number ◽  
Blood Parameters ◽  
Serum Chemistry ◽  
Independent Variables ◽  
Estimation Algorithms ◽  
Injury Assessment ◽  
Total Body

Multivariate radiation injury estimation algorithms were formulated for estimating severe hematopoietic acute radiation syndrome (H-ARS) injury (i.e., response category three or RC3) in a rhesus monkey total-body irradiation (TBI) model. Classical CBC and serum chemistry blood parameters were examined prior to irradiation (d 0) and on d 7, 10, 14, 21, and 25 after irradiation involving 24 nonhuman primates (NHP) (Macaca mulatta) given 6.5-Gy60CoΥ-rays (0.4 Gy min−1) TBI. A correlation matrix was formulated with the RC3 severity level designated as the “dependent variable” and independent variables down selected based on their radioresponsiveness and relatively low multicollinearity using stepwise-linear regression analyses. Final candidate independent variables included CBC counts (absolute number of neutrophils, lymphocytes, and platelets) in formulating the “CBC” RC3 estimation algorithm. Additionally, the formulation of a diagnostic CBC and serum chemistry “CBC-SCHEM” RC3 algorithm expanded upon the CBC algorithm model with the addition of hematocrit and the serum enzyme levels of aspartate aminotransferase, creatine kinase, and lactate dehydrogenase. Both algorithms estimated RC3 with over 90% predictive power. Only the CBC-SCHEM RC3 algorithm, however, met the critical three assumptions of linear least squares demonstrating slightly greater precision for radiation injury estimation, but with significantly decreased prediction error indicating increased statistical robustness.

Reliability of blood parameters to assess the nutritional status of caribou

1987 ◽  
Vol 65 (10) ◽  
pp. 2413-2416 ◽  
Author(s):  
François Messier ◽  
Jean Huot ◽  
François Goudreault ◽  
Armand V. Tremblay
Keyword(s):  
Nutritional Status ◽  
Predictive Power ◽  
Cumulative Effect ◽  
Blood Parameters ◽  
The Body ◽  
Blood Constituents ◽  
Fat Reserves ◽  
Total Body Fat ◽  
Total Body ◽  
George River

Total body fat reserves and 13 blood parameters were assessed from 58 female caribou. Animals were sampled in autumn and in spring from the George River caribou herd, northern Quebec and Labrador. Most blood constituents showed a remarkable homeostasis even though fat reserves ranged from 1.6 to 19.3% of the body mass. Total proteins versus fat content yielded a positive relationship, yet with a poor predictive power (r2 = 0.36). The lack of response in most blood parameters might be ascribable to small variations in diet among individual caribou. These variations in diet, although possibly small, may have a cumulative effect on fat reserve dynamics. Overall, we did not identify a set of blood parameters that could adequately reflect nutritional status of caribou. The limitations of blood parameters as indicators of the general nutritional status of caribou are discussed.

An Energy Efficient Estimation Algorithm for Wireless Sensor Network

2011 ◽  
Vol 216 ◽  
pp. 176-180
Author(s):  
Yong Ding ◽  
Yue Mei Su
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Current Energy

Wireless Sensor Networks functionality is closely related to network lifetime which depends on the energy consumption, so require energy- efficient protocols to improve the network lifetime. According to the analysis and summary of the current energy efficient estimation algorithms in wireless sensor network An energy-efficient algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional diffusion routing protocol. Simulation results show that this algorithm is to effectively balance the network energy consumption, improve the network life-cycle and ensure the communication quality.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

Predicting Voting Patterns in the General Assembly

1971 ◽  
Vol 65 (2) ◽  
pp. 471-498 ◽  
Author(s):  
Jack E. Vincent
Keyword(s):  
Economic Development ◽  
United Nations ◽  
Voting Behavior ◽  
Predictive Power ◽  
Correlation Technique ◽  
Factor Scores ◽  
Theoretical Level ◽  
Independent Variables ◽  
Attribute Data ◽  
The United Nations

This project attempts to relate a large number of potential predictors to voting data generated at the United Nations. Numerous associations were found when the predictors, 77 in all, were related to 13 different kinds of voting scores. Because of considerable redundancy in both sets of data, national attribute and voting, the results were factor analyzed and the original variables were reduced to 14 sets of factor scores representing the national attribute data, and 4 representing the voting data. Several significant associations emerged from the intercorrelation of these two sets of factor scores, with the independent variables “Economic Development,” “Democracy,” and “U.S. Relations” exhibiting considerable predictive power. When the overall relationships between the two sets of data were assessed by use of the canonical correlation technique, “Economic Development” received the greatest weight on the national attribute side, and “Eastern Voting” on the voting (dependent variable) side. These findings accord well with previous research, in that “Economic Development” seems to predict negativism as revealed by voting. Thus “Economic Development” appears to be fundamentally related to certain schisms at the United Nations, with the representatives from the most developed states appearing the most “negative” as evidenced by questionnaire responses and voting behavior. Such orientations are likely to have a significant impact on the evolution of the organization.At a theoretical level, the present findings may have considerable relevance for both Social Field theory and Attribute theory.

