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Sample records for model rem predictions

  1. Dream to predict? REM dreaming as prospective coding

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    Sue eLlewellyn

    2016-01-01

    Full Text Available The dream as prediction seems inherently improbable. The bizarre occurrences in dreams never characterize everyday life. Dreams do not come true! But assuming that bizarreness negates expectations may rest on a misunderstanding of how the predictive brain works. In evolutionary terms, the ability to rapidly predict what sensory input implies- through expectations derived from discerning patterns in associated past experiences- would have enhanced fitness and survival. For example, food and water are essential for survival, associating past experiences (to identify location patterns predicts where they can be found. Similarly, prediction may enable predator identification from what would have been only a fleeting and ambiguous stimulus- without prior expectations. To confront the many challenges associated with natural settings, visual perception is vital for humans (and most mammals and often responses must be rapid. Predictive coding during wake may, therefore, be based on unconscious imagery so that visual perception is maintained and appropriate motor actions triggered quickly. Speed may also dictate the form of the imagery. Bizarreness, during REM dreaming, may result from a prospective code fusing phenomena with the same meaning- within a particular context. For example, if the context is possible predation, from the perspective of the prey two different predators can both mean the same (i.e. immediate danger and require the same response (e.g. flight. Prospective coding may also prune redundancy from memories, to focus the image on the contextually-relevant elements only, thus, rendering the non-relevant phenomena indeterminate- another aspect of bizarreness. In sum, this paper offers an evolutionary take on REM dreaming as a form of prospective coding which identifies a probabilistic pattern in past events. This pattern is portrayed in an unconscious, associative, sensorimotor image which may support cognition in wake through being

  2. Dream to Predict? REM Dreaming as Prospective Coding

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    Llewellyn, Sue

    2016-01-01

    The dream as prediction seems inherently improbable. The bizarre occurrences in dreams never characterize everyday life. Dreams do not come true! But assuming that bizarreness negates expectations may rest on a misunderstanding of how the predictive brain works. In evolutionary terms, the ability to rapidly predict what sensory input implies—through expectations derived from discerning patterns in associated past experiences—would have enhanced fitness and survival. For example, food and water are essential for survival, associating past experiences (to identify location patterns) predicts where they can be found. Similarly, prediction may enable predator identification from what would have been only a fleeting and ambiguous stimulus—without prior expectations. To confront the many challenges associated with natural settings, visual perception is vital for humans (and most mammals) and often responses must be rapid. Predictive coding during wake may, therefore, be based on unconscious imagery so that visual perception is maintained and appropriate motor actions triggered quickly. Speed may also dictate the form of the imagery. Bizarreness, during REM dreaming, may result from a prospective code fusing phenomena with the same meaning—within a particular context. For example, if the context is possible predation, from the perspective of the prey two different predators can both mean the same (i.e., immediate danger) and require the same response (e.g., flight). Prospective coding may also prune redundancy from memories, to focus the image on the contextually-relevant elements only, thus, rendering the non-relevant phenomena indeterminate—another aspect of bizarreness. In sum, this paper offers an evolutionary take on REM dreaming as a form of prospective coding which identifies a probabilistic pattern in past events. This pattern is portrayed in an unconscious, associative, sensorimotor image which may support cognition in wake through being mobilized as a

  3. A mathematical model towards understanding the mechanism of neuronal regulation of wake-NREMS-REMS states.

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    Rupesh Kumar

    Full Text Available In this study we have constructed a mathematical model of a recently proposed functional model known to be responsible for inducing waking, NREMS and REMS. Simulation studies using this model reproduced sleep-wake patterns as reported in normal animals. The model helps to explain neural mechanism(s that underlie the transitions between wake, NREMS and REMS as well as how both the homeostatic sleep-drive and the circadian rhythm shape the duration of each of these episodes. In particular, this mathematical model demonstrates and confirms that an underlying mechanism for REMS generation is pre-synaptic inhibition from substantia nigra onto the REM-off terminals that project on REM-on neurons, as has been recently proposed. The importance of orexinergic neurons in stabilizing the wake-sleep cycle is demonstrated by showing how even small changes in inputs to or from those neurons can have a large impact on the ensuing dynamics. The results from this model allow us to make predictions of the neural mechanisms of regulation and patho-physiology of REMS.

  4. Coupled flip-flop model for REM sleep regulation in the rat.

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    Justin R Dunmyre

    Full Text Available Recent experimental studies investigating the neuronal regulation of rapid eye movement (REM sleep have identified mutually inhibitory synaptic projections among REM sleep-promoting (REM-on and REM sleep-inhibiting (REM-off neuronal populations that act to maintain the REM sleep state and control its onset and offset. The control mechanism of mutually inhibitory synaptic interactions mirrors the proposed flip-flop switch for sleep-wake regulation consisting of mutually inhibitory synaptic projections between wake- and sleep-promoting neuronal populations. While a number of synaptic projections have been identified between these REM-on/REM-off populations and wake/sleep-promoting populations, the specific interactions that govern behavioral state transitions have not been completely determined. Using a minimal mathematical model, we investigated behavioral state transition dynamics dictated by a system of coupled flip-flops, one to control transitions between wake and sleep states, and another to control transitions into and out of REM sleep. The model describes the neurotransmitter-mediated inhibitory interactions between a wake- and sleep-promoting population, and between a REM-on and REM-off population. We proposed interactions between the wake/sleep and REM-on/REM-off flip-flops to replicate the behavioral state statistics and probabilities of behavioral state transitions measured from experimental recordings of rat sleep under ad libitum conditions and after 24 h of REM sleep deprivation. Reliable transitions from REM sleep to wake, as dictated by the data, indicated the necessity of an excitatory projection from the REM-on population to the wake-promoting population. To replicate the increase in REM-wake-REM transitions observed after 24 h REM sleep deprivation required that this excitatory projection promote transient activation of the wake-promoting population. Obtaining the reliable wake-nonREM sleep transitions observed in the data

  5. Excessive Daytime Sleepiness Predicts Neurodegeneration in Idiopathic REM Sleep Behavior Disorder.

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    Zhou, Junying; Zhang, Jihui; Lam, Siu Ping; Chan, Joey Wy; Mok, Vincent; Chan, Anne; Li, Shirley Xin; Liu, Yaping; Tang, Xiangdong; Yung, Wing Ho; Wing, Yun Kwok

    2017-05-01

    To determine the association of excessive daytime sleepiness (EDS) with the conversion of neurodegenerative diseases in patients with idiopathic REM sleep behavior disorder (iRBD). A total of 179 patients with iRBD (79.1% males, mean age = 66.3 ± 9.8 years) were consecutively recruited. Forty-five patients with Epworth Sleepiness Scale score ≥14 were defined as having EDS. Demographic, clinical, and polysomnographic data were compared between iRBD patients with and without EDS. The risk of developing neurodegenerative diseases was examined using Cox proportional hazards model. After a mean follow-up of 5.8 years (SD = 4.3 years), 50 (27.9%) patients developed neurodegenerative diseases. There was a significantly higher proportion of conversion in patients with EDS compared to those without EDS (42.2 % vs. 23.1%, p = .01). EDS significantly predicted an increased risk of developing neurodegenerative diseases (adjusted hazard ratios [HR] = 2.56, 95% confidence interval [CI] 1.37 to 4.77) after adjusting for age, sex, body mass index, current depression, obstructive sleep apnea, and periodic limb movements during sleep. Further analyses demonstrated that EDS predicted the conversion of Parkinson's disease (PD) (adjusted HR = 3.55, 95% CI 1.59 to 7.89) but not dementia (adjusted HR = 1.48, 95% CI 0.44 to 4.97). EDS is associated with an increased risk of developing neurodegenerative diseases, especially PD, in patients with iRBD. Our findings suggest that EDS is a potential clinical biomarker of α-synucleinopathies in iRBD. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  6. A two-state stochastic model of REM sleep architecture in the rat.

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    Gregory, Gavin G; Cabeza, Rafael

    2002-11-01

    Rapid eye movement (REM) sleep is a recurring state throughout the sleeping period. Based on the examination of 45 sleep records of 3-mo-old male rats during the middle of the light phase, a stochastic model is proposed for the sequence X(1),Y(2), X(2),Y(2),. of REM sleep durations X and inter-REM sleep waiting times Y experienced by a rat during a sleeping period. In our model the probability distribution of any variable in the sequence, given the past, is allowed to depend on only the immediately previous variable. The conditional distributions f(y(i) | x(i)) and g(x(i+1) | y(i)) do not depend on the index i. It is shown that the marginal distributions tend to stationarity. Aggregations of the data on a discrete time scale suggest that the conditional distributions be formulated as two-component mixtures. These component distributions are modeled as Poisson and their means are called the means of short and long waiting time and the means of short and long REM sleep duration. Associated with each mean is a probability weight. Parametric forms are given to the means and probability weights. The model estimated by maximum likelihood shows a good fit to data of the 3-mo-old rats. The model fit to a smaller data set obtained from rats aged 15-22 mo shows a significant shortening of the means for both short and long REM sleep bout durations compared with the means of the 3-mo-old rats. Neuronal correlates for the behavior of the model are discussed in the context of the reciprocal interaction model of REM sleep regulation.

  7. Olfactory impairment is related to REM sleep deprivation in rotenone model of Parkinson's disease

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    Mariana F. Aurich

    Full Text Available Introduction: Olfactory dysfunction affects about 85-90% of Parkinson's disease (PD patients with severe deterioration in the ability of discriminate several types of odors. In addition, studies reported declines in olfactory performances during a short period of sleep deprivation. Besides, PD is also known to strongly affect the occurrence and maintenance of rapid eye movement (REM sleep. Methods: Therefore, we investigated the mechanisms involved on discrimination of a social odor (dependent on the vomeronasal system and a non-social odor (related to the main olfactory pathway in the rotenone model of PD. Also, a concomitant impairment in REM sleep was inflicted with the introduction of two periods (24 or 48 h of REM sleep deprivation (REMSD. Rotenone promoted a remarkable olfactory impairment in both social and non-social odors, with a notable modulation induced by 24 h of REMSD for the non-social odor. Results: Our findings demonstrated the occurrence of a strong association between the density of nigral TH-ir neurons and the olfactory discrimination capacity for both odorant stimuli. Specifically, the rotenone-induced decrease of these neurons tends to elicit reductions in the olfactory discrimination ability. Conclusions: These results are consistent with the participation of the nigrostriatal dopaminergic system mainly in the olfactory discrimination of a non-social odor, probably through the main olfactory pathway. Such involvement may have produce relevant impact in the preclinical abnormalities found in PD patients.

  8. REM sleep behavior disorder in the marmoset MPTP model of early Parkinson disease.

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    Verhave, Peternella S; Jongsma, Marjan J; Van den Berg, Roland M; Vis, José C; Vanwersch, Raymond A P; Smit, August B; Van Someren, Eus J W; Philippens, Ingrid H C H M

    2011-08-01

    Sleep problems are a common phenomenon in most neurological and psychiatric diseases. In Parkinson disease (PD), for instance, sleep problems may be the most common and burdensome non-motor symptoms in addition to the well-described classical motor symptoms. Since sleep disturbances generally become apparent in the disease before motor symptoms emerge, they may represent early diagnostic tools and a means to investigate early mechanisms in PD onset. The sleep disturbance, REM sleep behavior disorder (RBD), precedes PD in one-third of patients. We therefore investigated sleep changes in marmoset monkeys treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP), the non-human primate model for idiopathic PD. Mild parkinsonism was induced in 5 marmoset monkeys (3M/2F) over a 2-week period of subchronic MPTP treatment. Electroencephalograms (EEGs) and electromyograms (EMGs) were recorded weekly. Motor activity and hand-eye coordination were also measured weekly, and any signs of parkinsonism were noted each day. Sleep parameters, motor activity, and performance data before and after MPTP treatment were compared between MPTP-treated marmosets and 4 control marmosets (1M/3F). MPTP increased the number of sleep epochs with high-amplitude EMG bouts during REM sleep relative to control animals (mean ± SEM percentage of REM 58.2 ± 9.3 vs. 29.6 ± 7.7; P sleep parameters measured, RBD-like measures discriminated best between MPTP-treated and control animals. On the other hand, functional motor behavior, as measured by hand-eye coordination, was not affected by MPTP treatment (correct trials MPTP: 23.40 ± 3.56 vs. control: 36.13 ± 5.88 correct trials; P = 0.32). This REM sleep-specific change, in the absence of profound changes in wake motor behaviors, suggests that the MPTP marmoset model of PD could be used for further studies into the mechanisms and treatment of RBD and other sleep disorders in premotor symptom PD.

  9. Implementation and evaluation of pH-dependent cloud chemistry and wetdeposition in the chemical transport model REM-Calgrid

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    Banzhaf, S.; Schaap, M.; Kerschbaumer, A.; Reimer, E.; Stern, R.; Swaluw, E. van der; Builtjes, P.

    2012-01-01

    The Chemistry Transport Model REM-Calgrid (RCG) has been improved by implementing an enhanced description of aqueous-phase chemistry and wet deposition processes including droplet pH. A sensitivity study on cloud and rain droplet pH has been performed to investigate its impact on model sulphate

  10. REM and NREM sleep mentation.

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    McNamara, Patrick; Johnson, Patricia; McLaren, Deirdre; Harris, Erica; Beauharnais, Catherine; Auerbach, Sanford

    2010-01-01

    We review the literature on the neurobiology of rapid eye movement (REM) and non-rapid eye movement (NREM) sleep states and associated dreams. REM is associated with enhanced activation of limbic and amygdalar networks and decreased activation in dorsal prefrontal regions while stage II NREM is associated with greater cortical activation than REM. Not surprisingly, these disparate brain activation patterns tend to be associated with dramatically different dream phenomenologies and dream content. We present two recent studies which content-analyzed hundreds of dream reports from REM and NREM sleep states. These studies demonstrated that dreamer-initiated aggressive social interactions were more characteristic of REM than NREM, and dreamer-initiated friendliness was more characteristic of NREM than REM reports. Both REM and NREM dreams therefore may function to simulate opposing types of social interactions, with the REM state specializing in simulation of aggressive interactions and the NREM state specializing in simulation of friendly interactions. We close our review with a summary of evidence that dream content variables significantly predict daytime mood and social interactions. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. REM sleep rescues learning from interference.

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    McDevitt, Elizabeth A; Duggan, Katherine A; Mednick, Sara C

    2015-07-01

    Classical human memory studies investigating the acquisition of temporally-linked events have found that the memories for two events will interfere with each other and cause forgetting (i.e., interference; Wixted, 2004). Importantly, sleep helps consolidate memories and protect them from subsequent interference (Ellenbogen, Hulbert, Stickgold, Dinges, & Thompson-Schill, 2006). We asked whether sleep can also repair memories that have already been damaged by interference. Using a perceptual learning paradigm, we induced interference either before or after a consolidation period. We varied brain states during consolidation by comparing active wake, quiet wake, and naps with either non-rapid eye movement sleep (NREM), or both NREM and REM sleep. When interference occurred after consolidation, sleep and wake both produced learning. However, interference prior to consolidation impaired memory, with retroactive interference showing more disruption than proactive interference. Sleep rescued learning damaged by interference. Critically, only naps that contained REM sleep were able to rescue learning that was highly disrupted by retroactive interference. Furthermore, the magnitude of rescued learning was correlated with the amount of REM sleep. We demonstrate the first evidence of a process by which the brain can rescue and consolidate memories damaged by interference, and that this process requires REM sleep. We explain these results within a theoretical model that considers how interference during encoding interacts with consolidation processes to predict which memories are retained or lost. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Does the function of REM sleep concern non-REM sleep or waking?

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    Benington, J H; Heller, H C

    1994-12-01

    We have hypothesized that REM sleep is functionally and homeostatically related to NREM sleep rather than to waking. In other words, REM sleep rather than to waking. In other words, REM sleep occurs in response to NREM-sleep expression and compensates for some process that takes place during NREM sleep. Under normal conditions, the need for REM sleep does not accrue during waking. The primary basis for this hypothesis is the fact that REM-sleep expression is a function of prior NREM-sleep expression. That is, REM sleep follows NREM sleep within sleep periods, REM-sleep episodes occur at intervals determined by the amount of NREM-sleep time elapsed, and total time spent in REM sleep is consistently about 1/4 of prior NREM-sleep time, regardless of how much time is spent in NREM sleep. Our experimental tests of the hypothesis support it. (1) REM-sleep propensity accumulates quite rapidly during a 2-hr interval spent predominantly in NREM sleep. (2) The timing of individual REM-sleep episodes is controlled homeostatically, by accumulation within NREM sleep of a propensity for REM sleep. The NREM sleep-related model of REM-sleep regulation (Fig. 1) explains a number of phenomena of REM-sleep expression, including the frequent and periodic occurrence of REM-sleep episodes throughout sleep periods, that have been accommodated by the waking-related model but are not functionally accounted for by it. In our opinion, the NREM sleep-related model of REM-sleep regulation recommends itself partly by its simplicity. According to the waking-related model, two independent and competing sleep propensities accumulate during waking and are discharged in two distinct sleep states that perform different waking-related recovery processes. One behaviour, sleep, is thought to perform two independent and competing functions that alternate at regular intervals. In the NREM sleep-related model of REM-sleep regulation, sleep debt simply reflects a need for NREM sleep. That is, the cerebrally

  13. Water cycle at Gale crater through MSL/REMS observations

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    Harri, Ari-Matti; Genzer, Maria; Kemppinen, Osku; Gomez-Elvira, Javier; Savijärvi, Hannu; McConnochie, Tim; De la Torre, Manuel; Haberle, Robert; Polkko, Jouni; Paton, Mark; Richardson, Mark I.; Newman, Claire E.; Siili, Tero; Makinen, Terhi

    2016-10-01

    The Mars Science laboratory (MSL) has been successfully operating at the Gale crater since early August 2012 and has provided a wealth of extremely valuable data. That includes atmospheric observations by the REMS instrument performing atmospheric pressure, temperature of the air, ground temperature, wind speed and direction, relative humidity (REMS-H), and UV measurements.The REMS-H relative humidity device is based on polymeric capacitive humidity sensors developed by Vaisala Inc. and it makes use of three (3) humidity sensor heads. The humidity device is mounted on the REMS boom providing ventilation with the ambient atmosphere through a filter protecting the device from airborne dust.The REMS-H humidity instrument has created an unprecedented data record of more than two full Martian. REMS-H measured the relative humidity and temperature at 1.6 m height for a period of 5 minutes every hour as part of the MSL/REMS instrument package. We focus on describing the annual in situ water cycle with the new REMS-H instrument calibration for the period of two Martian years. The results will be constrained through comparison with independent indirect observations and through modeling efforts.We inferred the hourly atmospheric VMR from the REMS-H observations and compared these VMR measurements with predictions of VMR from our 1D column Martian atmospheric model and regolith to investigate the local water cycle, exchange processes and the local climate in Gale Crater. The strong diurnal variation suggests there are surface-atmosphere exchange processes at Gale Crater during all seasons, which depletes moisture to the ground in the evening and nighttime and release the moisture back to the atmosphere during the daytime. On the other hand, these processes do not result in significant water deposition on the ground, because frost has not been detected in Gale Crater by any of the MSL observations. Hence, our modelling results presumably indicate that adsorption processes take

  14. Spontaneous sleep interruptions during extended nights. Relationships with NREM and REM sleep phases and effects on REM sleep regulation.

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    Barbato, Giuseppe; Barker, Charles; Bender, Charles; Wehr, Thomas A

    2002-06-01

    There is no agreement in the literature as to whether sleep interruption causes rapid eye movement (REM) pressure to increase, and if so, whether this increase is expressed as shortened REM latency, increased REM density, or increased duration of REM sleep. The purpose of the present study was to examine the effect of different durations of spontaneous sleep interruptions on the regulation of REM sleep that occurs after return to sleep. The occurrence of spontaneous periods of wakefulness and their effects on subsequent REM sleep periods were analysed in a total sample of 1189 sleep interruptions which occurred across 364 extended nights in 13 normal subjects. Compared with sleep interruptions that last less than 10 min, sleep interruptions that last longer than 10 min occur preferentially out of REM sleep. In both the short and long types of sleep interruptions, the duration of REM periods that ended in wakefulness were shorter than the duration of those that were not interrupted by wakefulness. REM densities of the REM periods that terminated in periods of wakefulness were higher than those of uninterrupted REM periods. The proportion of episodes of wakefulness following REM sleep that were long-lasting progressively increased over the course of the extended night period. The sleep episodes that followed the periods of wakefulness were characterised by a short REM latency. REM duration was increased in episodes that followed long sleep interruptions compared to those that followed short sleep interruptions. REM density did not appear to change significantly in the episodes that followed sleep interruption. REM sleep mechanisms appear to be the main force controlling sleep after a spontaneous sleep interruption, presumably because during the second half of the night, where more sleep interruptions occur, the pressure for non-rapid eye movement sleep is reduced and the circadian rhythm in REM sleep propensity reaches its peak. Processes promoting REM sleep at the

  15. Humidity cycle at Gale crater through MSL/REMS observations

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    Harri, Ari-Matti; Genzer, Maria; Gomez-Elvira, Javier; Savijarvi, Hannu; McConnochie, Tim; De la Torre, Manuel; Martinez, German; Haberle, Robert; Polkko, Jouni; Paton, Mark; Newman, Claire; Makinen, Terhi; Vazquez, Luis

    2017-04-01

    Since early August 2012 the Mars Science laboratory (MSL) has been operating successfully with the REMS instrument providing extremely valuable atmospheric observations of atmospheric pressure, temperature of the air, ground temperature, wind speed and direction, relative humidity (REMS-H), and UV measurements. The REMS-H relative humidity device is based on polymeric capacitive humidity sensors developed by Vaisala Inc. and it makes use of three (3) humidity sensor heads. The humidity device is mounted on the REMS boom providing ventilation with the ambient atmosphere through a filter protecting the device from airborne dust. The REMS-H humidity instrument has created an unprecedented data record of more than two full Martian years. It has measured the relative humidity and temperature at 1.6 m height for a period of 5 minutes every hour as part of the MSL/REMS instrument package. We focus on describing the annual in situ water cycle with the new REMS-H instrument calibration for the period of two Martian years. The results will be constrained through comparison with independent indirect observations and through modeling efforts. We inferred the hourly atmospheric VMR from the REMS-H observations and compared these VMR measurements with predictions of VMR from our 1D column Martian atmospheric model and regolith to investigate the local water cycle, exchange processes and the local climate in Gale Crater. The strong diurnal variation suggests there are surface-atmosphere exchange processes at Gale Crater during all seasons, which depletes moisture to the ground in the evening and nighttime and release the moisture back to the atmosphere during the daytime. On the other hand, these processes do not result in significant water deposition on the ground, because frost has not been detected in Gale Crater by any of the MSL observations. Hence, our modelling results presumably indicate that adsorption processes take place during the nighttime and desorption during the

  16. Is the nonREM-REM sleep cycle reset by forced awakenings from REM sleep?

    NARCIS (Netherlands)

    Grozinger, M; Beersma, DGM; Fell, J; Roschke, J

    2002-01-01

    In selective REM sleep deprivation (SRSD), the occurrence of stage REM is repeatedly interrupted by short awakenings. Typically, the interventions aggregate in clusters resembling the REM episodes in undisturbed sleep. This salient phenomenon can easily be explained if the nonREM-REM sleep process

  17. The Biology of REM Sleep

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    Peever, John; Fuller, Patrick M.

    2018-01-01

    Considerable advances in our understanding of the mechanisms and functions of rapid-eye-movement (REM) sleep have occurred over the past decade. Much of this progress can be attributed to the development of new neuroscience tools that have enabled high-precision interrogation of brain circuitry linked with REM sleep control, in turn revealing how REM sleep mechanisms themselves impact processes such as sensorimotor function. This review is intended to update the general scientific community about the recent mechanistic, functional and conceptual developments in our current understanding of REM sleep biology and pathobiology. Specifically, this review outlines the historical origins of the discovery of REM sleep, the diversity of REM sleep expression across and within species, the potential functions of REM sleep (e.g., memory consolidation), the neural circuits that control REM sleep, and how dysfunction of REM sleep mechanisms underlie debilitating sleep disorders such as REM sleep behaviour disorder and narcolepsy. PMID:26766231

  18. The Inappropriate Occurrence of REM Sleep in Narcolepsy is not due to a Defect in Homeostatic Regulation of REM Sleep.

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    Roman, Alexis; Meftah, Soraya; Arthaud, Sébastien; Luppi, Pierre-Hervé; Peyron, Christelle

    2018-03-07

    Narcolepsy type 1 is a disabling disorder with four primary symptoms: excessive-daytime-sleepiness, cataplexy, hypnagogic hallucinations and sleep paralysis. The three latter symptoms together with a short REM sleep latency have suggested impairment in REM sleep homeostatic regulation with an enhanced propensity for (i.e. tendency to enter) REM sleep. To test this hypothesis, we challenged REM sleep homeostatic regulation in a recognized model of narcolepsy, the orexin knock-out (Orex-KO) mice and their wild-type (WT) littermates. We first performed 48hrs of REM sleep deprivation using the classic small-platforms-over-water method. We found that narcoleptic mice are similarly REM sleep deprived to WT mice. Although they had shorter sleep latency, Orex-KO mice recovered similarly to WT during the following 10hrs of recovery. Interestingly, Orex-KO mice also had cataplexy episodes immediately after REM sleep deprivation, anticipating REM sleep rebound, at a time of day when cataplexy does not occur in baseline condition. We then evaluated REM sleep propensity using our new automated method of deprivation that performs a specific and efficient REM sleep deprivation. We showed that REM sleep propensity is similar during light phase in Orex-KO and WT mice. However, during the dark phase REM sleep propensity was not suppressed in Orex-KO mice when hypocretin/orexin neuropeptides are normally released. Altogether our data suggest that in addition to the well-known wake-promoting role of hypocretin/orexin, these neuropeptides would also suppress REM sleep. Therefore, hypocretin/orexin deficiency would facilitate the occurrence of REM sleep at any time of day in an opportunistic manner as seen in human narcolepsy.

  19. MODEL PEMBELAJARAN NUMBERED HEAD TOGETHER (NHT UNTUK MENINGKATKAN PERAN AKTIF SISWA BELAJAR MATA PELAJARAN SISTEM REM

    Directory of Open Access Journals (Sweden)

    Subur Riyono

    2016-12-01

    Full Text Available The Implementation of Numbered Head Together (NHT learning model to Enhance the students Active Role in Learning the Brake System. A thesis of Machine Engginering Education Study Program Faculty of Teacher Training and Educatioon of Sarjanawiyata Tamansiswa University Yogyakarta, 2016.The type of this research is action research including three cycles. Each cycle is conducted by four stages including 1. Planning 2. Implementing 3. Observing and 4. Reflexting. In collecting the Data, the researcher appied test, observation as well as document. The technique used in analyzing the observation sheet and test is quantitive deskriptive.  The result of this research showed that the implementation of Numbered Head Together (NHT learning model had enhanched both the students learning Active Role and the students’ learning results of the brake system subject to each cycle. It is proved by the increasing result of the observation sheet of the students learning Active Role from which the first cycle 44,57% having increased to the second cycle 16,57% becoming 61,14% and in the third cycle having increased 25,57% becoming 86,71%. Furthermore, based on the learning result test  of the first cycle gave the average grade of the pre test 62,28%, the average grade of the post test then 60,71%, the average grade 69,57% so the learning result increased 8,86% and in the second cycle gave the average grade of pretest 62,28% and the average post test then 75,42% increased the learning result 13,14% and the the test of the third cycle, the average pre test 65,14% and the average post test 83,42%.. Due to the research findings, it can be concluded that the implementation of the Numbered Head Together (NHT learning model can enhance the students learning Active Role as well as the results of the students learning in the brake system.

  20. Evidence that non-dreamers do dream: a REM sleep behaviour disorder model.

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    Herlin, Bastien; Leu-Semenescu, Smaranda; Chaumereuil, Charlotte; Arnulf, Isabelle

    2015-12-01

    To determine whether non-dreamers do not produce dreams or do not recall them, subjects were identified with no dream recall with dreamlike behaviours during rapid eye movement sleep behaviour disorder, which is typically characterised by dream-enacting behaviours congruent with sleep mentation. All consecutive patients with idiopathic rapid eye movement sleep behaviour disorder or rapid eye movement sleep behaviour disorder associated with Parkinson's disease who underwent a video-polysomnography were interviewed regarding the presence or absence of dream recall, retrospectively or upon spontaneous arousals. The patients with no dream recall for at least 10 years, and never-ever recallers were compared with dream recallers with rapid eye movement sleep behaviour disorder regarding their clinical, cognitive and sleep features. Of the 289 patients with rapid eye movement sleep behaviour disorder, eight (2.8%) patients had no dream recall, including four (1.4%) patients who had never ever recalled dreams, and four patients who had no dream recall for 10-56 years. All non-recallers exhibited, daily or almost nightly, several complex, scenic and dreamlike behaviours and speeches, which were also observed during rapid eye movement sleep on video-polysomnography (arguing, fighting and speaking). They did not recall a dream following sudden awakenings from rapid eye movement sleep. These eight non-recallers with rapid eye movement sleep behaviour disorder did not differ in terms of cognition, clinical, treatment or sleep measures from the 17 dreamers with rapid eye movement sleep behaviour disorder matched for age, sex and disease. The scenic dreamlike behaviours reported and observed during rapid eye movement sleep in the rare non-recallers with rapid eye movement sleep behaviour disorder (even in the never-ever recallers) provide strong evidence that non-recallers produce dreams, but do not recall them. Rapid eye movement sleep behaviour disorder provides a new model to

  1. Sleep-stage sequencing of sleep-onset REM periods in MSLT predicts treatment response in patients with narcolepsy.

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    Drakatos, Panagis; Patel, Kishankumar; Thakrar, Chiraag; Williams, Adrian J; Kent, Brian D; Leschziner, Guy D

    2016-04-01

    Current treatment recommendations for narcolepsy suggest that modafinil should be used as a first-line treatment ahead of conventional stimulants or sodium oxybate. In this study, performed in a tertiary sleep disorders centre, treatment responses were examined following these recommendations, and the ability of sleep-stage sequencing of sleep-onset rapid eye movement periods in the multiple sleep latency test to predict treatment response. Over a 3.5-year period, 255 patients were retrospectively identified in the authors' database as patients diagnosed with narcolepsy, type 1 (with cataplexy) or type 2 (without) using clinical and polysomnographic criteria. Eligible patients were examined in detail, sleep study data were abstracted and sleep-stage sequencing of sleep-onset rapid eye movement periods were analysed. Response to treatment was graded utilizing an internally developed scale. Seventy-five patients were included (39% males). Forty (53%) were diagnosed with type 1 narcolepsy with a mean follow-up of 2.37 ± 1.35 years. Ninety-seven percent of the patients were initially started on modafinil, and overall 59% reported complete response on the last follow-up. Twenty-nine patients (39%) had the sequence of sleep stage 1 or wake to rapid eye movement in all of their sleep-onset rapid eye movement periods, with most of these diagnosed as narcolepsy type 1 (72%). The presence of this specific sleep-stage sequence in all sleep-onset rapid eye movement periods was associated with worse treatment response (P = 0.0023). Sleep-stage sequence analysis of sleep-onset rapid eye movement periods in the multiple sleep latency test may aid the prediction of treatment response in narcoleptics and provide a useful prognostic tool in clinical practice, above and beyond their classification as narcolepsy type 1 or 2. © 2015 European Sleep Research Society.

