Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning.

PubMed

Rajkomar, Alvin; Yim, Joanne Wing Lan; Grumbach, Kevin; Parekh, Ami

2016-10-14

Characterizing patient complexity using granular electronic health record (EHR) data regularly available to health systems is necessary to optimize primary care processes at scale. To characterize the utilization patterns of primary care patients and create weighted panel sizes for providers based on work required to care for patients with different patterns. We used EHR data over a 2-year period from patients empaneled to primary care clinicians in a single academic health system, including their in-person encounter history and virtual encounters such as telephonic visits, electronic messaging, and care coordination with specialists. Using a combination of decision rules and k-means clustering, we identified clusters of patients with similar health care system activity. Phenotypes with basic demographic information were used to predict future health care utilization using log-linear models. Phenotypes were also used to calculate weighted panel sizes. We identified 7 primary care utilization phenotypes, which were characterized by various combinations of primary care and specialty usage and were deemed clinically distinct by primary care physicians. These phenotypes, combined with age-sex and primary payer variables, predicted future primary care utilization with R 2 of .394 and were used to create weighted panel sizes. Individual patients' health care utilization may be useful for classifying patients by primary care work effort and for predicting future primary care usage.

Multidisciplinary Modeling Software for Analysis, Design, and Optimization of HRRLS Vehicles

NASA Technical Reports Server (NTRS)

Spradley, Lawrence W.; Lohner, Rainald; Hunt, James L.

2011-01-01

The concept for Highly Reliable Reusable Launch Systems (HRRLS) under the NASA Hypersonics project is a two-stage-to-orbit, horizontal-take-off / horizontal-landing, (HTHL) architecture with an air-breathing first stage. The first stage vehicle is a slender body with an air-breathing propulsion system that is highly integrated with the airframe. The light weight slender body will deflect significantly during flight. This global deflection affects the flow over the vehicle and into the engine and thus the loads and moments on the vehicle. High-fidelity multi-disciplinary analyses that accounts for these fluid-structures-thermal interactions are required to accurately predict the vehicle loads and resultant response. These predictions of vehicle response to multi physics loads, calculated with fluid-structural-thermal interaction, are required in order to optimize the vehicle design over its full operating range. This contract with ResearchSouth addresses one of the primary objectives of the Vehicle Technology Integration (VTI) discipline: the development of high-fidelity multi-disciplinary analysis and optimization methods and tools for HRRLS vehicles. The primary goal of this effort is the development of an integrated software system that can be used for full-vehicle optimization. This goal was accomplished by: 1) integrating the master code, FEMAP, into the multidiscipline software network to direct the coupling to assure accurate fluid-structure-thermal interaction solutions; 2) loosely-coupling the Euler flow solver FEFLO to the available and proven aeroelasticity and large deformation (FEAP) code; 3) providing a coupled Euler-boundary layer capability for rapid viscous flow simulation; 4) developing and implementing improved Euler/RANS algorithms into the FEFLO CFD code to provide accurate shock capturing, skin friction, and heat-transfer predictions for HRRLS vehicles in hypersonic flow, 5) performing a Reynolds-averaged Navier-Stokes computation on an HRRLS configuration; 6) integrating the RANS solver with the FEAP code for coupled fluid-structure-thermal capability; and 7) integrating the existing NASA SRGULL propulsion flow path prediction software with the FEFLO software for quasi-3D propulsion flow path predictions, 8) improving and integrating into the network, an existing adjoint-based design optimization code.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues.

PubMed

Guo, Song; Liu, Chunhua; Zhou, Peng; Li, Yanling

2016-01-01

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields.

A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues

PubMed Central

Liu, Chunhua; Zhou, Peng; Li, Yanling

2016-01-01

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields. PMID:27034949

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

PubMed

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-04-01

Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

Nomogram for suboptimal cytoreduction at primary surgery for advanced stage ovarian cancer.

PubMed

Gerestein, Cornelis G; Eijkemans, Marinus J; Bakker, Jeanette; Elgersma, Otto E; van der Burg, Maria E L; Kooi, Geertruida S; Burger, Curt W

2011-11-01

Maximal cytoreduction to minimal residual tumor is the most important determinant of prognosis in patients with advanced stage epithelial ovarian cancer (EOC). Preoperative prediction of suboptimal cytoreduction, defined as residual tumor >1 cm, could guide treatment decisions and improve counseling. The objective of this study was to identify predictive computed tomographic (CT) scan and clinical parameters for suboptimal cytoreduction at primary cytoreductive surgery for advanced stage EOC and to generate a nomogram with the identified parameters, which would be easy to use in daily clinical practice. Between October 2005 and December 2008, all patients with primary surgery for suspected advanced stage EOC at six participating teaching hospitals in the South Western part of the Netherlands entered the study protocol. To investigate independent predictors of suboptimal cytoreduction, a Cox proportional hazard model with backward stepwise elimination was utilized. One hundred and fifteen patients with FIGO stage III/IV EOC entered the study protocol. Optimal cytoreduction was achieved in 52 (45%) patients. A suboptimal cytoreduction was predicted by preoperative blood platelet count (p=0.1990; odds ratio (OR)=1.002), diffuse peritoneal thickening (DPT) (p=0.0074; OR=3.021), and presence of ascites on at least two thirds of CT scan slices (p=0.0385; OR=2.294) with a for-optimism corrected c-statistic of 0.67. Suboptimal cytoreduction was predicted by preoperative platelet count, DPT and presence of ascites. The generated nomogram can, after external validation, be used to estimate surgical outcome and to identify those patients, who might benefit from alternative treatment approaches.

Optimal pharmacological therapy in ST-elevation myocardial infarction-a review : A review of antithrombotic therapies in STEMI.

PubMed

Hermanides, R S; Kilic, S; van 't Hof, A W J

2018-04-23

Antithrombotic therapy is an essential component in the optimisation of clinical outcomes in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. There are currently several intravenous anticoagulant drugs available for primary percutaneous coronary intervention. Dual antiplatelet therapy comprising aspirin and P2Y12 inhibitor represents the cornerstone treatment for STEMI. However, these effective treatment strategies may be associated with bleeding complications. Compared with clopidogrel, prasugrel and ticagrelor are more potent and predictable, which translates into better clinical outcomes. Therefore, these agents are the first-line treatment in primary percutaneous coronary intervention. However, patients can still experience adverse ischaemic events, which might be in part attributed to alternative pathways triggering thrombosis. In this review, we provide a critical and updated review of currently available antithrombotic therapies used in patients with STEMI undergoing primary PCI. Finding a balance that minimises both thrombotic and bleeding risk is difficult, but crucial. Further randomised trials for this optimal balance are needed.

Fetal omphalocele ratios predict outcomes in prenatally diagnosed omphalocele.

PubMed

Montero, Freddy J; Simpson, Lynn L; Brady, Paula C; Miller, Russell S

2011-09-01

The objective of the study was to evaluate whether ratios considering omphalocele diameter relative to fetal biometric measurements perform better than giant omphalocele designation at predicting inability to achieve neonatal primary surgical closure. Cases of fetal omphalocele that underwent evaluation between May 2003 and July 2010 were identified. Inclusion was restricted to live births with plan for postnatal repair. Omphalocele diameter upon antenatal ultrasound was compared with abdominal circumference, femur length, and head circumference, yielding the respective omphalocele (O)/abdominal circumference (AC), O/femur length (FL), and O/head circumference (HC) ratios. The absolute measurements were used to classify giant lesions. Omphalocele ratios and giant omphalocele designations were evaluated as predictors of inability to achieve primary repair. Among 25 included cases, staged or delayed closure occurred in 52%. With an optimal cutoff of 0.21 or greater, O/HC best predicted the primary outcome (sensitivity, 84.6%; specificity, 58.3%; odds ratio, 7.7). The O/HC of 0.21 or greater outperformed giant designations. The O/HC of 0.21 or greater best predicted staged or delayed omphalocele closure. Giant omphalocele designation, regardless of definition, poorly predicted outcome. Copyright © 2011 Mosby, Inc. All rights reserved.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm

PubMed Central

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-01-01

Background Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. Aim To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Design and setting Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Method Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. Results From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The ‘predictAL-10’ risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the ‘predictAL-9’), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. Conclusion The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. PMID:28360074

Screening for Underage Drinking and Diagnostic and Statistical Manual of Mental Disorders, 5th Edition Alcohol Use Disorder in Rural Primary Care Practice.

PubMed

Clark, Duncan B; Martin, Christopher S; Chung, Tammy; Gordon, Adam J; Fiorentino, Lisa; Tootell, Mason; Rubio, Doris M

2016-06-01

To examine the National Institute on Alcohol Abuse and Alcoholism Youth Guide alcohol frequency screening thresholds when applied to Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) diagnostic criteria, and to describe alcohol use patterns and alcohol use disorder (AUD) characteristics in rural youth from primary care settings. Adolescents (n = 1193; ages 12 through 20 years) visiting their primary care practitioner for outpatient visits in six rural primary care clinics were assessed prior to their practitioner visit. A tablet computer collected youth self-report of past-year frequency and quantity of alcohol use and DSM-5 AUD symptoms. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. For early adolescents (ages 12 through 14 years), 1.9% met DSM-5 criteria for past-year AUD and ≥3 days with alcohol use in the past year yielded a screen for DSM-5 with optimal psychometric properties (sensitivity: 89%; specificity: 95%; PPV: 37%; NPV: 100%). For middle adolescents (ages 15 through 17 years), 9.5% met DSM-5 AUD criteria, and ≥3 past year drinking days showed optimal screening results (sensitivity: 91%; specificity: 89%; PPV: 50%; NPV: 99%). For late adolescents (ages 18 through 20 years), 10.0% met DSM-5 AUD criteria, and ≥12 past year drinking days showed optimal screening results (sensitivity: 92%; specificity: 75%; PPV: 31%; NPV: 99%). The age stratified National Institute on Alcohol Abuse and Alcoholism frequency thresholds also produced effective results. In rural primary care clinics, 10% of youth over age 14 years had a past-year DSM-5 AUD. These at-risk adolescents can be identified with a single question on alcohol use frequency. Copyright © 2016 Elsevier Inc. All rights reserved.

Heart-type fatty acid binding protein (H-FABP) in patients in an emergency department setting, suspected of acute coronary syndrome: optimal cut-off point, diagnostic value and future opportunities in primary care.

PubMed

Willemsen, Robert T A; van Severen, Evie; Vandervoort, Pieter M; Grieten, Lars; Buntinx, Frank; Glatz, Jan F C; Dinant, Geert Jan

2015-01-01

Most patients presenting chest complaints in primary care are referred to secondary care facilities, whereas only a few are diagnosed with acute coronary syndrome (ACS). The aim is to determine the optimal cut-off value for a point-of-care heart-type fatty acid binding protein (H-FABP) test in patients presenting to the emergency department and to evaluate a possible future role of H-FABP in safely ruling out ACS in primary care. Serial plasma H-FABP (index test) and high sensitivity troponin T (hs-cTnT) (reference test) were determined in patients with any new-onset chest complaint. In a receiver operating characteristic (ROC) curve, the optimal cut-off value of H-FABP for ACS was determined. Predictive values of H-FABP for ACS were calculated. For 202 consecutive patients (prevalence ACS 59%), the ROC curve based on the results of the first H-FABP was equal to the ROC curve of hs-cTnT (AUC 0.79 versus 0.80). Using a cut-off value of 4.0 ng/ml for H-FABP, sensitivity for ACS of the H-FABP (hs-cTnT) tests was 73.9% (70.6%). Negative predictive value (NPV) of H-FABP for ACS in a population representative for primary care (incidence of ACS 22%) thus could reach 90.8%. In patients presenting chest pain, plasma H-FABP reaches the highest diagnostic value when a cut-off value of 4 ng/ml is used. Diagnostic values of an algorithm combining point-of-care H-FABP measurement and a score of signs and symptoms should be studied in primary care, to learn if such an algorithm could safely reduce referral rate by GPs.

Development of a model with which to predict the life expectancy of patients with spinal epidural metastasis.

PubMed

Bartels, Ronald H M A; Feuth, Ton; van der Maazen, Richard; Verbeek, André L M; Kappelle, Arnoud C; André Grotenhuis, J; Leer, Jan Willem

2007-11-01

The surgical treatment of spinal epidural metastasis is evolving. To be a surgical candidate, a patient should have a life expectancy of at least 3 months. Estimation of survival by experienced specialists has proven to be unreliable. The Cox proportional hazards model was used to make a prediction model. To validate the model, Efron optimism correction by bootstrapping was performed. Retrospective data of patients treated for a spinal metastasis were used. Possible predictive factors were defined based on clinical experience and the literature. Statistical methods and clinical knowledge were also used to reveal an optimal set of predictors of survival. Data from patients treated at the Department of Radiation Oncology for spinal metastasis between 1998 and 2005 were evaluated. The case notes of 219 patients form the base of this study. In the final model, only 5 variables were required to predict the survival of a patient with spinal metastasis: sex, location of the primary lesion, intentional curative treatment of the primary tumor, cervical location of the spinal metastasis, and Karnofsky performance score. Examples with different predictors are given. The R(2) (N) index of Nagelkerke was 0.36 (95% confidence interval [95% CI], 0.28-0.48) and the c-index 0.72 (95% CI, 0.68-0.77). A reliable and simple model with which to predict the survival of a patient with spinal epidural metastasis is presented. Without the need for extensive investigations, survival can be predicted and only 5 easily obtainable parameters are required.

