Propofol decreases in vivo binding of 11C-PBR28 to translocator protein (18 kDa) in the human brain.

Hines, Christina S,Fujita, Masahiro,Zoghbi, Sami S,Kim, Jin Su,Quezado, Zenaide,Herscovitch, Peter,Miao, Ning,Ferraris Araneta, Maria D,Morse, Cheryl,Pike, Victor W,Labovsky, Julia,Innis, Robert B Society of Nuclear Medicine 2013 The Journal of nuclear medicine Vol.54 No.1

<P>The PET radioligand (11)C-PBR28 targets translocator protein (18 kDa) (TSPO) and is a potential marker of neuroimmune activation in vivo. Although several patient populations have been studied using (11)C-PBR28, no investigators have studied cognitively impaired patients who would require anesthesia for the PET procedure, nor have any reports investigated the effects that anesthesia may have on radioligand uptake. The purpose of this study was to determine whether the anesthetic propofol alters brain uptake of (11)C-PBR28 in healthy subjects.</P>

Osteonecrosis of the Femoral Head: an Updated Review of ARCO on Pathogenesis, Staging and Treatment

Hines Jeremy T.,Jo Woo-Lam,Cui Quanjun,Mont Michael A.,Koo Kyung-Hoi,Cheng Edward Y.,Goodman Stuart B.,Ha Yong-Chan,Hernigou Phillippe,Jones Lynne C.,Kim Shin-Yoon,Sakai Takashi,Sugano Nobuhiko,Yamamo 대한의학회 2021 Journal of Korean medical science Vol.36 No.24

Non-traumatic osteonecrosis of the femoral head (ONFH) usually affects adults younger than 50 years and frequently leads to femoral head collapse and subsequent arthritis of the hip. It is becoming more prevalent along with increasing use of corticosteroids for the adjuvant therapy of leukemia and other myelogenous diseases as well as management of organ transplantation. This review updated knowledge on the pathogenesis, classification criteria, staging system, and treatment of ONFH.

MSET PERFORMANCE OPTIMIZATION THROUGH REGULARIZATION

HINES J. WESLEY,USYNIN ALEXANDER Korean Nuclear Society 2005 Nuclear Engineering and Technology Vol.37 No.2

The Multivariate State Estimation Technique (MSET) is being used in Nuclear Power Plants for sensor and equipment condition monitoring. This paper presents the use of regularization methods for optimizing MSET's predictive performance. The techniques are applied to a simulated data set and a data set obtained from a nuclear power plant currently implementing empirical, on-line, equipment condition monitoring techniques. The results show that regularization greatly enhances the predictive performance. Additionally, the selection of prototype vectors is investigated and a local modeling method is presented that can be applied when computational speed is desired.

Linking the AEP and AOP frameworks using a high-throughput approach for mixtures toxicokinetic modeling

David Hines 환경독성보건학회 2023 한국독성학회 심포지움 및 학술발표회 Vol.2023 No.11

MSET Performance Optimization Through Regularization

J. WESLEY HINES,ALEXANDER USYNIN 한국원자력학회 2005 Nuclear Engineering and Technology Vol.37 No.2

The Multivariate State Estimation Technique (MSET) is being used in Nuclear Power Plants for sensor and equipment condition monitoring. This paper presents the use of regularization methods for optimizing MSET’s predictive performance. The techniques are applied to a simulated data set and a data set obtained from a nuclear power plant currently implementing empirical, on-line, equipment condition monitoring techniques. The results show that regularization greatly enhances the predictive performance. Additionally, the selection of prototype vectors is investigated and a local modeling method is presented that can be applied when computational speed is desired.

INCORPORATING PRIOR BELIEF IN THE GENERAL PATH MODEL: A COMPARISON OF INFORMATION SOURCES

Coble, Jamie,Hines, J. W esley Korean Nuclear Society 2014 Nuclear Engineering and Technology Vol.46 No.6

The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. The GPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failure threshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognostic parameter observations are available. However, the parametric fit can suffer significantly when few data are available or the data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conform to a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to include prior information in the form of distributions of expected model parameters. This requires a number of run-to-failure cases with tracked prognostic parameters; these data may not be readily available for many systems. Reliability information and stressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM. This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacy of including different information sources on two data sets.

Effects of Expander Conditioning of Complex Nursery Diets on Growth Performance of Weanling Pigs

Johnston, S.L.,Hines, R.H.,Hancock, J.D.,Behnke, K.C.,Traylor, S.L.,Chae, B.J.,Han, In K. Asian Australasian Association of Animal Productio 1999 Animal Bioscience Vol.12 No.3