  2. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is

  3. Winds Measured by the Rover Environmental Monitoring Station (REMS) During the Mars Science Laboratory (MSL) Rover's Bagnold Dunes Campaign and Comparison with Numerical Modeling Using MarsWRF

    Science.gov (United States)

    Newman, Claire E.; Gomez-Elvira, Javier; Marin, Mercedes; Navarro, Sara; Torres, Josefina; Richardson, Mark I.; Battalio, J. Michael; Guzewich, Scott D.; Sullivan, Robert; de la Torre, Manuel; hide

    2016-01-01

    A high density of REMS wind measurements were collected in three science investigations during MSL's Bagnold Dunes Campaign, which took place over approx. 80 sols around southern winter solstice (Ls approx. 90deg) and constituted the first in situ analysis of the environmental conditions, morphology, structure, and composition of an active dune field on Mars. The Wind Characterization Investigation was designed to fully characterize the near-surface wind field just outside the dunes and confirmed the primarily upslope/downslope flow expected from theory and modeling of the circulation on the slopes of Aeolis Mons in this season. The basic pattern of winds is 'upslope' (from the northwest, heading up Aeolis Mons) during the daytime (approx. 09:00-17:00 or 18:00) and 'downslope' (from the southeast, heading down Aeolis Mons) at night (approx. 20:00 to some time before 08:00). Between these times the wind rotates largely clockwise, giving generally westerly winds mid-morning and easterly winds in the early evening. The timings of these direction changes are relatively consistent from sol to sol; however, the wind direction and speed at any given time shows considerable intersol variability. This pattern and timing is similar to predictions from the MarsWRF numerical model, run at a resolution of approx. 490 m in this region, although the model predicts the upslope winds to have a stronger component from the E than the W, misses a wind speed peak at approx. 09:00, and under-predicts the strength of daytime wind speeds by approx. 2-4 m/s. The Namib Dune Lee Investigation reveals 'blocking' of northerly winds by the dune, leaving primarily a westerly component to the daytime winds, and also shows a broadening of the 1 Hz wind speed distribution likely associated with lee turbulence. The Namib Dune Side Investigation measured primarily daytime winds at the side of the same dune, in support of aeolian change detection experiments designed to put limits on the saltation

  4. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  5. Real Time Radioactivity Monitoring and its Interface with predictive atmospheric transport modelling

    International Nuclear Information System (INIS)

    Raes, F.

    1990-01-01

    After the Chernobyl accident, a programme was initiated at the Joint Research Centre of the Commission of the European Communities named 'Radioactivity Environmental Monitoring' (REM). The main aspects considered in REM are: data handling, atmospheric modelling and data quality control related to radioactivity in the environment. The first REM workshop was held in December 1987: 'Aerosol Measurements and Nuclear Accidents: A Reconsideration'. (CEC EUR 11755 EN). These are the proceedings of the second REM workshop, held in December 1989, dealing with real-time radioactivity monitoring and its interface with predictive atmospheric models. Atmospheric transport models, in applications extending over time scales of the order of a day or more become progressively less reliable to the extent that an interface with real-time radiological field data becomes highly desirable. Through international arrangements for early exchange of information in the event of a nuclear accident (European Community, IAEA) such data might become available on a quasi real-time basis. The question is how best to use such information to improve our predictive capabilities. During the workshop the present status of on-line monitoring networks for airborne radioactivity in the EC Member States has been reviewed. Possibilities were discussed to use data from such networks as soon as they become available, in order to update predictions made with long range transport models. This publication gives the full text of 13 presentations and a report of the Round Table Discussion held afterwards

  6. Combining weather radar nowcasts and numerical weather prediction models to estimate short-term quantitative precipitation and uncertainty

    DEFF Research Database (Denmark)

    Jensen, David Getreuer

    The topic of this Ph.D. thesis is short term forecasting of precipitation for up to 6 hours called nowcasts. The focus is on improving the precision of deterministic nowcasts, assimilation of radar extrapolation model (REM) data into Danish Meteorological Institutes (DMI) HIRLAM numerical weather...... prediction (NWP) model and produce quantitative estimations of nowcast uncertainty. In real time control of urban drainage systems, nowcasting is used to increase the margin for decision-making. The spatial extent of urban drainag e catchments is very small in a meteorological context. This is a problem...... by the relative standard deviation. A significant result of this Ph.D. study is major improvements in predictability of DMI HIRLAM NWP model by assimilation of REM data. A new nudging assimilation method developed at DMI was used to assimilate the REM data. The assimilation technique enhances convection in case...

  7. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  8. An extended range neutron rem counter

    International Nuclear Information System (INIS)

    Birattari, C.; Nuccetelli, C.; Pelliccioni, M.; Silari, M.

    1990-01-01

    Extensive Monte Carlo calculations have been carried out to assess the possibility of extending the sensitivity of a neutron rem counter of the Andersson-Braun type up to several hundred MeV. The validity of the model adopted has first been checked by comparing with experimental data the calculated response curve and the angular dependence of the sensitivity for a well known commercial rem counter. Next, a number of modifications to the configuration of the moderator-attenuator have been investigated. The response functions and angular distributions produced by two simple solutions yielding an instrument with a sensitivity extended up to 400 MeV are presented. The response of the original rem counter and of its two modified versions to nine test spectra has also been calculated. The resulting instrument is transportable rather than portable, but the availability of an extended range neutron survey meter would be of great advantage at medium and high energy particle accelerator facilities. (orig.)

  9. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  10. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg

    2001-01-01

    utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...

  11. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  12. REM sleep deprivation and dopaminergic D2 receptors modulation increase recognition memory in an animal model of Parkinson's disease.

    Science.gov (United States)

    Targa, Adriano D S; Noseda, Ana Carolina D; Rodrigues, Lais S; Aurich, Mariana F; Lima, Marcelo M S

    2018-02-26

    Cognitive impairment is an important non-motor symptom of Parkinson's disease (PD). The neuronal death in nigrostriatal pathway is the main factor for motor symptoms and recent studies indicate a possible influence in non-motor symptoms as well. The pedunculopontine tegmental nucleus (PPT) and basal ganglia are closely related anatomically and functionally and, since they are affected by neurodegeneration in PD, they might be involved in recognition memory. To investigate this, we promoted an ibotenic acid lesion within the PPT or a rotenone lesion within substantia nigra pars compacta (SNpc) of Wistar rats, followed by 24h of REM sleep deprivation (REMSD). Then, we administered a dopaminergic D2 receptor agonist (piribedil, 3μg/μl), antagonist (raclopride, 10μg/μl) or vehicle (dimethylsulfoxide) directly in the striatum and the animals were submitted to the object recognition test (ORT). We observed that raclopride administration impaired object recognition memory as well as rotenone and ibotenic acid lesion. Interestingly, REMSD reversed the deleterious effects induced by these drugs. Also, raclopride administration after rotenone lesion allowed the animal to explore the new object for a longer time compared to the familiar object, suggesting that raclopride has a dual effect, dependent of the treatments. These findings suggest a role for PPT, SNpc and striatum in recognition memory and points the D2 receptors modulation and REMSD as possible targets for cognitive deficits in Parkinson's disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. REM sleep and neural nets.

    Science.gov (United States)

    Crick, F; Mitchison, G

    1995-01-01

    The broad features of rapid eye movement (REM) sleep are reviewed. Memory storage in the brain is probably quite unlike that in a digital computer, being distributed, superimposed and robust. Such memory systems are easily overloaded. If the stored memories share common features, random stimulation often produces mixed outputs. Simulations show that such overloading can be reduced by a process we call 'reverse learning'. We propose that this process is what is happening in REM sleep and that it explains in an unforced manner the condensation commonly found in dreams. Evidence for and against the proposed theory is discussed and several alternative theories are briefly described. The absence of REM sleep in the Enchidna and in two species of dolphins (that have relatively large brains) suggests that REM may allow the brain to be smaller than if REM were lacking.

  14. Changes in rem dream content during the night: implications for a hypothesis about changes in cerebral dominance across rem periods.

    Science.gov (United States)

    Cohen, D B

    1977-06-01

    REM dream content was scored for categories suggesting the predominant influence of the left hemisphere, e.g., good ego functioning, verbalization, or the right hemisphere, e.g., music, spatial salience, bizarreness. Data from 5 samples of college men showed consistent evidence of an increase in the prominence of left-, but not right-, related categories from earlier to later REM periods. These data suggest there is an increase in left hemisphere control/dominance across the REM periods during the night. Two sets of predictions based on this hypothesis (using more direct estimates of the hypothesized change) yielded supportive evidence. First, as predicted, there was a positive relation between change in percentage of right eye movement (R%) and (a) temporal position of the REM period and (b) change in left-related categories; greater R% was associated with later REM periods and with more prominent left- (but not right-) hemisphere categories. Second, as predicted, there was a positive relation between the diminution of the ratio of left to right EEG amplitudes (L/R) and (a) temporal position of the REM period and (b) prominence of verbal activity. As expected, this relation was attenuated for those subjects showing a preference for left-handedness. Two possible explanations for the inferred increase in left-hemispheric influence during the night are suggested.

  15. Effects of psychot herapy on REM latency and REM time.

    Science.gov (United States)

    Karle, W; Hopper, M; Switzer, A; Corriere, R; Woldenberg, L

    1980-08-01

    This study investigated the effect of a functional psychotherapy on the sleep EEG patterns of 6 patients. Contrary to original expectations no significant group differences in REM time and REM latency were found between two nights following therapy sessions and two normal nights. However, across the 4 nights the patients exhibited an average REM latency of 71 min. which was significantly shorter than that recorded in an independent study with the same design and a similar subject population. Clausen, Sersen, and Lidsky (1974) reported an average REM latency of 107.3 min. for 10 normal subjects also recorded across four nights. This result is compared with those in several other studies and discussed in relation to possible changes in dream patterns.

  16. Shutdown radiation level and man-rem control for water cooled reactors

    International Nuclear Information System (INIS)

    Cripps, S.J.; Regan, J.D.

    1978-01-01

    The importance of controlling the formation and subsequent deposition of active corrosion products (crud) is highlighted as a method of reducing occupational exposure. A semi-empirical model is described and used to predict the effectiveness of various methods of crud control. The relative merits of reactor coolant clean-up techniques including ion-exchange and electromagnetic filtration are assessed in terms of man-rem savings and associated cost penalties. (author)

  17. The role of REM theta activity in emotional memory

    Directory of Open Access Journals (Sweden)

    Isabel Camilla Hutchison

    2015-10-01

    Full Text Available While NREM sleep has been strongly implicated in the reactivation and consolidation of memory traces, the role of REM sleep remains unclear. A growing body of research on humans and animals provide behavioral evidence for a role of REM sleep in the strengthening and modulation of emotional memories. Theta activity – which describes low frequency oscillations in the local field potential within the hippocampus, amygdala and neocortex – is a prominent feature of both wake and REM sleep in humans and rodents. Theta coherence between the hippocampus and amygdala drives large-scale PGO waves, the density of which predicts increases in plasticity-related gene expression. This could potentially facilitate the processing of emotional memory traces within the hippocampus during REM sleep. Further, the timing of hippocampal activity in relation to theta phase is vital in determining subsequent potentiation of neuronal activity. This could allow the emotionally modulated strengthening of novel and the gradual weakening of consolidated hippocampal memory traces observed in both wake and REM sleep. Hippocampal theta activity is also correlated with REM sleep acetylcholine levels – which are thought to reduce hippocampal afferent inputs in the neocortex. The additional low levels of noradrenaline during REM sleep, which facilitate recurrent activation within the neocortex, could allow the integration of novel memory traces previously consolidated during NREM sleep. We therefore propose that REM sleep mediates the prioritized processing of emotional memories within the hippocampus, the integration of previously consolidated memory traces within the neocortex, as well as the disengagement of consolidated neocortical memory traces from the hippocampus.

  18. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  19. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  20. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  1. Retention over a Period of REM or non-REM Sleep.

    Science.gov (United States)

    Tilley, Andrew J.

    1981-01-01

    Subjects, awaked, presented with a word list, and tested with arousal measures, were reawaked during REM or non-REM sleep and retested. Recall was facilitated by REM sleep. It was hypothesized that the high arousal level associated with REM sleep incidentally maintained the memory trace in a more retrievable form. (Author/SJL)

  2. Is the nonREM–REM sleep cycle reset by forced awakenings from REM sleep?

    NARCIS (Netherlands)

    Grözinger, Michael; Beersma, Domien G.M.; Fell, Jürgen; Röschke, Joachim

    2002-01-01

    In selective REM sleep deprivation (SRSD), the occurrence of stage REM is repeatedly interrupted by short awakenings. Typically, the interventions aggregate in clusters resembling the REM episodes in undisturbed sleep. This salient phenomenon can easily be explained if the nonREM–REM sleep process

  3. Increases in number of REMS and REM density in humans following an intensive learning period.

    Science.gov (United States)

    Smith, C; Lapp, L

    1991-08-01

    Animal studies have recently demonstrated that increases in rapid eye movement (REM) sleep and actual number of rapid eye movements (REMs) over normal levels followed successful learning of an avoidance task. These increases persisted for many days following the end of the training sessions. It was hypothesized that similar extended increases in REM sleep parameters would follow an intensive learning task in humans. Senior college students were sleep monitored following the end of their Christmas examinations. Results showed that there was a significant increase in the number of REMs observed following the exams as compared to baseline and control subject values. The number of extra REMs was mot prominent during the fifth REM period of the night. A significantly increased REM density was observed at the fourth REM sleep period of the night. Results support the idea of REM sleep and/or the REMs themselves being involved in long-term memory processing several days after the end of training.

  4. The Reciprocal Internal/External Frame of Reference Model Using Grades and Test Scores

    Science.gov (United States)

    Möller, Jens; Zimmermann, Friederike; Köller, Olaf

    2014-01-01

    Background: The reciprocal I/E model (RI/EM) combines the internal/external frame of reference model (I/EM) with the reciprocal effects model (REM). The RI/EM extends the I/EM longitudinally and the REM across domains. The model predicts that, within domains, mathematics and verbal achievement (VACH) and academic self-concept have positive effects…

  5. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  6. Why Does REM Sleep Occur? A Wake-up Hypothesis

    Directory of Open Access Journals (Sweden)

    Dr. W. R. eKlemm

    2011-09-01

    Full Text Available Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses REM to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, 1 when first going to sleep, the brain plunges into Stage N3 (formerly called Stage IV, a deep abyss of sleep, and, as the night progresses, the sleep is punctuated by episodes of REM that become longer and more frequent toward morning, 2 conscious-like dreams are a reliable component of the REM state in which the dreamer is an active mental observer or agent in the dream, 3 the last awakening during a night’s sleep usually occurs in a REM episode during or at the end of a dream, 4 both REM and awake consciousness seem to arise out of a similar brainstem ascending arousal system 5 N3 is a functionally perturbed state that eventually must be corrected so that embodied brain can direct adaptive behavior, and 6 corticofugal projections to brainstem arousal areas provide a way to trigger increased cortical activity in REM to progressively raise the sleeping brain to the threshold required for wakefulness. This paper shows how the hypothesis conforms to common experience and has substantial predictive and explanatory power regarding the phenomenology of sleep in terms of ontogeny, aging, phylogeny, abnormal/disease states, cognition, and behavioral physiology. That broad range of consistency is not matched by competing theories, which are summarized herein. Specific ways to test this wake-up hypothesis are suggested. Such research could lead to a better understanding of awake consciousness.

  7. Why does rem sleep occur? A wake-up hypothesis.

    Science.gov (United States)

    Klemm, W R

    2011-01-01

    Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses rapid eye movement (REM) to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, (1) when first going to sleep, the brain plunges into Stage N3 (formerly called Stage IV), a deep abyss of sleep, and, as the night progresses, the sleep is punctuated by episodes of REM that become longer and more frequent toward morning, (2) conscious-like dreams are a reliable component of the REM state in which the dreamer is an active mental observer or agent in the dream, (3) the last awakening during a night's sleep usually occurs in a REM episode during or at the end of a dream, (4) both REM and awake consciousness seem to arise out of a similar brainstem ascending arousal system (5) N3 is a functionally perturbed state that eventually must be corrected so that embodied brain can direct adaptive behavior, and (6) cortico-fugal projections to brainstem arousal areas provide a way to trigger increased cortical activity in REM to progressively raise the sleeping brain to the threshold required for wakefulness. This paper shows how the hypothesis conforms to common experience and has substantial predictive and explanatory power regarding the phenomenology of sleep in terms of ontogeny, aging, phylogeny, abnormal/disease states, cognition, and behavioral physiology. That broad range of consistency is not matched by competing theories, which are summarized herein. Specific ways to test this wake-up hypothesis are suggested. Such research could lead to a better understanding of awake consciousness.

  8. Cutamesine Overcomes REM Sleep Deprivation-Induced Memory Loss : Relationship to Sigma-1 Receptor Occupancy

    NARCIS (Netherlands)

    Kuzhuppilly Ramakrishnan, Nisha; Schepers, Marianne; Luurtsema, Gert; Nyakas, Csaba J.; Elsinga, Philip H.; Ishiwata, Kiichi; Dierckx, Rudi A. J. O.; van Waarde, Aren

    Rapid eye movement (REM) sleep deprivation (SD) decreases cerebral sigma-1 receptor expression and causes cognitive deficits. Sigma-1 agonists are cognitive enhancers. Here, we investigate the effect of cutamesine treatment in the REM SD model. Sigma-1 receptor occupancy (RO) in the rat brain by

  9. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  10. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...

  11. Olfactory impairment in the rotenone model of Parkinson's disease is associated with bulbar dopaminergic D2 activity after REM sleep deprivation

    Directory of Open Access Journals (Sweden)

    Laís Soares Rodrigues

    2014-12-01

    Full Text Available Olfactory and rapid eye movement (REM sleep deficits are commonly found in untreated subjects with a recent diagnosis of Parkinson's disease (PD. Besides different studies reported declines in olfactory performances during a short period of sleep deprivation. Mechanisms underlying these clinical manifestations are poorly understood although the impairment in the dopamine (DA neurotransmission in the olfactory bulb and in the nigrostriatal pathway may have important roles in olfactory as well as in REM sleep disturbances. Therefore, we have led to the hypothesis that a modulation of the dopaminergic D2 receptors in the olfactory bulb could provide a more comprehensive understanding of the olfactory deficits in PD and after a short period of REM sleep deprivation (REMSD. We decided to investigate the olfactory, neurochemical and histological alterations generated by the administration of piribedil (a selective D2 agonist or raclopride (a selective D2 antagonist, within the glomerular layer of the olfactory bulb, in rats submitted to intranigral rotenone and REMSD. Our findings provided a remarkable evidence of the occurrence of a negative correlation (r = - 0.52, P = 0.04 between the number of periglomerular TH-ir neurons and the bulbar levels of DA in the rotenone, but not sham groups. A significant positive correlation (r = 0.34, P = 0.03 was observed between nigral DA and olfactory discrimination index (DI, for the sham groups, indicating that increased DA levels in the substantia nigra pars compacta (SNpc are associated to enhanced olfactory discrimination performance. Also, increased levels in bulbar and striatal DA induced by piribedil in the rotenone control and rotenone REMSD groups were consistent with reduced amounts of DI. The present evidence reinforce that DA produced by periglomerular neurons, and particularly the bulbar dopaminergic D2 receptors, are essential participants in the olfactory discrimination processes, as well as SNpc

  12. In vitro model for the study of the role of the mesopontine region in rapid eye movement (REM sleep and wakefulness.

    Directory of Open Access Journals (Sweden)

    Esteban Pino

    2017-06-01

    Full Text Available O estudo de estratégias neurais para a organização do comportamento em vertebrados constitui um desafio maior para a neurociencia. O avanço do conhecimento nessa área depende criticamente da utilização de modelos experimentais adequados que suportem múltiplos níveis de análise (por exemplo: comportamental, circuital, celular,  sináptico e molecular e abordagens por múltiplas técnicas. Decidiu-se analisar in vitro uma rede neural da união mesopontina do tronco encefálico criticamente envolvida no controle do sono de movimentos oculares rápidos (S-REM. Apesar da riqueza de provas que sustentam o papel desta rede em relação ao S-REM, os mecanismos celulares e sinápticos subjacentes a este controle são pouco conhecidos e permanecem sob intensa investigação. Para avançar no conhecimento desses mecanismos, caracterizou-se morfológica e funcionalmente uma fatia de tronco encefálico de rato, na qual as estruturas críticas para o controle do S-REM, i.e.: núcleos tegmentais laterodorsal e pedunculopontino, e sua projeção para o núcleo reticular pontis oralis (PnO estão presentes e operantes. A inclusão do núcleo motor do trigêmeo na fatia permitiu detectar mudanças da excitabilidade das motoneuronas provocadas por manipulações farmacológicas do PnO, representativas das alterações do tônus muscular associados com operações semelhantes quando realizados in vivo. A utlização deste modelo in vitro de S-REM permitirá contribuir para a elucidação de estratégias neurais que operam em níveis intermedios de organização do SN de mamíferos para a geração e regulação de um estado comportamental.

  13. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.

  14. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  15. REM Sleep Phase Preference in the Crepuscular Octodon degus Assessed by Selective REM Sleep Deprivation

    Science.gov (United States)

    Ocampo-Garcés, Adrián; Hernández, Felipe; Palacios, Adrian G.

    2013-01-01

    Study Objectives: To determine rapid eye movement (REM) sleep phase preference in a crepuscular mammal (Octodon degus) by challenging the specific REM sleep homeostatic response during the diurnal and nocturnal anticrepuscular rest phases. Design: We have investigated REM sleep rebound, recovery, and documented REM sleep propensity measures during and after diurnal and nocturnal selective REM sleep deprivations. Subjects: Nine male wild-captured O. degus prepared for polysomnographic recordings Interventions: Animals were recorded during four consecutive baseline and two separate diurnal or nocturnal deprivation days, under a 12:12 light-dark schedule. Three-h selective REM sleep deprivations were performed, starting at midday (zeitgeber time 6) or midnight (zeitgeber time 18). Measurements and Results: Diurnal and nocturnal REM sleep deprivations provoked equivalent amounts of REM sleep debt, but a consistent REM sleep rebound was found only after nocturnal deprivation. The nocturnal rebound was characterized by a complete recovery of REM sleep associated with an augment in REM/total sleep time ratio and enhancement in REM sleep episode consolidation. Conclusions: Our results support the notion that the circadian system actively promotes REM sleep. We propose that the sleep-wake cycle of O. degus is modulated by a chorus of circadian oscillators with a bimodal crepuscular modulation of arousal and a unimodal promotion of nocturnal REM sleep. Citation: Ocampo-Garcés A; Hernández F; Palacios AG. REM sleep phase preference in the crepuscular Octodon degus assessed by selective REM sleep deprivation. SLEEP 2013;36(8):1247-1256. PMID:23904685

  16. REM sleep homeostasis in the absence of REM sleep: Effects of antidepressants.

    Science.gov (United States)

    McCarthy, Andrew; Wafford, Keith; Shanks, Elaine; Ligocki, Marcin; Edgar, Dale M; Dijk, Derk-Jan

    2016-09-01

    Most antidepressants suppress rapid eye movement (REM) sleep, which is thought to be important to brain function, yet the resulting REM sleep restriction is well tolerated. This study investigated the impact of antidepressants with different mechanisms of action, such as selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCA), on the regulation of REM sleep in rats. REM sleep was first demonstrated to be homeostatically regulated using 5, 8 and 10 h of REM-sleep specific restriction through EEG-triggered arousals, with an average of 91 ± 10% of lost REM sleep recovered following a 26-29 -hour recovery period. Acute treatment with the antidepressants paroxetine, citalopram and imipramine inhibited REM sleep by 84 ± 8, 84 ± 8 and 69 ± 9% respectively relative to vehicle control. The pharmacologically-induced REM sleep deficits by paroxetine and citalopram were not fully recovered, whereas, after imipramine the REM sleep deficit was fully compensated. Given the marked difference between REM sleep recovery following the administration of paroxetine, citalopram, imipramine and REM sleep restriction, the homeostatic response was further examined by pairing REM sleep specific restriction with the three antidepressants. Surprisingly, the physiologically-induced REM sleep deficits incurred prior to suppression of REM sleep by all antidepressants was consistently recovered. The data indicate that REM sleep homeostasis remains operative following subsequent treatment with antidepressants and is unaffected by additional pharmacological inhibition of REM sleep. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  19. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  20. Predictions models with neural nets

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2008-01-01

    Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.

  1. Overnight improvements in two REM sleep-sensitive tasks are associated with both REM and NREM sleep changes, sleep spindle features, and awakenings for dream recall.

    Science.gov (United States)

    Nielsen, T; O'Reilly, C; Carr, M; Dumel, G; Godin, I; Solomonova, E; Lara-Carrasco, J; Blanchette-Carrière, C; Paquette, T

    2015-07-01

    Memory consolidation is associated with sleep physiology but the contribution of specific sleep stages remains controversial. To clarify the contribution of REM sleep, participants were administered two REM sleep-sensitive tasks to determine if associated changes occurred only in REM sleep. Twenty-two participants (7 men) were administered the Corsi Block Tapping and Tower of Hanoi tasks prior to and again after a night of sleep. Task improvers and non-improvers were compared for sleep structure, sleep spindles, and dream recall. Control participants (N = 15) completed the tasks twice during the day without intervening sleep. Overnight Corsi Block improvement was associated with more REM sleep whereas Tower of Hanoi improvement was associated with more N2 sleep. Corsi Block improvement correlated positively with %REM sleep and Tower of Hanoi improvement with %N2 sleep. Post-hoc analyses suggest Tower of Hanoi effects-but not Corsi Block effects-are due to trait differences. Sleep spindle density was associated with Tower of Hanoi improvement whereas spindle amplitude correlated with Corsi Block improvement. Number of REM awakenings for dream reporting (but not dream recall per se) was associated with Corsi Block, but not Tower of Hanoi, improvement but was confounded with REM sleep time. This non-replication of one of 2 REM-sensitive task effects challenges both 'dual-process' and 'sequential' or 'sleep organization' models of sleep-dependent learning and points rather to capacity limitations on REM sleep. Experimental awakenings for sampling dream mentation may not perturb sleep-dependent learning effects; they may even enhance them. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. REM sleep and dreaming functions beyond reductionism.

    Science.gov (United States)

    Kirov, Roumen

    2013-12-01

    Brain activation patterns and mental, electrophysiological, and neurobiological features of rapid eye movement (REM) sleep suggest more functions than only elaborative encoding. Hence, the periodic occurrence of REM sleep episodes and dreaming may be regarded as a recurrent adaptive interference, which incorporates recent memories into a broader vital context comprising emotions, basic needs and individual genetic traits.

  3. Hign sensitivity neutron rem counter

    International Nuclear Information System (INIS)

    Jiang Jinling; Wen Youqin; Xie Jianlun; Chen Changmao

    1986-01-01

    The constructions of two counters (NR1 and NR2) and their characteristics are presented. In the region from thermal energy to 17 MeV, the detectable dose equivalent values range from 10 -4 to 10 2 mrem.h -1 and the neutron dose equivalent sensitivity is approximately 140 cps/mrem.h -1 for the counter NR1; the detectable dose equivalent values range from 10 -4 to 65 mrem.h -1 and the dose equivalent sensitivity approx.= 209 cps/mrem.h -1 for the counter NR2 in the energy range from thermal energy to 7 MeV. Compared with the rem counter SIUDSVIK 2202D, their dose equivalent readings are consistent within +- 10% when neutron beams are approximatly perpendicular to the counter axis

  4. Diagnostic REM sleep muscle activity thresholds in patients with idiopathic REM sleep behavior disorder with and without obstructive sleep apnea.

    Science.gov (United States)

    McCarter, Stuart J; St Louis, Erik K; Sandness, David J; Duwell, Ethan J; Timm, Paul C; Boeve, Bradley F; Silber, Michael H

    2017-05-01

    We aimed to determine whether visual and automated rapid eye movement (REM) sleep without atonia (RSWA) methods could accurately diagnose patients with idiopathic REM sleep behavior disorder (iRBD) and comorbid obstructive sleep apnea (OSA). In iRBD patients (n = 15) and matched controls (n = 30) with and without OSA, we visually analyzed RSWA phasic burst durations, phasic, tonic, and "any" muscle activity by 3-s mini-epochs, phasic activity by 30-s (AASM rules) epochs, and automated REM atonia index (RAI). Group RSWA metrics were analyzed with regression models. Receiver operating characteristic (ROC) curves were used to determine the best diagnostic cutoff thresholds for REM sleep behavior disorder (RBD). Both split-night and full-night polysomnographic studies were analyzed. All mean RSWA phasic burst durations and muscle activities were higher in iRBD patients than in controls (p sleep behavior disorder (PD-RBD), consistent with a common mechanism and presumed underlying etiology of synucleinopathy in both groups. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. What do saliency models predict?

    Science.gov (United States)

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.

    2014-01-01

    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  6. Wake-promoting agent modafinil worsened attentional performance following REM sleep deprivation in a young-adult rat model of 5-choice serial reaction time task.

    Science.gov (United States)

    Liu, Yia-Ping; Tung, Che-Se; Lin, Yu-Lung; Chuang, Chia-Hsin

    2011-01-01

    Individuals who experience sleep loss may exhibit certain physiological abnormalities. Central stimulant drugs have been studied in sleep-loss conditions, and some of them might be therapeutically beneficial. Modafinil (diphenyl-methyl-sulfinyl-2-acetamide, MOD) has been increasingly employed for elevating alertness and vigilance in recent years, yet the underlying mechanism of actions for MOD is not fully understood. To examine the behavioral effect of MOD following rapid eye movement sleep deprivation (REMD) in rats. A five-choice serial reaction time task (5-CSRTT) was employed to investigate animals' attentional performance and impulsive reactivity. Rats of different ages were trained to learn the 5-CSRTT. REMD with the water platform method was applied for 96 h. The impacts of REMD on 5-CSRTT in middle-age (32-weeks-old) and young-adult (12-week-old) rats were compared with baseline or a condition with shorter visual stimulus duration. The results revealed that following REMD, young-adult but not middle-age rats were liable to be affected in their performances of the 5-CSRTT. In young-adult rats, while MOD had no contributions to the effect of REMD, it worsened rats' performance following REMD when the stimulus duration was shortened, as shown by the reduced number of correct responses and prolonged magazine latency. These results suggest that aging might be a crucial factor for the physiological impact following REMD. MOD should be used cautiously, particularly, in conditions that require REM sleep.

  7. Rem : a los dos lados del espejo = (Rem at both sides of the mirror)

    OpenAIRE

    Butragueño Díaz-Guerra, Belén

    2016-01-01

    Cuando se trata de Rem Koolhaas, su espejo no refleja una sola imagen sino múltiples, es un prisma poliédrico. Su espejo nos devuelve el Rem mediático, el intelectual, el conceptualizador, el constructor, el analista, el periodista, el actor... En el caso de esta investigación, fijamos el punto de mira en el Rem COMUNICADOR. “Rem a los dos lados del espejo” se enmarca en una investigación sobre los medios de comunicación de arquitectura, su reflejo en la producción arquitectónica y viceversa....

  8. Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder

    DEFF Research Database (Denmark)

    Kempfner, J; Sørensen, Gertrud Laura; Sorensen, H B D

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG....

  9. Family history of idiopathic REM behavior disorder

    DEFF Research Database (Denmark)

    Dauvilliers, Yves; Postuma, Ronald B; Ferini-Strambi, Luigi

    2013-01-01

    To compare the frequency of proxy-reported REM sleep behavior disorder (RBD) among relatives of patients with polysomnogram-diagnosed idiopathic RBD (iRBD) in comparison to controls using a large multicenter clinic-based cohort.......To compare the frequency of proxy-reported REM sleep behavior disorder (RBD) among relatives of patients with polysomnogram-diagnosed idiopathic RBD (iRBD) in comparison to controls using a large multicenter clinic-based cohort....

  10. The first film presentation of REM sleep behavior disorder precedes its scientific debut by 35 years

    OpenAIRE

    Janković Slavko M.; Sokić Dragoslav V.; Vojvodić Nikola M.; Ristić Aleksandar J.