Novel determinants of mammalian primary microRNA processing revealed by systematic evaluation of hairpin-containing transcripts and human genetic variation

PubMed Central

Roden, Christine; Gaillard, Jonathan; Kanoria, Shaveta; Rennie, William; Barish, Syndi; Cheng, Jijun; Pan, Wen; Liu, Jun; Cotsapas, Chris; Ding, Ye; Lu, Jun

2017-01-01

Mature microRNAs (miRNAs) are processed from hairpin-containing primary miRNAs (pri-miRNAs). However, rules that distinguish pri-miRNAs from other hairpin-containing transcripts in the genome are incompletely understood. By developing a computational pipeline to systematically evaluate 30 structural and sequence features of mammalian RNA hairpins, we report several new rules that are preferentially utilized in miRNA hairpins and govern efficient pri-miRNA processing. We propose that a hairpin stem length of 36 ± 3 nt is optimal for pri-miRNA processing. We identify two bulge-depleted regions on the miRNA stem, located ∼16–21 nt and ∼28–32 nt from the base of the stem, that are less tolerant of unpaired bases. We further show that the CNNC primary sequence motif selectively enhances the processing of optimal-length hairpins. We predict that a small but significant fraction of human single-nucleotide polymorphisms (SNPs) alter pri-miRNA processing, and confirm several predictions experimentally including a disease-causing mutation. Our study enhances the rules governing mammalian pri-miRNA processing and suggests a diverse impact of human genetic variation on miRNA biogenesis. PMID:28087842

Optimal versus observed vegetation responses to CO2 over the last 40 years

NASA Astrophysics Data System (ADS)

Roderick, M. L.; Yang, Y.; Donohue, R. J.; Farquhar, G. D.; McVicar, T.

2016-12-01

The ongoing increase in atmospheric CO2 presents an interesting opportunity for primary producers. Understanding the impacts on agriculture, natural ecological communities and water resources presents considerable challenges. We investigate this problem using a Budyko-type framework based around two end-members: (i) warm arid environments (e.g. warm deserts) and (ii) warm wet environments (e.g. tropical rainforests). We first make predictions of the effect of a change in atmospheric CO2 on the partitioning of precipitation between evapotranspiration and streamflow. We then use satellite observation of greenness and in-situ streamflow data to assess the predictions. We finish by contrasting the observed responses against those expected from a purely theoretical construct: the so-called optimal vegetation.

Quantitative analysis of adipose tissue on chest CT to predict primary graft dysfunction in lung transplant recipients: a novel optimal biomarker approach

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, Jayaram K.; Wang, Chuang; Wu, Caiyun; Pednekar, Gargi; Restivo, Michaela D.; Lederer, David J.; Christie, Jason D.; Torigian, Drew A.

2018-02-01

In this study, patients who underwent lung transplantation are categorized into two groups of successful (positive) or failed (negative) transplantations according to primary graft dysfunction (PGD), i.e., acute lung injury within 72 hours of lung transplantation. Obesity or being underweight is associated with an increased risk of PGD. Adipose quantification and characterization via computed tomography (CT) imaging is an evolving topic of interest. However, very little research of PGD prediction using adipose quantity or characteristics derived from medical images has been performed. The aim of this study is to explore image-based features of thoracic adipose tissue on pre-operative chest CT to distinguish between the above two groups of patients. 140 unenhanced chest CT images from three lung transplant centers (Columbia, Penn, and Duke) are included in this study. 124 patients are in the successful group and 16 in failure group. Chest CT slices at the T7 and T8 vertebral levels are captured to represent the thoracic fat burden by using a standardized anatomic space (SAS) approach. Fat (subcutaneous adipose tissue (SAT)/ visceral adipose tissue (VAT)) intensity and texture properties (1142 in total) for each patient are collected, and then an optimal feature set is selected to maximize feature independence and separation between the two groups. Leave-one-out and leave-ten-out crossvalidation strategies are adopted to test the prediction ability based on those selected features all of which came from VAT texture properties. Accuracy of prediction (ACC), sensitivity (SEN), specificity (SPE), and area under the curve (AUC) of 0.87/0.97, 0.87/0.97, 0.88/1.00, and 0.88/0.99, respectively are achieved by the method. The optimal feature set includes only 5 features (also all from VAT), which might suggest that thoracic VAT plays a more important role than SAT in predicting PGD in lung transplant recipients.

International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol.

PubMed

Williams, Leanne M; Rush, A John; Koslow, Stephen H; Wisniewski, Stephen R; Cooper, Nicholas J; Nemeroff, Charles B; Schatzberg, Alan F; Gordon, Evian

2011-01-05

Clinically useful treatment moderators of Major Depressive Disorder (MDD) have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. The International Study to Predict Optimized Treatment - in Depression (iSPOT-D) is a multi-centre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65) from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls). Study-eligible patients are antidepressant medication (ADM) naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and event-related potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary) and self-reported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary). Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. International Study to Predict Optimised Treatment - in Depression (iSPOT-D) ClinicalTrials.gov Identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict+Optimized+Treatment+for+Depression&rank=1

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

PubMed Central

Acampora, Giovanni; Brown, David; Rees, Robert C.

2016-01-01

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582). PMID:27258119

Conditional power and predictive power based on right censored data with supplementary auxiliary information.

PubMed

Sun, Libo; Wan, Ying

2018-04-22

Conditional power and predictive power provide estimates of the probability of success at the end of the trial based on the information from the interim analysis. The observed value of the time to event endpoint at the interim analysis could be biased for the true treatment effect due to early censoring, leading to a biased estimate of conditional power and predictive power. In such cases, the estimates and inference for this right censored primary endpoint are enhanced by incorporating a fully observed auxiliary variable. We assume a bivariate normal distribution of the transformed primary variable and a correlated auxiliary variable. Simulation studies are conducted that not only shows enhanced conditional power and predictive power but also can provide the framework for a more efficient futility interim analysis in terms of an improved accuracy in estimator, a smaller inflation in type II error and an optimal timing for such analysis. We also illustrated the new approach by a real clinical trial example. Copyright © 2018 John Wiley & Sons, Ltd.

Reducing the uncertainty of parameters controlling seasonal carbon and water fluxes in Chinese forests and its implication for simulated climate sensitivities

NASA Astrophysics Data System (ADS)

Li, Yue; Yang, Hui; Wang, Tao; MacBean, Natasha; Bacour, Cédric; Ciais, Philippe; Zhang, Yiping; Zhou, Guangsheng; Piao, Shilong

2017-08-01

Reducing parameter uncertainty of process-based terrestrial ecosystem models (TEMs) is one of the primary targets for accurately estimating carbon budgets and predicting ecosystem responses to climate change. However, parameters in TEMs are rarely constrained by observations from Chinese forest ecosystems, which are important carbon sink over the northern hemispheric land. In this study, eddy covariance data from six forest sites in China are used to optimize parameters of the ORganizing Carbon and Hydrology In Dynamics EcosystEms TEM. The model-data assimilation through parameter optimization largely reduces the prior model errors and improves the simulated seasonal cycle and summer diurnal cycle of net ecosystem exchange, latent heat fluxes, and gross primary production and ecosystem respiration. Climate change experiments based on the optimized model are deployed to indicate that forest net primary production (NPP) is suppressed in response to warming in the southern China but stimulated in the northeastern China. Altered precipitation has an asymmetric impact on forest NPP at sites in water-limited regions, with the optimization-induced reduction in response of NPP to precipitation decline being as large as 61% at a deciduous broadleaf forest site. We find that seasonal optimization alters forest carbon cycle responses to environmental change, with the parameter optimization consistently reducing the simulated positive response of heterotrophic respiration to warming. Evaluations from independent observations suggest that improving model structure still matters most for long-term carbon stock and its changes, in particular, nutrient- and age-related changes of photosynthetic rates, carbon allocation, and tree mortality.

Dynamic determination of kinetic parameters and computer simulation of growth of Clostridium perfringens in cooked beef

USDA-ARS?s Scientific Manuscript database

The objective of this research was to develop a new one-step methodology that uses a dynamic approach to directly construct a tertiary model for prediction of the growth of C. perfringens in cooked beef. This methodology was based on numerical analysis and optimization of both primary and secondary...

Is there an additional benefit of serial NT-proBNP measurements in patients with stable chronic heart failure receiving individually optimized therapy?

PubMed

Franke, Jennifer; Frankenstein, Lutz; Schellberg, Dieter; Bajrovic, Amer; Wolter, Jan Sebastian; Ehlermann, Philipp; Doesch, Andreas O; Nelles, Manfred; Katus, Hugo A; Zugck, Christian

2011-12-01

The role of serial NT-proBNP measurements in patients suffering from chronic systolic heart failure (CHF) who already receive individually optimized pharmacotherapy is still unresolved. NT-proBNP was assessed at baseline and at 6 months follow-up in 504 stable CHF patients treated with individually optimized pharmacotherapy. After assessment of clinical stability at 6 months, patients were followed up for at least 1 year. The combined primary endpoint was defined as death, hospitalization due to cardiac reasons or heart transplantation in 1-year follow-up. We stratified our patients according to two principles: first, a percent change of value (CV) between the first and second measurement of NT-proBNP and secondly, the transformed logarithm of NT-proBNP measured at 6 months. During the follow-up period of 1 year, 50 patients (9.9%) reached the combined primary endpoint. Stratification according to percentage CV was less accurate in predicting endpoint-free survival compared to a classification in categories of lnNT-proBNP measured at 6 months (ROC AUC = 0.615; 95% CI 0.525-0.70 vs. ROC AUC = 0.790; 95% CI 0.721-0.856, respectively). When entered into proportional hazard regression analysis, lnNT-proBNP measured at 6 months remained an independent predictor of the combined primary endpoint with an associated HR of 2.53 (95% CI 1.385-4.280). To date, this is the largest analysis of serial NT-proBNP measurements in patients with CHF receiving individually optimized medical therapy. These data suggest that a single NT-proBNP measurement after 6 months in stable clinical conditions may have higher predictive value than stratification of change in serial measurements.

Strategy of arm movement control is determined by minimization of neural effort for joint coordination.

PubMed

Dounskaia, Natalia; Shimansky, Yury

2016-06-01

Optimality criteria underlying organization of arm movements are often validated by testing their ability to adequately predict hand trajectories. However, kinematic redundancy of the arm allows production of the same hand trajectory through different joint coordination patterns. We therefore consider movement optimality at the level of joint coordination patterns. A review of studies of multi-joint movement control suggests that a 'trailing' pattern of joint control is consistently observed during which a single ('leading') joint is rotated actively and interaction torque produced by this joint is the primary contributor to the motion of the other ('trailing') joints. A tendency to use the trailing pattern whenever the kinematic redundancy is sufficient and increased utilization of this pattern during skillful movements suggests optimality of the trailing pattern. The goal of this study is to determine the cost function minimization of which predicts the trailing pattern. We show that extensive experimental testing of many known cost functions cannot successfully explain optimality of the trailing pattern. We therefore propose a novel cost function that represents neural effort for joint coordination. That effort is quantified as the cost of neural information processing required for joint coordination. We show that a tendency to reduce this 'neurocomputational' cost predicts the trailing pattern and that the theoretically developed predictions fully agree with the experimental findings on control of multi-joint movements. Implications for future research of the suggested interpretation of the trailing joint control pattern and the theory of joint coordination underlying it are discussed.

Transferrin saturation phenotype and HFE genotype screening for hemochromatosis and primary iron overload: predictions from a model based on national, racial, and ethnic group composition in central Alabama.

PubMed

Barton, J C; Acton, R T

2000-01-01

There is interest in general population screening for hemochromatosis and other primary iron overload disorders, although not all persons are at equal risk. We developed a model to estimate the numbers of persons in national, racial, or ethnic population subgroups in Jefferson County, Alabama, who would be detected using transferrin saturation (phenotype) or HFE mutation analysis (genotype) screening. Approximately 62% are Caucasians, 37% are African Americans, and the remainder are Hispanics, Asians, or Native Americans. The predicted phenotype frequencies are greatest in a Caucasian subgroup, ethnicity unspecified, which consists predominantly of persons of Scotch and Irish descent (0.0065 men, 0.0046 women), and in African Americans (0.0089 men, 0.0085 women). Frequencies of the HFE genotype C282Y/C282Y > or = 0.0001 are predicted to occur only among Caucasians; the greatest frequency (0.0080) was predicted to occur in the ethnicity-unspecified Caucasian population. C282Y/C282Y frequency estimates were lower in Italian, Greek, and Jewish subgroups. There is excellent agreement in the numbers of the ethnicity-unspecified Caucasians who would be detected using phenotype and genotype criteria. Our model also indicates that phenotyping would identify more persons with primary iron overload than would genotyping in our Italian Caucasian, Hispanic, and African American subgroups. This is consistent with previous observations that indicate that primary iron overload disorders in persons of southern Italian descent and African Americans are largely attributable to non-HFE alleles. Because the proportions of population subgroups and their genetic constitution may differ significantly in other geographic regions, we suggest that models similar to the present one be constructed to predict optimal screening strategies for primary iron overload disorders.

[Fast optimization of stepwise gradient conditions for ternary mobile phase in reversed-phase high performance liquid chromatography].

PubMed

Shan, Yi-chu; Zhang, Yu-kui; Zhao, Rui-huan

2002-07-01

In high performance liquid chromatography, it is necessary to apply multi-composition gradient elution for the separation of complex samples such as environmental and biological samples. Multivariate stepwise gradient elution is one of the most efficient elution modes, because it combines the high selectivity of multi-composition mobile phase and shorter analysis time of gradient elution. In practical separations, the separation selectivity of samples can be effectively adjusted by using ternary mobile phase. For the optimization of these parameters, the retention equation of samples must be obtained at first. Traditionally, several isocratic experiments are used to get the retention equation of solute. However, it is time consuming especially for the separation of complex samples with a wide range of polarity. A new method for the fast optimization of ternary stepwise gradient elution was proposed based on the migration rule of solute in column. First, the coefficients of retention equation of solute are obtained by running several linear gradient experiments, then the optimal separation conditions are searched according to the hierarchical chromatography response function which acts as the optimization criterion. For each kind of organic modifier, two initial linear gradient experiments are used to obtain the primary coefficients of retention equation of each solute. For ternary mobile phase, only four linear gradient runs are needed to get the coefficients of retention equation. Then the retention times of solutes under arbitrary mobile phase composition can be predicted. The initial optimal mobile phase composition is obtained by resolution mapping for all of the solutes. A hierarchical chromatography response function is used to evaluate the separation efficiencies and search the optimal elution conditions. In subsequent optimization, the migrating distance of solute in the column is considered to decide the mobile phase composition and sustaining time of the latter steps until all the solutes are eluted out. Thus the first stepwise gradient elution conditions are predicted. If the resolution of samples under the predicted optimal separation conditions is satisfactory, the optimization procedure is stopped; otherwise, the coefficients of retention equation are adjusted according to the experimental results under the previously predicted elution conditions. Then the new stepwise gradient elution conditions are predicted repeatedly until satisfactory resolution is obtained. Normally, the satisfactory separation conditions can be found only after six experiments by using the proposed method. In comparison with the traditional optimization method, the time needed to finish the optimization procedure can be greatly reduced. The method has been validated by its application to the separation of several samples such as amino acid derivatives, aromatic amines, in which satisfactory separations were obtained with predicted resolution.

