Browsing by Author "Reifman, Jaques"
Now showing 1 - 20 of 49
Results Per Page
Sort Options
- Item2B-Alert App: A mobile application for real-time individualized prediction of alertness.(0000-00-00) Reifman, Jaques; Ramakrishnan, Sridhar; Liu, Jianbo; Kapela, Adam; Doty, Tracy J; Balkin, Thomas J; Kumar, Kamal; Khitrov, Maxim YKnowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies Here we describe and validate the 2B Alert App the first mobile application that progressively learns an individual s trait like response to sleep deprivation in real time to generate increasingly more accurate individualized predictions of alertness We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test We implemented the resulting model and the psychomotor vigilance test as a smartphone application 2B Alert App and prospectively validated its performance in a 62 hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real time individualized predictions after each test The temporal profiles of reaction times on the app conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device The app progressively learned each individual s trait like response to sleep deprivation throughout the study yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased After only 12 psychomotor vigilance tests the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests With the ability to make real time individualized predictions of the effects of sleep deprivation on future alertness the 2B Alert App can be used to tailor personalized fatigue management strategies facilitating self management of alertness and safety in operational and non operational settings
- ItemA 3-D mathematical model to identify organ-specific risks in rats during thermal stress.(2013-12-17) Rakesh, Vineet; Stallings, Jonathan D; Helwig, Bryan G; Leon, Lisa R; Jackson, David A; Reifman, JaquesEarly prediction of the adverse outcomes associated with heat stress is critical for effective management and mitigation of injury which may sometimes lead to extreme undesirable clinical conditions such as multiorgan dysfunction syndrome and death Here we developed a computational model to predict the spatiotemporal temperature distribution in a rat exposed to heat stress in an attempt to understand the correlation between heat load and differential organ dysfunction The model includes a three dimensional representation of the rat anatomy obtained from medical imaging and incorporates the key mechanisms of heat transfer during thermoregulation We formulated a novel approach to estimate blood temperature by accounting for blood mixing from the different organs and to estimate the effects of the circadian rhythm in body temperature by considering day night variations in metabolic heat generation and blood perfusion We validated the model using in vivo core temperature measurements in control and heat stressed rats and other published experimental data The model predictions were within 1 SD of the measured data The liver demonstrated the greatest susceptibility to heat stress with the maximum temperature reaching 2 C higher than the measured core temperature and 95 of its volume exceeding the targeted experimental core temperature Other organs also attained temperatures greater than the core temperature illustrating the need to monitor multiple organs during heat stress The model facilitates the identification of organ specific risks during heat stress and has the potential to aid in the development of improved clinical strategies for thermal injury prevention and management
- ItemAGeS: a software system for microbial genome sequence annotation.(2011-03-16) Kumar, Kamal; Desai, Valmik; Cheng, Li; Khitrov, Maxim; Grover, Deepak; Satya, Ravi Vijaya; Yu, Chenggang; Zavaljevski, Nela; Reifman, JaquesThe annotation of genomes from next generation sequencing platforms needs to be rapid high throughput and fully integrated and automated Although a few Web based annotation services have recently become available they may not be the best solution for researchers that need to annotate a large number of genomes possibly including proprietary data and store them locally for further analysis To address this need we developed a standalone software application the Annotation of microbial Genome Sequences AGeS system which incorporates publicly available and in house developed bioinformatics tools and databases many of which are parallelized for high throughput performance
- ItemAlternative methods for modeling fatigue and performance.(2004-03-15) Reifman, JaquesThe use of nonparametric approaches and semiparametric approaches for modeling fatigue and performance are analyzed Nonparametric approaches in the form of stand alone artificial neural networks and semiparametric hybrid approaches that combine neural networks with prior process knowledge are explored and compared with existing parametric approaches based on the two process model of sleep regulation Within the context of a military application we explore two notional semiparametric approaches for real time prediction of cognitive performance on the basis of individualized on line measurements of physiologic variables Initial analysis indicates that these alternative modeling approaches may address key technological gaps and advance fatigue and performance modeling Most notably these approaches seem amenable to predicting individual performance and quantitatively assessing the reliability of model predictions through estimation of statistical error bounds which have eluded researchers for the last two decades