    2006-01-01

    The perplexing and tantalizing disease of rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by peculiar, potentially dangerous behavior during REM sleep. It was described both in animals and humans. RBD in mammals was first described by Jouvet and Delorme in 1965, based on an experimental model induced by lesion in pontine region of cats [1]. In 1972, Passouant et al. described sleep with eye movements and persistent tonic muscle activity induced by tricyclic antidepress...

  11. REM sleep and dreaming: towards a theory of protoconsciousness.

    Science.gov (United States)

    Hobson, J Allan

    2009-11-01

    Dreaming has fascinated and mystified humankind for ages: the bizarre and evanescent qualities of dreams have invited boundless speculation about their origin, meaning and purpose. For most of the twentieth century, scientific dream theories were mainly psychological. Since the discovery of rapid eye movement (REM) sleep, the neural underpinnings of dreaming have become increasingly well understood, and it is now possible to complement the details of these brain mechanisms with a theory of consciousness that is derived from the study of dreaming. The theory advanced here emphasizes data that suggest that REM sleep may constitute a protoconscious state, providing a virtual reality model of the world that is of functional use to the development and maintenance of waking consciousness.

  12. REM sleep deprivation during 5 hours leads to an immediate REM sleep rebound and to suppression of non-REM sleep intensity

    NARCIS (Netherlands)

    Beersma, D.G.M.; Dijk, D.J.; Blok, Guus; Everhardus, I.

    Nine healthy male subjects were deprived of REM sleep during the first 5 h after sleep onset. Afterwards recovery sleep was undisturbed. During the deprivation period the non-REM EEG power spectrum was reduced when compared to baseline for the frequencies up to 7 Hz, despite the fact that non-REM

  13. REM sleep estimation only using respiratory dynamics

    International Nuclear Information System (INIS)

    Chung, Gih Sung; Choi, Byung Hoon; Lee, Jeong Su; Lee, Jin-Seong; Jeong, Do-Un; Park, Kwang Suk

    2009-01-01

    Polysomnography (PSG) is currently considered the gold standard for assessing sleep quality. However, the numerous sensors that must be attached to the subject can disturb sleep and limit monitoring to within hospitals and sleep clinics. If data could be obtained without such constraints, sleep monitoring would be more convenient and could be extended to ordinary homes. During rapid-eye-movement (REM) sleep, respiration rate and variability are known to be greater than in other sleep stages. Hence, we calculated the average rate and variability of respiration in an epoch (30 s) by applying appropriate smoothing algorithms. Increased and irregular respiratory patterns during REM sleep were extracted using adaptive and linear thresholds. When both parameters simultaneously showed higher values than the thresholds, the epochs were assumed to belong to REM sleep. Thermocouples and piezoelectric-type belts were used to acquire respiratory signals. Thirteen healthy adults and nine obstructive sleep apnea (OSA) patients participated in this study. Kappa statistics showed a substantial agreement (κ > 0.60) between the standard and respiration-based methods. One-way ANOVA analysis showed no significant difference between the techniques for total REM sleep. This approach can also be applied to the non-intrusive measurement of respiration signals, making it possible to automatically detect REM sleep without disturbing the subject

  14. Development of a spherical neutron rem monitor

    International Nuclear Information System (INIS)

    Panchal, C.G.; Madhavi, V.; Bansode, P.Y.; Jakati, R.K.; Ghodgaonkar, M.D.; Desai, S.S.; Shaikh, A.M.; Sathian, V.

    2007-01-01

    A new neutron rem monitor based on spherical LINUS with the state of art electronic circuits has been designed in Electronics Division. This prototype instrument encompasses a spherical double polythene moderator to improve an isotropic response and a lead layer to extend its energy response compared to the conventional neutron rem monitors. A systematic testing and calibration of the energy and directional response of the prototype monitor have been carried out. Although the monitor is expected to perform satisfactorily upto an energy ∼ 55 MeV, at present its response has been tested upto 5 MeV. (author)

  15. Effects of a lifestyle intervention on REM sleep-related OSA severity in obese individuals with type 2 diabetes.

    Science.gov (United States)

    Shechter, Ari; Foster, Gary D; Lang, Wei; Reboussin, David M; St-Onge, Marie-Pierre; Zammit, Gary; Newman, Anne B; Millman, Richard P; Wadden, Thomas A; Jakicic, John M; Strotmeyer, Elsa S; Wing, Rena R; Pi-Sunyer, F Xavier; Kuna, Samuel T

    2017-12-01

    The aim of this study was to determine if an intensive lifestyle intervention (ILI) reduces the severity of obstructive sleep apnea (OSA) in rapid-eye movement (REM) sleep, and to determine if longitudinal changes in glycaemic control are related to changes in OSA severity during REM sleep over a 4-year follow-up. This was a randomized controlled trial including 264 overweight/obese adults with type 2 diabetes (T2D) and OSA. Participants were randomized to an ILI targeted to weight loss or a diabetes support and education (DSE) control group. Measures included anthropometry, apnea-hypopnea index (AHI) during REM sleep (REM-AHI) and non-REM sleep (NREM-AHI) and glycated haemoglobin (HbA1c) at baseline and year 1, year 2 and year 4 follow-ups. Mean baseline values of REM-AHI were significantly higher than NREM-AHI in both groups. Both REM-AHI and NREM-AHI were reduced significantly more in ILI versus DSE, but these differences were attenuated slightly after adjustment for weight changes. Repeated-measure mixed-model analyses including data to year 4 demonstrated that changes in HbA1c were related significantly to changes in weight, but not to changes in REM-AHI and NREM-AHI. Compared to control, the ILI reduced REM-AHI and NREM-AHI during the 4-year follow-up. Weight, as opposed to REM-AHI and NREM-AHI, was related to changes in HbA1c. The findings imply that weight loss from a lifestyle intervention is more important than reductions in AHI for improving glycaemic control in T2D patients with OSA. © 2017 European Sleep Research Society.

  16. Management of REM sleep behavior disorder: An evidence based review

    OpenAIRE

    Preeti Devnani; Racheal Fernandes

    2015-01-01

    Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by dream enactment behavior resulting from a loss of REM skeletal muscle atonia. The neurobiology of REM sleep and the characteristic features of REM atonia have an important basis for understanding the aggravating etiologies the proposed pharmacological interventions in its management. This review outlines the evidence for behavioral and therapeutic measures along with evidence-based guidelines for their implementation, ...

  17. CAN NON-REM SLEEP BE DEPRESSOGENIC

    NARCIS (Netherlands)

    BEERSMA, DGM; VANDENHOOFDAKKER, RH

    Sleep and mood are clearly interrelated in major depression, as shown by the antidepressive effects of various experiments, such as total sleep deprivation, partial sleep deprivation, REM sleep deprivation, and temporal shifts of the sleep period. The prevailing hypotheses explaining these effects

  18. Can non-REM sleep be depressogenic?

    NARCIS (Netherlands)

    Beersma, Domien G.M.; Hoofdakker, Rutger H. van den

    1992-01-01

    Sleep and mood are clearly interrelated in major depression, as shown by the antidepressive effects of various experiments, such as total sleep deprivation, partial sleep deprivation, REM sleep deprivation, and temporal shifts of the sleep period. The prevailing hypotheses explaining these effects

  19. RemBench: A Digital Workbench for Rembrandt Research

    NARCIS (Netherlands)

    Verberne, Suzan; Van Leeuwen, Rudie; Gerritsen, G.H.; Boves, Lou

    2016-01-01

    In this paper, we present RemBench, a search engine for research into the life and works of Rembrandt van Rijn. RemBench combines the data from four different databases behind one interface using federated search technology. Metadata filtering is enabled through faceted search. RemBench enables art

  20. Automatic REM Sleep Detection Associated with Idiopathic REM Sleep Behavior Disorder

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sørensen, Gertrud Laura; Sørensen, Helge Bjarup Dissing

    2011-01-01

    Rapid eye movement sleep Behavior Disorder (RBD) is a strong early marker of later development of Parkinsonism. Currently there are no objective methods to identify and discriminate abnormal from normal motor activity during REM sleep. Therefore, a REM sleep detection without the use of chin...... electromyography (EMG) is useful. This is addressed by analyzing the classification performance when implementing two automatic REM sleep detectors. The first detector uses the electroencephalography (EEG), electrooculography (EOG) and EMG to detect REM sleep, while the second detector only uses the EEG and EOG....... Method: Ten normal controls and ten age matched patients diagnosed with RBD were enrolled. All subjects underwent one polysomnographic (PSG) recording, which was manual scored according to the new sleep-scoring standard from the American Academy of Sleep Medicine. Based on the manual scoring...

  1. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  2. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  3. Posttraining Increases in REM Sleep Intensity Implicate REM Sleep in Memory Processing and Provide a Biological Marker of Learning Potential

    Science.gov (United States)

    Nader, Rebecca S.; Smith, Carlyle T.; Nixon, Margaret R.

    2004-01-01

    Posttraining rapid eye movement (REM) sleep has been reported to be important for efficient memory consolidation. The present results demonstrate increases in the intensity of REM sleep during the night of sleep following cognitive procedural/implicit task acquisition. These REM increases manifest as increases in total number of rapid eye…

  4. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  5. Particle Reduction Strategies - PAREST. Influence of the boundary conditions from the global chemistry transport model TM5 on the regional aerosol chemistry transport model REM CALGRID; Strategien zur Verminderung der Feinstaubbelastung - PAREST. Einfluss der Randbedingungen aus dem globalen Chemie-Transport-Modell TM5 auf das regionale Aerosol-Chemie-Transport-Modell REM-CALGRID. Teilbericht

    Energy Technology Data Exchange (ETDEWEB)

    Kerschbaumer, Andreas; Hannig, Katrin [Freie Univ. Berlin (Germany). Inst. fuer Meteorologie, Troposphaerische Umweltforschung

    2013-06-15

    In this report the coupling of a global model is presented with a continental model. It examines how far the forecasts of regional air quality in Europe are affected by the choice of boundary conditions. The focus of this report is to analyze the influence of different boundary conditions on the calculated soil concentrations of ozone and PM10. A model evaluation, however, was not the aim of this study. [German] In diesem Bericht wird die Koppelung eines Globalmodells mit einem kontinentalen Modell vorgestellt. Es wird untersucht, wie weit die Prognosen der regionalen Luftqualitaet in Europa von der Wahl der Randbedingungen beeinflusst werden. Der Schwerpunkt des vorliegenden Berichts liegt in der Analyse des Einflusses der verschiedenen Randbedingungen auf die berechneten Bodenkonzentrationen von Ozon und PM10. Eine Modellevaluierung hingegen war nicht Ziel dieser Studie.

  6. Proposed man-rem reference values of occupational exposure

    International Nuclear Information System (INIS)

    Lombard, J.

    1988-04-01

    This report presents a proposal of referent collective dose (man-rem) values for occupational exposure related to operation of French pressurized water reactors. These values, permitting adequate choice of protection both at design and operation level, are dependent on the level of annual individual doses. The man-rem value, originating from annual individual doses less than 0.5 rem are estimated to 1 kf. The proposed value is 20 kf for annual individual exposures between 0.5 and 3 rem, and 90 kf for annual individual exposures between 3 and 5 rem. (author) [fr

  7. Dreamless: the silent epidemic of REM sleep loss.

    Science.gov (United States)

    Naiman, Rubin

    2017-10-01

    We are at least as dream deprived as we are sleep deprived. Many of the health concerns attributed to sleep loss result from a silent epidemic of REM sleep deprivation. REM/dream loss is an unrecognized public health hazard that silently wreaks havoc with our lives, contributing to illness, depression, and an erosion of consciousness. This paper compiles data about the causes and extent of REM/dream loss associated with commonly used medications, endemic substance use disorders, rampant sleep disorders, and behavioral and lifestyle factors. It examines the consequences of REM/dream loss and concludes with recommendations for restoring healthy REM/dreaming. © 2017 New York Academy of Sciences.

  8. Dicholine succinate, the neuronal insulin sensitizer, normalizes behavior, REM sleep, hippocampal pGSK3 beta and mRNAs of NMDA receptor subunits in mouse models of depression

    Directory of Open Access Journals (Sweden)

    Brandon H. Cline

    2015-02-01

    Full Text Available Central insulin receptor-mediated signalling is attracting the growing attention of researchers because of rapidly accumulating evidence implicating it in the mechanisms of plasticity, stress response and neuropsychiatric disorders including depression. Dicholine succinate (DS, a mitochondrial complex II substrate, was shown to enhance insulin-receptor mediated signaling in neurons and is regarded as a sensitizer of the neuronal insulin receptor. Compounds enhancing neuronal insulin receptor-mediated transmission exert an antidepressant-like effect in several pre-clinical paradigms of depression; similarly, such properties for DS were found with a stress-induced anhedonia model. Here, we additionally studied the effects of DS on several variables which were ameliorated by other insulin receptor sensitizers in mice. Pre-treatment with DS of chronically stressed C57BL6 mice rescued normal contextual fear conditioning, hippocampal gene expression of NMDA receptor subunit NR2A, the NR2A/NR2B ratio and increased REM sleep rebound after acute predation. In 18-month-old C57BL6 mice, a model of elderly depression, DS restored normal sucrose preference and activated the expression of neural plasticity factors in the hippocampus as shown by Illumina microarray. Finally, young naïve DS-treated C57BL6 mice had reduced depressive- and anxiety-like behaviours and, similarly to imipramine-treated mice, preserved hippocampal levels of the phosphorylated (inactive form of GSK3 beta that was lowered by forced swimming in pharmacologically naïve animals. Thus, DS can ameliorate behavioural and molecular outcomes under a variety of stress- and depression-related conditions. This further highlights neuronal insulin signalling as a new factor of pathogenesis and a potential pharmacotherapy of affective pathologies.

  9. Thermophysical Properties Along Curiosity's Traverse in Gale Crater, Mars, Derived from the REMS Ground Temperature Sensor

    Science.gov (United States)

    Vasavada, Ashwin R.; Piqueux, Sylvain; Lewis, Kevin W.; Lemmon, Mark T.; Smith, Michael Doyle

    2016-01-01

    The REMS instrument onboard the Mars Science Laboratory rover, Curiosity, has measured ground temperature nearly continuously at hourly intervals for two Mars years. Coverage of the entire diurnal cycle at 1 Hz is available every few martian days. We compare these measurements with predictions of surface atmosphere thermal models to derive the apparent thermal inertia and thermally derived albedo along the rovers traverse after accounting for the radiative effects of atmospheric water ice during fall and winter, as is necessary to match the measured seasonal trend. The REMS measurements can distinguish between active sand, other loose materials, mudstone, and sandstone based on their thermophysical properties. However, the apparent thermal inertias of bedrock dominated surfaces [approx. 350-550 J m(exp. -2) K(exp. -1 s(exp. -1/2 )] are lower than expected. We use rover imagery and the detailed shape of the diurnal ground temperature curve to explore whether lateral or vertical heterogeneity in the surface materials within the sensor footprint might explain the low inertias. We find that the bedrock component of the surface can have a thermal inertia as high as 650-1700 J m(exp. -2) K(exp. -1) s(exp. -1/2) for mudstone sites and approx. 700 J m(exp. -2) K(exp. -1) s(exp. - 1/2) for sandstone sites in models runs that include lateral and vertical mixing. Although the results of our forward modeling approach may be non-unique, they demonstrate the potential to extract information about lateral and vertical variations in thermophysical properties from temporally resolved measurements of ground temperature.

  10. Control of REM sleep by ventral medulla GABAergic neurons.

    Science.gov (United States)

    Weber, Franz; Chung, Shinjae; Beier, Kevin T; Xu, Min; Luo, Liqun; Dan, Yang

    2015-10-15

    Rapid eye movement (REM) sleep is a distinct brain state characterized by activated electroencephalogram and complete skeletal muscle paralysis, and is associated with vivid dreams. Transection studies by Jouvet first demonstrated that the brainstem is both necessary and sufficient for REM sleep generation, and the neural circuits in the pons have since been studied extensively. The medulla also contains neurons that are active during REM sleep, but whether they play a causal role in REM sleep generation remains unclear. Here we show that a GABAergic (γ-aminobutyric-acid-releasing) pathway originating from the ventral medulla powerfully promotes REM sleep in mice. Optogenetic activation of ventral medulla GABAergic neurons rapidly and reliably initiated REM sleep episodes and prolonged their durations, whereas inactivating these neurons had the opposite effects. Optrode recordings from channelrhodopsin-2-tagged ventral medulla GABAergic neurons showed that they were most active during REM sleep (REMmax), and during wakefulness they were preferentially active during eating and grooming. Furthermore, dual retrograde tracing showed that the rostral projections to the pons and midbrain and caudal projections to the spinal cord originate from separate ventral medulla neuron populations. Activating the rostral GABAergic projections was sufficient for both the induction and maintenance of REM sleep, which are probably mediated in part by inhibition of REM-suppressing GABAergic neurons in the ventrolateral periaqueductal grey. These results identify a key component of the pontomedullary network controlling REM sleep. The capability to induce REM sleep on command may offer a powerful tool for investigating its functions.

  11. Rapid Acute Physiology Score versus Rapid Emergency Medicine Score in Trauma Outcome Prediction; a Comparative Study

    Directory of Open Access Journals (Sweden)

    Babak Nakhjavan-Shahraki

    2017-01-01

    Full Text Available Introduction: Rapid acute physiology score (RAPS and rapid emergency medicine score (REMS are two physiologic models for measuring injury severity in emergency settings. The present study was designed to compare the two models in outcome prediction of trauma patients presenting to emergency department (ED.Methods: In this prospective cross-sectional study, the two models of RAPS and REMS were compared regarding prediction of mortality and poor outcome (severe disability based on Glasgow outcome scale of trauma patients presenting to the EDs of 5 educational hospitals in Iran (Tehran, Tabriz, Urmia, Jahrom and Ilam from May to October 2016. The discriminatory power and calibration of the models were calculated and compared using STATA 11.Results: 2148 patients with the mean age of 39.50±17.27 years were studied (75.56% males. The area under the curve of REMS and RAPS in predicting in-hospital mortality were calculated to be 0.93 (95% CI: 0.92-0.95 and 0.899 (95% CI: 0.86-0.93, respectively (p=0.02. These measures were 0.92 (95% CI: 0.90-0.94 and 0.86 (95% CI: 0.83-0.90, respectively, regarding poor outcome (p=0.001. The optimum cut-off point in predicting outcome was found to be 3 for REMS model and 2 for RAPS model. The sensitivity and specificity of REMS and RAPS in the mentioned cut offs were 95.93 vs. 85.37 and 77.63 vs. 83.51, respectively, in predicting mortality. Calibration and overall performance of the two models were acceptable.Conclusion: The present study showed that adding age and level of arterial oxygen saturation to the variables included in RAPS model can increase its predictive value. Therefore, it seems that REMS could be used for predicting mortality and poor outcome of trauma patients in emergency settings

  12. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  13. A Different Clinical Type of OSAS: REM-Related OSAS

    Directory of Open Access Journals (Sweden)

    Handan İnönü Köseoğlu

    2015-08-01

    Full Text Available Objective: Rapid eye movement (REM is an entity in which the collapsibility of upper respiratory tract increases. Different opinions have been proposed with regard to the definition of REM-related obstructive sleep apnea syndrome (OSAS. Some authors consider REM-related OSAS as the first presentation, and others consider it as a different clinical type of OSAS. We aimed to compare the clinical and polysomnographic findings of REM-related and non-REM-related OSAS patients to test whether REM-related OSAS is a different clinical type OSAS or the manifestation of early stage or the onset of OSAS. Methods: The study had a retrospective design. Patients with an initial diagnosis of sleep-related breathing disorders were later diagnosed to have OSAS based on an apnea–hypopnea index (AHI of ≥5 and were divided into the following two groups: patients with AHINREM of 2 whose REM recordings were obtained for at least 30 min were defined as having “REM-related OSAS,” and those who did not meet this description were defined as having “non-REM-related OSAS.” Results: A total of 329 patients with a mean age of 51±10 years were included in the study. Thirty-five (10.6% patients with OSAS were REM-related and 294 (89.4% were non-REM-related. Age, body mass index, smoking status, and concomitant diseases were comparable between groups (p>0.05. In REM-related patients, AHI was lower, REM duration was longer, and mean oxygen saturations were comparatively higher (p<0.05. Conclusion: Similarities between groups in age, body mass index, and concomitant disease suggest that REM-related OSAS is a different clinical type of OSAS, rather than the early phase of OSAS.

  14. Diagnostic thresholds for quantitative REM sleep phasic burst duration, phasic and tonic muscle activity, and REM atonia index in REM sleep behavior disorder with and without comorbid obstructive sleep apnea.

    Science.gov (United States)

    McCarter, Stuart J; St Louis, Erik K; Duwell, Ethan J; Timm, Paul C; Sandness, David J; Boeve, Bradley F; Silber, Michael H

    2014-10-01

    We aimed to determine whether phasic burst duration and conventional REM sleep without atonia (RSWA) methods could accurately diagnose REM sleep behavior disorder (RBD) patients with comorbid OSA. We visually analyzed RSWA phasic burst durations, phasic, "any," and tonic muscle activity by 3-s mini-epochs, phasic activity by 30-s (AASM rules) epochs, and conducted automated REM atonia index (RAI) analysis. Group RSWA metrics were analyzed and regression models fit, with receiver operating characteristic (ROC) curves determining the best diagnostic cutoff thresholds for RBD. Both split-night and full-night polysomnographic studies were analyzed. N/A. Parkinson disease (PD)-RBD (n = 20) and matched controls with (n = 20) and without (n = 20) OSA. N/A. All mean RSWA phasic burst durations and muscle activities were higher in PD-RBD patients than controls (P sleep without atonia diagnostic thresholds applicable in Parkinson disease-REM sleep behavior disorder (PD-RBD) patient populations with comorbid OSA that may be useful toward distinguishing PD-RBD in typical outpatient populations. © 2014 Associated Professional Sleep Societies, LLC.

  15. The neuronal transition probability (NTP model for the dynamic progression of non-REM sleep EEG: the role of the suprachiasmatic nucleus.

    Directory of Open Access Journals (Sweden)

    Helli Merica

    Full Text Available Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG, we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP--in fitting the data well--successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN activity we show that the SCN has the information required to provide a threshold-triggered flip-flop for TIMING the towards-and-away alternations, information provided by sleep-relevant feedback to the SCN. NTP then determines the PATTERN of spectral power within each dynamic-state. NTP was fitted to individual NREM episodes 1-4, using data from 30 healthy subjects aged 20-30 years, and the quality of fit for each NREM measured. We show that the model fits well all NREM episodes and the best-fit probability-set is found to be effectively the same in fitting all subject data. The significant model-data agreement, the constant probability parameter and the proposed role of the SCN add considerable strength to the model. With it we link for the first time findings at cellular level and detailed time-course data at EEG level, to give a coherent picture of NREM dynamics over the entire night and over hierarchic brain levels all the way from the SCN

  16. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  17. Predictive Model Assessment for Count Data

    National Research Council Canada - National Science Library

    Czado, Claudia; Gneiting, Tilmann; Held, Leonhard

    2007-01-01

    .... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...

  18. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...

  19. Validating severity of illness scoring systems in the prediction of outcomes in Staphylococcus aureus bacteremia.

    Science.gov (United States)

    Sharma, Mamta; Szpunar, Susan; Khatib, Riad

    2013-08-01

    Severity of illness scores are helpful in predicting mortality; however, no standardized scoring system has been validated in patients with Staphylococcus aureus bacteremia (SAB). The modified Rapid Emergency Medicine Score (REMS), the CURB-65 (confusion, urea, respiratory rate, blood pressure and age 65) and the Charlson weighted index of comorbidity (CWIC) were compared in predicting outcomes at the onset of SAB. All adult inpatients with SAB from July 15, 2008, to December 31, 2009, were prospectively assessed. The 3 scoring systems were applied: REMS, CURB-65 and CWIC. The end points were attributable and overall mortality. A total of 241 patients with SAB were reviewed during the study period. The all-cause mortality rate was 22.8% and attributable mortality 14.1%. Patients who died had higher mean CURB-65 score and REMS than those who lived, whereas the difference in the CWIC score was not significant. Two logistic regression models based on CURB-65 score or REMS, after controlling for CWIC, revealed that both scores were independent predictors of mortality, with an odds ratio of 3.38 (P < 0.0001) and 1.45 (P < 0.0001) for CURB-65 and REMS, respectively. Receiver operating characteristic analysis revealed that a cutoff point of 3.0 (CURB-65) and 6.0 (REMS) provided the highest sensitivity and specificity. The area under the curves for all-cause mortality were 0.832 and 0.806, and for attributable mortality 0.845 and 0.819, for CURB-65 and REMS, respectively. REMS and CURB-65 scores outperformed CWIC as predictors of mortality in SAB and may be effective in predicting the severity of illness at the onset of bacteremia.

  20. Diagnostic tools for REM sleep behavior disorder.

    Science.gov (United States)

    Neikrug, Ariel B; Ancoli-Israel, Sonia

    2012-10-01

    Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia during REM sleep that results in motor behaviors. Diagnosis of RBD involves a clinical interview in which history of dream enactment behaviors is elicited and a subsequent overnight polysomnography (PSG) evaluation to assess for REM sleep without atonia (RWA) and/or observe motor behaviors during REM sleep. Therefore, the nature of RBD diagnosis involves both subjective and objective measurements that attempt to qualify and quantify the different diagnostic sub-criteria. The primary aim of the current study was to identify and summarize the available clinical measurements that have been used for RBD assessment. Two major online databases (MEDLINE and PsycInfo) were searched for articles developing, validating, or evaluating psychometric properties of the RBD diagnostic criteria or methods used for diagnosis. Studies of adult subjects (18 years or more) that included sufficient psychometric data for validation were included. Fifty-eight studies were found to meet review criteria. The objective measurements for assessment of RBD reviewed included visual electromyographic (EMG) scoring methods, computerized EMG scoring methods, cardiac (123)I-metaiodobenzylguanidine ((123)I-MIBG) scintigraphy, actigraphy, behavioral classification and video analysis. Subjective measurements of RBD included interviews and questionnaires. Sleep history may be sufficient for diagnosis of RBD in some populations. However, PSG is necessary for a definitive diagnosis. EMG scoring methods vary in definition used and there is no single accepted approach to scoring muscle activity. Additional validation studies are required for establishing cutoff scores for the different methods. Questionnaires were shown to be appropriate screening tools, yet further validation in different populations is necessary. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. Predictive models for arteriovenous fistula maturation.

    Science.gov (United States)

    Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas

    2016-05-07

    Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.

  2. Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...

    African Journals Online (AJOL)

    Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...

  3. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...

  4. The Neuronal Transition Probability (NTP) Model for the Dynamic Progression of Non-REM Sleep EEG: The Role of the Suprachiasmatic Nucleus

    CERN Document Server

    Merica, H

    2011-01-01

    Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) - in fitting the data well - successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN...

  5. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  6. Hybrid approaches to physiologic modeling and prediction

    Science.gov (United States)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  7. PRESCILA: a new, lightweight neutron rem meter.

    Science.gov (United States)

    Olsher, Richard H; Seagraves, David T; Eisele, Shawna L; Bjork, Christopher W; Martinez, William A; Romero, Leonard L; Mallett, Michael W; Duran, Michael A; Hurlbut, Charles R

    2004-06-01

    Conventional neutron rem meters currently in use are based on 1960's technology that relies on a large neutron moderator assembly surrounding a thermal detector to achieve a rem-like response function over a limited energy range. Such rem meters present an ergonomic challenge, being heavy and bulky, and have caused injuries during radiation protection surveys. Another defect of traditional rem meters is a poor high-energy response above 10 MeV, which makes them unsuitable for applications at high-energy accelerator facilities. Proton Recoil Scintillator-Los Alamos (PRESCILA) was developed as a low-weight (2 kg) alternative capable of extended energy response, high sensitivity, and moderate gamma rejection. An array of ZnS(Ag) based scintillators is located inside and around a Lucite light guide, which couples the scintillation light to a sideview bialkali photomultiplier tube. The use of both fast and thermal scintillators allows the energy response function to be optimized for a wide range of operational spectra. The light guide and the borated polyethylene frame provide moderation for the thermal scintillator element. The scintillators represent greatly improved versions of the Hornyak and Stedman designs from the 1950's, and were developed in collaboration with Eljen Technology. The inherent pulse height advantage of proton recoils over electron tracks in the phosphor grains eliminates the need for pulse shape discrimination and makes it possible to use the PRESCILA probe with standard pulse height discrimination provided by off-the-shelf health physics counters. PRESCILA prototype probes have been extensively tested at both Los Alamos and the German Bureau of Standards, Physikalisch-Technische Bundesanstalt. Test results are presented for energy response, directional dependence, linearity, sensitivity, and gamma rejection. Initial field tests have been conducted at Los Alamos and these results are also given. It is concluded that PRESCILA offers a viable

  8. REM sleep enhancement of probabilistic classification learning is sensitive to subsequent interference.

    Science.gov (United States)

    Barsky, Murray M; Tucker, Matthew A; Stickgold, Robert

    2015-07-01

    During wakefulness the brain creates meaningful relationships between disparate stimuli in ways that escape conscious awareness. Processes active during sleep can strengthen these relationships, leading to more adaptive use of those stimuli when encountered during subsequent wake. Performance on the Weather Prediction Task (WPT), a well-studied measure of implicit probabilistic learning, has been shown to improve significantly following a night of sleep, with stronger initial learning predicting more nocturnal REM sleep. We investigated this relationship further, studying the effect on WPT performance of a daytime nap containing REM sleep. We also added an interference condition after the nap/wake period as an additional probe of memory strength. Our results show that a nap significantly boosts WPT performance, and that this improvement is correlated with the amount of REM sleep obtained during the nap. When interference training is introduced following the nap, however, this REM-sleep benefit vanishes. In contrast, following an equal period of wake, performance is both unchanged from training and unaffected by interference training. Thus, while the true probabilistic relationships between WPT stimuli are strengthened by sleep, these changes are selectively susceptible to the destructive effects of retroactive interference, at least in the short term. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  10. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  11. A Global Model for Bankruptcy Prediction.

    Science.gov (United States)

    Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel

    2016-01-01

    The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

  12. Comparisons Between Model Predictions and Spectral Measurements of Charged and Neutral Particles on the Martian Surface

    Science.gov (United States)

    Kim, Myung-Hee Y.; Cucinotta, Francis A.; Zeitlin, Cary; Hassler, Donald M.; Ehresmann, Bent; Rafkin, Scot C. R.; Wimmer-Schweingruber, Robert F.; Boettcher, Stephan; Boehm, Eckart; Guo, Jingnan; hide

    2014-01-01

    Detailed measurements of the energetic particle radiation environment on the surface of Mars have been made by the Radiation Assessment Detector (RAD) on the Curiosity rover since August 2012. RAD is a particle detector that measures the energy spectrum of charged particles (10 to approx. 200 MeV/u) and high energy neutrons (approx 8 to 200 MeV). The data obtained on the surface of Mars for 300 sols are compared to the simulation results using the Badhwar-O'Neill galactic cosmic ray (GCR) environment model and the high-charge and energy transport (HZETRN) code. For the nuclear interactions of primary GCR through Mars atmosphere and Curiosity rover, the quantum multiple scattering theory of nuclear fragmentation (QMSFRG) is used. For describing the daily column depth of atmosphere, daily atmospheric pressure measurements at Gale Crater by the MSL Rover Environmental Monitoring Station (REMS) are implemented into transport calculations. Particle flux at RAD after traversing varying depths of atmosphere depends on the slant angles, and the model accounts for shielding of the RAD "E" dosimetry detector by the rest of the instrument. Detailed comparisons between model predictions and spectral data of various particle types provide the validation of radiation transport models, and suggest that future radiation environments on Mars can be predicted accurately. These contributions lend support to the understanding of radiation health risks to astronauts for the planning of various mission scenarios

  13. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  14. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  15. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  16. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  8. Selective REM Sleep Deprivation Improves Expectation-Related Placebo Analgesia.