Decision Making in Concurrent Multitasking: Do People Adapt to Task Interference?

PubMed Central

Nijboer, Menno; Taatgen, Niels A.; Brands, Annelies; Borst, Jelmer P.; van Rijn, Hedderik

2013-01-01

While multitasking has received a great deal of attention from researchers, we still know little about how well people adapt their behavior to multitasking demands. In three experiments, participants were presented with a multicolumn subtraction task, which required working memory in half of the trials. This primary task had to be combined with a secondary task requiring either working memory or visual attention, resulting in different types of interference. Before each trial, participants were asked to choose which secondary task they wanted to perform concurrently with the primary task. We predicted that if people seek to maximize performance or minimize effort required to perform the dual task, they choose task combinations that minimize interference. While performance data showed that the predicted optimal task combinations indeed resulted in minimal interference between tasks, the preferential choice data showed that a third of participants did not show any adaptation, and for the remainder it took a considerable number of trials before the optimal task combinations were chosen consistently. On the basis of these results we argue that, while in principle people are able to adapt their behavior according to multitasking demands, selection of the most efficient combination of strategies is not an automatic process. PMID:24244527

Moving on from rigid plant stoichiometry: Optimal canopy nitrogen allocation within a novel land surface model

NASA Astrophysics Data System (ADS)

Caldararu, S.; Kern, M.; Engel, J.; Zaehle, S.

2016-12-01

Despite recent advances in global vegetation models, we still lack the capacity to predict observed vegetation responses to experimental environmental changes such as elevated CO2, increased temperature or nutrient additions. In particular for elevated CO2 (FACE) experiments, studies have shown that this is related in part to the models' inability to represent plastic changes in nutrient use and biomass allocation. We present a newly developed vegetation model which aims to overcome these problems by including optimality processes to describe nitrogen (N) and carbon allocation within the plant. We represent nitrogen allocation to the canopy and within the canopy between photosynthetic components as an optimal processes which aims to maximize net primary production (NPP) of the plant. We also represent biomass investment into aboveground and belowground components (root nitrogen uptake , biological N fixation) as an optimal process that maximizes plant growth by considering plant carbon and nutrient demands as well as acquisition costs. The model can now represent plastic changes in canopy N content and chlorophyll and Rubisco concentrations as well as in belowground allocation both on seasonal and inter-annual time scales. Specifically, we show that under elevated CO2 conditions, the model predicts a lower optimal leaf N concentration, which, combined with a redistribution of leaf N between the Rubisco and chlorophyll components, leads to a continued NPP response under high CO2, where models with a fixed canopy stoichiometry would predicts a quick onset of N limitation. In general, our model aims to include physiologically-based plant processes and avoid arbitrarily imposed parameters and thresholds in order to improve our predictive capability of vegetation responses under changing environmental conditions.

Impact of DOTS compared with DOTS-plus on multidrug resistant tuberculosis and tuberculosis deaths: decision analysis.

PubMed

Sterling, Timothy R; Lehmann, Harold P; Frieden, Thomas R

2003-03-15

This study sought to determine the impact of the World Health Organization's directly observed treatment strategy (DOTS) compared with that of DOTS-plus on tuberculosis deaths, mainly in the developing world. Decision analysis with Monte Carlo simulation of a Markov decision tree. People with smear positive pulmonary tuberculosis. Analyses modelled different levels of programme effectiveness of DOTS and DOTS-plus, and high (10%) and intermediate (3%) proportions of primary multidrug resistant tuberculosis, while accounting for exogenous reinfection. The cumulative number of tuberculosis deaths per 100 000 population over 10 years. The model predicted that under DOTS, 276 people would die from tuberculosis (24 multidrug resistant and 252 not multidrug resistant) over 10 years under optimal implementation in an area with 3% primary multidrug resistant tuberculosis. Optimal implementation of DOTS-plus would result in four (1.5%) fewer deaths. If implementation of DOTS-plus were to result in a decrease of just 5% in the effectiveness of DOTS, 16% more people would die with tuberculosis than under DOTS alone. In an area with 10% primary multidrug resistant tuberculosis, 10% fewer deaths would occur under optimal DOTS-plus than under optimal DOTS, but 16% more deaths would occur if implementation of DOTS-plus were to result in a 5% decrease in the effectiveness of DOTS CONCLUSIONS: Under optimal implementation, fewer tuberculosis deaths would occur under DOTS-plus than under DOTS. If, however, implementation of DOTS-plus were associated with even minimal decreases in the effectiveness of treatment, substantially more patients would die than under DOTS.

Optimizing an Actuator Array for the Control of Multi-Frequency Noise in Aircraft Interiors

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.

1997-01-01

Techniques developed for selecting an optimized actuator array for interior noise reduction at a single frequency are extended to the multi-frequency case. Transfer functions for 64 actuators were obtained at 5 frequencies from ground testing the rear section of a fully trimmed DC-9 fuselage. A single loudspeaker facing the left side of the aircraft was the primary source. A combinatorial search procedure (tabu search) was employed to find optimum actuator subsets of from 2 to 16 actuators. Noise reduction predictions derived from the transfer functions were used as a basis for evaluating actuator subsets during optimization. Results indicate that it is necessary to constrain actuator forces during optimization. Unconstrained optimizations selected actuators which require unrealistically large forces. Two methods of constraint are evaluated. It is shown that a fast, but approximate, method yields results equivalent to an accurate, but computationally expensive, method.

Genome-scale biological models for industrial microbial systems.

PubMed

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT).

PubMed

Reinhardt, Martin; Brandmaier, Philipp; Seider, Daniel; Kolesnik, Marina; Jenniskens, Sjoerd; Sequeiros, Roberto Blanco; Eibisberger, Martin; Voglreiter, Philip; Flanagan, Ronan; Mariappan, Panchatcharam; Busse, Harald; Moche, Michael

2017-12-01

Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available. ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction. This unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

The Optimal Screening for Prediction of Referral and Outcome (OSPRO) in patients with musculoskeletal pain conditions: a longitudinal validation cohort from the USA

PubMed Central

George, Steven Z; Beneciuk, Jason M; Lentz, Trevor A; Wu, Samuel S

2017-01-01

Purpose There is an increased need for determining which patients with musculoskeletal pain benefit from additional diagnostic testing or psychologically informed intervention. The Optimal Screening for Prediction of Referral and Outcome (OSPRO) cohort studies were designed to develop and validate standard assessment tools for review of systems and yellow flags. This cohort profile paper provides a description of and future plans for the validation cohort. Participants Patients (n=440) with primary complaint of spine, shoulder or knee pain were recruited into the OSPRO validation cohort via a national Orthopaedic Physical Therapy-Investigative Network. Patients were followed up at 4 weeks, 6 months and 12 months for pain, functional status and quality of life outcomes. Healthcare utilisation outcomes were also collected at 6 and 12 months. Findings to date There are no longitudinal findings reported to date from the ongoing OSPRO validation cohort. The previously completed cross-sectional OSPRO development cohort yielded two assessment tools that were investigated in the validation cohort. Future plans Follow-up data collection was completed in January 2017. Primary analyses will investigate how accurately the OSPRO review of systems and yellow flag tools predict 12-month pain, functional status, quality of life and healthcare utilisation outcomes. Planned secondary analyses include prediction of pain interference and/or development of chronic pain, investigation of treatment expectation on patient outcomes and analysis of patient satisfaction following an episode of physical therapy. Trial registration number The OSPRO validation cohort was not registered. PMID:28600371

Visual Perceptual Learning and Models.

PubMed

Dosher, Barbara; Lu, Zhong-Lin

2017-09-15

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

King’s College Hospital criteria for non-acetaminophen induced acute liver failure in an international cohort of children

PubMed Central

Sundaram, Vinay; Shneider, Benjamin L.; Dhawan, Anil; Ng, Vicky L.; Im, Kyungah; Belle, Steven; Squires, Robert H.

2012-01-01

Objective To validate King’s College Hospital criteria (KCHC) in children with non-acetaminophen (APAP) induced pediatric acute liver failure (PALF) and to determine whether re-optimizing the KCHC would improve predictive accuracy. Study design We utilized the PALF study group database. Primary outcomes were survival without liver transplantation (LT) versus death at 21 days following enrollment. Classification and Regression Tree (CART) analysis was used to determine if modification of KCHC parameters would improve classification of death versus survival. Results Among 163 patients who met KCHC, 54 patients (33.1%) died within 21 days. Sensitivity of KCHC in this cohort was significantly lower than in the original study (61% vs 91%, p=0.002), and specificity did not differ significantly. The positive predictive value (PPV) and negative predictive value (NPV) of KCHC for this cohort was 33% and 88% respectively. CART analysis yielded the following optimized parameters to predict death: grade 2–4 encephalopathy, international normalized ratio >4.02 and total bilirubin >2.02 mg/dL. These parameters did not improve PPV, but NPV was significantly better (88% vs. 92%, p<0.0001). Conclusions KCHC does not reliably predict death in PALF. With a PPV of 33%, twice as many participants who met KCHC recovered spontaneously than died, indicating that using KCHC may cause over utilization of LT. Re-optimized cutpoints for KCHC parameters improved NPV, but not PPV. Parameters beyond the KCHC should be evaluated to create a predictive model for PALF. PMID:22906509

Enhanced method of fast re-routing with load balancing in software-defined networks

NASA Astrophysics Data System (ADS)

Lemeshko, Oleksandr; Yeremenko, Oleksandra

2017-11-01

A two-level method of fast re-routing with load balancing in a software-defined network (SDN) is proposed. The novelty of the method consists, firstly, in the introduction of a two-level hierarchy of calculating the routing variables responsible for the formation of the primary and backup paths, and secondly, in ensuring a balanced load of the communication links of the network, which meets the requirements of the traffic engineering concept. The method provides implementation of link, node, path, and bandwidth protection schemes for fast re-routing in SDN. The separation in accordance with the interaction prediction principle along two hierarchical levels of the calculation functions of the primary (lower level) and backup (upper level) routes allowed to abandon the initial sufficiently large and nonlinear optimization problem by transiting to the iterative solution of linear optimization problems of half the dimension. The analysis of the proposed method confirmed its efficiency and effectiveness in terms of obtaining optimal solutions for ensuring balanced load of communication links and implementing the required network element protection schemes for fast re-routing in SDN.

Rapid freeze-drying cycle optimization using computer programs developed based on heat and mass transfer models and facilitated by tunable diode laser absorption spectroscopy (TDLAS).

PubMed

Kuu, Wei Y; Nail, Steven L

2009-09-01

Computer programs in FORTRAN were developed to rapidly determine the optimal shelf temperature, T(f), and chamber pressure, P(c), to achieve the shortest primary drying time. The constraint for the optimization is to ensure that the product temperature profile, T(b), is below the target temperature, T(target). Five percent mannitol was chosen as the model formulation. After obtaining the optimal sets of T(f) and P(c), each cycle was assigned with a cycle rank number in terms of the length of drying time. Further optimization was achieved by dividing the drying time into a series of ramping steps for T(f), in a cascading manner (termed the cascading T(f) cycle), to further shorten the cycle time. For the purpose of demonstrating the validity of the optimized T(f) and P(c), four cycles with different predicted lengths of drying time, along with the cascading T(f) cycle, were chosen for experimental cycle runs. Tunable diode laser absorption spectroscopy (TDLAS) was used to continuously measure the sublimation rate. As predicted, maximum product temperatures were controlled slightly below the target temperature of -25 degrees C, and the cascading T(f)-ramping cycle is the most efficient cycle design. In addition, the experimental cycle rank order closely matches with that determined by modeling.

A boundary element approach to optimization of active noise control sources on three-dimensional structures

NASA Technical Reports Server (NTRS)

Cunefare, K. A.; Koopmann, G. H.

1991-01-01

This paper presents the theoretical development of an approach to active noise control (ANC) applicable to three-dimensional radiators. The active noise control technique, termed ANC Optimization Analysis, is based on minimizing the total radiated power by adding secondary acoustic sources on the primary noise source. ANC Optimization Analysis determines the optimum magnitude and phase at which to drive the secondary control sources in order to achieve the best possible reduction in the total radiated power from the noise source/control source combination. For example, ANC Optimization Analysis predicts a 20 dB reduction in the total power radiated from a sphere of radius at a dimensionless wavenumber ka of 0.125, for a single control source representing 2.5 percent of the total area of the sphere. ANC Optimization Analysis is based on a boundary element formulation of the Helmholtz Integral Equation, and thus, the optimization analysis applies to a single frequency, while multiple frequencies can be treated through repeated analyses.

Transformational leadership in primary care: Clinicians' patterned approaches to care predict patient satisfaction and health expectations.

PubMed

Huynh, Ho Phi; Sweeny, Kate; Miller, Tricia

2018-04-01

Clinicians face the complex challenge of motivating their patients to achieve optimal health while also ensuring their satisfaction. Inspired by transformational leadership theory, we proposed that clinicians' motivational behaviors can be organized into three patient care styles (transformational, transactional, and passive-avoidant) and that these styles differentially predict patient health outcomes. In two studies using patient-reported data and observer ratings, we found that transformational patient care style positively predicted patients' satisfaction and health expectations above and beyond transactional and passive-avoidant patient care style. These findings provide initial support for the patient care style approach and suggest novel directions for the study of clinicians' motivational behaviors.

Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment.

PubMed

Li, Yinghui; Huang, Shuaijin; Qu, Xuexin

2017-10-27

The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter "Reservoir Area"). However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM) to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1) model, and build a new GM (1,1) model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1) model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area.