- ItemCaffeine dosing strategies to optimize alertness during sleep loss.(0000-00-00) Vital-Lopez, Francisco G; Ramakrishnan, Sridhar; Doty, Tracy J; Balkin, Thomas J; Reifman, JaquesSleep loss which affects about one third of the US population can severely impair physical and neurobehavioural performance Although caffeine the most widely used stimulant in the world can mitigate these effects currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits In this work we provide an optimization algorithm suited for mobile computing platforms to determine when and how much caffeine to consume so as to safely maximize neurobehavioural performance at the desired time of the day under any sleep loss condition The algorithm is based on our previously validated Unified Model of Performance which predicts the effect of caffeine consumption on a psychomotor vigilance task We assessed the algorithm by comparing the caffeine dosing strategies timing and amount it identified with the dosing strategies used in four experimental studies involving total and partial sleep loss Through computer simulations we showed that the algorithm yielded caffeine dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64 while using the same total amount of caffeine as in the original studies In addition the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65 Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep loss condition
- ItemCan a mathematical model predict an individual's trait-like response to both total and partial sleep loss?(2015-01-12) Ramakrishnan, Sridhar; Lu, Wei; Laxminarayan, Srinivas; Wesensten, Nancy J; Rupp, Tracy L; Balkin, Thomas J; Reifman, JaquesHumans display a trait like response to sleep loss However it is not known whether this trait like response can be captured by a mathematical model from only one sleep loss condition to facilitate neurobehavioural performance prediction of the same individual during a different sleep loss condition In this paper we investigated the extent to which the recently developed unified mathematical model of performance UMP captured such trait like features for different sleep loss conditions We used the UMP to develop two sets of individual specific models for 15 healthy adults who underwent two different sleep loss challenges order counterbalanced separated by 2 4 weeks i 64 h of total sleep deprivation TSD and ii chronic sleep restriction CSR of 7 days of 3 h nightly time in bed We then quantified the extent to which models developed using psychomotor vigilance task data under TSD predicted performance data under CSR and vice versa The results showed that the models customized to an individual under one sleep loss condition accurately predicted performance of the same individual under the other condition yielding on average up to 50 improvement over non individualized group average model predictions This finding supports the notion that the UMP captures an individual s trait like response to different sleep loss conditions
- ItemChanges in tibial bone microarchitecture in female recruits in response to 8 weeks of U.S. Army Basic Combat Training.(0000-00-00) Hughes, Julie M; Gaffney-Stomberg, Erin; Guerriere, Katelyn I; Taylor, Kathryn M; Popp, Kristin L; Xu, Chun; Unnikrishnan, Ginu; Staab, Jeffery S; Matheny, Ronald W; McClung, James P; Reifman, Jaques; Bouxsein, Mary LBACKGROUND U S Army Basic Combat Training BCT is a physically demanding program at the start of military service Whereas animal studies have shown that increased mechanical loading rapidly alters bone structure there is limited evidence of changes in bone density and structure in humans exposed to a brief period of unaccustomed physical activity PURPOSE We aimed to characterize changes in tibial bone density and microarchitecture and serum based biochemical markers of bone metabolism in female recruits as a result of 8 weeks of BCT METHODS We collected high resolution peripheral quantitative computed tomography images of the distal tibial metaphysis and diaphysis 4 and 30 of tibia length respectively and serum markers of bone metabolism before and after BCT Linear mixed models were used to estimate the mean difference for each outcome from pre to post BCT while controlling for race ethnicity age and body mass index RESULTS 91 female BCT recruits volunteered and completed this observational study age 21 5 3 3 yrs At the distal tibia cortical thickness trabecular thickness trabecular number bone volume total volume and total and trabecular volumetric bone density vBMD increased significantly by 1 2 all p Under 0 05 over the BCT period whereas trabecular separation