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"Dynamic linear model"

Yongqiu Xia, Donald E Weller, Meghan N Williams, Thomas E Jordan, Xiaoyuan Yan
Export coefficient models (ECMs) are often used to predict nutrient sources and sinks in watersheds because ECMs can flexibly incorporate processes and have minimal data requirements. However, ECMs do not quantify uncertainties in model structure, parameters, or predictions; nor do they account for spatial and temporal variability in land characteristics, weather, and management practices. We applied Bayesian hierarchical methods to address these problems in ECMs used to predict nitrate concentration in streams...
November 15, 2016: Water Research
C Hildebrandt Jørgensen, A R Kristensen, S Østergaard, T W Bennedsgaard
An automated method for determining whether dairy cows with subclinical mammary infections recover after antibiotic treatment would be a useful tool in dairy production. For that purpose, inline l-lactate dehydrogenase (LDH) measurements was modeled using a dynamic linear model; the variance parameters were estimated using the expectation-maximization algorithm. The method used to classify cows as infected or uninfected was based on a multiprocess Kalman filter. Two learning data sets were created: infected and uninfected...
October 2016: Journal of Dairy Science
Dan B Jensen, Henk Hogeveen, Albert De Vries
Rapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis...
September 2016: Journal of Dairy Science
A H Stygar, A R Kristensen
Application of BW monitoring methods for the whole batch of pigs is not common in commercial herds. Instead, farm managers may regularly weigh a chosen subset of pigs (observed group) and use the obtained information for monitoring, forecasting, and decision support. The objective of this study was to construct a model for growth monitoring and forecasting in pig fattening herds and use the developed model framework to quantify the value of information on BW. The dynamic process of pig growing was described by means of a dynamic linear model (DLM) with Kalman filtering...
March 2016: Journal of Animal Science
Francisco Torres-Avilés, Tomás Moraga, Loreto Núñez, Gloria Icaza
The objectives were to analyze lung cancer mortality trends in Chile from 1990 to 2009, and to project the rates six years forward. Lung cancer mortality data were obtained from the Chilean Ministry of Health. To obtain mortality rates, population projections were used, based on the 2002 National Census. Rates were adjusted using the world standard population as reference. Bayesian dynamic linear models were fitted to estimate trends from 1990 to 2009 and to obtain projections for 2010-2015. During the period under study, there was a 19...
September 2015: Cadernos de Saúde Pública
Christian Holm Hansen, Pamela Warner, Richard A Parker, Brian R Walker, Hilary Od Critchley, Christopher J Weir
It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. This article describes the preparatory simulation study for a Bayesian response-adaptive dose-finding trial design. Dexamethasone for Excessive Menstruation aims to assess the efficacy of Dexamethasone in reducing excessive menstrual bleeding and to determine the best dose for further study. To maximise learning about the dose response, patients receive placebo or an active dose with randomisation probabilities adapting based on evidence from patients already recruited...
September 30, 2015: Statistical Methods in Medical Research
J Kotoku, S Kumagai, A Haga, S Nakabayashi, N Arai, T Kobayashi
PURPOSE: The purpose of this study is to predict respiratory motion for a few seconds ahead by use of dynamic linear models. The models describe trends and periodic components of time series of respiratory curves obtained on patient's body. METHODS: To measure spatial coordinates of multiple points on patient's body during respiratory motion, we used a consumer depth camera (Microsoft Kinect) and obtained depth data via triangulation from infrared random dots patterns...
June 2015: Medical Physics
Robert A Lenz, Yili L Pritchett, Scott M Berry, Daniel A Llano, Shu Han, Donald A Berry, Carl H Sadowsky, Walid M Abi-Saab, Mario D Saltarelli
ABT-089, an α4β2 neuronal nicotinic receptor partial agonist, was evaluated for efficacy and safety in mild to moderate Alzheimer disease patients receiving stable doses of acetylcholinesterase inhibitors. This phase 2 double-blind, placebo-controlled, proof-of-concept, and dose-finding study adaptively randomized patients to receive ABT-089 (5, 10, 15, 20, 30, or 35 mg once daily) or placebo for 12 weeks. The primary efficacy endpoint was the Alzheimer's Disease Assessment Scale, cognition subscale (ADAS-Cog) total score...
July 2015: Alzheimer Disease and Associated Disorders
Tamás Fehér, Vincent Libis, Pablo Carbonell, Jean-Loup Faulon
Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical...
2015: Frontiers in Bioengineering and Biotechnology
Nabanita Modak, Kelley Spence, Saloni Sood, Jacky Ann Rosati
Air emissions from the U.S. pulp and paper sector have been federally regulated since 1978; however, regulations are periodically reviewed and revised to improve efficiency and effectiveness of existing emission standards. The Industrial Sectors Integrated Solutions (ISIS) model for the pulp and paper sector is currently under development at the U.S. Environmental Protection Agency (EPA), and can be utilized to facilitate multi-pollutant, sector-based analyses that are performed in conjunction with regulatory development...