    Science.gov (United States)

    Chouchou, Florian; Chauny, Jean-Marc; Rainville, Pierre; Lavigne, Gilles J

    2015-01-01

    The placebo effect is a neurobiological and psychophysiological process known to influence perceived pain relief. Optimization of placebo analgesia may contribute to the clinical efficacy and effectiveness of medication for acute and chronic pain management. We know that the placebo effect operates through two main mechanisms, expectations and learning, which is also influenced by sleep. Moreover, a recent study suggested that rapid eye movement (REM) sleep is associated with modulation of expectation-mediated placebo analgesia. We examined placebo analgesia following pharmacological REM sleep deprivation and we tested the hypothesis that relief expectations and placebo analgesia would be improved by experimental REM sleep deprivation in healthy volunteers. Following an adaptive night in a sleep laboratory, 26 healthy volunteers underwent classical experimental placebo analgesic conditioning in the evening combined with pharmacological REM sleep deprivation (clonidine: 13 volunteers or inert control pill: 13 volunteers). Medication was administered in a double-blind manner at bedtime, and placebo analgesia was tested in the morning. Results revealed that 1) placebo analgesia improved with REM sleep deprivation; 2) pain relief expectations did not differ between REM sleep deprivation and control groups; and 3) REM sleep moderated the relationship between pain relief expectations and placebo analgesia. These results support the putative role of REM sleep in modulating placebo analgesia. The mechanisms involved in these improvements in placebo analgesia and pain relief following selective REM sleep deprivation should be further investigated.

  9. Selective REM Sleep Deprivation Improves Expectation-Related Placebo Analgesia.

    Directory of Open Access Journals (Sweden)

    Florian Chouchou

    Full Text Available The placebo effect is a neurobiological and psychophysiological process known to influence perceived pain relief. Optimization of placebo analgesia may contribute to the clinical efficacy and effectiveness of medication for acute and chronic pain management. We know that the placebo effect operates through two main mechanisms, expectations and learning, which is also influenced by sleep. Moreover, a recent study suggested that rapid eye movement (REM sleep is associated with modulation of expectation-mediated placebo analgesia. We examined placebo analgesia following pharmacological REM sleep deprivation and we tested the hypothesis that relief expectations and placebo analgesia would be improved by experimental REM sleep deprivation in healthy volunteers. Following an adaptive night in a sleep laboratory, 26 healthy volunteers underwent classical experimental placebo analgesic conditioning in the evening combined with pharmacological REM sleep deprivation (clonidine: 13 volunteers or inert control pill: 13 volunteers. Medication was administered in a double-blind manner at bedtime, and placebo analgesia was tested in the morning. Results revealed that 1 placebo analgesia improved with REM sleep deprivation; 2 pain relief expectations did not differ between REM sleep deprivation and control groups; and 3 REM sleep moderated the relationship between pain relief expectations and placebo analgesia. These results support the putative role of REM sleep in modulating placebo analgesia. The mechanisms involved in these improvements in placebo analgesia and pain relief following selective REM sleep deprivation should be further investigated.

  10. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  11. Multiple Steps Prediction with Nonlinear ARX Models

    OpenAIRE

    Zhang, Qinghua; Ljung, Lennart

    2007-01-01

    NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...

  12. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  13. Model complexity control for hydrologic prediction

    Science.gov (United States)

    Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.

    2008-12-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.

  14. REM Sleep EEG Instability in REM Sleep Behavior Disorder and Clonazepam Effects.

    Science.gov (United States)

    Ferri, Raffaele; Rundo, Francesco; Silvani, Alessandro; Zucconi, Marco; Bruni, Oliviero; Ferini-Strambi, Luigi; Plazzi, Giuseppe; Manconi, Mauro

    2017-08-01

    We aimed to analyze quantitatively rapid eye movement (REM) sleep electroencephalogram (EEG) in controls, drug-naïve idiopathic REM sleep behavior disorder patients (iRBD), and iRBD patients treated with clonazepam. Twenty-nine drug-naïve iRBD patients (mean age 68.2 years), 14 iRBD patients under chronic clonazepam therapy (mean age 66.3 years), and 21 controls (mean age 66.8 years) were recruited. Power spectra were obtained from sleep EEG (central derivation), using a 2-second sliding window, with 1-second steps. The power values of each REM sleep EEG spectral band (one every second) were normalized with respect to the average power value obtained during sleep stage 2 in the same individual. In drug-naïve patients, the normalized power values showed a less pronounced REM-related decrease of power in all bands with frequency sleep EEG structure changes found in this study disclose subtle but significant alterations in the cortical electrophysiology of RBD that might represent the early expression of the supposed neurodegenerative processes already taking place at this stage of the disease and might be the target of better and effective future therapeutic strategies for this condition. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  15. Quantifying predictive accuracy in survival models.

    Science.gov (United States)

    Lirette, Seth T; Aban, Inmaculada

    2017-12-01

    For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

  16. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov

    2013-12-01

    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  17. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  18. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  19. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  20. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  1. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  2. Posterior predictive checking of multiple imputation models.

    Science.gov (United States)

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B

    2015-07-01

    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Sleepiness in Idiopathic REM Sleep Behavior Disorder and Parkinson Disease.

    Science.gov (United States)

    Arnulf, Isabelle; Neutel, Dulce; Herlin, Bastien; Golmard, Jean-Louis; Leu-Semenescu, Smaranda; Cochen de Cock, Valérie; Vidailhet, Marie

    2015-10-01

    To determine whether patients with idiopathic and symptomatic RBD were sleepier than controls, and if sleepiness in idiopathic RBD predicted earlier conversion to Parkinson disease. The Epworth Sleepiness Scale (ESS) and its determinants were compared at the time of a video-polysomnography for an RBD diagnosis in patients with idiopathic RBD, in patients with Parkinson disease, and in controls. Whether sleepiness at time of RBD diagnosis predicted an earlier conversion to neurodegenerative diseases was retrospectively analyzed in the followed-up patients. The 75 patients with idiopathic RBD were sleepier (ESS: 7.8 ± 4.6) at the time of RBD diagnosis than 74 age- and sex-matched controls (ESS: 5.0 ± 3.6, P sleep measures. Among the 69 patients with idiopathic RBD who were followed up for a median 3 years (1-15 years), 16 (23.2%) developed parkinsonism (n = 6), dementia (n = 6), dementia plus parkinsonism (n = 2), and multiple system atrophy (n = 2). An ESS greater than 8 at time of RBD diagnosis predicted a shorter time to phenoconversion to parkinsonism and dementia, from RBD onset, and from RBD diagnosis (when adjusted for age and time between RBD onset and diagnosis). Sleepiness is associated with idiopathic REM sleep behavior disorder and predicts more rapid conversion to parkinsonism and dementia, suggesting it is an early marker of neuronal loss in brainstem arousal systems. © 2015 Associated Professional Sleep Societies, LLC.

  4. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  5. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  6. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  7. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  8. Post training REMs coincident auditory stimulation enhances memory in humans.

    Science.gov (United States)

    Smith, C; Weeden, K

    1990-06-01

    Sleep activity was monitored in 20 freshman college students for two consecutive nights. Subjects were assigned to 4 equal groups and all were asked to learn a complex logic task before bed on the second night. Two groups of subjects learned the task with a constant clicking noise in the background (cued groups), while two groups simply learned the task (non cued). During the night, one cued and one non cued group were presented with auditory clicks during REM sleep such as to coincide with all REMs of at least 100 microvolts. The second cued group was given auditory clicks during REM sleep, but only during the REMs "quiet" times. The second non-cued control group was never given any nighttime auditory stimulations. The cued REMs coincident group showed a significant 23% improvement in task performance when tested one week later. The non cued REMs coincident group showed only an 8.8% improvement which was not significant. The cued REMs quiet and non-stimulated control groups showed no change in task performance when retested. The results were interpreted as support for the idea that the cued auditory stimulation induced a "recall" of the learned material during the REM sleep state in order for further memory processing to take place.

  9. Automatic detection of REM sleep in subjects without atonia

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Jennum, Poul; Nikolic, Miki

    2012-01-01

    Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored...

  10. REM Sleep at its Core – Circuits, Neurotransmitters, and Pathophysiology

    Science.gov (United States)

    Fraigne, Jimmy J.; Torontali, Zoltan A.; Snow, Matthew B.; Peever, John H.

    2015-01-01

    Rapid eye movement (REM) sleep is generated and maintained by the interaction of a variety of neurotransmitter systems in the brainstem, forebrain, and hypothalamus. Within these circuits lies a core region that is active during REM sleep, known as the subcoeruleus nucleus (SubC) or sublaterodorsal nucleus. It is hypothesized that glutamatergic SubC neurons regulate REM sleep and its defining features such as muscle paralysis and cortical activation. REM sleep paralysis is initiated when glutamatergic SubC cells activate neurons in the ventral medial medulla, which causes release of GABA and glycine onto skeletal motoneurons. REM sleep timing is controlled by activity of GABAergic neurons in the ventrolateral periaqueductal gray and dorsal paragigantocellular reticular nucleus as well as melanin-concentrating hormone neurons in the hypothalamus and cholinergic cells in the laterodorsal and pedunculo-pontine tegmentum in the brainstem. Determining how these circuits interact with the SubC is important because breakdown in their communication is hypothesized to underlie narcolepsy/cataplexy and REM sleep behavior disorder (RBD). This review synthesizes our current understanding of mechanisms generating healthy REM sleep and how dysfunction of these circuits contributes to common REM sleep disorders such as cataplexy/narcolepsy and RBD. PMID:26074874

  11. REM sleep at its core—Circuits, neurotransmitters and pathophysiology

    Directory of Open Access Journals (Sweden)

    John ePeever

    2015-05-01

    Full Text Available REM sleep is generated and maintained by the interaction of a variety of neurotransmitter systems in the brainstem, forebrain and hypothalamus. Within these circuits lies a core region that is active during REM sleep, known as the subcoeruleus nucleus (SubC or sublaterodorsal nucleus. It is hypothesized that glutamatergic SubC neurons regulate REM sleep and its defining features such as muscle paralysis and cortical activation. REM sleep paralysis is initiated when glutamatergic SubC activate neurons in the ventral medial medulla (VMM, which causes release of GABA and glycine onto skeletal motoneurons. REM sleep timing is controlled by activity of GABAergic neurons in the ventrolateral periaqueductal gray (vlPAG and dorsal paragigantocellular reticular nucleus (DPGi as well as melanin-concentrating hormone (MCH neurons in the hypothalamus and cholinergic cells in the laterodorsal (LDT and pedunculo-pontine tegmentum (PPT in the brainstem. Determining how these circuits interact with the SubC is important because breakdown in their communication is hypothesized to underlie cataplexy/narcolepsy and REM sleep behaviour disorder (RBD. This review synthesizes our current understanding of mechanisms generating healthy REM sleep and how dysfunction of these circuits contributes to common REM sleep disorders such as cataplexy/narcolepsy and RBD.

  12. REKAYASA MESIN PENCETAK KAMPAS REM SERAT PULP NON ASBESTOS

    Directory of Open Access Journals (Sweden)

    Wawan Kartiwa Haroen

    2017-04-01

    Full Text Available Mesin  pembuat  kampas rem non asbestos berbahan serat pulp merupakan kampas hasil inovasi komponen otomotif  yang memiliki  keunggulan diantaranya, sifat gesek tinggi, tahan panas dan bebas asbes.     Telah dilakukan penelitian perekayasaan mesin  pembuat kampas rem berbahan serat pulp skala kecil, dapat digunakan langsung sebagai alat produksi skala kecil atau untuk mensosialisasikan  proses penelitian berbagai variasi bahan baku kampas rem.      Mesin ini terdiri dari dua bagian   yaitu mesin pengurai serat , pengaduk dan  pembuat  kampas rem serat pulp jenis rem cakram (disk brake. Mesin pengurai   berfungsi untuk memisahkan serat pulp menjadi serat yang terurai secara individu, mesin pengaduk berfungsi untuk mengaduk atau mencampur anatar serat  pulp dengan bahan aditif pengikat. Alat pengurai dan pengaduk dioperasikan dengan motor listrik 1.000 watt, dilengkapi pengatur kecepatan (reducer speed dan screw  feeder. Alat pembuat kampas rem dioperasikan oleh motor listrik  2.000 watt, pompa press 10 ton dan  pemanas 40-200 oC.  Mesin ini dioperasikan semi otomatis dengan konsumsi listrik 3.000 watt,  dilengkapi pengontrol  temperatur, pengatur kecepatan putaran  (rpm dan  pengatur daya tekan. Mesin pencetak kampas rem hasil rekayasa dapat memproduksi 10-15 specimen kampas rem cakram /jam.

  13. Are animal models predictive for humans?

    Directory of Open Access Journals (Sweden)

    Greek Ray

    2009-01-01

    Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

  14. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  15. Medical image of the week: alpha intrusion into REM sleep

    Directory of Open Access Journals (Sweden)

    Shetty S

    2015-12-01

    Full Text Available A 45-year-old woman with a past medical history of hypertension and chronic headaches was referred to the sleep laboratory for high clinical suspicion for sleep apnea based on a history of snoring, witnessed apnea and excessive daytime sleepiness. An overnight sleep study was performed. Images during N3 Sleep and REM sleep are shown (Figures 1 and 2. Alpha intrusion in delta sleep is seen in patients with fibromyalgia, depression, chronic fatigue syndrome, anxiety disorder, and primary sleep disorders like psychophysiological insomnia, obstructive sleep apnea, circadian disorders and narcolepsy (1. Bursts of alpha waves during REM sleep may be more common during phasic REM than tonic REM. The REM alpha bursts (alpha activity lasting at least 3 seconds without an increase in EMG amplitude may represent microarousals (2. Hypersynchronous theta activity should be differentiated from the spike and waveform activity seen in seizures.

  16. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2014-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  17. A comparative simulation of feed and bleed operation during the total loss of feedwater event by RELAP5/MOD3 and CEFLASH-4AS/REM computer codes

    International Nuclear Information System (INIS)

    Kwon, Y.M.; Ro, T.S.; Song, J.H.

    1995-01-01

    The Ulchin 3 and 4 nuclear power plants, which are two-loop 2,825 MW(thermal) pressurized water reactors designed by the Korea Atomic Energy Research Institute, adopted a safety depressurization system (SDS) to mitigate the beyond-design-basis event of a total loss of feedwater (TLOFW). A comparative simulation by the CEFLASH-4AS/REM and RELAP5/MOD3 computer codes for the TLOFW event without operator recovery and the TLOFW event with feed and bleed (F and B) operation is performed for Ulchin 3 and 4. In the analyses, the SDS bleed paths are modeled by orifices located on the top of the pressurizer, where the analytical area of the bleed path is based on the Ulchin 3 and 4 SDS design flow capacity. An additional case, where the SDS piping and valves are modeled explicitly, is considered for the RELAP5 analysis. The predictions by the CEFLASH-4AS/REM of the transient two-phase system behavior show good qualitative and quantitative agreement with those by the RELAP5 simulation. The RELAP5 case with explicit piping results in less repressurization and lower reactor coolant system pressure than that of the case without explicit SDS modeling. However, the two cases of RELAP5 analyses result in essentially the same transient scenarios for TLOFW with F and B operation. The results of the simulation demonstrate the validity of the Ulchin 3 and 4 design approach, which employs CEFLASH-4AS/REM computer code and SDS bleed paths modeled by orifices located on the top of the pressurizer. The results also indicate that the decay heat removal and core inventory makeup function can be successfully accomplished by F and B operation by using the SDS for Ulchin 3 and 4

  18. L-carnitine prevents memory impairment induced by chronic REM-sleep deprivation.

    Science.gov (United States)

    Alzoubi, Karem H; Rababa'h, Abeer M; Owaisi, Amani; Khabour, Omar F

    2017-05-01

    Sleep deprivation (SD) negatively impacts memory, which was related to oxidative stress induced damage. L-carnitine is a naturally occurring compound, synthesized endogenously in mammalian species and known to possess antioxidant properties. In this study, the effect of L-carnitine on learning and memory impairment induced by rapid eye movement sleep (REM-sleep) deprivation was investigated. REM-sleep deprivation was induced using modified multiple platform model (8h/day, for 6 weeks). Simultaneously, L-carnitine was administered (300mg/kg/day) intraperitoneally for 6 weeks. Thereafter, the radial arm water maze (RAWM) was used to assess spatial learning and memory. Additionally, the hippocampus levels of antioxidant biomarkers/enzymes: reduced glutathione (GSH), oxidized glutathione (GSSG), GSH/GSSG ratio, glutathione peroxidase (GPx), catalase, and superoxide dismutase (SOD) and thiobarbituric acid reactive substance (TBARS) were assessed. The results showed that chronic REM-sleep deprivation impaired both short- and long-term memory (Psleep deprivation induced reduction in the hippocampus ratio of GSH/GSSG, activity of catalase, GPx, and SOD. No change was observed in TBARS among tested groups (P>0.05). In conclusion, chronic REM-sleep deprivation induced memory impairment, and treatment with L-carnitine prevented this impairment through normalizing antioxidant mechanisms in the hippocampus. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  20. Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models.

    Science.gov (United States)

    Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa

    2006-04-01

    A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.

  1. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  2. A revised prediction model for natural conception.

    Science.gov (United States)

    Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C

    2017-06-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.

  3. [Trazodone in REM sleep behavior disorder].

    Science.gov (United States)

    Chica-Urzola, Heydy Luz

    2015-01-01

    This case concerns an elderly man with a REM sleep behavior disorder, who was initially offered a pharmacological treatment with clonazepam, recommended by other articles, but with poor adherence due to its adverse reactions and persistence of symptoms. He was then offered a treatment with Trazodone was offered, achieving a complete remission of symptoms, with no reported side effects. It is clear that Trazodone has no known indication for this type of disorder; nevertheless, it was considered in this case because of its pharmacological profile, obtaining satisfactory results. Further research is needed in order to document thoroughly the mechanisms of action, efficacy and utility of this molecule in cases such as the one presented. Copyright © 2015 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

  4. Autonomic symptoms in idiopathic REM behavior disorder

    DEFF Research Database (Denmark)

    Ferini-Strambi, Luigi; Oertel, Wolfgang; Dauvilliers, Yves

    2014-01-01

    to study the disorders of the autonomic nervous system in Parkinson's disease (PD) patients, the SCOPA-AUT, was administered to all the patients and controls. The SCOPA-AUT consists of 25 items assessing the following domains: gastrointestinal, urinary, cardiovascular, thermoregulatory, pupillomotor......Patients with idiopathic REM sleep behavior disorder (iRBD) are at very high risk of developing neurodegenerative synucleinopathies, which are disorders with prominent autonomic dysfunction. Several studies have documented autonomic dysfunction in iRBD, but large-scale assessment of autonomic...... symptoms has never been systematically performed. Patients with polysomnography-confirmed iRBD (318 cases) and controls (137 healthy volunteers and 181 sleep center controls with sleep diagnoses other than RBD) were recruited from 13 neurological centers in 10 countries from 2008 to 2011. A validated scale...

  5. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  6. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  7. Antidepressants Increase REM Sleep Muscle Tone in Patients with and without REM Sleep Behavior Disorder.

    Science.gov (United States)

    McCarter, Stuart J; St Louis, Erik K; Sandness, David J; Arndt, Katlyn; Erickson, Maia; Tabatabai, Grace; Boeve, Bradley F; Silber, Michael H

    2015-06-01

    REM sleep behavior disorder (RBD) is associated with antidepressant treatment, especially in younger patients; but quantitative REM sleep without atonia (RSWA) analyses of psychiatric RBD patients remain limited. We analyzed RSWA in adults receiving antidepressants, with and without RBD. We comparatively analyzed visual, manual, and automated RSWA between RBD and control groups. RSWA metrics were compared between groups, and regression was used to explore associations with clinical variables. Tertiary-care sleep center. Participants included traditional RBD without antidepressant treatment (n = 30, 15 Parkinson disease [PD-RBD] and 15 idiopathic); psychiatric RBD receiving antidepressants (n = 30); and adults without RBD, including antidepressant-treated psychiatric (n = 30), untreated psychiatric (n = 15), and OSA (n = 60) controls. N/A. RSWA was highest in traditional and psychiatric RBD, intermediate in treated psychiatric controls, and lowest in untreated psychiatric and OSA controls (P sleep without atonia (RSWA) even without REM sleep behavior disorder (RBD), suggesting that antidepressants, not depression, promote RSWA. Differences in RSWA distribution and type were also seen, with higher anterior tibialis RSWA in antidepressant-treated patients and higher tonic RSWA in Parkinson disease-RBD patients, which could aid distinction between RBD subtypes. These findings suggest that antidepressants may mediate different RSWA mechanisms or, alternatively, that RSWA type and distribution evolve during progressive neurodegeneration. Further prospective RSWA analyses are necessary to clarify the relationships between antidepressant treatment, psychiatric disease, and RBD. © 2015 Associated Professional Sleep Societies, LLC.

  8. Targeted memory reactivation of newly learned words during sleep triggers REM-mediated integration of new memories and existing knowledge.

    Science.gov (United States)

    Tamminen, Jakke; Lambon Ralph, Matthew A; Lewis, Penelope A

    2017-01-01

    Recent memories are spontaneously reactivated during sleep, leading to their gradual strengthening. Whether reactivation also mediates the integration of new memories with existing knowledge is unknown. We used targeted memory reactivation (TMR) during slow-wave sleep (SWS) to selectively cue reactivation of newly learned spoken words. While integration of new words into their phonological neighbourhood was observed in both cued and uncued words after sleep, TMR-triggered integration was predicted by the time spent in rapid eye movement (REM) sleep. These data support complementary roles for SWS and REM in memory consolidation. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...

  10. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  11. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  12. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  13. Predictive modeling in homogeneous catalysis: a tutorial

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2010-01-01

    Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some

  14. Model predictive control of smart microgrids

    DEFF Research Database (Denmark)

    Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.

    2014-01-01

    required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...

  15. Feedback model predictive control by randomized algorithms

    NARCIS (Netherlands)

    Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep

    2001-01-01

    In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with

  16. A Robustly Stabilizing Model Predictive Control Algorithm

    Science.gov (United States)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  17. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...

  18. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...

  19. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  20. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  1. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang

    2016-01-01

    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  2. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  3. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Genetic models of homosexuality: generating testable predictions

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

  5. El trastorno de conducta del sueño rem

    OpenAIRE

    Alex Iranzo De Riquer, Dr.

    2013-01-01

    El trastorno de conducta durante el sueño REM (TCSR) se caracteriza por conductas motoras vigorosas, pesadillas y ausencia de atonía muscular durante el sueño REM. Se debe a la disfunción directa o indirecta de las estructuras del tronco cerebral que regulan el sueño REM, especialmente el núcleo subceruleus. El TCSR puede ser idiopático o asociado a enfermedades neurológicas como la enfermedad de Parkinson (EP), la demencia con cuerpos de Lewy (DCL), la atrofia multisistémica (AMS) y la narco...

  6. Form Follows Function: A Model for Clinical Supervision of Genetic Counseling Students.

    Science.gov (United States)

    Wherley, Colleen; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

    2015-10-01

    Supervision plays a vital role in genetic counselor training, yet models describing genetic counseling supervision processes and outcomes are lacking. This paper describes a proposed supervision model intended to provide a framework to promote comprehensive and consistent clinical supervision training for genetic counseling students. Based on the principle "form follows function," the model reflects and reinforces McCarthy Veach et al.'s empirically derived model of genetic counseling practice - the "Reciprocal Engagement Model" (REM). The REM consists of mutually interactive educational, relational, and psychosocial components. The Reciprocal Engagement Model of Supervision (REM-S) has similar components and corresponding tenets, goals, and outcomes. The 5 REM-S tenets are: Learning and applying genetic information are key; Relationship is integral to genetic counseling supervision; Student autonomy must be supported; Students are capable; and Student emotions matter. The REM-S outcomes are: Student understands and applies information to independently provide effective services, develop professionally, and engage in self-reflective practice. The 16 REM-S goals are informed by the REM of genetic counseling practice and supported by prior literature. A review of models in medicine and psychology confirms the REM-S contains supervision elements common in healthcare fields, while remaining unique to genetic counseling. The REM-S shows promise for enhancing genetic counselor supervision training and practice and for promoting research on clinical supervision. The REM-S is presented in detail along with specific examples and training and research suggestions.

  7. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  8. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  9. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  10. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  11. Treatment of REM Sleep Behavior Disorder.

    Science.gov (United States)

    Jung, Youngsin; St Louis, Erik K

    2016-11-01

    REM sleep behavior disorder (RBD) is a common parasomnia disorder affecting between 1 and 7 % of community-dwelling adults, most frequently older adults. RBD is characterized by nocturnal complex motor behavior and polysomnographic REM sleep without atonia. RBD is strongly associated with synucleinopathy neurodegeneration. The approach to RBD management is currently twofold: symptomatic treatment to prevent injury and prognostic counseling and longitudinal follow-up surveillance for phenoconversion toward overt neurodegenerative disorders. The focus of this review is symptomatic treatment for injury prevention. Injury occurs in up to 55 % of patients prior to treatment, even when most behaviors seem to be infrequent or minor, so patients with RBD should be treated promptly following diagnosis to prevent injury risk. A sound evidence basis for symptomatic treatment of RBD remains lacking, and randomized controlled treatment trials are needed. Traditional therapeutic mainstays with relatively robust retrospective case series level evidence include melatonin and clonazepam, which appear to be equally effective, although melatonin is more tolerable. Melatonin also has one small randomized controlled crossover trial supporting its use for RBD treatment. Melatonin dosed 3-12 mg at bedtime should be considered as the first-line therapy, followed by clonazepam 0.25-2.0 mg at bedtime if initial melatonin is judged ineffective or intolerable. However, neither agent is likely to completely stop dream enactment behaviors, so choosing a moderate target dosage of melatonin 6 mg or clonazepam 0.5 mg, or the highest tolerable dosage that reduces attack frequency and avoids adverse effects from overtreatment, is currently the most reasonable strategy. Alternative second- and third-line therapies with anecdotal efficacy include temazepam, lorazepam, zolpidem, zopiclone, pramipexole, donepezil, ramelteon, agomelatine, cannabinoids, and sodium oxybate. A novel non

  12. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  13. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  14. Field dependence and the effect of REM deprivation on thirst.

    Science.gov (United States)

    Koulack, D; De Koninck, J; Oczkowski, G

    1978-04-01

    Recently a number of studies have concerned the possible function of rapid eye movement (REM) sleep and the mastery of stress. The present study was designed to explore the possibility that REM sleep might play a function in reducing the potency of a stressful physiological stimulus, thirst, as well as the possibility that such a function might be specific to individuals falling at different points along the field-dependence dimension. While there was no difference between REM deprivation and non-REM awakening nights in subsequent morning thirst, there was a significant interaction between field dependence and night on morning thirst measures for 10 college students. These results are discussed in light of previous work on stylistic differences in dreaming and their possible role in adaptation to stress.

  15. Comorbidity and medication in REM sleep behavior disorder

    DEFF Research Database (Denmark)

    Frauscher, Birgit; Jennum, Poul; Ju, Yo-El S

    2014-01-01

    OBJECTIVE: This controlled study investigated associations between comorbidity and medication in patients with polysomnographically confirmed idiopathic REM sleep behavior disorder (iRBD), using a large multicenter clinic-based cohort. METHODS: Data of a self-administered questionnaire...

  16. Representation of the Self in REM and NREM Dreams

    Science.gov (United States)

    McNamara, Patrick; McLaren, Deirdre; Durso, Kate

    2008-01-01

    The authors hypothesized that representations of the Self (or the dreamer) in dreams would change systematically, from a prereflective form of Self to more complex forms, as a function of both age and sleep state (REM vs. non-REM). These hypotheses were partially confirmed. While the authors found that all the self-concept-related dream content indexes derived from the Hall/Van de Castle dream content scoring system did not differ significantly between the dreams of children and adults, adult Selves were more likely to engage in “successful” social interactions. The Self never acted as aggressor in NREM dream states and was almost always the befriender in friendly interactions in NREM dreams. Conversely, the REM-related dream Self preferred aggressive encounters. Our results suggests that while prereflective forms of Self are the norm in children’s dreams, two highly complex forms of Self emerge in REM and NREM dreams. PMID:19169371

  17. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  18. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  19. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  20. Predictive modelling of evidence informed teaching

    OpenAIRE

    Zhang, Dell; Brown, C.

    2017-01-01

    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...

  1. A Predictive Model for Cognitive Radio

    Science.gov (United States)

    2006-09-14

    response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery

  2. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  3. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  4. Medical image of the week: REM without atonia

    Directory of Open Access Journals (Sweden)

    Bartell J

    2015-03-01

    Full Text Available No abstract available. Article truncated after 150 words. A 78 year-old man with a past medical history of Parkinson’s disease (PD presented to the sleep medicine clinic for evaluation of obstructive sleep apnea (OSA. An overnight polysomnogram (PSG study was consistent with sleep apnea and revealed frequent leg and arm movements and sleep-talking during rapid eye movement (REM sleep. REM sleep behavior disorder (RBD is a parasomnia characterized by repeated episodes of abnormal behavior occurring during REM sleep (1,2. On PSG, REM sleep without atonia is seen while features of “normal REM” such as number of REM periods and REM cycling remain intact (2. RBD emerges most often in the context of alpha-synucleinopathies, and occurs in up to 60% of PD patients (3. The presence of RBD may be an important preclinical symptom prior to the onset of PD. Cases of PD with RBD are associated with a unique phenotype with an older age of onset, longer disease ...

  5. A dual-detector extended range rem-counter

    CERN Document Server

    Ferrarini, M; Silari, M; Agosteo, S

    2010-01-01

    The design and characterization of a dual-detector spherical rem counter is discussed in this paper. The rem counter is based on a polythene sphere with lead and cadmium insets, designed to host at its centre either an active (He-3 SP9 proportional counter) or a passive (CR39 + B-10 radiator) thermal neutron detector. Its sensitivity ranges from thermal energies up to 1 GeV. A Monte Carlo characterization of this dual-detector rem counter has shown no significant change in the shape of the response curve obtained with the two detectors. The rem counter has been calibrated with a Pu-Be source. An intercomparison in a high-energy neutron field has been carried out at the CERF facility at CERN among the rem counter in the two configurations, two commercial units and the original version of the active LINUS in use at CERN. Both the active and passive versions of the rem counter agree, within the statistical uncertainties, with the CERN LINUS and with the facility reference values. Both versions of the instrument ...

  6. Vocabulary learning benefits from REM after slow-wave sleep.

    Science.gov (United States)

    Batterink, Laura J; Westerberg, Carmen E; Paller, Ken A

    2017-10-01

    Memory reactivation during slow-wave sleep (SWS) influences the consolidation of recently acquired knowledge. This reactivation occurs spontaneously during sleep but can also be triggered by presenting learning-related cues, a technique known as targeted memory reactivation (TMR). Here we examined whether TMR can improve vocabulary learning. Participants learned the meanings of 60 novel words. Auditory cues for half the words were subsequently presented during SWS in an afternoon nap. Memory performance for cued versus uncued words did not differ at the group level but was systematically influenced by REM sleep duration. Participants who obtained relatively greater amounts of REM showed a significant benefit for cued relative to uncued words, whereas participants who obtained little or no REM demonstrated a significant effect in the opposite direction. We propose that REM after SWS may be critical for the consolidation of highly integrative memories, such as new vocabulary. Reactivation during SWS may allow newly encoded memories to be associated with other information, but this association can include disruptive linkages with pre-existing memories. Subsequent REM sleep may then be particularly beneficial for integrating new memories into appropriate pre-existing memory networks. These findings support the general proposition that memory storage benefits optimally from a cyclic succession of SWS and REM. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. REM Sleep Behavior Disorder and Prodromal Neurodegeneration - Where are We Headed?