Coordinated Optimization of Visual Cortical Maps (I) Symmetry-based Analysis

PubMed Central

Reichl, Lars; Heide, Dominik; Löwel, Siegrid; Crowley, Justin C.; Kaschube, Matthias; Wolf, Fred

2012-01-01

In the primary visual cortex of primates and carnivores, functional architecture can be characterized by maps of various stimulus features such as orientation preference (OP), ocular dominance (OD), and spatial frequency. It is a long-standing question in theoretical neuroscience whether the observed maps should be interpreted as optima of a specific energy functional that summarizes the design principles of cortical functional architecture. A rigorous evaluation of this optimization hypothesis is particularly demanded by recent evidence that the functional architecture of orientation columns precisely follows species invariant quantitative laws. Because it would be desirable to infer the form of such an optimization principle from the biological data, the optimization approach to explain cortical functional architecture raises the following questions: i) What are the genuine ground states of candidate energy functionals and how can they be calculated with precision and rigor? ii) How do differences in candidate optimization principles impact on the predicted map structure and conversely what can be learned about a hypothetical underlying optimization principle from observations on map structure? iii) Is there a way to analyze the coordinated organization of cortical maps predicted by optimization principles in general? To answer these questions we developed a general dynamical systems approach to the combined optimization of visual cortical maps of OP and another scalar feature such as OD or spatial frequency preference. From basic symmetry assumptions we obtain a comprehensive phenomenological classification of possible inter-map coupling energies and examine representative examples. We show that each individual coupling energy leads to a different class of OP solutions with different correlations among the maps such that inferences about the optimization principle from map layout appear viable. We systematically assess whether quantitative laws resembling experimental observations can result from the coordinated optimization of orientation columns with other feature maps. PMID:23144599

An improved simulation based biomechanical model to estimate static muscle loadings

NASA Technical Reports Server (NTRS)

Rajulu, Sudhakar L.; Marras, William S.; Woolford, Barbara

1991-01-01

The objectives of this study are to show that the characteristics of an intact muscle are different from those of an isolated muscle and to describe a simulation based model. This model, unlike the optimization based models, accounts for the redundancy in the musculoskeletal system in predicting the amount of forces generated within a muscle. The results of this study show that the loading of the primary muscle is increased by the presence of other muscle activities. Hence, the previous models based on optimization techniques may underestimate the severity of the muscle and joint loadings which occur during manual material handling tasks.

Annual Research Review: New Frontiers in Developmental Neuropharmacology--Can Long-Term Therapeutic Effects of Drugs Be Optimized through Carefully Timed Early Intervention?

ERIC Educational Resources Information Center

Andersen, Susan L.; Navalta, Carryl P.

2011-01-01

Our aim is to present a working model that may serve as a valuable heuristic to predict enduring effects of drugs when administered during development. Our primary tenet is that a greater understanding of neurodevelopment can lead to improved treatment that intervenes early in the progression of a given disorder and prevents symptoms from…

A Systems Approach to Designing Effective Clinical Trials Using Simulations

PubMed Central

Fusaro, Vincent A.; Patil, Prasad; Chi, Chih-Lin; Contant, Charles F.; Tonellato, Peter J.

2013-01-01

Background Pharmacogenetics in warfarin clinical trials have failed to show a significant benefit compared to standard clinical therapy. This study demonstrates a computational framework to systematically evaluate pre-clinical trial design of target population, pharmacogenetic algorithms, and dosing protocols to optimize primary outcomes. Methods and Results We programmatically created an end-to-end framework that systematically evaluates warfarin clinical trial designs. The framework includes options to create a patient population, multiple dosing strategies including genetic-based and non-genetic clinical-based, multiple dose adjustment protocols, pharmacokinetic/pharmacodynamics (PK/PD) modeling and international normalization ratio (INR) prediction, as well as various types of outcome measures. We validated the framework by conducting 1,000 simulations of the CoumaGen clinical trial primary endpoints. The simulation predicted a mean time in therapeutic range (TTR) of 70.6% and 72.2% (P = 0.47) in the standard and pharmacogenetic arms, respectively. Then, we evaluated another dosing protocol under the same original conditions and found a significant difference in TTR between the pharmacogenetic and standard arm (78.8% vs. 73.8%; P = 0.0065), respectively. Conclusions We demonstrate that this simulation framework is useful in the pre-clinical assessment phase to study and evaluate design options and provide evidence to optimize the clinical trial for patient efficacy and reduced risk. PMID:23261867

Finite element modelling of primary hip stem stability: the effect of interference fit.

PubMed

Abdul-Kadir, Mohammed Rafiq; Hansen, Ulrich; Klabunde, Ralf; Lucas, Duncan; Amis, Andrew

2008-01-01

The most commonly reported complications related to cementless hip stems are loosening and thigh pain; both of these have been attributed to high levels of relative micromotion at the bone-implant interface due to insufficient primary fixation. Primary fixation is believed by many to rely on achieving a sufficient interference fit between the implant and the bone. However, attempting to achieve a high interference fit not infrequently leads to femoral canal fracture either intra-operatively or soon after. The appropriate range of diametrical interference fit that ensures primary stability without risking femoral fracture is not well understood. In this study, a finite element model was constructed to predict micromotion and, therefore, instability of femoral stems. The model was correlated with an in vitro micromotion experiment carried out on four cadaver femurs. It was confirmed that interference fit has a very significant effect on micromotion and ignoring this parameter in an analysis of primary stability is likely to underestimate the stability of the stem. Furthermore, it was predicted that the optimal level of interference fit is around 50 microm as this is sufficient to achieve good primary fixation while having a safety factor of 2 against femoral canal fracture. This result is of clinical relevance as it indicates a recommendation for the surgeon to err on the side of a low interference fit rather than risking femoral fracture.

The Montreal Cognitive Assessment and the mini-mental state examination as screening instruments for cognitive impairment: item analyses and threshold scores.

PubMed

Damian, Anne M; Jacobson, Sandra A; Hentz, Joseph G; Belden, Christine M; Shill, Holly A; Sabbagh, Marwan N; Caviness, John N; Adler, Charles H

2011-01-01

To perform an item analysis of the Montreal Cognitive Assessment (MoCA) versus the Mini-Mental State Examination (MMSE) in the prediction of cognitive impairment, and to examine the characteristics of different MoCA threshold scores. 135 subjects enrolled in a longitudinal clinicopathologic study were administered the MoCA by a single physician and the MMSE by a trained research assistant. Subjects were classified as cognitively impaired or cognitively normal based on independent neuropsychological testing. 89 subjects were found to be cognitively normal, and 46 cognitively impaired (20 with dementia, 26 with mild cognitive impairment). The MoCA was superior in both sensitivity and specificity to the MMSE, although not all MoCA tasks were of equal predictive value. A MoCA threshold score of 26 had a sensitivity of 98% and a specificity of 52% in this population. In a population with a 20% prevalence of cognitive impairment, a threshold of 24 was optimal (negative predictive value 96%, positive predictive value 47%). This analysis suggests the potential for creating an abbreviated MoCA. For screening in primary care, the MoCA threshold of 26 appears optimal. For testing in a memory disorders clinic, a lower threshold has better predictive value. Copyright © 2011 S. Karger AG, Basel.

Predictability of Seasonal Rainfall over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2016-12-01

The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the negative phase of ENSO (La Niña) leads to dry conditions while the positive phase of ENSO (El Niño) anticipates enhanced wet conditions

Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

PubMed Central

Migliavacca, Mirco; Meroni, Michele; Busetto, Lorenzo; Colombo, Roberto; Zenone, Terenzio; Matteucci, Giorgio; Manca, Giovanni; Seufert, Guenther

2009-01-01

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale. PMID:22399948

Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model.

PubMed

Migliavacca, Mirco; Meroni, Michele; Busetto, Lorenzo; Colombo, Roberto; Zenone, Terenzio; Matteucci, Giorgio; Manca, Giovanni; Seufert, Guenther

2009-01-01

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.

A novel structural risk index for primary spontaneous pneumothorax: Ankara Numune Risk Index.

PubMed

Akkas, Yucel; Peri, Neslihan Gulay; Kocer, Bulent; Kaplan, Tevfik; Alhan, Aslihan

2017-07-01

In this study, we aimed to reveal a novel risk index as a structural risk marker for primary spontanoeus pneumothorax using body mass index and chest height, structural risk factors for pneumothorax development. Records of 86 cases admitted between February 2014 and January 2015 with or without primary spontaneous pneumothorax were analysed retrospectively. The patients were allocated to two groups as Group I and Group II. The patients were evaluated with regard to age, gender, pneumothorax side, duration of hospital stay, treatment type, recurrence, chest height and transverse diameter on posteroanterior chest graphy and body mass index. Body mass index ratio per cm of chest height was calculated by dividing body mass index with chest height. We named this risk index ratio which is defined first as 'Ankara Numune Risk Index'. Diagnostic value of Ankara Numune Risk Index value for prediction of primary spontaneous pneumothorax development was analysed with Receiver Operating Characteristics curver. Of 86 patients, 69 (80.2%) were male and 17 (19.8%) were female. Each group was composed of 43 (50%) patients. When Receiver Operating Characteristics curve analysis was done for optimal limit value 0.74 of Ankara Numune Risk Index determined for prediction of pneumothorax development risk, area under the curve was 0.925 (95% Cl, 0.872-0.977, p < 0.001). Ankara Numune Risk Index is one of the structural risk factors for prediction of primary spontaneous pneumothorax development however it is insufficient for determining recurrence. Copyright © 2015. Published by Elsevier Taiwan.

Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment

PubMed Central

Huang, Shuaijin; Qu, Xuexin

2017-01-01

The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter “Reservoir Area”). However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM) to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1) model, and build a new GM (1,1) model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1) model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area. PMID:29077006

Inducible fluorescent speckle microscopy

PubMed Central

Aguiar, Paulo; Belsley, Michael; Maiato, Helder

2016-01-01

The understanding of cytoskeleton dynamics has benefited from the capacity to generate fluorescent fiducial marks on cytoskeleton components. Here we show that light-induced imprinting of three-dimensional (3D) fluorescent speckles significantly improves speckle signal and contrast relative to classic (random) fluorescent speckle microscopy. We predict theoretically that speckle imprinting using photobleaching is optimal when the laser energy and fluorophore responsivity are related by the golden ratio. This relation, which we confirm experimentally, translates into a 40% remaining signal after speckle imprinting and provides a rule of thumb in selecting the laser power required to optimally prepare the sample for imaging. This inducible speckle imaging (ISI) technique allows 3D speckle microscopy to be performed in readily available libraries of cell lines or primary tissues expressing fluorescent proteins and does not preclude conventional imaging before speckle imaging. As a proof of concept, we use ISI to measure metaphase spindle microtubule poleward flux in primary cells and explore a scaling relation connecting microtubule flux to metaphase duration. PMID:26783303

Inducible fluorescent speckle microscopy.

PubMed

Pereira, António J; Aguiar, Paulo; Belsley, Michael; Maiato, Helder

2016-01-18

The understanding of cytoskeleton dynamics has benefited from the capacity to generate fluorescent fiducial marks on cytoskeleton components. Here we show that light-induced imprinting of three-dimensional (3D) fluorescent speckles significantly improves speckle signal and contrast relative to classic (random) fluorescent speckle microscopy. We predict theoretically that speckle imprinting using photobleaching is optimal when the laser energy and fluorophore responsivity are related by the golden ratio. This relation, which we confirm experimentally, translates into a 40% remaining signal after speckle imprinting and provides a rule of thumb in selecting the laser power required to optimally prepare the sample for imaging. This inducible speckle imaging (ISI) technique allows 3D speckle microscopy to be performed in readily available libraries of cell lines or primary tissues expressing fluorescent proteins and does not preclude conventional imaging before speckle imaging. As a proof of concept, we use ISI to measure metaphase spindle microtubule poleward flux in primary cells and explore a scaling relation connecting microtubule flux to metaphase duration. © 2016 Pereira et al.

Random Wiring, Ganglion Cell Mosaics, and the Functional Architecture of the Visual Cortex

PubMed Central

Coppola, David; White, Leonard E.; Wolf, Fred

2015-01-01

The architecture of iso-orientation domains in the primary visual cortex (V1) of placental carnivores and primates apparently follows species invariant quantitative laws. Dynamical optimization models assuming that neurons coordinate their stimulus preferences throughout cortical circuits linking millions of cells specifically predict these invariants. This might indicate that V1’s intrinsic connectome and its functional architecture adhere to a single optimization principle with high precision and robustness. To validate this hypothesis, it is critical to closely examine the quantitative predictions of alternative candidate theories. Random feedforward wiring within the retino-cortical pathway represents a conceptually appealing alternative to dynamical circuit optimization because random dimension-expanding projections are believed to generically exhibit computationally favorable properties for stimulus representations. Here, we ask whether the quantitative invariants of V1 architecture can be explained as a generic emergent property of random wiring. We generalize and examine the stochastic wiring model proposed by Ringach and coworkers, in which iso-orientation domains in the visual cortex arise through random feedforward connections between semi-regular mosaics of retinal ganglion cells (RGCs) and visual cortical neurons. We derive closed-form expressions for cortical receptive fields and domain layouts predicted by the model for perfectly hexagonal RGC mosaics. Including spatial disorder in the RGC positions considerably changes the domain layout properties as a function of disorder parameters such as position scatter and its correlations across the retina. However, independent of parameter choice, we find that the model predictions substantially deviate from the layout laws of iso-orientation domains observed experimentally. Considering random wiring with the currently most realistic model of RGC mosaic layouts, a pairwise interacting point process, the predicted layouts remain distinct from experimental observations and resemble Gaussian random fields. We conclude that V1 layout invariants are specific quantitative signatures of visual cortical optimization, which cannot be explained by generic random feedforward-wiring models. PMID:26575467

Project STOP (Spectral Thermal Optimization Program)

NASA Technical Reports Server (NTRS)

Goldhammer, L. J.; Opjorden, R. W.; Goodelle, G. S.; Powe, J. S.

1977-01-01

The spectral thermal optimization of solar cell configurations for various solar panel applications is considered. The method of optimization depends upon varying the solar cell configuration's optical characteristics to minimize panel temperatures, maximize power output and decrease the power delta from beginning of life to end of life. Four areas of primary investigation are: (1) testing and evaluation of ultraviolet resistant coverslide adhesives, primarily FEP as an adhesive; (2) examination of solar cell absolute spectral response and corresponding cell manufacturing processes that affect it; (3) experimental work with solar cell manufacturing processes that vary cell reflectance (solar absorptance); and (4) experimental and theoretical studies with various coverslide filter designs, mainly a red rejection filter. The Hughes' solar array prediction program has been modified to aid in evaluating the effect of each of the above four areas on the output of a solar panel in orbit.