cortical tissue mineral density TMD and cortical vBMD decreased significantly by 0 3 1 0 all p Under 0 05 At the tibia diaphysis cortical vBMD and cortical TMD decreased significantly both 0 7 p Under 0 001 Bone strength estimated by micro finite element analysis increased by 2 5 and 0 7 at the distal tibial metaphysis and diaphysis respectively both p Under 0 05 Among the biochemical markers of bone metabolism sclerostin decreased 5 7 whereas bone alkaline phosphatase C telopeptide cross links of type 1 collagen tartrate resistance acid phosphatase and 25 OH D increased by 10 28 all p Under 0 05 CONCLUSION BCT leads to improvements in trabecular bone microarchitecture and increases in serum bone formation markers indicative of new bone formation as well as increases in serum bone resorption markers and decreases in cortical vBMD consistent with intracortical remodeling and or deposition of new less mineralized bone tissue This study demonstrates specific changes in trabecular and cortical bone density and microarchitecture following 8 weeks of unaccustomed physical activity in women
- ItemCollective interaction effects associated with mammalian behavioral traits reveal genetic factors connecting fear and hemostasis.(0000-00-00) Woo, Hyung Jun; Reifman, JaquesBACKGROUND Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes These traits however are often highly polygenic with individual loci contributing only small effects to the overall association The polygenicity makes it challenging to explain for example the widely observed comorbidity between stress and cardiac disease METHODS We present an algorithm for inferring the collective association of a large number of interacting gene variants with a quantitative trait Using simulated data we demonstrate that by taking into account the non uniform distribution of genotypes within a cohort we can achieve greater power than regression based methods for high dimensional inference RESULTS We analyzed genome wide data sets of outbred mice and pet dogs and found neurobiological pathways whose associations with behavioral traits arose primarily from interaction effects carboxylated coagulation factors and downstream neuronal signaling were highly associated with conditioned fear consistent with our previous finding in human post traumatic stress disorder PTSD data Prepulse inhibition in mice was associated with serotonin transporter and platelet homeostasis and noise induced fear in dogs with hemostasis CONCLUSIONS Our findings suggest a novel explanation for the observed comorbidity between PTSD anxiety and cardiovascular diseases key coagulation factors modulating hemostasis also regulate synaptic plasticity affecting the learning and extinction of fear
- ItemCommentary on the three-process model of alertness and broader modeling issues.(2004-03-15) Reifman, Jaques; Gander, Philippa
- ItemComputational approach to characterize causative factors and molecular indicators of chronic wound inflammation.(2014-02-10) Nagaraja, Sridevi; Wallqvist, Anders; Reifman, Jaques; Mitrophanov, Alexander YChronic inflammation is rapidly becoming recognized as a key contributor to numerous pathologies Despite detailed investigations understanding of the molecular mechanisms regulating inflammation is incomplete Knowledge of such critical regulatory processes and informative indicators of chronic inflammation is necessary for efficacious therapeutic interventions and diagnostic support to clinicians We used a computational modeling approach to elucidate the critical factors responsible for chronic inflammation and to identify robust molecular indicators of chronic inflammatory conditions Our kinetic model successfully captured experimentally observed cell and cytokine dynamics for both acute and chronic inflammatory responses Using sensitivity analysis we identified macrophage influx and efflux rate modulation as the strongest inducing factor of chronic inflammation for a wide range of scenarios Moreover our model predicted that among all major inflammatory mediators IL 6 TGF and PDGF may generally be considered the most sensitive and robust indicators of chronic inflammation which is supported by existing but limited experimental evidence
- ItemA computational study of the respiratory airflow characteristics in normal and obstructed human airways.