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

2007 ◽  
Vol 111 (1120) ◽  
pp. 389-396 ◽  
Author(s):  
G. Campa ◽  
M. R. Napolitano ◽  
M. Perhinschi ◽  
M. L. Fravolini ◽  
L. Pollini ◽  
...  
Keyword(s):  
Machine Vision ◽  
Pose Estimation ◽  
Estimation Algorithm ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Light Sources ◽  
Estimation Algorithms ◽  
Trade Offs ◽  
Labeling Algorithms ◽  
Selection Of

Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

scholarly journals Comparative analysis of the in vivo radioprotective effects of recombinant granulocyte colony-stimulating factor (G-CSF), recombinant granulocyte-macrophage CSF, and their combination

Blood ◽  
1991 ◽  
Vol 77 (11) ◽  
pp. 2364-2371 ◽  
Author(s):  
KG Waddick ◽  
CW Song ◽  
L Souza ◽  
FM Uckun
Keyword(s):  
Radiation Injury ◽  
Direct Evidence ◽  
Granulocyte Colony Stimulating Factor ◽  
Colony Stimulating Factor ◽  
Probability Of Survival ◽  
Total Body ◽  
Different Strains ◽  
In Vivo Administration ◽  
Stimulating Factor

The purpose of the present study was to evaluate and compare the in vivo radioprotective effects of pre-total body irradiation (TBI) conditioning with recombinant granulocyte colony-stimulating factor (rG- CSF) and recombinant granulocyte-macrophage CSF (rGM-CSF) in a large series of lethally and supralethally irradiated mice. Also analyzed were the radioprotective effects of simultaneous as well as sequential combinations of rG-CSF and rGM-CSF. Our findings in 1,180 mice provide direct evidence that in vivo administration of rG-CSF or rGM-CSF before TBI protects a significant fraction of mice from the lethal effects of LD100/30 TBI. At equivalent doses, rG-CSF displayed a more potent radioprotective activity than rGM-CSF. Not only was rG-CSF radioprotective at much smaller doses than rGM-CSF, the survival rate after lethal TBI was also significantly higher in mice receiving optimally radioprotective doses of rG-CSF as compared with mice receiving optimally radioprotective doses of rGM-CSF. Pretreatment of mice with rGM-CSF markedly attenuated the radioprotective affects of rG- CSF in lethally as well as supralethally irradiated mice. Pretreatment with rG-CSF followed by rGM-CSF was slightly more effective than rG-CSF alone in supralethally irradiated mice but not in lethally irradiated mice. Notably, marked differences among different strains of mice were noted regarding the optimally radioprotective doses of rG-CSF or rGM- CSF as well as probability of survival and median survival time after lethal or supralethal TBI. This report confirms and extends previous studies concerning the potential of cytokines in prevention or therapy of lethal radiation injury.

Third-Person Effects of News Coverage: Orientations Toward Media

1997 ◽  
Vol 74 (3) ◽  
pp. 525-540 ◽  
Author(s):  
Vincent Price ◽  
Li-Ning Huang ◽  
David Tewksbury
Keyword(s):  
Individual Differences ◽  
News Media ◽  
Predictive Power ◽  
Media Use ◽  
News Coverage ◽  
Regression Analyses ◽  
Political Factors ◽  
Independent Variables ◽  
Third Person

This research focused on empirical connections between third-person effects and media orientations - general beliefs about news and characteristic uses of the news media. The study examined the contributions of three groups of independent variables, including political factors, media schemas, and media use, to third-person effects. Results of regression analyses suggest that each of the three groups of variables is modestly related to the magnitude of third-person effects, but none individually has great predictive power or necessarily alters third-person effects in a given news scenario. Finally, the mechanisms by which different variables influence the magnitude of third-person effects clearly vary. Overall, the results suggest only modest connections between individual differences in media orientations and the tendency to exhibit third- person effects.

A computational scheme to estimate the Leontief model matrix coefficients according to input-output table data for the southern regions of the Tyumen Oblast

2021 ◽  
Vol 19 (12) ◽  
pp. 2360-2383
Author(s):  
Denis A. GOVORKOV ◽  
Viktor P. NOVIKOV ◽  
Il'ya G. SOLOV'EV ◽  
Vladimir R. TSIBUL'SKII
Keyword(s):  
Least Squares Method ◽  
Estimation Algorithm ◽  
Balance Model ◽  
Input Output ◽  
Output Data ◽  
Tyumen Oblast ◽  
Estimation Algorithms ◽  
Additional Information ◽  
Output Matrix ◽  
Reference Input

Subject. This article deals with the control and management aspects of regional development on the basis of Leontief’s balance model. Objectives. The article aims to develop schemes for stable estimation of aggregate parameters of region balance models based on a shortened sample of input-output statistical data and rules for their subsequent regularization. Methods. For the study, we used multiple forms of regional economic balance model transformation based on the aggregation of data of the selected regional subsystems. Results. The primary estimates of aggregate input-output matrix for the southern regions of the Tyumen Oblast were obtained from the statistical input-output data for 2014–2018. To comply with the productivity conditions, additional information was introduced into the estimation algorithm reflecting the balance dependence for the reference input-output matrix for the Russian Federation and for the southern regions of the Tyumen Oblast in retrospective (2004–2013). Conclusions. The result of regularization of aggregate input-output matrix for the southern regions of the Tyumen Oblast obtained from the statistical input-output data on the basis of the least squares method indicates that the backward estimation technique cannot act as a basic tool for the primary construction of balance models of regional economies. However, backward estimation algorithms with subsequent regularization are effective in correcting the reference input-output matrix using actual data of the region’s socio-economic development.

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