Four Bed Molecular Sieve - Exploration (4BMS-X) Virtual Heater Design and Optimization

NASA Technical Reports Server (NTRS)

Schunk, R. Gregory; Peters, Warren T.; Thomas, John T., Jr.

2017-01-01

A 4BMS-X (Four Bed Molecular Sieve - Exploration) design and heater optimization study for CO2 sorbent beds in proposed exploration system architectures is presented. The primary objectives of the study are to reduce heater power and thermal gradients within the CO2 sorbent beds while minimizing channeling effects. Some of the notable changes from the ISS (International Space Station) CDRA (Carbon Dioxide Removal Assembly) to the proposed exploration system architecture include cylindrical beds, alternate sorbents and an improved heater core. Results from both 2D and 3D sorbent bed thermal models with integrated heaters are presented. The 2D sorbent bed models are used to optimize heater power and fin geometry while the 3D models address end effects in the beds for more realistic thermal gradient and heater power predictions.

Assimilation of seasonal chlorophyll and nutrient data into an adjoint three-dimensional ocean carbon cycle model: Sensitivity analysis and ecosystem parameter optimization

NASA Astrophysics Data System (ADS)

Tjiputra, Jerry F.; Polzin, Dierk; Winguth, Arne M. E.

2007-03-01

An adjoint method is applied to a three-dimensional global ocean biogeochemical cycle model to optimize the ecosystem parameters on the basis of SeaWiFS surface chlorophyll observation. We showed with identical twin experiments that the model simulated chlorophyll concentration is sensitive to perturbation of phytoplankton and zooplankton exudation, herbivore egestion as fecal pellets, zooplankton grazing, and the assimilation efficiency parameters. The assimilation of SeaWiFS chlorophyll data significantly improved the prediction of chlorophyll concentration, especially in the high-latitude regions. Experiments that considered regional variations of parameters yielded a high seasonal variance of ecosystem parameters in the high latitudes, but a low variance in the tropical regions. These experiments indicate that the adjoint model is, despite the many uncertainties, generally capable to optimize sensitive parameters and carbon fluxes in the euphotic zone. The best fit regional parameters predict a global net primary production of 36 Pg C yr-1, which lies within the range suggested by Antoine et al. (1996). Additional constraints of nutrient data from the World Ocean Atlas showed further reduction in the model-data misfit and that assimilation with extensive data sets is necessary.

Co-optimization of CO 2 -EOR and Storage Processes under Geological Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ampomah, William; Balch, Robert; Will, Robert

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parametersmore » as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization process predicted more than 94% of CO 2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO 2 storage. The Farnsworth CO 2 project will serve as a benchmark for future CO 2–EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.« less

Co-optimization of CO 2 -EOR and Storage Processes under Geological Uncertainty

DOE PAGES

Ampomah, William; Balch, Robert; Will, Robert; ...

2017-07-01

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parametersmore » as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization process predicted more than 94% of CO 2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO 2 storage. The Farnsworth CO 2 project will serve as a benchmark for future CO 2–EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.« less

Adaptation, acclimation, and assembly: How optimality principles govern the scaling of form, function, and diversity of ecosystem function in the light of climate change.

NASA Astrophysics Data System (ADS)

Enquist, B. J.

2016-12-01

The link between variation in species-specific traits - due to acclimation, adaptation, and how ecological communities assemble in time and space - and larger scale ecosystem processes is an important focus for global change research. Understanding such linkages requires synthesis of evolutionary, biogeograpahic, and biogeochemical approaches. Recent observations reveal several paradoxical patterns across ecosystems. Optimality principles provide a novel framework for generating numerous predictions for how ecosystems have and will reorganize and respond to climate change. Tropical elevation gradients are natural laboratories to assess how changing climate can ramify to influence tropical forest diversity and ecosystem functioning. We tested several new predictions from trait- and metabolic scaling theories by assessing the covariation between climate, traits, biomass and gross and net primary productivity (GPP and NPP) across tropical forest plots spanning elevation gradients. We measured multiple leaf physiological, morphological, and stoichiometric traits linked to variation in tree growth. Consistent with theory, observed decreases in NPP and GPP with temperature were best predicted by forest biomass, and scaled allometrically as predicted by theory but the effect of temperature was much less, characterized by a kinetic response much lower ( 0.1eV) than predicted ( 0.65eV). This is likely due to an observed exponential increase in the mean community leaf P:N ratio and photosynthetic nutrient use efficiency with decreases in temperature. Our results are consistent with predictions from Trait Driver Theory, where adaptive/acclamatory shifts in plant traits compensate for the kinetic effects of temperature on tree growth. Further, most of the traits measured showed significantly skewed trait distributions consistent with recent observations that observed shifts in species composition. The development of trait-based scaling theory provides a robust basis to predict how shifts in climate have and will influence functional composition and ecosystem functioning. Together, these results highlight the potential critical importance optimality principles for understanding the role of the biosphere within the integrated earth system.

A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo

DOE PAGES

Zhao, Luning; Neuscamman, Eric

2017-05-17

We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recently-introduced variational principle for excited states. For wave function ansatzes with tens of thousands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much better fit for modern supercomputer architectures in which data communication and per-process memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involvingmore » both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multi-Slater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we demonstrate on a hydrogen ring a prototype approach for making systematically convergent, non-perturbative predictions of Mott-insulators’ optical band gaps.« less

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.

2018-01-01

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

Upper Limits for Power Yield in Thermal, Chemical, and Electrochemical Systems

NASA Astrophysics Data System (ADS)

Sieniutycz, Stanislaw

2010-03-01

We consider modeling and power optimization of energy converters, such as thermal, solar and chemical engines and fuel cells. Thermodynamic principles lead to expressions for converter's efficiency and generated power. Efficiency equations serve to solve the problems of upgrading or downgrading a resource. Power yield is a cumulative effect in a system consisting of a resource, engines, and an infinite bath. While optimization of steady state systems requires using the differential calculus and Lagrange multipliers, dynamic optimization involves variational calculus and dynamic programming. The primary result of static optimization is the upper limit of power, whereas that of dynamic optimization is a finite-rate counterpart of classical reversible work (exergy). The latter quantity depends on the end state coordinates and a dissipation index, h, which is the Hamiltonian of the problem of minimum entropy production. In reacting systems, an active part of chemical affinity constitutes a major component of the overall efficiency. The theory is also applied to fuel cells regarded as electrochemical flow engines. Enhanced bounds on power yield follow, which are stronger than those predicted by the reversible work potential.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Luning; Neuscamman, Eric

We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recently-introduced variational principle for excited states. For wave function ansatzes with tens of thousands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much better fit for modern supercomputer architectures in which data communication and per-process memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involvingmore » both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multi-Slater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we demonstrate on a hydrogen ring a prototype approach for making systematically convergent, non-perturbative predictions of Mott-insulators’ optical band gaps.« less

Surgery on spinal epidural metastases (SEM) in renal cell carcinoma: a plea for a new paradigm.

PubMed

Bakker, Nicolaas A; Coppes, Maarten H; Vergeer, Rob A; Kuijlen, Jos M A; Groen, Rob J M

2014-09-01

Prediction models for outcome of decompressive surgical resection of spinal epidural metastases (SEM) have in common that they have been developed for all types of SEM, irrespective of the type of primary tumor. It is our experience in clinical practice, however, that these models often fail to accurately predict outcome in the individual patient. To investigate whether decision making could be optimized by applying tumor-specific prediction models. For the proof of concept, we analyzed patients with SEM from renal cell carcinoma that we have operated on. Retrospective chart analysis 2006 to 2012. Twenty-one consecutive patients with symptomatic SEM of renal cell carcinoma. Predictive factors for survival. Next to established predictive factors for survival, we analyzed the predictive value of the Motzer criteria in these patients. The Motzer criteria comprise a specific and validated risk model for survival in patients with renal cell carcinoma. After multivariable analysis, only Motzer intermediate (hazard ratio [HR] 17.4, 95% confidence interval [CI] 1.82-166, p=.01) and high risk (HR 39.3, 95% CI 3.10-499, p=.005) turned out to be significantly associated with survival in patients with renal cell carcinoma that we have operated on. In this study, we have demonstrated that decision making could have been optimized by implementing the Motzer criteria next to established prediction models. We, therefore, suggest that in future, in patients with SEM from renal cell carcinoma, the Motzer criteria are also taken into account. Copyright © 2014 Elsevier Inc. All rights reserved.

Optimization of Primary Drying in Lyophilization during Early Phase Drug Development using a Definitive Screening Design with Formulation and Process Factors.

PubMed

Goldman, Johnathan M; More, Haresh T; Yee, Olga; Borgeson, Elizabeth; Remy, Brenda; Rowe, Jasmine; Sadineni, Vikram

2018-06-08

Development of optimal drug product lyophilization cycles is typically accomplished via multiple engineering runs to determine appropriate process parameters. These runs require significant time and product investments, which are especially costly during early phase development when the drug product formulation and lyophilization process are often defined simultaneously. Even small changes in the formulation may require a new set of engineering runs to define lyophilization process parameters. In order to overcome these development difficulties, an eight factor definitive screening design (DSD), including both formulation and process parameters, was executed on a fully human monoclonal antibody (mAb) drug product. The DSD enables evaluation of several interdependent factors to define critical parameters that affect primary drying time and product temperature. From these parameters, a lyophilization development model is defined where near optimal process parameters can be derived for many different drug product formulations. This concept is demonstrated on a mAb drug product where statistically predicted cycle responses agree well with those measured experimentally. This design of experiments (DoE) approach for early phase lyophilization cycle development offers a workflow that significantly decreases the development time of clinically and potentially commercially viable lyophilization cycles for a platform formulation that still has variable range of compositions. Copyright © 2018. Published by Elsevier Inc.

The Representation of Prediction Error in Auditory Cortex

PubMed Central

Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali

2016-01-01

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. PMID:27490251

Do difficulties in accessing in-hours primary care predict higher use of out-of-hours GP services? Evidence from an English National Patient Survey.

PubMed

Zhou, Yin; Abel, Gary; Warren, Fiona; Roland, Martin; Campbell, John; Lyratzopoulos, Georgios

2015-05-01

It is believed that some patients are more likely to use out-of-hours primary care services because of difficulties in accessing in-hours care, but substantial evidence about any such association is missing. We analysed data from 567,049 respondents to the 2011/2012 English General Practice Patient Survey who reported at least one in-hours primary care consultation in the preceding 6 months. Of those respondents, 7% also reported using out-of-hours primary care. We used logistic regression to explore associations between use of out-of-hours primary care and five measures of in-hours access (ease of getting through on the telephone, ability to see a preferred general practitioner, ability to get an urgent or routine appointment and convenience of opening hours). We illustrated the potential for reduction in use of out-of-hours primary care in a model where access to in-hours care was made optimal. Worse in-hours access was associated with greater use of out-of-hours primary care for each access factor. In multivariable analysis adjusting for access and patient characteristic variables, worse access was independently associated with increased out-of-hours use for all measures except ease of telephone access. Assuming these associations were causal, we estimated that an 11% relative reduction in use of out-of-hours primary care services in England could be achievable if access to in-hours care were optimal. This secondary quantitative analysis provides evidence for an association between difficulty in accessing in-hours care and use of out-of-hours primary care services. The findings can motivate the development of interventions to improve in-hour access. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Ship and satellite bio-optical research in the California Bight

NASA Technical Reports Server (NTRS)

Smith, R. C.; Baker, K. S.

1982-01-01

Mesoscale biological patterns and processes in productive coastal waters were studied. The physical and biological processes leading to chlorophyll variability were investigated. The ecological and evolutionary significance of this variability, and its relation to the prediction of fish recruitment and marine mammal distributions was studied. Seasonal primary productivity (using chlorophyll as an indication of phytoplankton biomass) for the entire Southern California Bight region was assessed. Complementary and contemporaneous ship and satellite (Nimbus 7-CZCS) bio-optical data from the Southern California Bight and surrounding waters were obtained and analyzed. These data were also utilized for the development of multi-platform sampling strategies and the optimization of algorithms for the estimation of phytoplankton biomass and primary production from satellite imagery.

A quantitative comparison of physiologic indicators of cardiopulmonary resuscitation quality: Diastolic blood pressure versus end-tidal carbon dioxide.

PubMed

Morgan, Ryan W; French, Benjamin; Kilbaugh, Todd J; Naim, Maryam Y; Wolfe, Heather; Bratinov, George; Shoap, Wesley; Hsieh, Ting-Chang; Nadkarni, Vinay M; Berg, Robert A; Sutton, Robert M

2016-07-01

The American Heart Association (AHA) recommends monitoring invasive arterial diastolic blood pressure (DBP) and end-tidal carbon dioxide (ETCO2) during cardiopulmonary resuscitation (CPR) when available. In intensive care unit patients, both may be available to the rescuer. The objective of this study was to compare DBP vs. ETCO2 during CPR as predictors of cardiac arrest survival. In two models of cardiac arrest (primary ventricular fibrillation [VF] and asphyxia-associated VF), 3-month old swine received either standard AHA guideline-based CPR or patient-centric, BP-guided CPR. Mean values of DBP and ETCO2 in the final 2min before the first defibrillation attempt were compared using receiver operating characteristic curves (area under curve [AUC] analysis). The optimal DBP cut point to predict survival was derived and subsequently validated in two independent, randomly generated cohorts. Of 60 animals, 37 (61.7%) survived to 45min. DBP was higher in survivors than in non-survivors (40.6±1.8mmHg vs. 25.9±2.4mmHg; p<0.001), while ETCO2 was not different (30.0±1.5mmHg vs. 32.5±1.8mmHg; p=0.30). By AUC analysis, DBP was superior to ETCO2 (0.82 vs. 0.60; p=0.025) in discriminating survivors from non-survivors. The optimal DBP cut point in the derivation cohort was 34.1mmHg. In the validation cohort, this cut point demonstrated a sensitivity of 0.78, specificity of 0.81, positive predictive value of 0.64, and negative predictive value of 0.89 for survival. In both primary and asphyxia-associated VF porcine models of cardiac arrest, DBP discriminates survivors from non-survivors better than ETCO2. Failure to attain a DBP >34mmHg during CPR is highly predictive of non-survival. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Quantitative Comparison of Physiologic Indicators of Cardiopulmonary Resuscitation Quality: Diastolic Blood Pressure Versus End-Tidal Carbon Dioxide

PubMed Central

Morgan, Ryan W.; French, Benjamin; Kilbaugh, Todd J.; Naim, Maryam Y.; Wolfe, Heather; Bratinov, George; Shoap, Wesley; Hsieh, Ting-Chang; Nadkarni, Vinay M.; Berg, Robert A.; Sutton, Robert M.