2015: PloS One
P Warner, C J Weir, C H Hansen, A Douglas, M Madhra, S G Hillier, P T K Saunders, J P Iredale, S Semple, B R Walker, H O D Critchley
INTRODUCTION: Heavy menstrual bleeding (HMB) diminishes individual quality-of-life and poses substantial societal burden. In HMB endometrium, inactivation of cortisol (by enzyme 11β hydroxysteroid dehydrogenase type 2 (11βHSD2)), may cause local endometrial glucocorticoid deficiency and hence increased angiogenesis and impaired vasoconstriction. We propose that 'rescue' of luteal phase endometrial glucocorticoid deficiency could reduce menstrual bleeding. METHODS AND ANALYSIS: DexFEM is a double-blind response-adaptive parallel-group placebo-controlled trial in women with HMB (108 to be randomised), with active treatment the potent oral synthetic glucocorticoid dexamethasone, which is relatively resistant to 11βHSD2 inactivation...
2015: BMJ Open
Ali Yousefi, Alireza A Dibazar, Theodore W Berger
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed...
August 2014: Neural Networks: the Official Journal of the International Neural Network Society
Pei-Hua Cao, Xin Wang, Shi-Song Fang, Xiao-Wen Cheng, King-Pan Chan, Xi-Ling Wang, Xing Lu, Chun-Li Wu, Xiu-Juan Tang, Ren-Li Zhang, Han-Wu Ma, Jin-Quan Cheng, Chit-Ming Wong, Lin Yang
BACKGROUND: Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China. METHODS: Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen...
2014: PloS One
Achilleas Achilleos, Charalambos Loizides, Marios Hadjiandreou, Triantafyllos Stylianopoulos, Georgios D Mitsis
We propose a sequential probabilistic mixture model for individualized tumor growth forecasting. In contrast to conventional deterministic methods for estimation and prediction of tumor evolution, we utilize all available tumor-specific observations up to the present time to approximate the unknown multi-scale process of tumor growth over time, in a stochastic context. The suggested mixture model uses prior information obtained from the general population and becomes more individualized as more observations from the tumor are sequentially taken into account...
May 2014: Annals of Biomedical Engineering
M Aldrin, E Damsleth, H V Sæbø
The upper parts of the river Gaula in Central Norway are heavily contaminated by toxic metals-particularily copper (Cu).A monitoring program for the river was established in early 1986, and the concentration of Cu, among other variables, has been measured.There is a fairly strong temporal component in the Cu measurements, which calls for some sort of time series model. The irregular pattern of the observation times, however, makes the usual models infeasible, as they assume equi-spaced observations.In the paper we present a simple DLM (Dynamic Linear Model) which gives a satisfactory description of the Cu concentration series...
November 1989: Environmental Monitoring and Assessment
Lucie Michel, David Makowski
The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail...
2013: PloS One
R M de Mol, G André, E J B Bleumer, J T N van der Werf, Y de Haas, C G van Reenen
Lameness is a major problem in modern dairy husbandry and has welfare implications and other negative consequences. The behavior of dairy cows is influenced by lameness. Automated lameness detection can, among other methods, be based on day-to-day variation in animal behavior. Activity sensors that measure lying time, number of lying bouts, and other parameters were used to record behavior per cow per day. The objective of this research was to develop and validate a lameness detection model based on daily activity data...
June 2013: Journal of Dairy Science
Ali Yousefi, Alireza A Dibazar, Theodore W Berger
Linear model for synapse temporal dynamics and learning algorithm for synaptic adaptation in spiking neural networks are presented. The proposed linear model substantially simplifies analysis and training of spiking neural networks, meanwhile accurately models facilitation and depression dynamics in synapse. The learning rule is biologically plausible and is capable of simultaneously adjusting both of LTP and STP parameters of individual synapses in a network. To prove efficiency of the system, a small size spiking neural network is trained for generating different spike and bursting patterns of cortical neurons...
2012: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Margaret R Neff, Satyendra P Bhavsar, George B Arhonditsis, Rachael Fletcher, Donald A Jackson
The English-Wabigoon River system in Northwestern Ontario, Canada, was one of the most heavily mercury-contaminated waterways in the world due to historical discharges in the 1960s from a chlor-alkali plant. This study examines long-term (1970-2010) monitoring data to assess temporal trends in mercury contamination in Walleye, Northern Pike and Lake Whitefish, three species important for sport and subsistence fishing in this region, using dynamic linear modeling and piecewise regression. For all lakes and species, there is a significant decline (36-94%) in mercury concentrations through time; however, there is evidence that this decline is either slowing down or levelling off...
September 2012: Journal of Environmental Monitoring: JEM
Eric H Y Lau, Calvin K Y Cheng, Dennis K M Ip, Benjamin J Cowling
BACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data...
2012: PloS One
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