(2014-08-18) Sul, Bora; Wallqvist, Anders; Morris, Michael J; Reifman, Jaques; Rakesh, VineetObstructive lung diseases in the lower airways are a leading health concern worldwide To improve our understanding of the pathophysiology of lower airways we studied airflow characteristics in the lung between the 8th and the 14th generations using a three dimensional computational fluid dynamics model where we compared normal and obstructed airways for a range of breathing conditions We employed a novel technique based on computing the Pearson s correlation coefficient to quantitatively characterize the differences in airflow patterns between the normal and obstructed airways We found that the airflow patterns demonstrated clear differences between normal and diseased conditions for high expiratory flow rates 2300ml s but not for inspiratory flow rates Moreover airflow patterns subjected to filtering demonstrated higher sensitivity than airway resistance for differentiating normal and diseased conditions Further we showed that wall shear stresses were not only dependent on breathing rates but also on the distribution of the obstructed sites in the lung for the same degree of obstruction and breathing rate we observed as much as two fold differences in shear stresses In contrast to previous studies that suggest increased wall shear stress due to obstructions as a possible damage mechanism for small airways our model demonstrated that for flow rates corresponding to heavy activities the wall shear stress in both normal and obstructed airways was Under0 3Pa which is within the physiological limit needed to promote respiratory defense mechanisms In summary our model enables the study of airflow characteristics that may be impractical to assess experimentally
- ItemDBSecSys: a database of Burkholderia mallei secretion systems.(2014-07-25) Memišević, Vesna; Kumar, Kamal; Cheng, Li; Zavaljevski, Nela; DeShazer, David; Wallqvist, Anders; Reifman, JaquesBacterial pathogenicity represents a major public health concern worldwide Secretion systems are a key component of bacterial pathogenicity as they provide the means for bacterial proteins to penetrate host cell membranes and insert themselves directly into the host cells cytosol Burkholderia mallei is a Gram negative bacterium that uses multiple secretion systems during its host infection life cycle To date the identities of secretion system proteins for B mallei are not well known and their pathogenic mechanisms of action and host factors are largely uncharacterized
- ItemDecision tool for the early diagnosis of trauma patient hypovolemia.(2008-06-05) Chen, Liangyou; McKenna, Thomas M; Reisner, Andrew T; Gribok, Andrei; Reifman, JaquesWe present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital when reliable acquisition of vital sign data may be difficult The decision tool uses basic vital sign variables as input into linear classifiers which are then combined into an ensemble classifier The classifier identifies hypovolemic patients with an area under a receiver operating characteristic curve AUC of 0 76 standard deviation 0 05 for 100 randomly reselected patient subsets The ensemble classifier is robust classification performance degrades only slowly as variables are dropped and the ensemble structure does not require identification of a set of variables for use as best feature inputs into the classifier The ensemble classifier consistently outperforms best features based linear classifiers the classification AUC is greater and the standard deviation is smaller pUnder0 05 The simple computational requirements of ensemble classifiers will permit them to function in small fieldable devices for continuous monitoring of trauma patients
- ItemThe development of PIPA: an integrated and automated pipeline for genome-wide protein function annotation.(2008-03-03) Yu, Chenggang; Zavaljevski, Nela; Desai, Valmik; Johnson, Seth; Stevens, Fred J; Reifman, JaquesAutomated protein function prediction methods are needed to keep pace with high throughput sequencing With the existence of many programs and databases for inferring different protein functions a pipeline that properly integrates these resources will benefit from the advantages of each method However integrated systems usually do not provide mechanisms to generate customized databases to predict particular protein functions Here we describe a tool termed PIPA Pipeline for Protein Annotation that has these capabilities
- ItemDiagnosis of hemorrhage in a prehospital trauma population using linear and nonlinear multiparameter analysis of vital signs.(2007-11-16) Chen, Liangyou; Reisner, Andrew T; McKenna, Thomas M; Gribok, Andrei; Reifman, JaquesIn this study we analyzed a dataset of time series vital signs data collected by standard Propaq travel monitor during helicopter transport of 898 civilian trauma casualties from the scene of injury to a receiving trauma center The goals of the analysis are two fold First to determine which combination of the automatically collected and qualified vital signs provides the best discrimination between casualties with and without major hemorrhage Second to determine whether nonlinear classifiers provide improved discrimination over simpler linear classifiers Major hemorrhage is defined by the presence of injuries consistent with hemorrhage in casualties who received one or more units of blood We randomly selected a subset of the casualties to train and test the classifiers with multiple combinations of the vital signs variables and used the area under the receiver operating characteristic curve ROC AUC as a decision metric Based on the results of 100 simulations we observe that i the best two features obtained are systolic blood pressure and heart rate mean AUC 0 75 from a linear classifier and ii the use of nonlinear classifiers does not improve discrimination These results support earlier findings that the interaction of systolic blood pressure and heart rate is useful for the identification of trauma hemorrhage and that linear classifiers are adequate for many real world applications