2016-01-01

Aim The American Heart Association (AHA) recommends monitoring invasive arterial diastolic blood pressure (DBP) and end-tidal carbon dioxide (ETCO2) during cardiopulmonary resuscitation (CPR) when available. In intensive care unit patients, both may be available to the rescuer. The objective of this study was to compare DBP versus ETCO2 during CPR as predictors of cardiac arrest survival. Methods In two models of cardiac arrest (primary ventricular fibrillation [VF] and asphyxia-associated VF), 3-month old swine received either standard AHA guideline-based CPR or patient-centric, BP-guided CPR. Mean values of DBP and ETCO2 in the final two minutes before the first defibrillation attempt were compared using receiver operating characteristic curves (area under curve [AUC] analysis). The optimal DBP cut point to predict survival was derived and subsequently validated in two independent, randomly generated cohorts. Results Of 60 animals, 37 (61.7%) survived to 45 minutes. DBP was higher in survivors than in non-survivors (40.6±1.8mmHg vs. 25.9±2.4mmHg; p<0.001), while ETCO2 was not different (30.0±1.5mmHg vs. 32.5±1.8mmHg; p=0.30). By AUC analysis, DBP was superior to ETCO2 (0.82 vs. 0.60; p=0.025) in discriminating survivors from non-survivors. The optimal DBP cut point in the derivation cohort was 34.1mmHg. In the validation cohort, this cut point demonstrated a sensitivity of 0.78, specificity of 0.81, positive predictive value of 0.64, and negative predictive value of 0.89 for survival. Conclusions In both primary and asphyxia-associated VF porcine models of cardiac arrest, DBP discriminates survivors from non-survivors better than ETCO2. Failure to attain a DBP >34mmHg during CPR is highly predictive of non-survival. PMID:27107688

Research and development of a dedicated collimator for 14.2 MeV fast neutrons for imaging using a D-T generator

NASA Astrophysics Data System (ADS)

Sabo-Napadensky, I.; Weiss-Babai, R.; Gayer, A.; Vartsky, D.; Bar, D.; Mor, I.; Chacham-Zada, R.; Cohen, M.; Tamim, N.

2012-06-01

One of the main problems in neutron imaging is the scattered radiation that accompanies the direct neutrons that reach the imaging detectors and affect the image quality. We have developed a dedicated collimator for 14.2 MeV fast neutrons. The collimator optimizes the amount of scattered radiation to primary neutrons that arrive at the imaging plane. We have used different materials within the collimator in order to lower the scattered radiation that arrives at the scanned object. The image quality and the signal to noise ratios that are measured show that a mixture of BORAX (Na2B4O7ṡ10H2O) and water in the experimental beam collimator give the best results. We have used GEANT4 to simulate the collimator performance, the simulations predict the optimized material looking on the ratios of the scattered to primary neutrons that contribute in the detector. We present our experimental setup, report the results of the experimental and related simulation studies with neutrons beam generated by a 14.2 MeV D-T neutron generator.

Efficient Power-Transfer Capability Analysis of the TET System Using the Equivalent Small Parameter Method.

PubMed

Yanzhen Wu; Hu, A P; Budgett, D; Malpas, S C; Dissanayake, T

2011-06-01

Transcutaneous energy transfer (TET) enables the transfer of power across the skin without direct electrical connection. It is a mechanism for powering implantable devices for the lifetime of a patient. For maximum power transfer, it is essential that TET systems be resonant on both the primary and secondary sides, which requires considerable design effort. Consequently, a strong need exists for an efficient method to aid the design process. This paper presents an analytical technique appropriate to analyze complex TET systems. The system's steady-state solution in closed form with sufficient accuracy is obtained by employing the proposed equivalent small parameter method. It is shown that power-transfer capability can be correctly predicted without tedious iterative simulations or practical measurements. Furthermore, for TET systems utilizing a current-fed push-pull soft switching resonant converter, it is found that the maximum energy transfer does not occur when the primary and secondary resonant tanks are "tuned" to the nominal resonant frequency. An optimal turning point exists, corresponding to the system's maximum power-transfer capability when optimal tuning capacitors are applied.

Optimal cut-off levels to define obesity: body mass index and waist circumference, and their relationship to cardiovascular disease, dyslipidaemia, hypertension and diabetes in Malaysia.

PubMed

Zaher, Zaki Morad Mohd; Zambari, Robayaah; Pheng, Chan Siew; Muruga, Vadivale; Ng, Bernard; Appannah, Geeta; Onn, Lim Teck

2009-01-01

Many studies in Asia have demonstrated that Asian populations may require lower cut-off levels for body mass index (BMI) and waist circumference to define obesity and abdominal obesity respectively, compared to western populations. Optimal cut-off levels for body mass index and waist circumference were determined to assess the relationship between the two anthropometric- and cardiovascular indices. Receiver operating characteristics analysis was used to determine the optimal cut-off levels. The study sample included 1833 subjects (mean age of 44+/-14 years) from 93 primary care clinics in Malaysia. Eight hundred and seventy two of the subjects were men and 960 were women. The optimal body mass index cut-off values predicting dyslipidaemia, hypertension, diabetes mellitus, or at least one cardiovascular risk factor varied from 23.5 to 25.5 kg/m2 in men and 24.9 to 27.4 kg/m2 in women. As for waist circumference, the optimal cut-off values varied from 83 to 92 cm in men and from 83 to 88 cm in women. The optimal cut-off values from our study showed that body mass index of 23.5 kg/m2 in men and 24.9 kg/m2 in women and waist circumference of 83 cm in men and women may be more suitable for defining the criteria for overweight or obesity among adults in Malaysia. Waist circumference may be a better indicator for the prediction of obesity-related cardiovascular risk factors in men and women compared to BMI. Further investigation using a bigger sample size in Asia needs to be done to confirm our findings.

Men's Health Index: a pragmatic approach to stratifying and optimizing men's health.

PubMed

Tan, Hui Meng; Tan, Wei Phin; Wong, Jun Hoe; Ho, Christopher Chee Kong; Teo, Chin Hai; Ng, Chirk Jenn

2014-11-01

The proposed Men's Health Index (MHI) aims to provide a practical and systematic framework for comprehensively assessing and stratifying older men with the intention of optimising their health and functional status. A literature search was conducted using PubMed from 1980 to 2012. We specifically looked for instruments which: assess men's health, frailty and fitness; predict life expectancy, mortality and morbidities. The instruments were assessed by the researchers who then agreed on the tools to be included in the MHI. When there was disagreements, the researchers discussed and reached a consensus guided by the principle that the MHI could be used in the primary care setting targetting men aged 55-65 years. The instruments chosen include the Charlson's Combined Comorbidity-Age Index; the International Index of Erectile Function-5; the International Prostate Symptom Score; the Androgen Deficiency in Aging Male; the Survey of Health, Ageing and Retirement in Europe Frailty Instrument; the Sitting-Rising Test; the Senior Fitness Test; the Fitness Assessment Score; and the Depression Anxiety Stress Scale-21. A pilot test on eight men was carried out and showed that the men's health index is viable. The concept of assessing, stratifying, and optimizing men's health should be incorporated into routine health care, and this can be implemented by using the MHI. This index is particularly useful to primary care physicians who are in a strategic position to engage men at the peri-retirement age in a conversation about their life goals based on their current and predicted health status.

Fluid Dynamic Modeling to Support the Development of Flow-Based Hepatocyte Culture Systems for Metabolism Studies

PubMed Central

Pedersen, Jenny M.; Shim, Yoo-Sik; Hans, Vaibhav; Phillips, Martin B.; Macdonald, Jeffrey M.; Walker, Glenn; Andersen, Melvin E.; Clewell, Harvey J.; Yoon, Miyoung

2016-01-01

Accurate prediction of metabolism is a significant outstanding challenge in toxicology. The best predictions are based on experimental data from in vitro systems using primary hepatocytes. The predictivity of the primary hepatocyte-based culture systems, however, is still limited due to well-known phenotypic instability and rapid decline of metabolic competence within a few hours. Dynamic flow bioreactors for three-dimensional cell cultures are thought to be better at recapitulating tissue microenvironments and show potential to improve in vivo extrapolations of chemical or drug toxicity based on in vitro test results. These more physiologically relevant culture systems hold potential for extending metabolic competence of primary hepatocyte cultures as well. In this investigation, we used computational fluid dynamics to determine the optimal design of a flow-based hepatocyte culture system for evaluating chemical metabolism in vitro. The main design goals were (1) minimization of shear stress experienced by the cells to maximize viability, (2) rapid establishment of a uniform distribution of test compound in the chamber, and (3) delivery of sufficient oxygen to cells to support aerobic respiration. Two commercially available flow devices – RealBio® and QuasiVivo® (QV) – and a custom developed fluidized bed bioreactor were simulated, and turbulence, flow characteristics, test compound distribution, oxygen distribution, and cellular oxygen consumption were analyzed. Experimental results from the bioreactors were used to validate the simulation results. Our results indicate that maintaining adequate oxygen supply is the most important factor to the long-term viability of liver bioreactor cultures. Cell density and system flow patterns were the major determinants of local oxygen concentrations. The experimental results closely corresponded to the in silico predictions. Of the three bioreactors examined in this study, we were able to optimize the experimental conditions for long-term hepatocyte cell culture using the QV bioreactor. This system facilitated the use of low system volumes coupled with higher flow rates. This design supports cellular respiration by increasing oxygen concentrations in the vicinity of the cells and facilitates long-term kinetic studies of low clearance test compounds. These two goals were achieved while simultaneously keeping the shear stress experienced by the cells within acceptable limits. PMID:27747210

Fine Motor Skills Predict Maths Ability Better than They Predict Reading Ability in the Early Primary School Years

PubMed Central

Pitchford, Nicola J.; Papini, Chiara; Outhwaite, Laura A.; Gulliford, Anthea

2016-01-01

Fine motor skills have long been recognized as an important foundation for development in other domains. However, more precise insights into the role of fine motor skills, and their relationships to other skills in mediating early educational achievements, are needed to support the development of optimal educational interventions. We explored concurrent relationships between two components of fine motor skills, Fine Motor Precision and Fine Motor Integration, and early reading and maths development in two studies with primary school children of low-to-mid socio-economic status in the UK. Two key findings were revealed. First, despite being in the first 2 years of primary school education, significantly better performance was found in reading compared to maths across both studies. This may reflect the protective effects of recent national-level interventions to promote early literacy skills in young children in the UK that have not been similarly promoted for maths. Second, fine motor skills were a better predictor of early maths ability than they were of early reading ability. Hierarchical multiple regression revealed that fine motor skills did not significantly predict reading ability when verbal short-term memory was taken into account. In contrast, Fine Motor Integration remained a significant predictor of maths ability, even after the influence of non-verbal IQ had been accounted for. These results suggest that fine motor skills should have a pivotal role in educational interventions designed to support the development of early mathematical skills. PMID:27303342

Fine Motor Skills Predict Maths Ability Better than They Predict Reading Ability in the Early Primary School Years.

PubMed

Pitchford, Nicola J; Papini, Chiara; Outhwaite, Laura A; Gulliford, Anthea

2016-01-01

Fine motor skills have long been recognized as an important foundation for development in other domains. However, more precise insights into the role of fine motor skills, and their relationships to other skills in mediating early educational achievements, are needed to support the development of optimal educational interventions. We explored concurrent relationships between two components of fine motor skills, Fine Motor Precision and Fine Motor Integration, and early reading and maths development in two studies with primary school children of low-to-mid socio-economic status in the UK. Two key findings were revealed. First, despite being in the first 2 years of primary school education, significantly better performance was found in reading compared to maths across both studies. This may reflect the protective effects of recent national-level interventions to promote early literacy skills in young children in the UK that have not been similarly promoted for maths. Second, fine motor skills were a better predictor of early maths ability than they were of early reading ability. Hierarchical multiple regression revealed that fine motor skills did not significantly predict reading ability when verbal short-term memory was taken into account. In contrast, Fine Motor Integration remained a significant predictor of maths ability, even after the influence of non-verbal IQ had been accounted for. These results suggest that fine motor skills should have a pivotal role in educational interventions designed to support the development of early mathematical skills.

Supersonic Flight Dynamics Test 1 - Post-Flight Assessment of Simulation Performance

NASA Technical Reports Server (NTRS)

Dutta, Soumyo; Bowes, Angela L.; Striepe, Scott A.; Davis, Jody L.; Queen, Eric M.; Blood, Eric M.; Ivanov, Mark C.

2015-01-01

NASA's Low Density Supersonic Decelerator (LDSD) project conducted its first Supersonic Flight Dynamics Test (SFDT-1) on June 28, 2014. Program to Optimize Simulated Trajectories II (POST2) was one of the flight dynamics codes used to simulate and predict the flight performance and Monte Carlo analysis was used to characterize the potential flight conditions experienced by the test vehicle. This paper compares the simulation predictions with the reconstructed trajectory of SFDT-1. Additionally, off-nominal conditions seen during flight are modeled in post-flight simulations to find the primary contributors that reconcile the simulation with flight data. The results of these analyses are beneficial for the pre-flight simulation and targeting of the follow-on SFDT flights currently scheduled for summer 2015.

The comparative oncologic effectiveness of available management strategies for clinically localized prostate cancer.