    Directory of Open Access Journals (Sweden)

    Ronald B. Postuma

    2013-04-01

    Full Text Available Rapid eye movement (REM sleep behavior disorder (RBD is characterized by loss of normal atonia during REM sleep, such that patients appear to act out their dreams. The most important implication of research into this area is that patients with idiopathic RBD are at very high risk of developing synucleinmediated neurodegenerative disease (Parkinson's disease [PD], dementia with Lewy bodies [DLB], and multiple system atrophy, with risk estimates that approximate 40–65% at 10 years. Thus, RBD disorder is a very strong feature of prodromal synucleinopathy. This provides several opportunities for future research. First, patients with REM sleep behavior disorder can be studied to test other predictors of disease, which could potentially be applied to the general population. These studies have demonstrated that olfactory loss, decreased color vision, slowing on quantitative motor testing, and abnormal substantia nigra neuroimaging findings can predict clinical synucleinopathy. Second, prospectively studying patients with RBD allows a completely unprecedented opportunity to directly evaluate patients as they transition into clinical neurodegenerative disease. Studies assessing progression of markers of neurodegeneration in prodromal PD are beginning to appear. Third, RBD are very promising subjects for neuroprotective therapy trials because they have a high risk of disease conversion with a sufficiently long latency, which provides an opportunity for early intervention. As RBD research expands, collaboration between centers will become increasingly essential.

  8. Predictive Modeling by the Cerebellum Improves Proprioception

    Science.gov (United States)

    Bhanpuri, Nasir H.; Okamura, Allison M.

    2013-01-01

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance. PMID:24005283

  9. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  10. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  11. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  12. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  13. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  14. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  15. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  16. Predictive Models for Normal Fetal Cardiac Structures.

    Science.gov (United States)

    Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A

    2016-12-01

    Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational

  17. Genetic inactivation of glutamate neurons in the rat sublaterodorsal tegmental nucleus recapitulates REM sleep behaviour disorder.

    Science.gov (United States)

    Valencia Garcia, Sara; Libourel, Paul-Antoine; Lazarus, Michael; Grassi, Daniela; Luppi, Pierre-Hervé; Fort, Patrice

    2017-02-01

    -enacting behaviours. These animals display symptoms and behaviours during paradoxical sleep that closely mimic human REM sleep behaviour disorder. Altogether, our data demonstrate that glutamate sublaterodorsal tegmental nucleus neurons generate muscle atonia during paradoxical sleep likely through descending projections to glycine/GABA premotor neurons in the ventral medulla. Although playing a role in paradoxical sleep regulation, they are, however, not necessary for inducing the state itself. The present work further validates a potent new preclinical REM sleep behaviour disorder model that opens avenues for studying and treating this disabling sleep disorder, and advances potential regions implicated in prodromal stages of synucleinopathies such as Parkinson's disease. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  18. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  19. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  20. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  1. [Endometrial cancer: Predictive models and clinical impact].

    Science.gov (United States)

    Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile

    2017-12-01

    In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  2. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  3. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  4. Cutamesine Overcomes REM Sleep Deprivation-Induced Memory Loss: Relationship to Sigma-1 Receptor Occupancy.

    Science.gov (United States)

    Ramakrishnan, Nisha K; Schepers, Marianne; Luurtsema, Gert; Nyakas, Csaba J; Elsinga, Philip H; Ishiwata, Kiichi; Dierckx, Rudi A J O; van Waarde, Aren

    2015-06-01

    Rapid eye movement (REM) sleep deprivation (SD) decreases cerebral sigma-1 receptor expression and causes cognitive deficits. Sigma-1 agonists are cognitive enhancers. Here, we investigate the effect of cutamesine treatment in the REM SD model. Sigma-1 receptor occupancy (RO) in the rat brain by cutamesine was determined using 1-[2-(3,4-dimethoxyphenethyl)]-4-(3-phenylpropyl)piperazine ([(11)C]SA4503) and positron emission tomography (PET), and tissue cutamesine levels were measured by ultra performance liquid chromatography (UPLC)-MS. RO was calculated from a Cunningham-Lassen plot, based on the total distribution volume of [(11)C]SA4503 determined by Logan graphical analysis. Cognitive performance was assessed using the passive avoidance (PA) test. Cutamesine at a dose of 1.0 mg/kg reversed REM SD-induced cognitive deficit and occupied 92 % of the sigma-1 receptor population. A lower dose (0.3 mg/kg) occupied 88 % of the receptors but did not significantly improve cognition. The anti-amnesic effect of cutamesine in this animal model may be related to longer exposure at a higher dose and/or drug binding to secondary targets.

  5. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  6. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  7. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  8. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...

  9. Screening for idiopathic REM sleep behavior disorder: usefulness of actigraphy.

    Science.gov (United States)

    Stefani, A; Heidbreder, A; Brandauer, E; Guaita, M; Neier, L M; Mitterling, T; Santamaria, J; Iranzo, A; Videnovic, A; Trenkwalder, C; Sixel-Döring, F; Wenning, G K; Chade, A; Poewe, W; Gershanik, O S; Högl, B

    2018-03-15

    To evaluate the utility of multimodal low-cost approaches including actigraphy, a wrist-worn device monitoring rest/activity cycles, in identifying patients with idiopathic REM sleep behavior disorder (iRBD). Seventy patients diagnosed with sleep disorders causing different motor manifestations during sleep (iRBD, sleep apnea, restless legs syndrome) and 20 subjects without any relevant motor manifestation during sleep, underwent video-polysomnography (vPSG) and two-week actigraphy, completed six validated RBD screening questionnaires, and sleep apps use was assessed. Actigraphy was analyzed automatically, and visually by seven blinded sleep medicine experts who rated as "no", "possible" and "probable" RBD. Quantitative actigraphy analysis distinguished patients from controls, but not between patients with different types of motor activity during sleep. Visual actigraphy rating by blinded experts in sleep medicine using pattern recognition identified vPSG confirmed iRBD with 85-95% sensitivity, 79-91% specificity, 81-91% accuracy, 57.7±11.3% positive predictive value, 95.1±3.3% negative predictive value, 6.8±2.2 positive likelihood ratio, 0.14±0.05 negative likelihood ratio and 0.874-0.933 AUC. AUC of the best performing questionnaire was 0.868. Few patients used sleep apps, therefore their potential utility in the evaluated patients' groups is limited. Visual analysis of actigraphy using pattern recognition can identify subjects with iRBD, is able to distinguish iRBD from other motor activity during sleep, even when patients are not aware of the disease in contrast to questionnaires. Therefore, actigraphy can be a reliable screening instrument for RBD potentially useful in the general population. Therefore, actigraphy can be a reliable screening instrument for RBD potentially useful in the general population.

  10. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  11. A Direct Test of the Differentiation Mechanism: REM, BCDMEM, and the Strength-Based Mirror Effect in Recognition Memory

    Science.gov (United States)

    Starns, Jeffrey J.; White, Corey N.; Ratcliff, Roger

    2010-01-01

    We explore competing explanations for the reduction in false alarm rate observed when studied items are strengthened. Some models, such as Retrieving Effectively from Memory (REM; Shiffrin & Steyvers, 1997), attribute the false alarm rate reduction to differentiation, a process in which strengthening memory traces at study directly reduces the…

  12. Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...... model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model...

  13. REM sleep respiratory behaviours mental content in narcoleptic lucid dreamers.

    Science.gov (United States)

    Oudiette, Delphine; Dodet, Pauline; Ledard, Nahema; Artru, Emilie; Rachidi, Inès; Similowski, Thomas; Arnulf, Isabelle

    2018-02-08

    Breathing is irregular during rapid eye-movement (REM) sleep, whereas it is stable during non-REM sleep. Why this is so remains a mystery. We propose that irregular breathing has a cortical origin and reflects the mental content of dreams, which often accompany REM sleep. We tested 21 patients with narcolepsy who had the exceptional ability to lucid dream in REM sleep, a condition in which one is conscious of dreaming during the dream and can signal lucidity with an ocular code. Sleep and respiration were monitored during multiple naps. Participants were instructed to modify their dream scenario so that it involved vocalizations or an apnoea, -two behaviours that require a cortical control of ventilation when executed during wakefulness. Most participants (86%) were able to signal lucidity in at least one nap. In 50% of the lucid naps, we found a clear congruence between the dream report (e.g., diving under water) and the observed respiratory behaviour (e.g., central apnoea) and, in several cases, a preparatory breath before the respiratory behaviour. This suggests that the cortico-subcortical networks involved in voluntary respiratory movements are preserved during REM sleep and that breathing irregularities during this stage have a cortical/subcortical origin that reflects dream content.

  14. REM SLEEP REBOUND AS AN ADAPTIVE RESPONSE TO STRESSFUL SITUATIONS

    Directory of Open Access Journals (Sweden)

    Deborah eSuchecki

    2012-04-01

    Full Text Available Stress and sleep are related to each other in a bidirectional way. If on one hand poor or inadequate sleep exacerbates emotional, behavioral and stress-related responses, on the other hand acute stress induces sleep rebound, most likely as a form to cope with the adverse stimuli. Chronic stress, conversely, has been claimed to be one of the triggering factors of emotional-related sleep disorders, such as insomnia, depressive- and anxiety-disorders. These outcomes are dependent on individual psychobiological characteristics, which confer more complexity to the stress-sleep relationship. Its neurobiology has only recently begun to be explored, through animal models, which are also valuable for the development of potential therapeutic agents and preventive actions. This review seeks to present data on the effects of stress on sleep and the different approaches used to study this relationship as well as possible neurobiological underpinnings and mechanisms involved. The results of numerous studies in humans and animals indicate that increased sleep, especially the REM phase, following a stressful situation is an important adaptive behavior for recovery. However, this endogenous advantage appears to be impaired in human beings and rodent strains that exhibit high levels of anxiety and anxiety-like behavior.

  15. REM Sleep Behavior and Motor Findings in Parkinson's Disease: A Cross-sectional Analysis

    Directory of Open Access Journals (Sweden)

    Abhimanyu Mahajan

    2014-06-01

    Full Text Available Background: Parkinson's disease (PD represents a major public health challenge that will only grow in our aging population. Understanding the connection between PD and associated prodromal conditions, such as rapid eye movement sleep behavioral disorder (RBD, is critical to identifying prevention strategies. However, the relationship between RBD and severity of motor findings in early PD is unknown. This study aims to examine this relationship. Methods: The study population consisted of 418 PD patients who completed the Movement Disorders Society‐United Parkinson's Disease Rating Scale (MDS‐UPDRS and rapid eye movement sleep (REM disorder questionnaires at the baseline visit of the Michael J. Fox's Parkinson's Progression Markers Initiative (PPMI. Cross‐sectional analysis was carried out to assess the association between REM Sleep Behavior Screening Questionnaire score and MDS UPDRS‐3 (motor score categories. Correlation with a higher score category was described as “worse motor findings”. A score of 5 on the REM disorder questionnaire was defined as predictive of RBD.Results: Out of the 418 PD patients, 113 (27.0% had RBD. With univariate logistic regression analysis, individuals with scores predictive of RBD were 1.66 times more likely to have worse motor findings (p = 0.028. Even with age, gender, and Geriatric Depression Scale scores taken into account, individuals with scores predictive of RBD were 1.69 times more likely to have worse motor findings (p = 0.025.Discussion: PD patients with RBD symptoms had worse motor findings than those unlikely to have RBD. This association provides further evidence for the relationship between RBD and PD.

  16. Predictive modeling: potential application in prevention services.

    Science.gov (United States)

    Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G

    2015-05-01

    In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  17. A systematic search of sudden pressure drops on Gale crater during two Martian years derived from MSL/REMS data

    Science.gov (United States)

    Ordonez-Etxeberria, Iñaki; Hueso, Ricardo; Sánchez-Lavega, Agustín

    2018-01-01

    The Mars Science Laboratory (MSL) rover carries a suite of meteorological detectors that constitute the Rover Environmental Monitoring Station (REMS) instrument. REMS investigates the meteorological conditions at Gale crater by obtaining high-frequency data of pressure, air and ground temperature, relative humidity, UV flux at the surface and wind intensity and direction with some limitations in the wind data. We have run a search of atmospheric pressure drops of short duration (works. The higher number of detections could be linked to a combination of different topography, higher altitudes (120 m above the landing site) and true inter-annual meteorological variability. We found 1129 events with a pressure drop larger than 0.5 Pa. Of these, 635 occurred during the local daytime (∼56%) and 494 were nocturnal. The most intense pressure drop (4.2 Pa) occurred at daytime on sol 1417 (areocentric solar longitude Ls = 195°) and was accompanied by a simultaneous decrease in the UV signal of 7.1%, pointing to a true dust devil. We also discuss similar but less intense simultaneous pressure and UV radiation drops that constitute 0.7% of all daytime events. Most of the intense daytime pressure drops with variations larger than 1.0 Pa occur when the difference between air and ground temperature is larger than 15 K. Statistically, the frequency of daytime pressure drops peaks close to noon (12:00-13:00 Local True Solar Time or LTST) with more events in spring and summer (Ls from 180° to 360°). The nocturnal sudden pressure drops concentrate in the 20:00-23:00 LTST time interval and they only occur in spring and summer. We interpret these nocturnal events as a consequence of local mechanically forced turbulence. This interpretation is consistent with published results from simulations with the MRAMS model (Rafkin et al., 2016) that predict a competition between local orographic circulation and global Hadley cell circulation at Gale crater at summer night-time that can

  18. Heuristic Modeling for TRMM Lifetime Predictions

    Science.gov (United States)

    Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.

    1996-01-01

    Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

  19. A Computational Model for Predicting Gas Breakdown

    Science.gov (United States)

    Gill, Zachary

    2017-10-01

    Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.

  20. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  1. Which method predicts recidivism best?: A comparison of statistical, machine learning, and data mining predictive models

    OpenAIRE

    Tollenaar, N.; van der Heijden, P.G.M.

    2012-01-01

    Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...

  2. A Temporally Controlled Inhibitory Drive Coordinates Twitch Movements during REM Sleep.

    Science.gov (United States)

    Brooks, Patricia L; Peever, John

    2016-05-09

    During REM sleep, skeletal muscles are paralyzed in one moment but twitch and jerk in the next. REM sleep twitches are traditionally considered random motor events that result from momentary lapses in REM sleep paralysis [1-3]. However, recent evidence indicates that twitches are not byproducts of REM sleep, but are in fact self-generated events that could function to promote motor learning and development [4-6]. If REM twitches are indeed purposefully generated, then they should be controlled by a coordinated and definable mechanism. Here, we used behavioral, electrophysiological, pharmacological, and neuroanatomical methods to demonstrate that an inhibitory drive onto skeletal motoneurons produces a temporally coordinated pattern of muscle twitches during REM sleep. First, we show that muscle twitches in adult rats are not uniformly distributed during REM sleep, but instead follow a well-defined temporal trajectory. They are largely absent during REM initiation but increase steadily thereafter, peaking toward REM termination. Next, we identify the transmitter mechanism that controls the temporal nature of twitch activity. Specifically, we show that a GABA and glycine drive onto motoneurons prevents twitch activity during REM initiation, but progressive weakening of this drive functions to promote twitch activity during REM termination. These results demonstrate that REM twitches are not random byproducts of REM sleep, but are instead rather coherently generated events controlled by a temporally variable inhibitory drive. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. REM behaviour disorder detection associated with neurodegenerative diseases

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sorensen, Gertrud; Zoetmulder, Marielle

    2010-01-01

    Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection...... is proposed by analyzing the motor activity during sleep. Method: A total number of twelve patients have been involved in this study, six normal controls and six patients diagnosed with Parkinsons Disease (PD) with RBD. All subjects underwent at least one ambulant polysomnographic (PSG) recording. The sleep...

  4. REM Behaviour Disorder Detection Associated with Neurodegerative Diseases

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sørensen, Gertrud Laura; Zoetmulder, Marielle

    2010-01-01

    Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection...... recordings were scored, according to the new sleep-scoring standard from the American Academy of Sleep Medicine, by two independent sleep specialists. A follow-up analysis of the scoring consensus between the two specialists has been conducted. Based on the agreement of the two manual scorings...

  5. REM behaviour disorder detection associated with neurodegenerative diseases

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sorensen, Gertrud; Zoetmulder, Marielle

    2010-01-01

    Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection...... recordings were scored, according to the new sleep-scoring standard from the American Academy of Sleep Medicine, by two independent sleep specialists. A follow-up analysis of the scoring consensus between the two specialists has been conducted. Based on the agreement of the two manual scorings...

  6. REM Sleep Behavior Disorder and Medico-legal Aspects

    Directory of Open Access Journals (Sweden)

    Nida Taşçılar

    2008-04-01

    Full Text Available Scientific BACKGROUND: Rapid eye movement (REM sleep behavior disorder (RBD is a parasomnia characterised by dream-enacting behaviors and loss of normal REM sleep muscle atonia. It could occur idiopathically or accompanying neurodegenerative diseases. “Acting out of dreams” permits violent or injurious behaviors. These behaviors could result in laceration, fractures, subdural haematomas and so on. In developed countries, the legal implications of these behaviors have been discussing and debating in medical and legal literature. But in Turkey, legal aspects of RBD have not discussed yet. CONCLUSION: In this review, the clinic, pathopysiology, therapy and medicolegal aspects of RBD is discussed

  7. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...

  8. Increased Motor Activity During REM Sleep Is Linked with Dopamine Function in Idiopathic REM Sleep Behaviour Disorder and Parkinson Disease

    DEFF Research Database (Denmark)

    Zoetmulder, Marielle; Nikolic, Miki; Biernat, Heidi

    2016-01-01

    STUDY OBJECTIVES: Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by impaired motor inhibition during REM sleep, and dream-enacting behavior. RBD is especially associated with α-synucleinopathies, such as Parkinson disease (PD). Follow-up studies have shown...... that patients with idiopathic RBD (iRBD) have an increased risk of developing an α-synucleinopathy in later life. Although abundant studies have shown that degeneration of the nigrostriatal dopaminergic system is associated with daytime motor function in Parkinson disease, only few studies have investigated...... in the putamen. In PD patients, EMG-activity was correlated to anti-Parkinson medication. CONCLUSIONS: Our results support the hypothesis that increased EMG-activity during REM sleep is at least partly linked to the nigrostriatal dopamine system in iRBD, and with dopamine function in PD....

  9. Increased Motor Activity During REM Sleep Is Linked with Dopamine Function in Idiopathic REM Sleep Behavior Disorder and Parkinson Disease

    DEFF Research Database (Denmark)

    Zoetmulder, Marielle; Nikolic, Miki; Biernat, Heidi B

    2016-01-01

    STUDY OBJECTIVES: Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by impaired motor inhibition during REM sleep, and dream-enacting behavior. RBD is especially associated with α-synucleinopathies, such as Parkinson disease (PD). Follow-up studies have shown...... that patients with idiopathic RBD (iRBD) have an increased risk of developing an α-synucleinopathy in later life. Although abundant studies have shown that degeneration of the nigrostriatal dopaminergic system is associated with daytime motor function in Parkinson disease, only few studies have investigated......-FP-CIT uptake in the putamen. In PD patients, EMG-activity was correlated to anti-Parkinson medication. CONCLUSIONS: Our results support the hypothesis that increased EMG-activity during REM sleep is at least partly linked to the nigrostriatal dopamine system in iRBD, and with dopamine function in PD....

  10. Model for predicting mountain wave field uncertainties

    Science.gov (United States)

    Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal

    2017-04-01

    Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of

  11. Model Predictive Control for an Industrial SAG Mill

    DEFF Research Database (Denmark)

    Ohan, Valeriu; Steinke, Florian; Metzger, Michael

    2012-01-01

    We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...

  12. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.

    2002-01-01

    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The

  13. Dealing with missing predictor values when applying clinical prediction models.

    NARCIS (Netherlands)

    Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.

    2009-01-01

    BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with

  14. Knockdown of orexin type 2 receptor in the lateral pontomesencephalic tegmentum of rats increases REM sleep

    OpenAIRE

    Chen, Lichao; McKenna, James T.; Bolortuya, Yunren; Brown, Ritchie E.

    2013-01-01

    Dysfunction of the orexin/hypocretin neurotransmitter system causes the sleep disorder narcolepsy, characterized by intrusion of rapid-eye-movement (REM) sleep-like events into normal wakefulness. The sites where orexins act to suppress REM sleep are incompletely understood. Previous studies suggested that the lateral pontomesencephalic tegmentum (lPMT) contains an important REM sleep inhibitory area, and proposed that orexins inhibit REM sleep via orexin type 2 receptors (OxR2) in this regio...

  15. The hypocretins (orexins mediate the “phasic” components of REM sleep: A new hypothesis

    Directory of Open Access Journals (Sweden)

    Pablo Torterolo

    2014-03-01

    The hypocretinergic neurons are active during wakefulness in conjunction with the presence of motor activity that occurs during survival-related behaviors. These neurons decrease their firing rate during non-REM sleep; however there is still controversy upon the activity and role of these neurons during REM sleep. Hence, in the present report we conducted a critical review of the literature of the hypocretinergic system during REM sleep, and hypothesize a possible role of this system in the generation of REM sleep.

  16. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  17. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  18. Predictive capabilities of various constitutive models for arterial tissue.

    Science.gov (United States)

    Schroeder, Florian; Polzer, Stanislav; Slažanský, Martin; Man, Vojtěch; Skácel, Pavel

    2018-02-01

    Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R 2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling

    Science.gov (United States)

    Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.

    2017-12-01

    The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found

  20. Environmental risk factors for REM sleep behavior disorder

    DEFF Research Database (Denmark)

    Postuma, R B; Montplaisir, J Y; Pelletier, A

    2012-01-01

    Idiopathic REM sleep behavior disorder is a parasomnia characterized by dream enactment and is commonly a prediagnostic sign of parkinsonism and dementia. Since risk factors have not been defined, we initiated a multicenter case-control study to assess environmental and lifestyle risk factors...

  1. Management of REM sleep behavior disorder: An evidence based review.

    Science.gov (United States)

    Devnani, Preeti; Fernandes, Racheal

    2015-01-01

    Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by dream enactment behavior resulting from a loss of REM skeletal muscle atonia. The neurobiology of REM sleep and the characteristic features of REM atonia have an important basis for understanding the aggravating etiologies the proposed pharmacological interventions in its management. This review outlines the evidence for behavioral and therapeutic measures along with evidence-based guidelines for their implementation, impact on falls, and effect on polysomnography (PSG) while highlighting the non-motor, autonomic, and cognitive impact of this entity. PubMed databases were reviewed upto May 2013 in peer-reviewed scientific literature regarding the pathophysiology and management of RBD in adults. The literature was graded according to the Oxford centre of evidence-based Medicine Levels. An early intervention that helps prevent consequences such as falls and provides a base for intervention with neuroprotective mechanisms and allocates a unique platform that RBD portrays with its high risk of disease conversion with a sufficiently long latency. RBD provides a unique platform with its high risk of disease conversion with a sufficiently long latency, providing an opportunity for early intervention both to prevent consequences such as falls and provide a base for intervention with neuroprotective mechanisms.

  2. Management of REM sleep behavior disorder: An evidence based review

    Directory of Open Access Journals (Sweden)

    Preeti Devnani

    2015-01-01

    Full Text Available Rapid eye movement (REM sleep behavior disorder (RBD is characterized by dream enactment behavior resulting from a loss of REM skeletal muscle atonia. The neurobiology of REM sleep and the characteristic features of REM atonia have an important basis for understanding the aggravating etiologies the proposed pharmacological interventions in its management. This review outlines the evidence for behavioral and therapeutic measures along with evidence-based guidelines for their implementation, impact on falls, and effect on polysomnography (PSG while highlighting the non-motor, autonomic, and cognitive impact of this entity. PubMed databases were reviewed upto May 2013 in peer-reviewed scientific literature regarding the pathophysiology and management of RBD in adults. The literature was graded according to the Oxford centre of evidence-based Medicine Levels. An early intervention that helps prevent consequences such as falls and provides a base for intervention with neuroprotective mechanisms and allocates a unique platform that RBD portrays with its high risk of disease conversion with a sufficiently long latency. RBD provides a unique platform with its high risk of disease conversion with a sufficiently long latency, providing an opportunity for early intervention both to prevent consequences such as falls and provide a base for intervention with neuroprotective mechanisms.

  3. Rapid Eye Movements (REMs) and visual dream recall in both congenitally blind and sighted subjects

    Science.gov (United States)

    Bértolo, Helder; Mestre, Tiago; Barrio, Ana; Antona, Beatriz

    2017-08-01

    Our objective was to evaluate rapid eye movements (REMs) associated with visual dream recall in sighted subjects and congenital blind. During two consecutive nights polysomnographic recordings were performed at subjects home. REMs were detected by visual inspection on both EOG channels (EOG-H, EOG-V) and further classified as occurring isolated or in bursts. Dream recall was defined by the existence of a dream report. The two groups were compared using t-test and also the two-way ANOVA and a post-hoc Fisher test (for the features diagnosis (blind vs. sighted) and dream recall (yes or no) as a function of time). The average of REM awakenings per subject and the recall ability were identical in both groups. CB had a lower REM density than CS; the same applied to REM bursts and isolated eye movements. In the two-way ANOVA, REM bursts and REM density were significantly different for positive dream recall, mainly for the CB group and for diagnosis; furthermore for both features significant results were obtained for the interaction of time, recall and diagnosis; the interaction of recall and time was however, stronger. In line with previous findings the data show that blind have lower REMs density. However the ability of dream recall in congenitally blind and sighted controls is identical. In both groups visual dream recall is associated with an increase in REM bursts and density. REM bursts also show differences in the temporal profile. REM visual dream recall is associated with increased REMs activity.

  4. Predictive models for moving contact line flows

    Science.gov (United States)

    Rame, Enrique; Garoff, Stephen

    2003-01-01

    Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.

  5. Developmental prediction model for early alcohol initiation in Dutch adolescents

    NARCIS (Netherlands)

    Geels, L.M.; Vink, J.M.; Beijsterveldt, C.E.M. van; Bartels, M.; Boomsma, D.I.

    2013-01-01

    Objective: Multiple factors predict early alcohol initiation in teenagers. Among these are genetic risk factors, childhood behavioral problems, life events, lifestyle, and family environment. We constructed a developmental prediction model for alcohol initiation below the Dutch legal drinking age

  6. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  7. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  8. Predictability in models of the atmospheric circulation

    NARCIS (Netherlands)

    Houtekamer, P.L.

    1992-01-01

    It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error

  9. Morning REM Sleep Naps Facilitate Broad Access to Emotional Semantic Networks

    Science.gov (United States)

    Carr, Michelle; Nielsen, Tore

    2015-01-01

    Study Objectives: The goals of the study were to assess semantic priming to emotion and nonemotion cue words using a novel measure of associational breadth for participants who either took rapid eye movement (REM) or nonrapid eye movement (NREM) naps or who remained awake, and to assess the relation of priming to REM sleep consolidation and REM sleep inertia effects. Design: The associational breadth task was applied in both a priming condition, where cue words were signaled to be memorized prior to sleep (primed), and a nonpriming condition, where cue words were not memorized (nonprimed). Cue words were either emotional (positive, negative) or nonemotional. Participants were randomly assigned to either an awake (WAKE) or a sleep condition, which was subsequently split into NREM or REM groups depending on stage at awakening. Setting: Hospital-based sleep laboratory. Participants: Fifty-eight healthy participants (22 male) ages 18 to 35 y (mean age = 23.3 ± 4.08 y). Measurements and Results: The REM group scored higher than the NREM or WAKE groups on primed, but not nonprimed emotional cue words; the effect was stronger for positive than for negative cue words. However, REM time and percent correlated negatively with degree of emotional priming. Priming occurred for REM awakenings but not for NREM awakenings, even when the latter sleep episodes contained some REM sleep. Conclusions: Associational breadth may be selectively consolidated during REM sleep for stimuli that have been tagged as important for future memory retrieval. That priming decreased with REM time and was higher only for REM sleep awakenings is consistent with two explanatory REM sleep processes: REM sleep consolidation serving emotional downregulation and REM sleep inertia. Citation: Carr M, Nielsen T. Morning REM sleep naps facilitate broad access to emotional semantic networks. SLEEP 2015;38(3):433–443. PMID:25409100

  10. Required Collaborative Work in Online Courses: A Predictive Modeling Approach

    Science.gov (United States)

    Smith, Marlene A.; Kellogg, Deborah L.

    2015-01-01

    This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…

  11. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  12. Effects of daytime activity upon the timing of REM sleep periods during a night.

    Science.gov (United States)

    Kobayashi, T; Ishikawa, T; Arakawa, K

    1998-04-01

    The effects of mental and physical daytime activities upon REM sleep cycle (REM cycle) during 1 night was studied in five university students (aged 19-25 years). Mental activity with high tension has effects upon the timing of REM sleep periods in the later part of the night. Physical activity has effects upon the timing of REM sleep in the early part of the night. The result suggests that mental and/or physical activities during daytime modulate REM cycle during the night.

  13. Levels of Interference in Long and Short-Term Memory Differentially Modulate Non-REM and REM Sleep.

    Science.gov (United States)

    Fraize, Nicolas; Carponcy, Julien; Joseph, Mickaël Antoine; Comte, Jean-Christophe; Luppi, Pierre-Hervé; Libourel, Paul-Antoine; Salin, Paul-Antoine; Malleret, Gaël; Parmentier, Régis

    2016-12-01

    It is commonly accepted that sleep is beneficial to memory processes, but it is still unclear if this benefit originates from improved memory consolidation or enhanced information processing. It has thus been proposed that sleep may also promote forgetting of undesirable and non-essential memories, a process required for optimization of cognitive resources. We tested the hypothesis that non-rapid eye movement sleep (NREMS) promotes forgetting of irrelevant information, more specifically when processing information in working memory (WM), while REM sleep (REMS) facilitates the consolidation of important information. We recorded sleep patterns of rats trained in a radial maze in three different tasks engaging either the long-term or short-term storage of information, as well as a gradual level of interference. We observed a transient increase in REMS amount on the day the animal learned the rule of a long-term/reference memory task (RM), and, in contrast, a positive correlation between the performance of rats trained in a WM task involving an important processing of interference and the amount of NREMS or slow wave activity. Various oscillatory events were also differentially modulated by the type of training involved. Notably, NREMS spindles and REMS rapid theta increase with RM training, while sharp-wave ripples increase with all types of training. These results suggest that REMS, but also rapid oscillations occurring during NREMS would be specifically implicated in the long-term memory in RM, whereas NREMS and slow oscillations could be involved in the forgetting of irrelevant information required for WM. © 2016 Associated Professional Sleep Societies, LLC.

  14. Regression models for predicting anthropometric measurements of ...

    African Journals Online (AJOL)

    measure anthropometric dimensions to predict difficult-to-measure dimensions required for ergonomic design of school furniture. A total of 143 students aged between 16 and 18 years from eight public secondary schools in Ogbomoso, Nigeria ...

  15. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.

  16. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...... visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks...

  17. Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models

    Directory of Open Access Journals (Sweden)

    Cheng-Hung Hsieh

    2007-09-01

    Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.

  18. From Predictive Models to Instructional Policies

    Science.gov (United States)

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  19. Evaluating the evidence surrounding pontine cholinergic involvement in REM sleep generation

    Directory of Open Access Journals (Sweden)

    Kevin P Grace

    2015-09-01

    Full Text Available Rapid eye movement (REM sleep - characterized by vivid dreaming, motor paralysis, and heightened neural activity - is one of the fundamental states of the mammalian central nervous system. Initial theories of rapid eye movement (REM sleep generation posited that induction of the state required activation of the ‘pontine REM sleep generator’ by cholinergic inputs. Here we review and evaluate the evidence surrounding cholinergic involvement in REM sleep generation. We submit that: (i the capacity of pontine cholinergic neurotransmission to generate REM sleep has been firmly established by gain-of-function experiments, (ii the function of endogenous cholinergic input to REM sleep generating sites cannot be determined by gain-of-function experiments; rather, loss-of-function studies are required, (iii loss-of-function studies show that endogenous cholinergic input to the PFT is not required for REM sleep generation, and (iv Cholinergic input to the pontine REM sleep generating sites serve an accessory role in REM sleep generation: reinforcing non-REM-to-REM sleep transitions making them quicker and less likely to fail.