Three experiments were conducted to determine the effects of conditioning a complex (20% whey, 10% lactose, 4% plasma protein, 4% wheat gluten and 2% blood meal) diet on growth performance of weanling pigs. In Exp. 1,180 pigs (average initial BW of 6.4 kg) were fed the experimental diet (1.7% lysine) during a 7-d growth assay. Treatments were a meal control (M), standard (ST), and expander (EX) conditioned mash or pellets. Rate and efficiency of gain were decreased by 39% and 21% (p<0.005) respectively, for pigs fed EX diets compared to those fed the ST diet. In Exp. 2,196 pigs (average initial BW of 6.5 kg) were used to determine the effects of EX operating conditions on nutritional value of a pelleted complex diet. When steam conditioning temperature (prior to expanding) was $54^{\circ}C$, increasing cone pressure of the EX from 0 to 7 to 14 kg/cm2 resulted in linear decreases in rate of gain of weaned pigs (p<0.006), suggesting heat damage of the diet. Increasing conditioning temperature (i.e., adding steam) of the diets from 46 to 54 to $63^{\circ}C$ (cone pressure at $12kg/cm^2$) resulted in improved rate of gain (p<0.04) of the pigs. However, none of the pigs fed expanded diets compared favorably to the pigs fed the conditioned $(54^{\circ}C)$ pellets processed with no cone pressure. In Exp. 3,168 pigs (average initial BW of 6.6 kg) were used to determine the effects of expanding the various components of the diet. Treatments were M and ST pellets as controls, EX-corn, EX-corn soybean-meal, EX corn-soybean meal-oil, and EX-complete diet. Efficiency of gain was increased by 13% with EX portions of the diet compared to the mash control, but there was a marked decrease in performance when the complete diet was expanded (p<0.001). Expanded corn-soybean meal-oil supported the greatest ADG with a 19% increase compared to the average of the EX corn and EX corn-soybean meal treatments (p<0.005). In conclusion, our results suggest no benefit from expanding complete phase-I diets.

Effects of Conditioners (Standard, Long-Term and Expander) on Pellet Quality and Growth Performance in Nursery and Finishing Pigs

Johnston, S.L.,Hines, R.H.,Hancock, J.D.,Behnke, K.C.,Traylor, S.L.,Chae, B.J.,Han, In K. Asian Australasian Association of Animal Productio 1999 Animal Bioscience Vol.12 No.4

A series of experiments were conducted to determine the effects of standard (ST), long-term (LT), and expander (EX) conditioners on nutritional value of phase-three nursery and finishing swine diets. In Exp. 1, 180 pigs (average initial BW of 11.7 kg) were fed com-soybean meal based diets (1.3% lysine) during a 28 d growth assay. Gain/feed was improved (p<0.004) with pelleting and pellet durability index (PDI) increased with degree of conditioning (LT>ST). However, there was no advantage for LT vs ST conditioning in rate or efficiency of gain (p>0.5). In Exp. 2, 180 pigs (average initial BW of 10.4 kg) were fed con-soybean meal based diets (0.9% lysine) during a 28 d growth assay, Pelleted diets tended to support greater ADG (p<0.08) and gain/feed (p<0.002) with no marked advantage from EX vs ST conditioning. In Exp 3, a total of 70 barrows (average initial BW of 54 kg) was used in a growth assay to determine the effects of feeding a com-soybean meal based diet processed with a standard (ST) steam conditioner, a long-term (LT) steam conditioner, and an expander (EX) conditioner. The conditioned diets were fed as mash (M) or pellets (P) to give a $2{\times}3$ factorial plus a meal control. PDI increased with degree of conditioning (EX>LT>ST). There was a trend (p<0.07) for greater ADG in pigs fed diets that had been thermally conditioned. Also, there was a general advantage in gain/feed with pelleting (p<0.04), but this advantage was pronounced only with standard conditioning. Indeed, the greatest gain/feed was observed for pigs fed the expander treatments (p<0.03) and the expander mash was used as efficiently as the expander pellets. There was no difference in backfat thickness among pigs fed the treatments (p>0.3). but the more extreme the processing technique. the greater the incidence and severity of stomach lesions (p<0.04). These results suggest maximum rate and efficiency of growth with pelleting after standard steam conditioning or simply feeding an expanded mash.

Incorporating Prior Belief in the General Path Model: A Comparison of Information Sources

Jamie Coble,WESLEY HINES 한국원자력학회 2014 Nuclear Engineering and Technology Vol.46 No.6

The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. TheGPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failurethreshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognosticparameter observations are available. However, the parametric fit can suffer significantly when few data are available orthe data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conformto a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to includeprior information in the form of distributions of expected model parameters. This requires a number of run-to-failure caseswith tracked prognostic parameters; these data may not be readily available for many systems. Reliability information andstressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM. This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacyof including different information sources on two data sets.

COMPUTATIONAL INTELLIGENCE IN NUCLEAR ENGINEERING

UHRIG ROBERT E.,HINES J. WESLEY Korean Nuclear Society 2005 Nuclear Engineering and Technology Vol.37 No.2

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization of ill-posed surveillance and diagnostic measurements, 4) transient identification, 5) artificial intelligence-based core monitoring and diagnostic system, 6) continuous efficiency improvement of nuclear power plants, and 7) autonomous anticipatory control and intelligent-agents. Several changes to the focus of Computational Intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.