PubMed

Tyson, Mark D; Penson, David F; Resnick, Matthew J

2017-02-01

The primary goal of modern prostate cancer treatment paradigms is to optimize the balance of predicted benefits associated with prostate cancer treatment against the predicted harms of therapy. However, given the limitations in the existing evidence as well as the significant tradeoffs posed by each treatment, there remain myriad challenges associated with individualized prostate cancer treatment decision-making. In this review, we summarize the existing comparative effectiveness evidence of treatments for localized prostate cancer with an emphasis on oncologic control. While we focus on the major treatment categories of radical prostatectomy, radiation therapy, and observation, we also provide a review of emerging therapies such as cryotherapy and high-intensity frequency ultrasound (HIFU). Copyright © 2017 Elsevier Inc. All rights reserved.

Cytokine Profiling of Ascites at Primary Surgery Identifies an Interaction of Tumor Necrosis Factor-α and Interleukin-6 in Predicting Reduced Progression-Free Survival in Epithelial Ovarian Cancer

PubMed Central

Kolomeyevskaya, Nonna; Eng, Kevin H.; Khan, Anm Nazmul H.; Grzankowski, Kassondra S.; Singel, Kelly L.; Moysich, Kirsten; Segal, Brahm H.

2015-01-01

Objectives Epithelial ovarian cancer (EOC) typically presents with advanced disease. Even with optimal debulking and response to adjuvant chemotherapy, the majority of patients will have disease relapse. We evaluated cytokine and chemokine profiles in ascites at primary surgery as biomarkers for progression-free survival (PFS) and overall survival (OS) in patients with advanced EOC. Methods Retrospective analysis of patients (n =70) who underwent surgery at Roswell Park Cancer Institute between 2002-12, followed by platinum-based chemotherapy. Results The mean age at diagnosis was 61.8 years, 85.3% had serous EOC, and 95.7% had stage IIIB, IIIC, or IV disease. Univariate analysis showed that ascites levels of tumor necrosis factor (TNF)-α were associated with reduced PFS after primary surgery. Although the ascites concentration of interleukin (IL)-6 was not by itself predictive of PFS, we found that stratifying patients by high TNF-α and high IL-6 levels identified a sub-group of patients at high risk for rapid disease relapse. This effect was largely independent of clinical prognostic variables. Conclusions The combination of high TNF-α and high IL-6 ascites levels at primary surgery predicts worse PFS in patients with advanced EOC. These results suggest an interaction between ascites TNF-α and IL-6 in driving tumor progression and resistance to chemotherapy in advanced EOC, and raise the potential for pre-treatment ascites levels of these cytokines as prognostic biomarkers. This study involved a small sample of patients and was an exploratory analysis; therefore, findings require validation in a larger independent cohort. PMID:26001328

Cytokine profiling of ascites at primary surgery identifies an interaction of tumor necrosis factor-α and interleukin-6 in predicting reduced progression-free survival in epithelial ovarian cancer.

PubMed

Kolomeyevskaya, Nonna; Eng, Kevin H; Khan, Anm Nazmul H; Grzankowski, Kassondra S; Singel, Kelly L; Moysich, Kirsten; Segal, Brahm H

2015-08-01

Epithelial ovarian cancer (EOC) typically presents with advanced disease. Even with optimal debulking and response to adjuvant chemotherapy, the majority of patients will have disease relapse. We evaluated cytokine and chemokine profiles in ascites at primary surgery as biomarkers for progression-free survival (PFS) and overall survival (OS) in patients with advanced EOC. Retrospective analysis of patients (n =70) who underwent surgery at Roswell Park Cancer Institute between 2002 and 2012, followed by platinum-based chemotherapy. The mean age at diagnosis was 61.8 years, 85.3% had serous EOC, and 95.7% had stage IIIB, IIIC, or IV disease. Univariate analysis showed that ascites levels of tumor necrosis factor (TNF)-α were associated with reduced PFS after primary surgery. Although the ascites concentration of interleukin (IL)-6 was not by itself predictive of PFS, we found that stratifying patients by high TNF-α and high IL-6 levels identified a sub-group of patients at high risk for rapid disease relapse. This effect was largely independent of clinical prognostic variables. The combination of high TNF-α and high IL-6 ascites levels at primary surgery predicts worse PFS in patients with advanced EOC. These results suggest an interaction between ascites TNF-α and IL-6 in driving tumor progression and resistance to chemotherapy in advanced EOC, and raise the potential for pre-treatment ascites levels of these cytokines as prognostic biomarkers. This study involved a small sample of patients and was an exploratory analysis; therefore, findings require validation in a larger independent cohort. Copyright © 2015 Elsevier Inc. All rights reserved.

Fabrication of Thermoplastic Composite Laminates Having Film Interleaves By Automated Fiber Placement

NASA Technical Reports Server (NTRS)

Hulcher, A. B.; Tiwari, S. N.; Marchello, J. M.; Johnston, Norman J. (Technical Monitor)

2001-01-01

Experiments were carried out at the NASA Langley Research Center automated Fiber placement facility to determine an optimal process for the fabrication of composite materials having polymer film interleaves. A series of experiments was conducted to determine an optimal process for the composite prior to investigation of a process to fabricate laminates with polymer films. The results of the composite tests indicated that a well-consolidated, void-free laminate could be attained. Preliminary interleaf processing trials were then conducted to establish some broad guidelines for film processing. The primary finding of these initial studies was that a two-stage process was necessary in order to process these materials adequately. A screening experiment was then performed to determine the relative influence of the process variables on the quality of the film interface as determined by the wedge peel test method. Parameters that were found to be of minor influence on specimen quality were subsequently held at fixed values enabling a more rapid determination of an optimal process. Optimization studies were then performed by varying the remaining parameters at three film melt processing rates. The resulting peel data were fitted with quadratic response surfaces. Additional specimens were fabricated at levels of high peel strength as predicted by the regression models in an attempt to gage the accuracy of the predicted response and to assess the repeatability of the process. The overall results indicate that quality laminates having film interleaves can be successfully and repeatably fabricated by automated fiber placement.

Application of Multivariable Model Predictive Advanced Control for a 2×310T/H CFB Boiler Unit

NASA Astrophysics Data System (ADS)

Weijie, Zhao; Zongllao, Dai; Rong, Gou; Wengan, Gong

When a CFB boiler is in automatic control, there are strong interactions between various process variables and inverse response characteristics of bed temperature control target. Conventional Pill control strategy cannot deliver satisfactory control demand. Kalman wave filter technology is used to establish a non-linear combustion model, based on the CFB combustion characteristics of bed fuel inventory, heating values, bed lime inventory and consumption. CFB advanced combustion control utilizes multivariable model predictive control technology to optimize primary and secondary air flow, bed temperature, air flow, fuel flow and heat flux. In addition to providing advanced combustion control to 2×310t/h CFB+1×100MW extraction condensing turbine generator unit, the control also provides load allocation optimization and advanced control for main steam pressure, combustion and temperature. After the successful implementation, under 10% load change, main steam pressure varied less than ±0.07MPa, temperature less than ±1°C, bed temperature less than ±4°C, and air flow (O2) less than ±0.4%.

FEV1/FEV6 in Primary Care Is a Reliable and Easy Method for the Diagnosis of COPD.

PubMed

Wang, Shengyu; Gong, Wei; Tian, Yao; Zhou, Jing

2016-03-01

FEV6 can be used as a convenient alternative to FVC. The aim of this study was to determine an alternative to the fixed cutoff points of FEV1/FVC <0.70 suitable for FEV1/FEV6 in primary care. Pulmonary function testing was conducted on volunteers recruited from 4 community centers in Xi'an, China, between July and August 2012. Participants underwent 3 FVC maneuvers. The maneuver with the best FEV1 was retained. FVC, FEV1, and FEV6 were measured by portable spirometer. The receiver operating characteristic curves that corresponded to the optimal combination of sensitivity and specificity for FEV1/FEV6 were determined. A kappa test was used to compare the agreement between FEV1/FVC and FEV1/FEV6. The positive predictive value and negative predictive value were also calculated. A total of 767 volunteers participated in this study, of whom 297 were male and 470 were female. Considering FEV1/FVC <0.70 as the accepted standard for COPD, the area under the curve was 98% (P < .001), and the FEV1/FEV6 cutoff, corresponding to the greatest sum of sensitivity and specificity, was 0.72. For the total population, the FEV1/FEV6 sensitivity, specificity, positive predictive value, and negative predictive value were 96.9, 98.8, 95.8, and 99.2%, respectively. The agreement between the 2 cutoff points was excellent, and the kappa value was 0.954. FEV1/FEV6 <0.72 can be used in primary care as a valid alternative to FEV1/FVC <0.70 as a fixed cutoff point for the detection of COPD in adults. This study suggests that FEV1/FEV6 is an effective and well validated option that should be used in primary care to detect COPD, which is a rampant problem. Copyright © 2016 by Daedalus Enterprises.

Estimating cellular parameters through optimization procedures: elementary principles and applications.

PubMed

Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki

2015-01-01

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

Simultaneous prediction of muscle and contact forces in the knee during gait.

PubMed

Lin, Yi-Chung; Walter, Jonathan P; Banks, Scott A; Pandy, Marcus G; Fregly, Benjamin J

2010-03-22

Musculoskeletal models are currently the primary means for estimating in vivo muscle and contact forces in the knee during gait. These models typically couple a dynamic skeletal model with individual muscle models but rarely include articular contact models due to their high computational cost. This study evaluates a novel method for predicting muscle and contact forces simultaneously in the knee during gait. The method utilizes a 12 degree-of-freedom knee model (femur, tibia, and patella) combining muscle, articular contact, and dynamic skeletal models. Eight static optimization problems were formulated using two cost functions (one based on muscle activations and one based on contact forces) and four constraints sets (each composed of different combinations of inverse dynamic loads). The estimated muscle and contact forces were evaluated using in vivo tibial contact force data collected from a patient with a force-measuring knee implant. When the eight optimization problems were solved with added constraints to match the in vivo contact force measurements, root-mean-square errors in predicted contact forces were less than 10 N. Furthermore, muscle and patellar contact forces predicted by the two cost functions became more similar as more inverse dynamic loads were used as constraints. When the contact force constraints were removed, estimated medial contact forces were similar and lateral contact forces lower in magnitude compared to measured contact forces, with estimated muscle forces being sensitive and estimated patellar contact forces relatively insensitive to the choice of cost function and constraint set. These results suggest that optimization problem formulation coupled with knee model complexity can significantly affect predicted muscle and contact forces in the knee during gait. Further research using a complete lower limb model is needed to assess the importance of this finding to the muscle and contact force estimation process. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Free energy, precision and learning: the role of cholinergic neuromodulation

PubMed Central

Moran, Rosalyn J.; Campo, Pablo; Symmonds, Mkael; Stephan, Klaas E.; Dolan, Raymond J.; Friston, Karl J.

2014-01-01

Acetylcholine (ACh) is a neuromodulatory transmitter implicated in perception and learning under uncertainty. This study combined computational simulations and pharmaco-electroencephalography in humans, to test a formulation of perceptual inference based upon the free energy principle. This formulation suggests that acetylcholine enhances the precision of bottom-up synaptic transmission in cortical hierarchies by optimising the gain of supragranular pyramidal cells. Simulations of a mismatch negativity paradigm predicted a rapid trial-by-trial suppression of evoked sensory prediction error (PE) responses that is attenuated by cholinergic neuromodulation. We confirmed this prediction empirically with a placebo-controlled study of cholinesterase inhibition. Furthermore – using dynamic causal modelling – we found that drug-induced differences in PE responses could be explained by gain modulation in supragranular pyramidal cells in primary sensory cortex. This suggests that acetylcholine adaptively enhances sensory precision by boosting bottom-up signalling when stimuli are predictable, enabling the brain to respond optimally under different levels of environmental uncertainty. PMID:23658161

Predictive factor and antihypertensive usage of tyrosine kinase inhibitor-induced hypertension in kidney cancer patients

PubMed Central

IZUMI, KOUJI; ITAI, SHINGO; TAKAHASHI, YOSHIKO; MAOLAKE, AERKEN; NAMIKI, MIKIO

2014-01-01

Hypertension (HT) is the common adverse event associated with vascular endothelial growth factor receptor-tyrosine kinase inhibitors (VEGFR-TKI). The present study was performed to identify the predictive factors of TKI-induced HT and to determine the classes of antihypertensive agents (AHTA) that demonstrate optimal efficacy against this type of HT. The charts of 50 cases of patients that had received VEGFR-TKI treatment were retrospectively examined. The association between patient background and TKI-induced HT, and the effect of administering AHTA were analyzed. High systolic blood pressure at baseline was identified to be a predictive factor for HT. In addition, there was no difference observed between calcium channel blockers (CCBs) and angiotensin receptor II blockers (ARBs) as first-line AHTA for the control of HT. The findings of the present study may aid with predicting the onset of TKI-induced HT, as well as for its management via the primary use of either CCBs or ARBs. PMID:24959266

Atlas of optimal coil orientation and position for TMS: A computational study.

PubMed

Gomez-Tames, Jose; Hamasaka, Atsushi; Laakso, Ilkka; Hirata, Akimasa; Ugawa, Yoshikazu

2018-04-17

Transcranial magnetic stimulation (TMS) activates target brain structures in a non-invasive manner. The optimal orientation of the TMS coil for the motor cortex is well known and can be estimated using motor evoked potentials. However, there are no easily measurable responses for activation of other cortical areas and the optimal orientation for these areas is currently unknown. This study investigated the electric field strength, optimal coil orientation, and relative locations to optimally stimulate the target cortex based on computed electric field distributions. A total of 518,616 stimulation scenarios were studied using realistic head models (2401 coil locations × 12 coil angles × 18 head models). Inter-subject registration methods were used to generate an atlas of optimized TMS coil orientations on locations on the standard brain. We found that the maximum electric field strength is greater in primary somatosensory cortex and primary motor cortex than in other cortical areas. Additionally, a universal optimal coil orientation applicable to most subjects is more feasible at the primary somatosensory cortex and primary motor cortex. We confirmed that optimal coil angle follows the anatomical shape of the hand motor area to realize personalized optimization of TMS. Finally, on average, the optimal coil positions for TMS on the scalp deviated 5.5 mm from the scalp points with minimum cortex-scalp distance. This deviation was minimal at the premotor cortex and primary motor cortex. Personalized optimal coil orientation is preferable for obtaining the most effective stimulation. Copyright © 2018. Published by Elsevier Inc.