  20. REM sleep selectively prunes and maintains new synapses in development and learning.

    Science.gov (United States)

    Li, Wei; Ma, Lei; Yang, Guang; Gan, Wen-Biao

    2017-03-01

    The functions and underlying mechanisms of rapid eye movement (REM) sleep remain unclear. Here we show that REM sleep prunes newly formed postsynaptic dendritic spines of layer 5 pyramidal neurons in the mouse motor cortex during development and motor learning. This REM sleep-dependent elimination of new spines facilitates subsequent spine formation during development and when a new motor task is learned, indicating a role for REM sleep in pruning to balance the number of new spines formed over time. Moreover, REM sleep also strengthens and maintains newly formed spines, which are critical for neuronal circuit development and behavioral improvement after learning. We further show that dendritic calcium spikes arising during REM sleep are important for pruning and strengthening new spines. Together, these findings indicate that REM sleep has multifaceted functions in brain development, learning and memory consolidation by selectively eliminating and maintaining newly formed synapses via dendritic calcium spike-dependent mechanisms.

  1. ALARA beyond dollars per person-rem

    International Nuclear Information System (INIS)

    Waite, D.; Harper, W.

    1983-01-01

    The trend in radiological assessments over the past decade is to extract more and more information from each bit of data. In the 1960's it was sufficient to calculate doses to the maximum individual and populations from exposure to critical nuclides in critical pathways. The 1970's brought the UNSCEAR and BEIR Reports and made the extension of calculated doses to predicted health effects fashionable. The 1980's may see data analysis extended still further to the use of an index of lost productivity. Advantages of these types of uses of dose calculations include the derivation of further insight into the system being analyzed and the attainment of a greater measure of comparability with impacts from agents other than radiological. The question is, where does the uncertainty in the analytical result overwhelm any possibility of its usefulness. This presentation addresses this question in the context of a performance analysis of a high-level nuclear waste repository. Estimates of uncertainty in dose calculations, health effects predictions and loss of productivity predictions are derived from examination of the open literature and the use of propagation of uncertainty techniques. The expected values and uncertainties are compounded as they would be in an ALARA analysis and the outcomes are analyzed. The advantages and disadvantages of truncating the analysis at each stage of extension are discussed. 21 references, 3 tables

  2. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  3. One step replica symmetry breaking and extreme order statistics of logarithmic REMs

    Directory of Open Access Journals (Sweden)

    Xiangyu Cao, Yan V. Fyodorov, Pierre Le Doussal

    2016-12-01

    Full Text Available Building upon the one-step replica symmetry breaking formalism, duly understood and ramified, we show that the sequence of ordered extreme values of a general class of Euclidean-space logarithmically correlated random energy models (logREMs behave in the thermodynamic limit as a randomly shifted decorated exponential Poisson point process. The distribution of the random shift is determined solely by the large-distance ("infra-red", IR limit of the model, and is equal to the free energy distribution at the critical temperature up to a translation. the decoration process is determined solely by the small-distance ("ultraviolet", UV limit, in terms of the biased minimal process. Our approach provides connections of the replica framework to results in the probability literature and sheds further light on the freezing/duality conjecture which was the source of many previous results for log-REMs. In this way we derive the general and explicit formulae for the joint probability density of depths of the first and second minima (as well its higher-order generalizations in terms of model-specific contributions from UV as well as IR limits. In particular, we show that the second min statistics is largely independent of details of UV data, whose influence is seen only through the mean value of the gap. For a given log-correlated field this parameter can be evaluated numerically, and we provide several numerical tests of our theory using the circular model of $1/f$-noise.

  4. A model to predict the beginning of the pollen season

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo

    1991-01-01

    In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...

  5. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  6. Evaluation of the US Army fallout prediction model

    International Nuclear Information System (INIS)

    Pernick, A.; Levanon, I.

    1987-01-01

    The US Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the US Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the US Army's method predicts danger zone lengths that are commonly shorter than the corresponding hot line distances of SIMFIC, the US Army's method is not reliably conservative

  7. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

  8. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected.

  9. Unilateral hemidiaphragm weakness is associated with positional hypoxemia in REM sleep.

    Science.gov (United States)

    Baltzan, Marcel A; Scott, Adrienne S; Wolkove, Norman

    2012-02-15

    Patients with unilateral diaphragmatic paralysis (UDP) have been reported to have varied respiratory symptoms and often reduced lung function. We sought to describe the polysomnographic respiratory characteristics in patients with UDP without obstructive sleep apnea. We prospectively collected 5 cases with clinical investigation regarding symptoms, lung function, and nocturnal polysomnography. The respiratory sleep characteristics were analyzed with standardized scoring of respiratory events in 30-sec epochs and comparison according to sleep-wake stages and body position with respect to oximetry. The cases were compared to 5 controls matched for age, gender, and body mass index. Three of 5 patients had significant awake lung restriction with a mean (range) forced vital capacity of 1.89 (1.48-2.24) liters, 72% (45% to 102%) predicted. All had REM sleep with few apneas and episodes of prolonged hypopneas characterized by important desaturation noted on oximetry. These desaturations were greatest during REM sleep when the patients slept supine with a mean (SD) saturation of 90.8% (4.5%) and minimum of 64% or on the side unaffected by UDP with a mean saturation of 87.8% (5.3%) and minimum of 67% (p sleep stages had few, if any significant desaturations and these events rarely occurred when the patient slept in the supine position. Saturation was lower in all sleep-wake stages and sleep positions compared to controls (p sleep with frequent desaturations.

  10. Molecular and functional characterization of mulberry EST encoding remorin (MiREM) involved in abiotic stress.

    Science.gov (United States)

    Checker, Vibha G; Khurana, Paramjit

    2013-11-01

    Group1 remorins may help the plants to optimize their growth under adverse conditions by their involvement in mediating osmotic stress responses in plants. Mulberry (Morus indica), a deciduous woody tree, serves as the cardinal component of the sericulture industry. Genomic endeavors in sequencing of mulberry ESTs provided clues to stress-specific clones, but their functional relevance remains fragmentary. Therefore in this study, we assessed the functional significance of a remorin gene family member that was identified in leaf ESTs. Remorins represent a large, plant-specific multigene family gaining importance in recent times with respect to their role in plant-microbe interactions, although their role in response to environmental stresses remains speculative as in vivo functions of remorin genes are limited. Mulberry remorin (MiREM) localizes to plasma membrane and is ubiquitously present in all plant organs. Expression analysis of MiREM by northern analysis reveals that its transcript increases under different abiotic stress conditions especially during dehydration and salt stress, implicating it in regulation of stress signaling pathways. Concomitantly, transgenic Arabidopsis plants overexpressing heterologous remorin show tolerance to dehydration and salinity at the germination and seedling stages as revealed by percentage germination, root inhibition assays, fresh weight and activity of photosystem II. This study predicts the possible function of group 1 remorin gene in mediating osmotic stress thus bringing novel perspectives in understanding the function of remorins in plant abiotic stress responses.

  11. Prediction of speech intelligibility based on an auditory preprocessing model

    DEFF Research Database (Denmark)

    Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten

    2010-01-01

    Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...

  12. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  13. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  14. A Prediction Model of the Capillary Pressure J-Function.

    Directory of Open Access Journals (Sweden)

    W S Xu

    Full Text Available The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative.

  15. REM-3D Reference Datasets: Reconciling large and diverse compilations of travel-time observations

    Science.gov (United States)

    Moulik, P.; Lekic, V.; Romanowicz, B. A.

    2017-12-01

    A three-dimensional Reference Earth model (REM-3D) should ideally represent the consensus view of long-wavelength heterogeneity in the Earth's mantle through the joint modeling of large and diverse seismological datasets. This requires reconciliation of datasets obtained using various methodologies and identification of consistent features. The goal of REM-3D datasets is to provide a quality-controlled and comprehensive set of seismic observations that would not only enable construction of REM-3D, but also allow identification of outliers and assist in more detailed studies of heterogeneity. The community response to data solicitation has been enthusiastic with several groups across the world contributing recent measurements of normal modes, (fundamental mode and overtone) surface waves, and body waves. We present results from ongoing work with body and surface wave datasets analyzed in consultation with a Reference Dataset Working Group. We have formulated procedures for reconciling travel-time datasets that include: (1) quality control for salvaging missing metadata; (2) identification of and reasons for discrepant measurements; (3) homogenization of coverage through the construction of summary rays; and (4) inversions of structure at various wavelengths to evaluate inter-dataset consistency. In consultation with the Reference Dataset Working Group, we retrieved the station and earthquake metadata in several legacy compilations and codified several guidelines that would facilitate easy storage and reproducibility. We find strong agreement between the dispersion measurements of fundamental-mode Rayleigh waves, particularly when made using supervised techniques. The agreement deteriorates substantially in surface-wave overtones, for which discrepancies vary with frequency and overtone number. A half-cycle band of discrepancies is attributed to reversed instrument polarities at a limited number of stations, which are not reflected in the instrument response history

  16. REM and NREM power spectral analysis on two consecutive nights in psychophysiological and paradoxical insomnia sufferers.

    Science.gov (United States)

    St-Jean, Geneviève; Turcotte, Isabelle; Pérusse, Alexandra D; Bastien, Célyne H

    2013-08-01

    The objectives of the study were to examine EEG activities using power spectral analysis (PSA) of good sleepers (GS), psychophysiological (PsyI) and paradoxical (ParI) insomnia sufferers on two consecutive nights. Participants completed three nights of PSG recordings in a sleep laboratory following a clinical evaluation. Participants were 26 PsyI, 20 ParI and 21 GS (mean age=40 years, SD=9.4). All sleep cycles of Nights 2 and 3 were retained for PSA. The absolute and relative activity in frequency bands (0.00 to 125.00 Hz) were computed at multiple frontal, central and parietal sites in REM and NREM sleep. Mixed model ANOVAs were performed with absolute and relative PSA data to assess differences between groups and nights. Over the course of the two nights, more absolute delta activity at F3, C3, and P3 was observed in ParI compared with PsyI suggesting deactivation of the left hemisphere in ParI and/or hyperactivation in PsyI. Further analysis on absolute PSA data revealed that differences between groups relate mostly to NREM. In REM, lower relative activity in slower frequency bands was found in ParI in comparison with GS and less relative theta activity was found in PsyI compared with GS implying higher activation in insomnia. In addition, between nights variability has been found in absolute powers of faster frequency bands (beta to omega). Signs of decreased cortical activity in absolute PSA in NREM combined with increased relative cortical activation in REM were found in ParI which might contribute to the misperception of sleep in ParI. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  18. Orexin and Epilepsy: Potential Role of REM Sleep.

    Science.gov (United States)

    Ng, Marcus C

    2017-03-01

    Interest in orexin receptor antagonism as a novel mechanism of action against seizures and epilepsy has increased in recent years. Loss of orexinergic activity is associated with rapid eye movement (REM) sleep onset, and REM sleep is generally protective against seizures. This paper discusses the dynamic modulation of seizure threshold by orexin through a postulated "orexi-cortical" axis in which the specific type of orexinergic activity exquisitely regulates sleep-wake states to modify ascending subcortical influences on cortical synchronization with profound subsequent consequences on seizure threshold. This paper also explores the current state of research into experimental orexinergic modulation of seizure threshold and suggests possible future research directions to fully understand the promise and peril of orexinergic manipulation in seizures and epilepsy. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  19. comparative analysis of two mathematical models for prediction

    African Journals Online (AJOL)

    Abstract. A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data ob- tained from experimental work done in this study. The models used are Scheffes and Osadebes optimization theories to predict the compressive strength of ...

  20. Comparison of predictive models for the early diagnosis of diabetes

    NARCIS (Netherlands)

    M. Jahani (Meysam); M. Mahdavi (Mahdi)

    2016-01-01

    textabstractObjectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve

  1. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  2. Hidden Markov Model for quantitative prediction of snowfall

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  3. Bayesian variable order Markov models: Towards Bayesian predictive state representations

    NARCIS (Netherlands)

    Dimitrakakis, C.

    2009-01-01

    We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more

  4. Demonstrating the improvement of predictive maturity of a computational model

    Energy Technology Data Exchange (ETDEWEB)

    Hemez, Francois M [Los Alamos National Laboratory; Unal, Cetin [Los Alamos National Laboratory; Atamturktur, Huriye S [CLEMSON UNIV.

    2010-01-01

    We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.

  5. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  6. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  7. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  8. Hidden Markov Model for quantitative prediction of snowfall and ...

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  9. Model predictive control of a 3-DOF helicopter system using ...

    African Journals Online (AJOL)

    ... by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC). Keywords: nonlinear systems, helicopter dynamics, MIMO systems, model predictive control, successive linearization. International Journal of Engineering, Science and Technology, Vol. 2, No. 10, 2010, pp. 9-19 ...

  10. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Science.gov (United States)

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  11. Comparative Analysis of Two Mathematical Models for Prediction of ...

    African Journals Online (AJOL)

    A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data obtained from experimental work done in this study. The models used are Scheffe's and Osadebe's optimization theories to predict the compressive strength of sandcrete ...

  12. A mathematical model for predicting earthquake occurrence ...

    African Journals Online (AJOL)

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  13. Model for predicting the injury severity score.

    Science.gov (United States)

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

    2015-07-01

    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  14. Ischemic stroke selectively inhibits REM sleep of rats

    OpenAIRE

    Ahmed, Samreen; Meng, He; Liu, Tiecheng; Sutton, Blair; Opp, Mark R.; Borjigin, Jimo; Wang, Michael M.

    2011-01-01

    Sleep disorders are important risk factors for stroke; conversely, stroke patients suffer from sleep disturbances including disruptions of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep and a decrease in total sleep. This study was performed to characterize the effect of stroke on sleep architecture of rats using continuous electroencephalography (EEG) and activity monitoring. Rats were implanted with transmitters which enabled continuous real time recording of EEG, electrom...

  15. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  16. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  17. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  18. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  19. El trastorno de conducta del sueño rem

    Directory of Open Access Journals (Sweden)

    Alex Iranzo De Riquer, Dr.

    2013-05-01

    Full Text Available El trastorno de conducta durante el sueño REM (TCSR se caracteriza por conductas motoras vigorosas, pesadillas y ausencia de atonía muscular durante el sueño REM. Se debe a la disfunción directa o indirecta de las estructuras del tronco cerebral que regulan el sueño REM, especialmente el núcleo subceruleus. El TCSR puede ser idiopático o asociado a enfermedades neurológicas como la enfermedad de Parkinson (EP, la demencia con cuerpos de Lewy (DCL, la atrofia multisistémica (AMS y la narcolepsia. Los pacientes con la forma idiopática, especialmente los que tienen alterados el SPECT del transportador de la dopamina, la sonografía de la sustancia negra, los test de olfato y de la visión de colores, tienen un elevado riesgo de desarrollar la E P, DCL y AMS. El TCSR no debe considerarse como un simple trastorno del sueño, sino como una manifestación de una enfermedad neurológica. El clonazepam (0.25-4 mg y la melatonina (3-12 mg a la hora de acostarse mejoran los síntomas del TCSR pero no evitan, en la forma idiopática, la conversión a una enfermedad neurodegenerativa.

  20. Models Predicting Success of Infertility Treatment: A Systematic Review

    Science.gov (United States)

    Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi

    2016-01-01

    Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461

  1. Towards a generalized energy prediction model for machine tools.

    Science.gov (United States)

    Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

    2017-04-01

    Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

  2. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  3. Comparison of Predictive Models for the Early Diagnosis of Diabetes.

    Science.gov (United States)

    Jahani, Meysam; Mahdavi, Mahdi

    2016-04-01

    This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

  4. The Reciprocal Effects Model Revisited: Extending Its Reach to Gifted Students Attending Academically Selective Schools

    Science.gov (United States)

    Seaton, Marjorie; Marsh, Herbert W.; Parker, Philip D.; Craven, Rhonda G.; Yeung, Alexander S.

    2015-01-01

    The reciprocal effects model (REM) predicts a reciprocal relation between academic self-concept and academic achievement, whereby prior academic self-concept is associated with future gains in achievement, and prior achievement is related to subsequent academic self-concept. Although research in this area has been extensive, there has been a…

  5. Applications of modeling in polymer-property prediction

    Science.gov (United States)

    Case, F. H.

    1996-08-01

    A number of molecular modeling techniques have been applied for the prediction of polymer properties and behavior. Five examples illustrate the range of methodologies used. A simple atomistic simulation of small polymer fragments is used to estimate drug compatibility with a polymer matrix. The analysis of molecular dynamics results from a more complex model of a swollen hydrogel system is used to study gas diffusion in contact lenses. Statistical mechanics are used to predict conformation dependent properties — an example is the prediction of liquid-crystal formation. The effect of the molecular weight distribution on phase separation in polyalkanes is predicted using thermodynamic models. In some cases, the properties of interest cannot be directly predicted using simulation methods or polymer theory. Correlation methods may be used to bridge the gap between molecular structure and macroscopic properties. The final example shows how connectivity-indices-based quantitative structure-property relationships were used to predict properties for candidate polyimids in an electronics application.

  6. Artificial Neural Network Model for Predicting Compressive

    OpenAIRE

    Salim T. Yousif; Salwa M. Abdullah

    2013-01-01

      Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum...

  7. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  8. Quantitative EEG amplitude across REM sleep periods in depression: preliminary report.

    Science.gov (United States)

    Liscombe, Marcus P; Hoffmann, Robert F; Trivedi, Madhukar H; Parker, Marc K; Rush, A John; Armitage, Roseanne

    2002-01-01

    To determine if there are significant differences in the temporal organization of rapid eye movement (REM) sleep microarchitecture between healthy controls and outpatients with major depressive disorder (MDD). Forty age-matched subjects, 20 men and 20 women, half with MDD, were selected from an archive of sleep electroencephalography (EEG) data collected under identical conditions. Each participant spent 2 consecutive nights in the Sleep Study Unit of the University of Texas Southwestern Medical Center at Dallas, the first of which served as adaptation. The average amplitude in each of 5 conventional EEG frequency bands was computed for each REM period across the second night. Data were then coded for group and sex. Aside from REM latency, none of the key sleep macroarchitectural variables differentiated MDD patients from controls. REM latency was longest in men with MDD. Sleep microarchitecture, however, did show a number of between-group differences. In general, slower frequencies declined across REM periods, with a significant REM period effect for delta, theta and alpha amplitude. Group x sex interactions were also obtained for theta and alpha. Beta activity showed a unique temporal profile in each group, supported by a significant REM period x group x sex interaction. In addition, the temporal change in theta amplitude across REM periods was most striking in women with MDD. This study suggests that, like during non-REM sleep, EEG amplitude shows a systematic temporal change over successive REM sleep periods and also shows elements that are both disease- and sex-dependent.

  9. Cognitive flexibility across the sleep-wake cycle: REM-sleep enhancement of anagram problem solving.

    Science.gov (United States)

    Walker, Matthew P; Liston, Conor; Hobson, J Allan; Stickgold, Robert

    2002-11-01

    Flexible or 'fluid' cognitive processes are regarded as fundamental to problem solving and creative ability, requiring a specific neurophysiological milieu. REM-sleep dreaming is associated with creative processes and abstract reasoning with increased strength of weak associations in cognitive networks. REM sleep is also mediated by a distinctive neurophysiological profile, different to that of wake and NREM sleep. This study compared the performance of 16 subjects on a test of cognitive flexibility using anagram word puzzles following REM and NREM awakenings across the night, and waking performances during the day. REM awakenings provided a significant 32% advantage in the number of anagrams solved compared with NREM awakenings and was equal to that of wake time trials. Correlations of individual performance profiles suggest that REM sleep may offer a different mode of problem solving compared with wake and NREM. When early and late REM and NREM awakening data were separated, a dissociation was evident, with NREM task performance becoming more REM-like later in the night, while REM performance remained constant. These data suggest that the neurophysiology of REM sleep represents a brain state more amenable to flexible cognitive processing than NREM and different from that in wake, and may offer insights into the neurocognitive properties of REM-sleep dreaming.

  10. Post-learning REM sleep deprivation impairs long-term memory: reversal by acute nicotine treatment.

    Science.gov (United States)

    Aleisa, A M; Alzoubi, K H; Alkadhi, K A

    2011-07-15

    Rapid eye movement sleep deprivation (REM-SD) is associated with spatial learning and memory impairment. During REM-SD, an increase in nicotine consumption among habitual smokers and initiation of tobacco use by non-smokers have been reported. We have shown recently that nicotine treatment prevented learning and memory impairments associated with REM-SD. We now report the interactive effects of post-learning REM-SD and/or nicotine. The animals were first trained on the radial arm water maze (RAWM) task, then they were REM-sleep deprived using the modified multiple platform paradigm for 24h. During REM-SD period, the rats were injected with saline or nicotine (1mg/kg s.c. every 12h: a total of 3 injections). The animals were tested for long-term memory in the RAWM at the end of the REM-SD period. The 24h post-learning REM-SD significantly impaired long-term memory. However, nicotine treatment reversed the post-learning REM-SD-induced impairment of long-term memory. On the other hand, post-learning treatment of normal rats with nicotine for 24h enhanced long-term memory. These results indicate that post-learning acute nicotine treatment prevented the deleterious effect of REM-SD on cognitive abilities. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  11. Identification of the transmitter and receptor mechanisms responsible for REM sleep paralysis.

    Science.gov (United States)

    Brooks, Patricia L; Peever, John H

    2012-07-18

    During REM sleep the CNS is intensely active, but the skeletal motor system is paradoxically forced into a state of muscle paralysis. The mechanisms that trigger REM sleep paralysis are a matter of intense debate. Two competing theories argue that it is caused by either active inhibition or reduced excitation of somatic motoneuron activity. Here, we identify the transmitter and receptor mechanisms that function to silence skeletal muscles during REM sleep. We used behavioral, electrophysiological, receptor pharmacology and neuroanatomical approaches to determine how trigeminal motoneurons and masseter muscles are switched off during REM sleep in rats. We show that a powerful GABA and glycine drive triggers REM paralysis by switching off motoneuron activity. This drive inhibits motoneurons by targeting both metabotropic GABA(B) and ionotropic GABA(A)/glycine receptors. REM paralysis is only reversed when motoneurons are cut off from GABA(B), GABA(A) and glycine receptor-mediated inhibition. Neither metabotropic nor ionotropic receptor mechanisms alone are sufficient for generating REM paralysis. These results demonstrate that multiple receptor mechanisms trigger REM sleep paralysis. Breakdown in normal REM inhibition may underlie common sleep motor pathologies such as REM sleep behavior disorder.

  12. Automatic detection of rapid eye movements (REMs): A machine learning approach.

    Science.gov (United States)

    Yetton, Benjamin D; Niknazar, Mohammad; Duggan, Katherine A; McDevitt, Elizabeth A; Whitehurst, Lauren N; Sattari, Negin; Mednick, Sara C

    2016-02-01

    Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process. Therefore, reliable, automated REM detection software is a valuable research tool. We developed an automatic REM detection algorithm combining a novel set of extracted features and the 'AdaBoost' classification algorithm to detect the presence of REMs in Electrooculogram data collected from the right and left outer canthi (ROC/LOC). Algorithm performance measures of Recall (percentage of REMs detected) and Precision (percentage of REMs detected that are true REMs) were calculated and compared to the gold standard of human detection by three expert sleep scorers. REM detection by four non-experts were also investigated and compared to expert raters and the algorithm. The algorithm performance (78.1% Recall, 82.6% Precision) surpassed that of the average (expert & non-expert) single human detection performance (76% Recall, 83% Precision). Agreement between non-experts (Cronbach Alpha=0.65) is markedly lower than experts (Cronbach Alpha=0.80). By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others. The automatic detection algorithm presented is a viable and efficient method of REM detection as it reliably matches the performance of human scorers and outperforms all other known LOC- and ROC-based detection algorithms. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Posterior Predictive Model Checking for Multidimensionality in Item Response Theory

    Science.gov (United States)

    Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip

    2009-01-01

    If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…

  14. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd

    1999-04-01

    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  15. Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

    Science.gov (United States)

    Liu, Z.; Merwade, V.

    2017-12-01

    Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

  16. Directional information flows between brain hemispheres across waking, non-REM and REM sleep states: an EEG study.

    Science.gov (United States)

    Bertini, Mario; Ferrara, Michele; De Gennaro, Luigi; Curcio, Giuseppe; Moroni, Fabio; Babiloni, Claudio; Infarinato, Francesco; Rossini, Paolo Maria; Vecchio, Fabrizio

    2009-03-30

    The present electroencephalographic (EEG) study evaluated the hypothesis of a preferred directionality of communication flows between brain hemispheres across 24 h (i.e., during the whole daytime and nighttime), as an extension of a recent report showing changes in preferred directionality from pre-sleep wake to early sleep stages. Scalp EEGs were recorded in 10 normal volunteers during daytime wakefulness (eyes closed; first period: from 10:00 to 13:00 h; second period: from 14:00 to 18:00 h; third period: from 19:00 to 22:00 h) and nighttime sleep (four NREM-REM cycles). EEG rhythms of interest were delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz) and beta (16-28 Hz). The direction of the inter-hemispheric information flow was evaluated by computing the directed transfer function (DTF) from these EEG rhythms. Inter-hemispheric directional flows varied as a function of the state of consciousness (wake, NREM sleep, REM sleep) and in relation to different cerebral areas. During the daytime, alpha and beta rhythms conveyed inter-hemispheric signals with preferred Left-to-Right hemisphere direction in parietal and central areas, respectively. During the NREM sleep periods of nighttime, the direction of inter-hemispheric DTF information flows conveyed by central beta rhythms was again preponderant from Left-to-Right hemisphere in the stage 2, independent of cortical areas. No preferred direction emerged across the REM periods. These results support the hypothesis that specific directionality of communication flows between brain hemispheres is associated with wakefulness, NREM (particularly stage 2) and REM states during daytime and nighttime. They also reinforce the suggestive hypothesis of a relationship between inter-hemispheric directionality of EEG functional coupling and frequency of the EEG rhythms.

  17. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  18. Predictive models for acute kidney injury following cardiac surgery.

    Science.gov (United States)

    Demirjian, Sevag; Schold, Jesse D; Navia, Jose; Mastracci, Tara M; Paganini, Emil P; Yared, Jean-Pierre; Bashour, Charles A

    2012-03-01

    Accurate prediction of cardiac surgery-associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery-associated AKI using presurgical and combined pre- and intrasurgical variables. Prospective observational cohort. 25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008. Presurgical and combined pre- and intrasurgical variables were used to develop predictive models. Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery. Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P predictive models in other cohorts is required before wide-scale application. We developed and internally validated 4 new models that accurately predict cardiac surgery-associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical

  19. Modeling number of claims and prediction of total claim amount

    Science.gov (United States)

    Acar, Aslıhan Şentürk; Karabey, Uǧur

    2017-07-01

    In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.

  20. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  1. Processing of a Subliminal Rebus during Sleep: Idiosyncratic Primary versus Secondary Process Associations upon Awakening from REM- versus Non-REM-Sleep

    Directory of Open Access Journals (Sweden)

    Jana Steinig

    2017-11-01

    Full Text Available Primary and secondary processes are the foundational axes of the Freudian mental apparatus: one horizontally as a tendency to associate, the primary process, and one vertically as the ability for perspective taking, the secondary process. Primary process mentation is not only supposed to be dominant in the unconscious but also, for example, in dreams. The present study tests the hypothesis that the mental activity during REM-sleep has more characteristics of the primary process, while during non-REM-sleep more secondary process operations take place. Because the solving of a rebus requires the ability to non-contexually condensate the literal reading of single stimuli into a new one, rebus solving is a primary process operation by excellence. In a replication of the dream-rebus study of Shevrin and Fisher (1967, a rebus, which consisted of an image of a comb (German: “Kamm” and an image of a raft (German: “Floß”, resulting in the German rebus word “kampflos” (Engl.: without a struggle, was flashed subliminally (at 1 ms to 20 participants before going to sleep. Upon consecutive awakenings participants were asked for a dream report, free associations and an image description. Based on objective association norms, there were significantly more conceptual associations referring to Kamm and Floß indexing secondary process mentation when subjects were awakened from non-REM sleep as compared to REM-awakenings. There were not significantly more rebus associations referring to kampflos indexing primary process mentation when awakened from REM-sleep as compared to non-REM awakenings. However, when the associations were scored on the basis of each subject’s individual norms, there was a rebus effect with more idiosyncratic rebus associations in awakenings after REM than after non-REM-sleep. Our results support the general idea that REM-sleep is characterized by primary process thinking, while non-REM-sleep mentation follows the rules of the

  2. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate......Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions......, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current...

  3. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Science.gov (United States)

    Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho

    2015-01-01

    Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope. During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  4. AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Kottalanka Srikanth

    2017-07-01

    Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

  5. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  6. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Directory of Open Access Journals (Sweden)

    Bang Wool Eom

    Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  7. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. Evaluation of wave runup predictions from numerical and parametric models

    Science.gov (United States)

    Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

    2014-01-01

    Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

  9. Femtocells Sharing Management using mobility prediction model

    OpenAIRE

    Barth, Dominique; Choutri, Amira; Kloul, Leila; Marcé, Olivier

    2013-01-01

    Bandwidth sharing paradigm constitutes an incentive solution for the serious capacity management problem faced by operators as femtocells owners are able to offer a QoS guaranteed network access to mobile users in their femtocell coverage. In this paper, we consider a technico-economic bandwidth sharing model based on a reinforcement learning algorithm. Because such a model does not allow the convergence of the learning algorithm, due to the small size of the femtocells, the mobile users velo...

  10. Validating predictions from climate envelope models

    Science.gov (United States)

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  11. Validating predictions from climate envelope models.

    Directory of Open Access Journals (Sweden)

    James I Watling

    Full Text Available Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species' distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967-1971 (t1 and evaluated using occurrence data from 1998-2002 (t2. Model sensitivity (the ability to correctly classify species presences was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on

  12. North Atlantic climate model bias influence on multiyear predictability

    Science.gov (United States)

    Wu, Y.; Park, T.; Park, W.; Latif, M.

    2018-01-01

    The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

  13. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2017-10-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  14. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

    2017-12-01

    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  15. Micro-mechanical studies on graphite strength prediction models

    Science.gov (United States)

    Kanse, Deepak; Khan, I. A.; Bhasin, V.; Vaze, K. K.

    2013-06-01

    The influence of type of loading and size-effects on the failure strength of graphite were studied using Weibull model. It was observed that this model over-predicts size effect in tension. However, incorporation of grain size effect in Weibull model, allows a more realistic simulation of size effects. Numerical prediction of strength of four-point bend specimen was made using the Weibull parameters obtained from tensile test data. Effective volume calculations were carried out and subsequently predicted strength was compared with experimental data. It was found that Weibull model can predict mean flexural strength with reasonable accuracy even when grain size effect was not incorporated. In addition, the effects of microstructural parameters on failure strength were analyzed using Rose and Tucker model. Uni-axial tensile, three-point bend and four-point bend strengths were predicted using this model and compared with the experimental data. It was found that this model predicts flexural strength within 10%. For uni-axial tensile strength, difference was 22% which can be attributed to less number of tests on tensile specimens. In order to develop failure surface of graphite under multi-axial state of stress, an open ended hollow tube of graphite was subjected to internal pressure and axial load and Batdorf model was employed to calculate failure probability of the tube. Bi-axial failure surface was generated in the first and fourth quadrant for 50% failure probability by varying both internal pressure and axial load.

  16. New Approaches for Channel Prediction Based on Sinusoidal Modeling

    Directory of Open Access Journals (Sweden)

    Ekman Torbjörn

    2007-01-01

    Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.