- ItemDose-dependent model of caffeine effects on human vigilance during total sleep deprivation.(2014-08-05) Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Wesensten, Nancy J; Kamimori, Gary H; Balkin, Thomas J; Reifman, JaquesCaffeine is the most widely consumed stimulant to counter sleep loss effects While the pharmacokinetics of caffeine in the body is well understood its alertness restoring effects are still not well characterized In fact mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available In this paper we describe a phenomenological model of the dose dependent effects of caffeine on psychomotor vigilance task PVT performance of sleep deprived subjects We used the two process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses We developed and validated the model fits and predictions on PVT lapse number of reaction times exceeding 500 ms data from two separate laboratory studies At the population average level the model captured the effects of a range of caffeine doses 50 300 mg yielding up to a 90 improvement over the two process model Individual specific caffeine models on average predicted the effects up to 23 better than population average caffeine models The proposed model serves as a useful tool for predicting the dose dependent effects of caffeine on the PVT performance of sleep deprived subjects and therefore can be used for determining caffeine doses that optimize the timing and duration of peak performance
- ItemDOVIS: an implementation for high-throughput virtual screening using AutoDock.(2008-03-27) Zhang, Shuxing; Kumar, Kamal; Jiang, Xiaohui; Wallqvist, Anders; Reifman, JaquesMolecular docking based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated In addition to the selection of a sound docking strategy with appropriate scoring functions another technical challenge is to in silico screen millions of compounds in a reasonable time To meet this challenge it is necessary to use high performance computing HPC platforms and techniques However the development of an integrated HPC system that makes efficient use of its elements is not trivial
- ItemEvidence of probabilistic behaviour in protein interaction networks.(2008-03-27) Ivanic, Joseph; Wallqvist, Anders; Reifman, JaquesData from high throughput experiments of protein protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour
- ItemExploiting large-scale drug-protein interaction information for computational drug repurposing.(2014-07-03) Liu, Ruifeng; Singh, Narender; Tawa, Gregory J; Wallqvist, Anders; Reifman, JaquesDespite increased investment in pharmaceutical research and development fewer and fewer new drugs are entering the marketplace This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect For this approach to be statistically sound it requires a large number of drugs in both classes However given few or no approved drugs for the diseases of highest medical urgency and interest different strategies need to be investigated
- ItemGenetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks.(0000-00-00) Woo, Hyung Jun; Reifman, JaquesBACKGROUND Common metabolic diseases including type 2 diabetes coronary artery disease and hypertension arise from disruptions of the body s metabolic homeostasis with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity Although genome wide association studies have revealed many genomic loci robustly associated with these diseases biological interpretation of such association is challenging because of the difficulty in mapping single nucleotide polymorphisms SNPs onto the underlying causal genes and pathways Furthermore common diseases are typically highly polygenic and conventional single variant based association testing does not adequately capture potentially important large scale interaction effects between multiple genetic factors METHODS We analyzed moderately sized case control data sets for type 2 diabetes coronary artery disease and hypertension to characterize the genetic risk factors arising from non additive collective interaction effects using a recently developed algorithm discrete discriminant analysis We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group RESULTS In contrast to non interacting SNP mapping which produced few genome wide significant loci our analysis revealed extensive arrays of pathways many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies They comprised cell stress and apoptotic pathways for insulin producing cells in type 2 diabetes processes covering different atherosclerotic stages in coronary artery disease and elements of both type 2 diabetes and coronary artery disease risk factors cell cycle apoptosis and hemostasis associated with hypertension CONCLUSIONS Our results support the view that non additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs
- «
- 1 (current)
- 2
- 3
- »