  17. Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

    Directory of Open Access Journals (Sweden)

    Volker J. Schmid

    2007-10-01

    Full Text Available The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

  18. Modeling for prediction of restrained shrinkage effect in concrete repair

    International Nuclear Information System (INIS)

    Yuan Yingshu; Li Guo; Cai Yue

    2003-01-01

    A general model of autogenous shrinkage caused by chemical reaction (chemical shrinkage) is developed by means of Arrhenius' law and a degree of chemical reaction. Models of tensile creep and relaxation modulus are built based on a viscoelastic, three-element model. Tests of free shrinkage and tensile creep were carried out to determine some coefficients in the models. Two-dimensional FEM analysis based on the models and other constitutions can predict the development of tensile strength and cracking. Three groups of patch-repaired beams were designed for analysis and testing. The prediction from the analysis shows agreement with the test results. The cracking mechanism after repair is discussed

  19. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...

  20. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  1. Validation of a tuber blight (Phytophthora infestans) prediction model

    Science.gov (United States)

    Potato tuber blight caused by Phytophthora infestans accounts for significant losses in storage. There is limited published quantitative data on predicting tuber blight. We validated a tuber blight prediction model developed in New York with cultivars Allegany, NY 101, and Katahdin using independent...

  2. Geospatial application of the Water Erosion Prediction Project (WEPP) Model

    Science.gov (United States)

    D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

    2011-01-01

    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

  3. Reduced order modelling and predictive control of multivariable ...

    Indian Academy of Sciences (India)

    Anuj Abraham

    2018-03-16

    Mar 16, 2018 ... The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE. Keywords. Predictive control; distillation column; reduced order model; dominant pole; ...

  4. Selective REM-Sleep Deprivation Does Not Diminish Emotional Memory Consolidation in Young Healthy Subjects

    OpenAIRE

    Morgenthaler, Jarste; Wiesner, Christian D.; Hinze, Karoline; Abels, Lena C.; Prehn-Kristensen, Alexander; Göder, Robert

    2014-01-01

    Sleep enhances memory consolidation and it has been hypothesized that rapid eye movement (REM) sleep in particular facilitates the consolidation of emotional memory. The aim of this study was to investigate this hypothesis using selective REM-sleep deprivation. We used a recognition memory task in which participants were shown negative and neutral pictures. Participants (N = 29 healthy medical students) were separated into two groups (undisturbed sleep and selective REM-sleep deprived). Both ...

  5. Mixed models for predictive modeling in actuarial science

    NARCIS (Netherlands)

    Antonio, K.; Zhang, Y.

    2012-01-01

    We start with a general discussion of mixed (also called multilevel) models and continue with illustrating specific (actuarial) applications of this type of models. Technical details on (linear, generalized, non-linear) mixed models follow: model assumptions, specifications, estimation techniques

  6. Consensus models to predict endocrine disruption for all ...

    Science.gov (United States)

    Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

  7. Dietary information improves cardiovascular disease risk prediction models.

    Science.gov (United States)

    Baik, I; Cho, N H; Kim, S H; Shin, C

    2013-01-01

    Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI NRI (category-free NRI=0.14, P NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. We suggest that dietary information may be useful in constructing CVD risk prediction models.

  8. Scanpath Based N-Gram Models for Predicting Reading Behavior

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...

  9. Unsupervised ship trajectory modeling and prediction using compression and clustering

    NARCIS (Netherlands)

    de Vries, G.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.

    2009-01-01

    In this paper we show how to build a model of ship trajectories in a certain maritime region and use this model to predict future ship movements. The presented method is unsupervised and based on existing compression (line-simplification) and clustering techniques. We evaluate the model with a

  10. Prediction of annual rainfall pattern using Hidden Markov Model ...

    African Journals Online (AJOL)

    A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual ...

  11. The Selection of Turbulence Models for Prediction of Room Airflow

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation...

  12. Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

    2013-01-01

    The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...

  13. Using Pareto points for model identification in predictive toxicology

    Science.gov (United States)

    2013-01-01

    Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

  14. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    constructed from geological and hydrological data. However, geophysical data are increasingly used to inform hydrogeologic models because they are collected at lower cost and much higher density than geological and hydrological data. Despite increased use of geophysics, it is still unclear whether...... the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... collecting geophysical data. At a minimum, an analysis should be conducted assuming settings that are favorable for the chosen geophysical method. If the analysis suggests that data collected by the geophysical method is unlikely to improve model prediction performance under these favorable settings...

  15. Hybrid Corporate Performance Prediction Model Considering Technical Capability

    Directory of Open Access Journals (Sweden)

    Joonhyuck Lee

    2016-07-01

    Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.

  16. Preoperative prediction model of outcome after cholecystectomy for symptomatic gallstones

    DEFF Research Database (Denmark)

    Borly, L; Anderson, I B; Bardram, Linda

    1999-01-01

    and sonography evaluated gallbladder motility, gallstones, and gallbladder volume. Preoperative variables in patients with or without postcholecystectomy pain were compared statistically, and significant variables were combined in a logistic regression model to predict the postoperative outcome. RESULTS: Eighty...... and by the absence of 'agonizing' pain and of symptoms coinciding with pain (P model 15 of 18 predicted patients had postoperative pain (PVpos = 0.83). Of 62 patients predicted as having no pain postoperatively, 56 were pain-free (PVneg = 0.90). Overall accuracy...... was 89%. CONCLUSION: From this prospective study a model based on preoperative symptoms was developed to predict postcholecystectomy pain. Since intrastudy reclassification may give too optimistic results, the model should be validated in future studies....

  17. Prediction of Chemical Function: Model Development and Application

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

  18. Linear regression crash prediction models : issues and proposed solutions.

    Science.gov (United States)

    2010-05-01

    The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

  19. FPGA implementation of predictive degradation model for engine oil lifetime

    Science.gov (United States)

    Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.

    2018-03-01

    This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.

  20. Predictive Modeling: A New Paradigm for Managing Endometrial Cancer.

    Science.gov (United States)

    Bendifallah, Sofiane; Daraï, Emile; Ballester, Marcos

    2016-03-01

    With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches.

  1. On the Predictiveness of Single-Field Inflationary Models

    CERN Document Server

    Burgess, C.P.; Trott, Michael

    2014-01-01

    We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in prin...

  2. Predictive modeling in catalysis - from dream to reality

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2009-01-01

    In silico catalyst optimization is the ultimate application of computers in catalysis. This article provides an overview of the basic concepts of predictive modeling and describes how this technique can be used in catalyst and reaction design.

  3. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  4. Compensatory versus noncompensatory models for predicting consumer preferences

    Directory of Open Access Journals (Sweden)

    Anja Dieckmann

    2009-04-01

    Full Text Available Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007 to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.

  5. Predictive Modeling of Partitioned Systems: Implementation and Applications

    OpenAIRE

    Latten, Christine

    2014-01-01

    A general mathematical methodology for predictive modeling of coupled multi-physics systems is implemented and has been applied without change to an illustrative heat conduction example and reactor physics benchmarks.

  6. A new, accurate predictive model for incident hypertension

    DEFF Research Database (Denmark)

    Völzke, Henry; Fung, Glenn; Ittermann, Till

    2013-01-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

  7. Model Predictive Control for Ethanol Steam Reformers

    OpenAIRE

    Li, Mingming

    2014-01-01

    This thesis firstly proposes a new approach of modelling an ethanol steam reformer (ESR) for producing pure hydrogen. Hydrogen has obvious benefits as an alternative for feeding the proton exchange membrane fuel cells (PEMFCs) to produce electricity. However, an important drawback is that the hydrogen distribution and storage have high cost. So the ESR is regarded as a way to overcome these difficulties. Ethanol is currently considered as a promising energy source under the res...

  8. Haskell financial data modeling and predictive analytics

    CERN Document Server

    Ryzhov, Pavel

    2013-01-01

    This book is a hands-on guide that teaches readers how to use Haskell's tools and libraries to analyze data from real-world sources in an easy-to-understand manner.This book is great for developers who are new to financial data modeling using Haskell. A basic knowledge of functional programming is not required but will be useful. An interest in high frequency finance is essential.

  9. Wireless model predictive control: Application to water-level system

    Directory of Open Access Journals (Sweden)

    Ramdane Hedjar

    2016-04-01

    Full Text Available This article deals with wireless model predictive control of a water-level control system. The objective of the model predictive control algorithm is to constrain the control signal inside saturation limits and maintain the water level around the desired level. Linear modeling of any nonlinear plant leads to parameter uncertainties and non-modeled dynamics in the linearized mathematical model. These uncertainties induce a steady-state error in the output response of the water level. To eliminate this steady-state error and increase the robustness of the control algorithm, an integral action is included in the closed loop. To control the water-level system remotely, the communication between the controller and the process is performed using radio channel. To validate the proposed scheme, simulation and real-time implementation of the algorithm have been conducted, and the results show the effectiveness of wireless model predictive control with integral action.

  10. Phasic Motor Activity of Respiratory and Non-Respiratory Muscles in REM Sleep

    Science.gov (United States)

    Fraigne, Jimmy J.; Orem, John M.

    2011-01-01

    Objectives: In this study, we quantified the profiles of phasic activity in respiratory muscles (diaphragm, genioglossus and external intercostal) and non-respiratory muscles (neck and extensor digitorum) across REM sleep. We hypothesized that if there is a unique pontine structure that controls all REM sleep phasic events, the profiles of the phasic twitches of different muscle groups should be identical. Furthermore, we described how respiratory parameters (e.g., frequency, amplitude, and effort) vary across REM sleep to determine if phasic processes affect breathing. Methods: Electrodes were implanted in Wistar rats to record brain activity and muscle activity of neck, extensor digitorum, diaphragm, external intercostal, and genioglossal muscles. Ten rats were studied to obtain 313 REM periods over 73 recording days. Data were analyzed offline and REM sleep activity profiles were built for each muscle. In 6 animals, respiratory frequency, effort, amplitude, and inspiratory peak were also analyzed during 192 REM sleep periods. Results: Respiratory muscle phasic activity increased in the second part of the REM period. For example, genioglossal activity increased in the second part of the REM period by 63.8% compared to the average level during NREM sleep. This profile was consistent between animals and REM periods (η2 = 0.58). This increased activity seen in respiratory muscles appeared as irregular bursts and trains of activity that could affect rythmo-genesis. Indeed, the increased integrated activity seen in the second part of the REM period in the diaphragm was associated with an increase in the number (28.3%) and amplitude (30%) of breaths. Non-respiratory muscle phasic activity in REM sleep did not have a profile like the phasic activity of respiratory muscles. Time in REM sleep did not have an effect on nuchal activity (P = 0.59). Conclusion: We conclude that the concept of a common pontine center controlling all REM phasic events is not supported by our

  11. REM Sleep and Endothermy: Potential Sites and Mechanism of a Reciprocal Interference

    Directory of Open Access Journals (Sweden)

    Matteo Cerri

    2017-08-01

    Full Text Available Numerous data show a reciprocal interaction between REM sleep and thermoregulation. During REM sleep, the function of thermoregulation appears to be impaired; from the other hand, the tonic activation of thermogenesis, such as during cold exposure, suppresses REM sleep occurrence. Recently, both the central neural network controlling REM sleep and the central neural network controlling thermoregulation have been progressively unraveled. Thermoregulation was shown to be controlled by a central “core” circuit, responsible for the maintenance of body temperature, modulated by a set of accessory areas. REM sleep was suggested to be controlled by a group of hypothalamic neurons overlooking at the REM sleep generating circuits within the brainstem. The two networks overlap in a few areas, and in this review, we will suggest that in such overlap may reside the explanation of the reciprocal interaction between REM sleep and thermoregulation. Considering the peculiar modulation of thermoregulation by REM sleep the result of their coincidental evolution, REM sleep may therefore be seen as a period of transient heterothermy.

  12. Enhanced emotional reactivity after selective REM sleep deprivation in humans: an fMRI study

    Directory of Open Access Journals (Sweden)

    Alejandra eRosales-Lagarde

    2012-06-01

    Full Text Available Converging evidence from animal and human studies suggest that REM sleep modulates emotional processing. The aim of the present study was to explore the effects of selective REM sleep deprivation on emotional responses to threatening visual stimuli and their brain correlates using functional magnetic resonance imaging (fMRI. Twenty healthy subjects were randomly assigned to two groups: selective REM sleep deprivation (REM-D, by awakening them at each REM sleep onset, or NREM sleep interruptions (NREM-I as control for potential non-specific effects of awakenings and lack of sleep. In a within-subject design, a visual emotional-reactivity task was performed in the scanner before and 24 hours after sleep manipulation. Behaviorally, emotional reactivity was enhanced relative to baseline in the REM deprived group only. In terms of fMRI signal, there was an overall decrease in activity in the NREM-I group the second time subjects performed the task, particularly in regions involved in emotional processing, such as occipital and temporal areas, as well as in the ventrolateral prefrontal cortex, involved in top-down emotion regulation. In contrast, activity in these areas remained the same level or even increased in the REM-D group, compared to their baseline level.Taken together, these results suggest that lack of REM sleep in humans is associated with enhanced emotional reactivity, both at behavioral and neural levels, and thus highlight the specific role of REM sleep in regulating the neural substrates for emotional responsiveness.

  13. Molecular cloning and functional analysis of the Populus deltoides remorin gene PdREM.

    Science.gov (United States)

    Li, Shaofeng; Su, Xiaohua; Zhang, Bingyu; Huang, Qinjun; Hu, Zanmin; Lu, Mengzhu

    2013-10-01

    Remorins play vital roles in signal transduction, energy transformation, ion flow and transport in plants. Upregulation of remorins correlates with dehiscence and cell maturation; however, no studies have been performed to elucidate the function of remorins in tree species. In this study, a Populus deltoides (Marsh.) plasma membrane-binding protein remorin gene (PdREM) was cloned and characterized by investigating its expression pattern and creating transgenic hybrid poplar (P. davidiana Dode × P. bolleana Lauche) lines expressing sense or antisense PdREM. PdREM was specifically expressed in leaf buds, and immature and mature phloem in P. deltoides. Downregulation of PdREM increased plant height, stem diameter, number of leaves, size of the xylem and phloem zones and induced expression of cell wall biosynthesis- and microfibril angle (MFA)-related genes. Overexpression of PdREM retarded vegetative growth. PdREM may negatively regulate vascular growth by inhibiting secondary cell wall expansion in poplar. In addition, antisense PdREM transgenic poplar had a lower MFA, suggesting that PdREM might contribute to sheet strength and wood properties in poplar. This study sheds light on the molecular mechanism of PdREM in P. deltoides growth and development, and lays the foundation for future functional genomics research into wood formation and the genetic engineering of forest trees with improved wood quality traits.

  14. REM sleep modifications following a Morse code learning session in humans.

    Science.gov (United States)

    Mandai, O; Guerrien, A; Sockeel, P; Dujardin, K; Leconte, P

    1989-10-01

    Various experimental data indicate that rapid eye movement (REM) sleep is involved in learning processes. In animals, any complex task in a learning environment leads to an increase of the consecutive total REM sleep time, especially just before learning completion. In humans, the oculomotor activity during REM sleep seems to constitute an interesting marker of learning performance. In this work, we focus on the qualitative analysis of REM sleep characteristics after a Morse code learning session. Eight male subjects were polygraphically recorded during three consecutive nights. A computer aided teaching session was performed just before bedrest onset of the experimental night. The learning performance (percentage of saving) was checked on awakening. The Morse code learning led to some modifications in REM sleep components, particularly increases of REM sleep time and number of REM episodes. We did not observe any significant modification in the total number of REMs in the experimental night. However, the correlative analysis between learning performance and sleep parameters indicates a superior r for the oculomotor activity than for the tonic components. This is consistent with the information processing hypothesis in which the temporal distribution of REMs reflects the subject's ability to increase the signal-noise ratio from environmental information intake.

  15. Respiratory muscle activity during REM sleep in patients with diaphragm paralysis.

    Science.gov (United States)

    Bennett, J R; Dunroy, H M A; Corfield, D R; Hart, N; Simonds, A K; Polkey, M I; Morrell, M J

    2004-01-13

    The diaphragm is the main inspiratory muscle during REM sleep. It was hypothesized that patients with isolated bilateral diaphragm paralysis (BDP) might not be able to sustain REM sleep. Polysomnography with EMG recordings was undertaken from accessory respiratory muscles in patients with BDP and normal subjects. Patients with BDP had a normal quantity of REM sleep (mean +/- SD, 18.6 +/- 7.5% of total sleep time) achieved by inspiratory recruitment of extradiaphragmatic muscles in both tonic and phasic REM, suggesting brainstem reorganization.

  16. Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

    Science.gov (United States)

    Boone, Spencer

    2017-01-01

    This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

  17. Approximating prediction uncertainty for random forest regression models

    Science.gov (United States)

    John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

    2016-01-01

    Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

  18. Prediction of cloud droplet number in a general circulation model

    Energy Technology Data Exchange (ETDEWEB)

    Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States)

    1996-04-01

    We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.

  19. The Next Page Access Prediction Using Makov Model

    OpenAIRE

    Deepti Razdan

    2011-01-01

    Predicting the next page to be accessed by the Webusers has attracted a large amount of research. In this paper, anew web usage mining approach is proposed to predict next pageaccess. It is proposed to identify similar access patterns from weblog using K-mean clustering and then Markov model is used forprediction for next page accesses. The tightness of clusters isimproved by setting similarity threshold while forming clusters.In traditional recommendation models, clustering by nonsequentiald...

  20. Working Towards a Risk Prediction Model for Neural Tube Defects

    Science.gov (United States)

    Agopian, A.J.; Lupo, Philip J.; Tinker, Sarah C.; Canfield, Mark A.; Mitchell, Laura E.

    2015-01-01

    BACKGROUND Several risk factors have been consistently associated with neural tube defects (NTDs). However, the predictive ability of these risk factors in combination has not been evaluated. METHODS To assess the predictive ability of established risk factors for NTDs, we built predictive models using data from the National Birth Defects Prevention Study, which is a large, population-based study of nonsyndromic birth defects. Cases with spina bifida or anencephaly, or both (n = 1239), and controls (n = 8494) were randomly divided into separate training (75% of cases and controls) and validation (remaining 25%) samples. Multivariable logistic regression models were constructed with the training samples. The predictive ability of these models was evaluated in the validation samples by assessing the area under the receiver operator characteristic curves. An ordinal predictive risk index was also constructed and evaluated. In addition, the ability of classification and regression tree (CART) analysis to identify subgroups of women at increased risk for NTDs in offspring was evaluated. RESULTS The predictive ability of the multivariable models was poor (area under the receiver operating curve: 0.55 for spina bifida only, 0.59 for anencephaly only, and 0.56 for anencephaly and spina bifida combined). The predictive abilities of the ordinal risk indexes and CART models were also low. CONCLUSION Current established risk factors for NTDs are insufficient for population-level prediction of a women’s risk for having affected offspring. Identification of genetic risk factors and novel nongenetic risk factors will be critical to establishing models, with good predictive ability, for NTDs. PMID:22253139

  1. Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

    Science.gov (United States)

    Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng

    2017-10-09

    Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  2. Predictive QSAR Models for the Toxicity of Disinfection Byproducts

    Directory of Open Access Journals (Sweden)

    Litang Qin

    2017-10-01

    Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

  3. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp

    2013-01-01

    function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...... capacity associated with large penetration of intermittent renewable energy sources in a future smart grid....

  4. Maxent modelling for predicting the potential distribution of Thai Palms

    DEFF Research Database (Denmark)

    Tovaranonte, Jantrararuk; Barfod, Anders S.; Overgaard, Anne Blach

    2011-01-01

    Increasingly species distribution models are being used to address questions related to ecology, biogeography and species conservation on global and regional scales. We used the maximum entropy approach implemented in the MAXENT programme to build a habitat suitability model for Thai palms based...... overprediction of species distribution ranges. The models with the best predictive power were found by calculating the area under the curve (AUC) of receiver-operating characteristic (ROC). Here, we provide examples of contrasting predicted species distribution ranges as well as a map of modeled palm diversity...

  5. Validation of Fatigue Modeling Predictions in Aviation Operations

    Science.gov (United States)

    Gregory, Kevin; Martinez, Siera; Flynn-Evans, Erin

    2017-01-01

    Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

  6. Aero-acoustic noise of wind turbines. Noise prediction models

    Energy Technology Data Exchange (ETDEWEB)

    Maribo Pedersen, B. [ed.

    1997-12-31

    Semi-empirical and CAA (Computational AeroAcoustics) noise prediction techniques are the subject of this expert meeting. The meeting presents and discusses models and methods. The meeting may provide answers to the following questions: What Noise sources are the most important? How are the sources best modeled? What needs to be done to do better predictions? Does it boil down to correct prediction of the unsteady aerodynamics around the rotor? Or is the difficult part to convert the aerodynamics into acoustics? (LN)

  7. Using a Prediction Model to Manage Cyber Security Threats.

    Science.gov (United States)

    Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

    2015-01-01

    Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  8. Using a Prediction Model to Manage Cyber Security Threats

    Directory of Open Access Journals (Sweden)

    Venkatesh Jaganathan

    2015-01-01

    Full Text Available Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  9. Predictions for mt and MW in minimal supersymmetric models

    International Nuclear Information System (INIS)

    Buchmueller, O.; Ellis, J.R.; Flaecher, H.; Isidori, G.

    2009-12-01

    Using a frequentist analysis of experimental constraints within two versions of the minimal supersymmetric extension of the Standard Model, we derive the predictions for the top quark mass, m t , and the W boson mass, m W . We find that the supersymmetric predictions for both m t and m W , obtained by incorporating all the relevant experimental information and state-of-the-art theoretical predictions, are highly compatible with the experimental values with small remaining uncertainties, yielding an improvement compared to the case of the Standard Model. (orig.)

  10. Lucid dreaming verified by volitional communication during REM sleep.

    Science.gov (United States)

    La Berge, S P; Nagel, L E; Dement, W C; Zarcone, V P

    1981-06-01

    The occurrence of lucid dreaming (dreaming while being conscious that one is dreaming) has been verified for 5 selected subjects who signaled that they knew they were dreaming while continuing to dream during unequivocal REM sleep. The signals consisted of particular dream actions having observable concomitants and were performed in accordance with pre-sleep agreement. The ability of proficient lucid dreamers to signal in this manner makes possible a new approach to dream research--such subjects, while lucid, could carry out diverse dream experiments marking the exact time of particular dream events, allowing derivation of of precise psychophysiological correlations and methodical testing of hypotheses.

  11. Webinar of paper 2013, Which method predicts recidivism best? A comparison of statistical, machine learning and data mining predictive models

    NARCIS (Netherlands)

    Tollenaar, N.; Van der Heijden, P.G.M.

    2013-01-01

    Using criminal population criminal conviction history information, prediction models are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining

  12. Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

    Science.gov (United States)

    Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong

    2017-02-03

    Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  13. Risk Prediction Models for Oral Clefts Allowing for Phenotypic Heterogeneity

    Directory of Open Access Journals (Sweden)

    Yalu eWen

    2015-08-01

    Full Text Available Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip and palate have similar genetic etiologies (AUC=0.572 with subtypes of cleft lip only (AUC=0.589, while the subtypes of cleft palate only (CPO have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively. This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.

  14. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A.; Giebel, G.; Landberg, L. [Risoe National Lab., Roskilde (Denmark); Madsen, H.; Nielsen, H.A. [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  15. Survival prediction model for postoperative hepatocellular carcinoma patients.

    Science.gov (United States)

    Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

    2017-09-01

    This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

  16. A prediction model for assessing residential radon concentration in Switzerland

    International Nuclear Information System (INIS)

    Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin

    2012-01-01

    Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be

  17. Numerical modeling capabilities to predict repository performance

    International Nuclear Information System (INIS)

    1979-09-01

    This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used

  18. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    In this thesis, we consider control strategies for flexible distributed energy resources in the future intelligent energy system – the Smart Grid. The energy system is a large-scale complex network with many actors and objectives in different hierarchical layers. Specifically the power system must...... significantly. A Smart Grid calls for flexible consumers that can adjust their consumption based on the amount of green energy in the grid. This requires coordination through new large-scale control and optimization algorithms. Trading of flexibility is key to drive power consumption in a sustainable direction....... In Denmark, we expect that distributed energy resources such as heat pumps, and batteries in electric vehicles will mobilize part of the needed flexibility. Our primary objectives in the thesis were threefold: 1.Simulate the components in the power system based on simple models from literature (e.g. heat...

  19. Model Predictive Control of Wind Turbines

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian

    Wind turbines play a major role in the transformation from a fossil fuel based energy production to a more sustainable production of energy. Total-cost-of-ownership is an important parameter when investors decide in which energy technology they should place their capital. Modern wind turbines...... are controlled by pitching the blades and by controlling the electro-magnetic torque of the generator, thus slowing the rotation of the blades. Improved control of wind turbines, leading to reduced fatigue loads, can be exploited by using less materials in the construction of the wind turbine or by reducing...... the need for maintenance of the wind turbine. Either way, better total-cost-of-ownership for wind turbine operators can be achieved by improved control of the wind turbines. Wind turbine control can be improved in two ways, by improving the model on which the controller bases its design or by improving...

  20. Comparison of Linear Prediction Models for Audio Signals

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

  1. Model Predictive Control of a Wave Energy Converter

    DEFF Research Database (Denmark)

    Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard

    2015-01-01

    In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model......'s are designed for each sea state using a model assuming a linear loss torque. The mean power results from two controllers are compared using both loss models. Simulation results show that MPC can outperform a reactive controller if a good model of the conversion losses is available....... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...

  2. Review of Model Predictions for Extensive Air Showers

    Science.gov (United States)

    Pierog, Tanguy

    In detailed air shower simulations, the uncertainty in the prediction of shower observable for different primary particles and energies is currently dominated by differences between hadronic interaction models. With the results of the first run of the LHC, the difference between post-LHC model predictions has been reduced at the same level as experimental uncertainties of cosmic ray experiments. At the same time new types of air shower observables, like the muon production depth, have been measured, adding new constraints on hadronic models. Currently no model is able to reproduce consistently all mass composition measurements possible with the Pierre Auger Observatory for instance. We review the current model predictions for various particle production observables and their link with air shower observables and discuss the future possible improvements.

  3. Integrating predictive frameworks and cognitive models of face perception.

    Science.gov (United States)

    Trapp, Sabrina; Schweinberger, Stefan R; Hayward, William G; Kovács, Gyula

    2018-02-08

    The idea of a "predictive brain"-that is, the interpretation of internal and external information based on prior expectations-has been elaborated intensely over the past decade. Several domains in cognitive neuroscience have embraced this idea, including studies in perception, motor control, language, and affective, social, and clinical neuroscience. Despite the various studies that have used face stimuli to address questions related to predictive processing, there has been surprisingly little connection between this work and established cognitive models of face recognition. Here we suggest that the predictive framework can serve as an important complement of established cognitive face models. Conversely, the link to cognitive face models has the potential to shed light on issues that remain open in predictive frameworks.

  4. A model for predicting lung cancer response to therapy

    International Nuclear Information System (INIS)

    Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-01-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  5. Predictive modeling of coupled multi-physics systems: I. Theory

    International Nuclear Information System (INIS)

    Cacuci, Dan Gabriel

    2014-01-01

    Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially

  6. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  7. Comparison of Predictive Modeling Methods of Aircraft Landing Speed

    Science.gov (United States)

    Diallo, Ousmane H.

    2012-01-01

    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  8. Model Predictive Control of Three Phase Inverter for PV Systems

    OpenAIRE

    Irtaza M. Syed; Kaamran Raahemifar

    2015-01-01

    This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize the TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of a boost converter (BC), maximum power point tracking (MPPT) control, and a three-leg voltage source inverter (VSI). The operational model of ...

  9. Prediction error, ketamine and psychosis: An updated model.

    Science.gov (United States)

    Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

    2016-11-01

    In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

  10. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

    Science.gov (United States)

    Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

    2018-02-01

    To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

  11. Fournier's gangrene: a model for early prediction.

    Science.gov (United States)

    Palvolgyi, Roland; Kaji, Amy H; Valeriano, Javier; Plurad, David; Rajfer, Jacob; de Virgilio, Christian

    2014-10-01

    Early diagnosis remains the cornerstone of management of Fournier's gangrene. As a result of variable progression of disease, identifying early predictors of necrosis becomes a diagnostic challenge. We present a scoring system based on objective admission criteria, which can help distinguish Fournier's gangrene from nonnecrotizing scrotal infections. Ninety-six patients were identified, 38 diagnosed with Fournier's gangrene and 58 diagnosed with scrotal cellulitis or abscess. Statistical analyses comparing admission vital signs, laboratory values, and imaging studies were performed and Classification and Regression Tree analysis was used to construct a scoring system. Admission heart rate greater than 110 beats/minute, serum sodium less than 135 mmol/L, blood urea nitrogen greater than 15 mg/dL, and white blood cell count greater than 15 × 10(3)/μL were significant predictors of Fournier's gangrene. Using a threshold score of two or greater, our model differentiates patients with Fournier's gangrene from those with nonnecrotizing infections with a sensitivity of 84.2 per cent. Only 34.2 per cent of patients with Fournier's gangrene had hard signs of necrotizing infection on admission, which were not observed in patients with nonnecrotizing infections. Objective admission criteria assist in distinguishing Fournier's gangrene from scrotal cellulitis or abscess. In situations in which results of the physical examination are ambiguous, this scoring system can heighten the index of suspicion for Fournier's gangrene and prompt rapid surgical intervention.

  12. Phasic motor activity of respiratory and non-respiratory muscles in REM sleep.

    Science.gov (United States)

    Fraigne, Jimmy J; Orem, John M

    2011-04-01

    In this study, we quantified the profiles of phasic activity in respiratory muscles (diaphragm, genioglossus and external intercostal) and non-respiratory muscles (neck and extensor digitorum) across REM sleep. We hypothesized that if there is a unique pontine structure that controls all REM sleep phasic events, the profiles of the phasic twitches of different muscle groups should be identical. Furthermore, we described how respiratory parameters (e.g., frequency, amplitude, and effort) vary across REM sleep to determine if phasic processes affect breathing. Electrodes were implanted in Wistar rats to record brain activity and muscle activity of neck, extensor digitorum, diaphragm, external intercostal, and genioglossal muscles. Ten rats were studied to obtain 313 REM periods over 73 recording days. Data were analyzed offline and REM sleep activity profiles were built for each muscle. In 6 animals, respiratory frequency, effort, amplitude, and inspiratory peak were also analyzed during 192 REM sleep periods. Respiratory muscle phasic activity increased in the second part of the REM period. For example, genioglossal activity increased in the second part of the REM period by 63.8% compared to the average level during NREM sleep. This profile was consistent between animals and REM periods (η(2)=0.58). This increased activity seen in respiratory muscles appeared as irregular bursts and trains of activity that could affect rythmo-genesis. Indeed, the increased integrated activity seen in the second part of the REM period in the diaphragm was associated with an increase in the number (28.3%) and amplitude (30%) of breaths. Non-respiratory muscle phasic activity in REM sleep did not have a profile like the phasic activity of respiratory muscles. Time in REM sleep did not have an effect on nuchal activity (P=0.59). We conclude that the concept of a common pontine center controlling all REM phasic events is not supported by our data. There is a drive in REM sleep that

  13. A deep auto-encoder model for gene expression prediction.

    Science.gov (United States)

    Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

    2017-11-17

    Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

  14. Predicting the Yield Stress of SCC using Materials Modelling

    DEFF Research Database (Denmark)

    Thrane, Lars Nyholm; Hasholt, Marianne Tange; Pade, Claus

    2005-01-01

    A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickne...... and distribution were varied between SCC types. The results indicate that yield stress of SCC may be predicted using the model.......A conceptual model for predicting the Bingham rheological parameter yield stress of SCC has been established. The model used here is inspired by previous work of Oh et al. (1), predicting that the yield stress of concrete relative to the yield stress of paste is a function of the relative thickness...... of excess paste around the aggregate. The thickness of excess paste is itself a function of particle shape, particle size distribution, and particle packing. Seven types of SCC were tested at four different excess paste contents in order to verify the conceptual model. Paste composition and aggregate shape...

  15. Predictive models of prolonged mechanical ventilation yield moderate accuracy.

    Science.gov (United States)

    Figueroa-Casas, Juan B; Dwivedi, Alok K; Connery, Sean M; Quansah, Raphael; Ellerbrook, Lowell; Galvis, Juan

    2015-06-01

    To develop a model to predict prolonged mechanical ventilation within 48 hours of its initiation. In 282 general intensive care unit patients, multiple variables from the first 2 days on mechanical ventilation and their total ventilation duration were prospectively collected. Three models accounting for early deaths were developed using different analyses: (a) multinomial logistic regression to predict duration > 7 days vs duration ≤ 7 days alive vs duration ≤ 7 days death; (b) binary logistic regression to predict duration > 7 days for the entire cohort and for survivors only, separately; and (c) Cox regression to predict time to being free of mechanical ventilation alive. Positive end-expiratory pressure, postoperative state (negatively), and Sequential Organ Failure Assessment score were independently associated with prolonged mechanical ventilation. The multinomial regression model yielded an accuracy (95% confidence interval) of 60% (53%-64%). The binary regression models yielded accuracies of 67% (61%-72%) and 69% (63%-75%) for the entire cohort and for survivors, respectively. The Cox regression model showed an equivalent to area under the curve of 0.67 (0.62-0.71). Different predictive models of prolonged mechanical ventilation in general intensive care unit patients achieve a moderate level of overall accuracy, likely insufficient to assist in clinical decisions. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Comparison of the models of financial distress prediction

    Directory of Open Access Journals (Sweden)

    Jiří Omelka

    2013-01-01

    Full Text Available Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company. The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.

  17. Plant water potential improves prediction of empirical stomatal models.

    Directory of Open Access Journals (Sweden)

    William R L Anderegg

    Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

  18. Maternal Stress Induces Adult Reduced REM sleep and Melatonin Level

    Science.gov (United States)

    Feng, Pingfu; Hu, Yufen; Vurbic, Drina; Guo, Yang

    2013-01-01

    Objectives We have previously reported that neonatal maternal deprivation (MD) resulted in a decrease of total sleep and an increase of orexin A in adult rats. Now, we characterized features of sleep, activity, and melatonin levels in rats neonatally treated with MD and control (MC) procedures. Design Adult male Sprague Dawley rats were treated with either MD or MC procedures for ten days starting at postnatal day 4. At three months of age, sleep was recorded for 48 hours in one set of MD and MC rats while another set of MD and MC rats were measured for locomotor activity (under LD=12:12). Melatonin levels in the blood, pineal gland, and hypothalamus were measured as well as clock protein level in the hypothalamus. Results Compared with the MC rats, REM sleep in the MD rats was significantly reduced in the light periods but not in the dark periods. Both quiet wake and total wake in the MD rats were significantly increased during the light period compared to the MC rats. The weight of the pineal gland of the MD rats was significantly smaller than in MC rats. Melatonin levels of the MD group were significantly reduced in the pineal gland and hypothalamus compared with the MC group. No significant difference was identified between groups in the expression of the clock protein in the hypothalamus. Conclusion Neonatal MD resulted in reduced REM sleep and melatonin levels, without changes of circadian cycle of locomotor activity and levels of clock protein. PMID:21805687

  19. Abandoned Remšnik mine with ramsbeckite and namuwite(?

    Directory of Open Access Journals (Sweden)

    Mirka Trajanova

    2013-06-01

    Full Text Available The polymetallic Rem{nik ore deposit is situated at the Kobansko area in northern Slovenia. Mineralizationoccurs in the thrust zone of the weakly metamorphosed old Palaeozoic rocks of the Magdalensberg formation, theRem{nik nappe, onto the retrogressed schists of the Austroalpine metamorphic basement. Hydrothermal ore mineralizationand silicification follow slaty cleavage in partly brecciated marmorized dolomite lenses and subordinatelyin metatuffites and phyllites. Its origin is most probably connected to the lively Tertiary magmatism, occurring atthe Pohorje Mountains. Mineral paragenesis of the predominant Pb, Cu and Zn silver bearing sulphide ore is associatedwith numerous secondary minerals. Among them, two rare sulphates with H2O of Cu and Zn occur, foundfor the first time in Slovenia. The green, transparent monoclinic crystals, only some tenth of millimetre in size weredetermined by SEM as ramsbeckite (Cu, Zn15(SO44(OH22 · 6(H2O. Greenish in colour are also leafy, hexagonal,flowery intergrown crystals, which most probably belong to namuwite (Cu, Zn4(SO4(OH6 · 4(H2O. Its submicroscopicsize and small quantity did not permit reliable determination, yet.

  20. Predicting the ungauged basin: model validation and realism assessment

    NARCIS (Netherlands)

    van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert

    2015-01-01

    The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

  1. Predicting the ungauged basin : Model validation and realism assessment

    NARCIS (Netherlands)

    van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert

    2015-01-01

    The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

  2. A Mathematical Model for the Prediction of Injectivity Decline | Odeh ...

    African Journals Online (AJOL)

    Injectivity impairment due to invasion of solid suspensions has been studied by several investigators and some modelling approaches have also been reported. Worthy of note is the development of analytical models for internal and external filtration coupled with transition time concept for predicting the overall decline in ...

  3. Mathematical Model for Prediction of Flexural Strength of Mound ...

    African Journals Online (AJOL)

    The mound soil-cement blended proportions were mathematically optimized by using scheffe's approach and the optimization model developed. A computer program predicting the mix proportion for the model was written. The optimal proportion by the program was used prepare beam samples measuring 150mm x 150mm ...

  4. Katz model prediction of Caenorhabditis elegans mutagenesis on STS-42

    Science.gov (United States)

    Cucinotta, Francis A.; Wilson, John W.; Katz, Robert; Badhwar, Gautam D.

    1992-01-01

    Response parameters that describe the production of recessive lethal mutations in C. elegans from ionizing radiation are obtained with the Katz track structure model. The authors used models of the space radiation environment and radiation transport to predict and discuss mutation rates for C. elegans on the IML-1 experiment aboard STS-42.

  5. Accident Prediction Models for Akure – Ondo Carriageway, Ondo ...

    African Journals Online (AJOL)

    FIRST LADY

    traffic exposure and intersection effects as independent variables. They suggested that the Poisson distribution allows for the relationship between exposure and crashes to be more accurately modeled as opposed to. Accident Prediction Models for Akure-Ondo Carriageway…Using Multiple Linear Regression ...

  6. Multi-model prediction of downward short-wave radiation

    Czech Academy of Sciences Publication Activity Database

    Eben, Kryštof; Resler, Jaroslav; Krč, Pavel; Juruš, Pavel; Pelikán, Emil

    2012-01-01

    Roč. 9, - (2012), EMS2012-384 [EMS Annual Meeting /12./ and European Conference on Applied Climatology /9./. 10.09.2012-14.09.2012, Lodz] Institutional support: RVO:67985807 Keywords : multi-model prediction * NWP * model postprocessing Subject RIV: DG - Athmosphere Sciences, Meteorology

  7. Atmospheric modelling for seasonal prediction at the CSIR

    CSIR Research Space (South Africa)

    Landman, WA

    2014-10-01

    Full Text Available by observed monthly sea-surface temperature (SST) and sea-ice fields. The AGCM is the conformal-cubic atmospheric model (CCAM) administered by the Council for Scientific and Industrial Research. Since the model is forced with observed rather than predicted...

  8. Prediction Models and Decision Support: Chances and Challenges

    NARCIS (Netherlands)

    Kappen, T.H.

    2015-01-01

    A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimating the prognosis. By utilizing various patient- and disease-related properties, such models can yield objective estimations of the risk of a disease or the probability of a certain disease course for

  9. Validation of a multi-objective, predictive urban traffic model

    NARCIS (Netherlands)

    Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.

    2013-01-01

    This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a

  10. Predictive ability of boiler production models | Ogundu | Animal ...

    African Journals Online (AJOL)

    The weekly body weight measurements of a growing strain of Ross broiler were used to compare the of ability of three mathematical models (the multi, linear, quadratic and Exponential) to predict 8 week body weight from early body measurements at weeks I, II, III, IV, V, VI and VII. The results suggest that the three models ...

  11. Predictive modelling of noise level generated during sawing of rocks ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... Influence of the operating variables and rock properties on the noise level are investigated and analysed. Statistical analyses are then employed and models are built for the prediction of noise levels depending on the operating variables and the rock properties. The derived models are validated through ...

  12. Modelling and prediction of non-stationary optical turbulence behaviour

    NARCIS (Netherlands)

    Doelman, N.J.; Osborn, J.

    2016-01-01

    There is a strong need to model the temporal fluctuations in turbulence parameters, for instance for scheduling, simulation and prediction purposes. This paper aims at modelling the dynamic behaviour of the turbulence coherence length r0, utilising measurement data from the Stereo-SCIDAR instrument

  13. Inferential ecosystem models, from network data to prediction

    Science.gov (United States)

    James S. Clark; Pankaj Agarwal; David M. Bell; Paul G. Flikkema; Alan Gelfand; Xuanlong Nguyen; Eric Ward; Jun. Yang

    2011-01-01

    Recent developments suggest that predictive modeling could begin to play a larger role not only for data analysis, but also for data collection. We address the example of efficient wireless sensor networks, where inferential ecosystem models can be used to weigh the value of an observation against the cost of data collection. Transmission costs make observations ‘‘...

  14. Model prediction of maize yield responses to climate change in ...

    African Journals Online (AJOL)

    Observed data of the last three decades (1971 to 2000) from several climatological stations in north-eastern Zimbabwe and outputs from several global climate models were used. The downscaled model simulations consistently predicted a warming of between 1 and 2 ºC above the baseline period (1971-2000) at most of ...

  15. A theoretical model for predicting neutron fluxes for cyclic Neutron ...

    African Journals Online (AJOL)

    A theoretical model has been developed for prediction of thermal neutron fluxes required for cyclic irradiations of a sample to obtain the same activity previously used for the detection of any radionuclide of interest. The model is suitable for radiotracer production or for long-lived neutron activation products where the ...

  16. A model to predict the sound reflection from forests

    NARCIS (Netherlands)

    Wunderli, J.M.; Salomons, E.M.

    2009-01-01

    A model is presented to predict the reflection of sound at forest edges. A single tree is modelled as a vertical cylinder. For the reflection at a cylinder an analytical solution is given based on the theory of scattering of spherical waves. The entire forest is represented by a line of cylinders

  17. Model Predictive Control for Offset-Free Reference Tracking

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav

    2016-01-01

    Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf

  18. Multi-model ensemble schemes for predicting northeast monsoon ...

    Indian Academy of Sciences (India)

    An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among ...

  19. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted

    2017-01-01

    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized contro...

  20. Evaluation of preformance of Predictive Models for Deoxynivalenol in Wheat

    NARCIS (Netherlands)

    Fels, van der H.J.

    2014-01-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields

  1. Three-model ensemble wind prediction in southern Italy

    Directory of Open Access Journals (Sweden)

    R. C. Torcasio

    2016-03-01

    Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  2. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  3. The predictive performance and stability of six species distribution models.

    Science.gov (United States)

    Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pSDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  4. SHMF: Interest Prediction Model with Social Hub Matrix Factorization

    Directory of Open Access Journals (Sweden)

    Chaoyuan Cui

    2017-01-01

    Full Text Available With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.

  5. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

  6. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Directory of Open Access Journals (Sweden)

    Jaime Cuevas

    2017-01-01

    Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u   and   f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .

  7. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

    2017-01-05

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

  8. Prognostic and symptomatic aspects of rapid eye movement sleep in a mouse model of posttraumatic stress disorder

    Directory of Open Access Journals (Sweden)

    Stephanie A. Polta

    2013-05-01

    Full Text Available Not every individual develops Posttraumatic Stress Disorder (PTSD after the exposure to a potentially traumatic event. Therefore, the identification of pre-existing risk factors and early diagnostic biomarkers is of high medical relevance. However, no objective biomarker has yet progressed into clinical practice. Sleep disturbances represent commonly reported complaints in PTSD patients. In particular, changes in rapid eye movement sleep (REMS properties are frequently observed in PTSD patients. Here, we examined in a mouse model of PTSD whether (1 mice developed REMS alterations after trauma and (2 whether REMS architecture before and/or shortly after trauma predicted the development of PTSD-like symptoms. We monitored sleep-wake behavior via combined EEG/EMG recordings immediately before (24 h pre, immediately after (0-48 h post and two months after exposure to an electric foot shock in male C57BL/6N mice (n=15. PTSD-like symptoms, including hyperarousal, contextual and generalized fear, were assessed one month post-trauma.Shocked mice showed early-onset and sustained elevation of REMS compared to non-shocked controls. In addition, REMS architecture before trauma was correlated with the intensity of acoustic startle responses, but not contextual fear, one month after trauma.Our data suggest REMS as prognostic (pre-trauma and symptomatic (post-trauma marker of PTSD-like symptoms in mice. Translated to the situation in humans, REMS may constitute a viable, objective and non-invasive biomarker in PTSD and other trauma-related psychiatric disorders, which could guide pharmacological interventions in humans at high risk.

  9. Stochastic models for predicting pitting corrosion damage of HLRW containers

    International Nuclear Information System (INIS)

    Henshall, G.A.

    1991-10-01

    Stochastic models for predicting aqueous pitting corrosion damage of high-level radioactive-waste containers are described. These models could be used to predict the time required for the first pit to penetrate a container and the increase in the number of breaches at later times, both of which would be useful in the repository system performance analysis. Monte Carlo implementations of the stochastic models are described, and predictions of induction time, survival probability and pit depth distributions are presented. These results suggest that the pit nucleation probability decreases with exposure time and that pit growth may be a stochastic process. The advantages and disadvantages of the stochastic approach, methods for modeling the effects of environment, and plans for future work are discussed

  10. Verification and improvement of a predictive model for radionuclide migration

    International Nuclear Information System (INIS)

    Miller, C.W.; Benson, L.V.; Carnahan, C.L.

    1982-01-01

    Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

  11. A novel Bayesian hierarchical model for road safety hotspot prediction.

    Science.gov (United States)

    Fawcett, Lee; Thorpe, Neil; Matthews, Joseph; Kremer, Karsten

    2017-02-01

    In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation - commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period - to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our

  12. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

    International Nuclear Information System (INIS)

    Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure

    2012-01-01

    We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.

  13. A simplified building airflow model for agent concentration prediction.

    Science.gov (United States)

    Jacques, David R; Smith, David A

    2010-11-01

    A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.

  14. Discrete fracture modelling for the Stripa tracer validation experiment predictions

    International Nuclear Information System (INIS)

    Dershowitz, W.; Wallmann, P.

    1992-02-01

    Groundwater flow and transport through three-dimensional networks of discrete fractures was modeled to predict the recovery of tracer from tracer injection experiments conducted during phase 3 of the Stripa site characterization and validation protect. Predictions were made on the basis of an updated version of the site scale discrete fracture conceptual model used for flow predictions and preliminary transport modelling. In this model, individual fractures were treated as stochastic features described by probability distributions of geometric and hydrologic properties. Fractures were divided into three populations: Fractures in fracture zones near the drift, non-fracture zone fractures within 31 m of the drift, and fractures in fracture zones over 31 meters from the drift axis. Fractures outside fracture zones are not modelled beyond 31 meters from the drift axis. Transport predictions were produced using the FracMan discrete fracture modelling package for each of five tracer experiments. Output was produced in the seven formats specified by the Stripa task force on fracture flow modelling. (au)

  15. Predicting nucleic acid binding interfaces from structural models of proteins.

    Science.gov (United States)

    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  16. Adaptive Gaussian Predictive Process Models for Large Spatial Datasets

    Science.gov (United States)

    Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.

    2011-01-01

    Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952

  17. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

    Science.gov (United States)

    Liu, Bowen; Ramsundar, Bharath; Kawthekar, Prasad; Shi, Jade; Gomes, Joseph; Luu Nguyen, Quang; Ho, Stephen; Sloane, Jack; Wender, Paul; Pande, Vijay

    2017-10-25

    We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.

  18. The supramammillary nucleus and the claustrum activate the cortex during REM sleep

    Science.gov (United States)

    Renouard, Leslie; Billwiller, Francesca; Ogawa, Keiko; Clément, Olivier; Camargo, Nutabi; Abdelkarim, Mouaadh; Gay, Nadine; Scoté-Blachon, Céline; Touré, Rouguy; Libourel, Paul-Antoine; Ravassard, Pascal; Salvert, Denise; Peyron, Christelle; Claustrat, Bruno; Léger, Lucienne; Salin, Paul; Malleret, Gael; Fort, Patrice; Luppi, Pierre-Hervé

    2015-01-01

    Evidence in humans suggests that limbic cortices are more active during rapid eye movement (REM or paradoxical) sleep than during waking, a phenomenon fitting with the presence of vivid dreaming during this state. In that context, it seemed essential to determine which populations of cortical neurons are activated during REM sleep. Our aim in the present study is to fill this gap by combining gene expression analysis, functional neuroanatomy, and neurochemical lesions in rats. We find in rats that, during REM sleep hypersomnia compared to control and REM sleep deprivation, the dentate gyrus, claustrum, cortical amygdaloid nucleus, and medial entorhinal and retrosplenial cortices are the only cortical structures containing neurons with an increased expression of Bdnf, FOS, and ARC, known markers of activation and/or synaptic plasticity. Further, the dentate gyrus is the only cortical structure containing more FOS-labeled neurons during REM sleep hypersomnia than during waking. Combining FOS staining, retrograde labeling, and neurochemical lesion, we then provide evidence that FOS overexpression occurring in the cortex during REM sleep hypersomnia is due to projections from the supramammillary nucleus and the claustrum. Our results strongly suggest that only a subset of cortical and hippocampal neurons are activated and display plasticity during REM sleep by means of ascending projections from the claustrum and the supramammillary nucleus. Our results pave the way for future studies to identify the function of REM sleep with regard to dreaming and emotional memory processing. PMID:26601158

  19. Analysis of automated quantification of motor activity in REM sleep behaviour disorder

    DEFF Research Database (Denmark)

    Frandsen, Rune; Nikolic, Miki; Zoetmulder, Marielle

    2015-01-01

    . The method was stable and can be used to differentiate RBD from controls and to quantify motor activity during REM sleep in patients with neurodegeneration. No control had more than 30% of REM sleep with increased motor activity; patients with known RBD had as low activity as 4.5%. We developed and applied...

  20. H-reflex suppression and autonomic activation during lucid REM sleep: a case study.

    Science.gov (United States)

    Brylowski, A; Levitan, L; LaBerge, S

    1989-08-01

    A single subject, a proficient lucid dreamer experienced with signaling the onset of lucidity (reflective consciousness of dreaming) by means of voluntary eye movements, spent 4 nonconsecutive nights in the sleep laboratory. The subject reported becoming lucid and signaling in 8 of the 18 rapid-eye movement (REM) periods recorded. Ten lucid dream reports were verified by polygraphic examination of signals, providing a total of 12.5 min of signal-verified lucid REM. H-Reflex amplitude was recorded every 5 s, along with continuous recording of electroencephalogram, electrooculogram, electromyogram, electrocardiogram, finger pulse, and respiration. Significant findings included greater mean H-reflex suppression during lucid REM sleep than during nonlucid REM and correlations of H-reflex suppression with increased eye movement density, heart rate, and respiration rate. These results support previous studies reporting that lucid REM is not, as might be supposed, a state closer to awakening than ordinary, or nonlucid, REM; rather, lucid dreaming occurs during unequivocal REM sleep and is characteristically associated with phasic REM activation.

  1. Pulsatile fluidic pump demonstration and predictive model application

    International Nuclear Information System (INIS)

    Morgan, J.G.; Holland, W.D.

    1986-04-01

    Pulsatile fluidic pumps were developed as a remotely controlled method of transferring or mixing feed solutions. A test in the Integrated Equipment Test facility demonstrated the performance of a critically safe geometry pump suitable for use in a 0.1-ton/d heavy metal (HM) fuel reprocessing plant. A predictive model was developed to calculate output flows under a wide range of external system conditions. Predictive and experimental flow rates are compared for both submerged and unsubmerged fluidic pump cases

  2. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    Directory of Open Access Journals (Sweden)

    Milan Vukićević

    2014-01-01

    Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.

  3. Models for Predicting and Explaining Citation Count of Biomedical Articles

    OpenAIRE

    Fu, Lawrence D.; Aliferis, Constantin

    2008-01-01

    The single most important bibliometric criterion for judging the impact of biomedical papers and their authors’ work is the number of citations received which is commonly referred to as “citation count”. This metric however is unavailable until several years after publication time. In the present work, we build computer models that accurately predict citation counts of biomedical publications within a deep horizon of ten years using only predictive information available at publication time. O...

  4. Predictive Models of Li-ion Battery Lifetime

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Kandler; Wood, Eric; Santhanagopalan, Shriram; Kim, Gi-heon; Shi, Ying; Pesaran, Ahmad

    2015-06-15

    It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.

  5. Modelling personality, plasticity and predictability in shelter dogs

    Science.gov (United States)

    2017-01-01

    Behavioural assessments of shelter dogs (Canis lupus familiaris) typically comprise standardized test batteries conducted at one time point, but test batteries have shown inconsistent predictive validity. Longitudinal behavioural assessments offer an alternative. We modelled longitudinal observational data on shelter dog behaviour using the framework of behavioural reaction norms, partitioning variance into personality (i.e. inter-individual differences in behaviour), plasticity (i.e. inter-individual differences in average behaviour) and predictability (i.e. individual differences in residual intra-individual variation). We analysed data on interactions of 3263 dogs (n = 19 281) with unfamiliar people during their first month after arrival at the shelter. Accounting for personality, plasticity (linear and quadratic trends) and predictability improved the predictive accuracy of the analyses compared to models quantifying personality and/or plasticity only. While dogs were, on average, highly sociable with unfamiliar people and sociability increased over days since arrival, group averages were unrepresentative of all dogs and predictions made at the individual level entailed considerable uncertainty. Effects of demographic variables (e.g. age) on personality, plasticity and predictability were observed. Behavioural repeatability was higher one week after arrival compared to arrival day. Our results highlight the value of longitudinal assessments on shelter dogs and identify measures that could improve the predictive validity of behavioural assessments in shelters. PMID:28989764

  6. Prediction of type A behaviour: A structural equation model

    Directory of Open Access Journals (Sweden)

    René van Wyk

    2009-05-01

    Full Text Available The predictability of Type A behaviour was measured in a sample of 375 professionals with a shortened version of the Jenkins Activity Survey (JAS. Two structural equation models were constructed with the Type A behaviour achievement sub-scale and global (total Type A as the predictor variables. The indices showed a reasonable-to-promising fit with the data. Type A achievement was reasonably predicted by service-career orientation, internal locus of control, power self-concept and economic innovation. Type A global was also predicted by internal locus of control, power self-concept and the entrepreneurial attitude of achievement and personal control.

  7. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom...

  8. Determining the prediction limits of models and classifiers with applications for disruption prediction in JET

    Science.gov (United States)

    Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET

    2017-01-01

    Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.

  9. Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

    Science.gov (United States)

    Dedes, I.; Dudek, J.

    2018-03-01

    We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

  10. GA-ARMA Model for Predicting IGS RTS Corrections

    Directory of Open Access Journals (Sweden)

    Mingyu Kim

    2017-01-01

    Full Text Available The global navigation satellite system (GNSS is widely used to estimate user positions. For precise positioning, users should correct for GNSS error components such as satellite orbit and clock errors as well as ionospheric delay. The international GNSS service (IGS real-time service (RTS can be used to correct orbit and clock errors in real-time. Since the IGS RTS provides real-time corrections via the Internet, intermittent data loss can occur due to software or hardware failures. We propose applying a genetic algorithm autoregressive moving average (GA-ARMA model to predict the IGS RTS corrections during data loss periods. The RTS orbit and clock corrections are predicted up to 900 s via the GA-ARMA model, and the prediction accuracies are compared with the results from a generic ARMA model. The orbit prediction performance of the GA-ARMA is nearly equivalent to that of ARMA, but GA-ARMA’s clock prediction performance is clearly better than that of ARMA, achieving a 32% error reduction. Predicted RTS corrections are applied to the broadcast ephemeris, and precise point positioning accuracies are compared. GA-ARMA shows a significant accuracy improvement over ARMA, particularly in terms of vertical positioning.

  11. Modeling the prediction of business intelligence system effectiveness.

    Science.gov (United States)

    Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

    2016-01-01

    Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

  12. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  13. Cardiopulmonary Circuit Models for Predicting Injury to the Heart

    Science.gov (United States)

    Ward, Richard; Wing, Sarah; Bassingthwaighte, James; Neal, Maxwell

    2004-11-01

    Circuit models have been used extensively in physiology to describe cardiopulmonary function. Such models are being used in the DARPA Virtual Soldier (VS) Project* to predict the response to injury or physiological stress. The most complex model consists of systemic circulation, pulmonary circulation, and a four-chamber heart sub-model. This model also includes baroreceptor feedback, airway mechanics, gas exchange, and pleural pressure influence on the circulation. As part of the VS Project, Oak Ridge National Laboratory has been evaluating various cardiopulmonary circuit models for predicting the effects of injury to the heart. We describe, from a physicist's perspective, the concept of building circuit models, discuss both unstressed and stressed models, and show how the stressed models are used to predict effects of specific wounds. *This work was supported by a grant from the DARPA, executed by the U.S. Army Medical Research and Materiel Command/TATRC Cooperative Agreement, Contract # W81XWH-04-2-0012. The submitted manuscript has been authored by the U.S. Department of Energy, Office of Science of the Oak Ridge National Laboratory, managed for the U.S. DOE by UT-Battelle, LLC, under contract No. DE-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purpose.

  14. Prediction of gas compressibility factor using intelligent models

    Directory of Open Access Journals (Sweden)

    Mohamad Mohamadi-Baghmolaei

    2015-10-01

    Full Text Available The gas compressibility factor, also known as Z-factor, plays the determinative role for obtaining thermodynamic properties of gas reservoir. Typically, empirical correlations have been applied to determine this important property. However, weak performance and some limitations of these correlations have persuaded the researchers to use intelligent models instead. In this work, prediction of Z-factor is aimed using different popular intelligent models in order to find the accurate one. The developed intelligent models are including Artificial Neural Network (ANN, Fuzzy Interface System (FIS and Adaptive Neuro-Fuzzy System (ANFIS. Also optimization of equation of state (EOS by Genetic Algorithm (GA is done as well. The validity of developed intelligent models was tested using 1038 series of published data points in literature. It was observed that the accuracy of intelligent predicting models for Z-factor is significantly better than conventional empirical models. Also, results showed the improvement of optimized EOS predictions when coupled with GA optimization. Moreover, of the three intelligent models, ANN model outperforms other models considering all data and 263 field data points of an Iranian offshore gas condensate with R2 of 0.9999, while the R2 for best empirical correlation was about 0.8334.

  15. Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance.

    Science.gov (United States)

    Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M

    2017-09-01

    Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Selective REM-sleep deprivation does not diminish emotional memory consolidation in young healthy subjects.

    Directory of Open Access Journals (Sweden)

    Jarste Morgenthaler

    Full Text Available Sleep enhances memory consolidation and it has been hypothesized that rapid eye movement (REM sleep in particular facilitates the consolidation of emotional memory. The aim of this study was to investigate this hypothesis using selective REM-sleep deprivation. We used a recognition memory task in which participants were shown negative and neutral pictures. Participants (N=29 healthy medical students were separated into two groups (undisturbed sleep and selective REM-sleep deprived. Both groups also worked on the memory task in a wake condition. Recognition accuracy was significantly better for negative than for neutral stimuli and better after the sleep than the wake condition. There was, however, no difference in the recognition accuracy (neutral and emotional between the groups. In summary, our data suggest that REM-sleep deprivation was successful and that the resulting reduction of REM-sleep had no influence on memory consolidation whatsoever.

  17. Estimate of man-rem expenditures for a mature CANDU 600 MW(e) station

    International Nuclear Information System (INIS)

    Kuperman, I.

    1978-08-01

    In recent years, man-rem expenditures at operating stations have come under close scrutiny in order to reduce operating personnel dosage. This increased awareness has led to concerted efforts to improve station design and to improve operating procedures to achieve lower man-rem expenditures. This paper is intended to highlight design improvements that have been made in the CANDU 600 MW(e) design and to show how these improvements will reduce man-rem expenditures. Other considerations, such as station decontaminations of the primary heat transport system and the fuelling machines and stricter chemistry control are presently available to help reduce man-rem consumption. Also, station management operating policy should emphasize man-rem awareness. (author)

  18. Visual hallucinations and pontine demyelination in a child: possible REM dissociation?

    Science.gov (United States)

    Vita, Maria Gabriella; Batocchi, Anna Paola; Dittoni, Serena; Losurdo, Anna; Cianfoni, Alessandro; Stefanini, Maria Chiara; Vollono, Catello; Della Marca, Giacomo; Mariotti, Paolo

    2008-12-15

    An 11 year-old-boy acutely developed complex visual and acoustic hallucinations. Hallucinations, consisting of visions of a threatening, evil character of the Harry Potter saga, persisted for 3 days. Neurological and psychiatric examinations were normal. Ictal EEG was negative. MRI documented 3 small areas of hyperintense signal in the brainstem, along the paramedian and lateral portions of pontine tegmentum, one of which showed post-contrast enhancement. These lesions were likely of inflammatory origin, and treatment with immunoglobulins was started. Polysomnography was normal, multiple sleep latency test showed a mean sleep latency of 8 minutes, with one sleep-onset REM period. The pontine tegmentum is responsible for REM sleep regulation, and contains definite "REM-on" and "REM-off" regions. The anatomical distribution of the lesions permits us to hypothesize that hallucinations in this boy were consequent to a transient impairment of REM sleep inhibitory mechanisms, with the appearance of dream-like hallucinations during wake.

  19. Selective REM-sleep deprivation does not diminish emotional memory consolidation in young healthy subjects.

    Science.gov (United States)

    Morgenthaler, Jarste; Wiesner, Christian D; Hinze, Karoline; Abels, Lena C; Prehn-Kristensen, Alexander; Göder, Robert

    2014-01-01

    Sleep enhances memory consolidation and it has been hypothesized that rapid eye movement (REM) sleep in particular facilitates the consolidation of emotional memory. The aim of this study was to investigate this hypothesis using selective REM-sleep deprivation. We used a recognition memory task in which participants were shown negative and neutral pictures. Participants (N=29 healthy medical students) were separated into two groups (undisturbed sleep and selective REM-sleep deprived). Both groups also worked on the memory task in a wake condition. Recognition accuracy was significantly better for negative than for neutral stimuli and better after the sleep than the wake condition. There was, however, no difference in the recognition accuracy (neutral and emotional) between the groups. In summary, our data suggest that REM-sleep deprivation was successful and that the resulting reduction of REM-sleep had no influence on memory consolidation whatsoever.

  20. The effect of a REM sleep deprivation procedure on different aspects of memory function in humans.

    Science.gov (United States)

    Saxvig, Ingvild West; Lundervold, Astri Johansen; Grønli, Janne; Ursin, Reidun; Bjorvatn, Bjørn; Portas, Chiara Maria

    2008-03-01

    Previous studies have suggested that memory is dependent on the occurrence of REM sleep. Research has mainly focused on two distinct types of memory function, declarative and procedural, and it seems that the latter may more directly depend on REM sleep. Memory consolidation has been more investigated than acquisition, maintenance, and recall, despite the fact that sleep may affect flow of information into/from storage. Moreover, tests have often been limited to stimuli within only one modality (usually visual or verbal). This study aimed to clarify the role of REM sleep in memory by investigating aspects of memory function, processing, and modality in the same experimental setting. Tests of acquisition and consolidation of multiple aspects of memory function within the visual and verbal modalities were administrated to subjects before and after REM sleep deprivation. Results show that test performance was not affected by REM sleep deprivation.