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

PubMed

Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

2015-06-01

Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Experimental Optimization of a Free-to-Rotate Wing for Small UAS

NASA Technical Reports Server (NTRS)

Logan, Michael J.; DeLoach, Richard; Copeland, Tiwana; Vo, Steven

2014-01-01

This paper discusses an experimental investigation conducted to optimize a free-to-rotate wing for use on a small unmanned aircraft system (UAS). Although free-to-rotate wings have been used for decades on various small UAS and small manned aircraft, little is known about how to optimize these unusual wings for a specific application. The paper discusses some of the design rationale of the basic wing. In addition, three main parameters were selected for "optimization", wing camber, wing pivot location, and wing center of gravity (c.g.) location. A small apparatus was constructed to enable some simple experimental analysis of these parameters. A design-of-experiment series of tests were first conducted to discern which of the main optimization parameters were most likely to have the greatest impact on the outputs of interest, namely, some measure of "stability", some measure of the lift being generated at the neutral position, and how quickly the wing "recovers" from an upset. A second set of tests were conducted to develop a response-surface numerical representation of these outputs as functions of the three primary inputs. The response surface numerical representations are then used to develop an "optimum" within the trade space investigated. The results of the optimization are then tested experimentally to validate the predictions.

A computational study of thrust augmenting ejectors based on a viscous-inviscid approach

NASA Technical Reports Server (NTRS)

Lund, Thomas S.; Tavella, Domingo A.; Roberts, Leonard

1987-01-01

A viscous-inviscid interaction technique is advocated as both an efficient and accurate means of predicting the performance of two-dimensional thrust augmenting ejectors. The flow field is subdivided into a viscous region that contains the turbulent jet and an inviscid region that contains the ambient fluid drawn into the device. The inviscid region is computed with a higher-order panel method, while an integral method is used for the description of the viscous part. The strong viscous-inviscid interaction present within the ejector is simulated in an iterative process where the two regions influence each other en route to a converged solution. The model is applied to a variety of parametric and optimization studies involving ejectors having either one or two primary jets. The effects of nozzle placement, inlet and diffuser shape, free stream speed, and ejector length are investigated. The inlet shape for single jet ejectors is optimized for various free stream speeds and Reynolds numbers. Optimal nozzle tilt and location are identified for various dual-ejector configurations.

Laser-assisted in situ keratomileusis with optimized, fast-repetition, and cyclotorsion control excimer laser to treat hyperopic astigmatism with high cylinder.

PubMed

Alió Del Barrio, Jorge L; Tiveron, Mauro; Plaza-Puche, Ana B; Amesty, María A; Casanova, Laura; García, María J; Alió, Jorge L

2017-10-18

To evaluate the visual outcomes after femtosecond laser-assisted laser in situ keratomileusis (LASIK) surgery to correct primary compound hyperopic astigmatism with high cylinder using a fast repetition rate excimer laser platform with optimized aspheric profiles and cyclotorsion control. Eyes with primary simple or compound hyperopic astigmatism and a cylinder power ≥3.00 D had uneventful femtosecond laser-assisted LASIK with a fast repetition rate excimer laser ablation, aspheric profiles, and cyclotorsion control. Visual, refractive, and aberrometric results were evaluated at the 3- and 6-month follow-up. The astigmatic outcome was evaluated using the Alpins method and ASSORT software. This study enrolled 80 eyes at 3 months and 50 eyes at 6 months. The significant reduction in refractive sphere and cylinder 3 and 6 months postoperatively (p<0.01) was associated with an improved uncorrected distance visual acuity (p<0.01). A total of 23.75% required retreatment 3 months after surgery. Efficacy and safety indices at 6 months were 0.90 and 1.00, respectively. At 6 months, 80% of eyes had an SE within ±0.50 D and 96% within ±1.00 D. No significant differences were detected between the third and the sixth postoperative months in refractive parameters. A significant increase in the spherical aberration was detected, but not in coma. The correction index was 0.94 at 3 months. Laser in situ keratomileusis for primary compound hyperopic astigmatism with high cylinder (>3.00 D) using the latest excimer platforms with cyclotorsion control, fast repetition rate, and optimized aspheric profiles is safe, moderately effective, and predictable.

Laser in situ keratomileusis using optimized aspheric profiles and cyclotorsion control to treat compound myopic astigmatism with high cylinder.

PubMed

Alió, Jorge L; Plaza-Puche, Ana B; Martinez, Lorena M; Torky, Magda; Brenner, Luis F

2013-01-01

To evaluate the visual outcomes after laser in situ keratomileusis (LASIK) surgery to correct primary compound myopic astigmatism with high cylinder performed using a fast-repetition-rate excimer laser platform with optimized aspheric profiles and cyclotorsion control. Vissum Corporation and Division of Ophthalmology, Universidad Miguel Hernández, Alicante, Spain. Retrospective consecutive observational nonrandomized noncomparative case series. Eyes with primary compound myopic astigmatism and a cylinder power over 3.00 diopters (D) had uneventful LASIK with femtosecond flap creation and fast-repetition-rate excimer laser ablation with aspheric profiles and cyclotorsion control. Visual, refractive, and aberrometric outcomes were evaluated at the 6-month follow-up. The astigmatic correction was evaluated using the Alpins method and Assort software. The study enrolled 37 eyes (29 patients; age range 19 to 55 years). The significant reduction in refractive sphere and cylinder 3 months and 6 months postoperatively (P<.01) was associated with improved uncorrected distance visual acuity (P<.01). Eighty-seven percent of eyes had a spherical equivalent within ±0.50 D; 7.5% of eyes were retreated. There was no significant induction of higher-order aberrations (HOAs). The targeted and surgically induced astigmatism magnitudes were 3.23 D and 2.96 D, respectively, and the correction index was 0.91. The safety and efficacy indices were 1.05 and 0.95, respectively. Laser in situ keratomileusis for primary compound myopic astigmatism with high cylinder (>3.00 D) performed using a fast-repetition-rate excimer laser with optimized aspheric profiles and cyclotorsion control was safe, effective, and predictable and did not cause significant induction of HOAs. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Analysis of postoperative complications associated with the use of anti-adhesion sodium hyaluronate-carboxymethylcellulose (HA-CMC) barrier after cytoreductive surgery for ovarian, fallopian tube and peritoneal cancers.

PubMed

Krill, Lauren S; Ueda, Stefanie M; Gerardi, Melissa; Bristow, Robert E

2011-02-01

To evaluate the risk of postoperative complications related to HA-CMC use in patients undergoing optimal cytoreductive surgery for primary and recurrent ovarian, fallopian tube, and peritoneal cancers. A single institution retrospective review identified all patients undergoing optimal (≤1 cm) cytoreductive surgery for primary or recurrent ovarian, fallopian tube, and peritoneal cancers between 1/95 and 12/08. Operative details and post-operative complications (<30 days) were extracted from the medical record. Fisher's exact test, Mann-Whitney-U, and multiple regression analyses were performed to identify factors, including HA-CMC use, associated with post-operative complications. Three hundred seventy-five cases were analyzed: HA-CMC was utilized in 168 debulking procedures. There was no difference in the incidence of overall morbidity for patients with HA-CMC compared to those without HA-CMC (OR 1.07; 95% CI: 0.68-1.67). On univariate analysis, application of HA-CMC increased the risk of pelvic abscess (OR 2.66; 95% CI: 1.21-5.86), particularly in the primary surgery setting (OR 4.65; 95% CI: 1.67-12.98) and in patients undergoing hysterectomy (OR 3.36; 95% CI: 1.18-9.53). After controlling for confounding factors using multiple linear regression, HA-CMC use approached statistical significance in predicting an increased risk of pelvic abscess but not major postoperative morbidity. HA-CMC adhesion barrier placement at the time of optimal cytoreductive surgery for ovarian, fallopian tube, and peritoneal cancer is not associated with major postoperative complications but may be associated with increased risk of pelvic abscess. Copyright © 2010 Elsevier Inc. All rights reserved.

Assessing FPAR Source and Parameter Optimization Scheme in Application of a Diagnostic Carbon Flux Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turner, D P; Ritts, W D; Wharton, S

2009-02-26

The combination of satellite remote sensing and carbon cycle models provides an opportunity for regional to global scale monitoring of terrestrial gross primary production, ecosystem respiration, and net ecosystem production. FPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) is a critical input to diagnostic models, however little is known about the relative effectiveness of FPAR products from different satellite sensors nor about the sensitivity of flux estimates to different parameterization approaches. In this study, we used multiyear observations of carbon flux at four eddy covariance flux tower sites within the conifer biome to evaluate these factors.more » FPAR products from the MODIS and SeaWiFS sensors, and the effects of single site vs. cross-site parameter optimization were tested with the CFLUX model. The SeaWiFs FPAR product showed greater dynamic range across sites and resulted in slightly reduced flux estimation errors relative to the MODIS product when using cross-site optimization. With site-specific parameter optimization, the flux model was effective in capturing seasonal and interannual variation in the carbon fluxes at these sites. The cross-site prediction errors were lower when using parameters from a cross-site optimization compared to parameter sets from optimization at single sites. These results support the practice of multisite optimization within a biome for parameterization of diagnostic carbon flux models.« less

Optimal experimental design for improving the estimation of growth parameters of Lactobacillus viridescens from data under non-isothermal conditions.

PubMed

Longhi, Daniel Angelo; Martins, Wiaslan Figueiredo; da Silva, Nathália Buss; Carciofi, Bruno Augusto Mattar; de Aragão, Gláucia Maria Falcão; Laurindo, João Borges

2017-01-02

In predictive microbiology, the model parameters have been estimated using the sequential two-step modeling (TSM) approach, in which primary models are fitted to the microbial growth data, and then secondary models are fitted to the primary model parameters to represent their dependence with the environmental variables (e.g., temperature). The Optimal Experimental Design (OED) approach allows reducing the experimental workload and costs, and the improvement of model identifiability because primary and secondary models are fitted simultaneously from non-isothermal data. Lactobacillus viridescens was selected to this study because it is a lactic acid bacterium of great interest to meat products preservation. The objectives of this study were to estimate the growth parameters of L. viridescens in culture medium from TSM and OED approaches and to evaluate both the number of experimental data and the time needed in each approach and the confidence intervals of the model parameters. Experimental data for estimating the model parameters with TSM approach were obtained at six temperatures (total experimental time of 3540h and 196 experimental data of microbial growth). Data for OED approach were obtained from four optimal non-isothermal profiles (total experimental time of 588h and 60 experimental data of microbial growth), two profiles with increasing temperatures (IT) and two with decreasing temperatures (DT). The Baranyi and Roberts primary model and the square root secondary model were used to describe the microbial growth, in which the parameters b and T min (±95% confidence interval) were estimated from the experimental data. The parameters obtained from TSM approach were b=0.0290 (±0.0020) [1/(h 0.5 °C)] and T min =-1.33 (±1.26) [°C], with R 2 =0.986 and RMSE=0.581, and the parameters obtained with the OED approach were b=0.0316 (±0.0013) [1/(h 0.5 °C)] and T min =-0.24 (±0.55) [°C], with R 2 =0.990 and RMSE=0.436. The parameters obtained from OED approach presented smaller confidence intervals and best statistical indexes than those from TSM approach. Besides, less experimental data and time were needed to estimate the model parameters with OED than TSM. Furthermore, the OED model parameters were validated with non-isothermal experimental data with great accuracy. In this way, OED approach is feasible and is a very useful tool to improve the prediction of microbial growth under non-isothermal condition. Copyright © 2016 Elsevier B.V. All rights reserved.

Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Atienzar, Franck A., E-mail: franck.atienzar@ucb.com; Novik, Eric I.; Gerets, Helga H.

Drug Induced Liver Injury (DILI) is a major cause of attrition during early and late stage drug development. Consequently, there is a need to develop better in vitro primary hepatocyte models from different species for predicting hepatotoxicity in both animals and humans early in drug development. Dog is often chosen as the non-rodent species for toxicology studies. Unfortunately, dog in vitro models allowing long term cultures are not available. The objective of the present manuscript is to describe the development of a co-culture dog model for predicting hepatotoxic drugs in humans and to compare the predictivity of the canine modelmore » along with primary human hepatocytes and HepG2 cells. After rigorous optimization, the dog co-culture model displayed metabolic capacities that were maintained up to 2 weeks which indicates that such model could be also used for long term metabolism studies. Most of the human hepatotoxic drugs were detected with a sensitivity of approximately 80% (n = 40) for the three cellular models. Nevertheless, the specificity was low approximately 40% for the HepG2 cells and hepatocytes compared to 72.7% for the canine model (n = 11). Furthermore, the dog co-culture model showed a higher superiority for the classification of 5 pairs of close structural analogs with different DILI concerns in comparison to both human cellular models. Finally, the reproducibility of the canine system was also satisfactory with a coefficient of correlation of 75.2% (n = 14). Overall, the present manuscript indicates that the dog co-culture model may represent a relevant tool to perform chronic hepatotoxicity and metabolism studies. - Highlights: • Importance of species differences in drug development. • Relevance of dog co-culture model for metabolism and toxicology studies. • Hepatotoxicity: higher predictivity of dog co-culture vs HepG2 and human hepatocytes.« less

Optimal Timing for Elective Early Primary Repair of Tetralogy of Fallot: Analysis of Intermediate Term Outcomes.

PubMed

Cunningham, Michael E A; Donofrio, Mary T; Peer, Syed Murfad; Zurakowski, David; Jonas, Richard A; Sinha, Pranava

2017-03-01

We have previously demonstrated that early primary repair of tetralogy of Fallot with pulmonary stenosis (TOF) can be safely performed without increase in hospital resource utilization or compromise to surgical technical performance scores (TPS). We sought to identify the optimal timing for elective early primary repair of TOF with respect to intermediate-term reintervention. Retrospective review of all patients with TOF undergoing elective primary repair between September 2004 and December 2013 was performed. Patients were stratified into reintervention group or no reintervention group. Multivariable Cox regression analysis identified independent predictors of reintervention. Youden's J-index in receiver operating characteristic analysis identified optimal age cutoff predictive of reintervention. Kaplan-Meier analysis with the log-rank test compared reintervention rates stratified by age and TPS. A total of 129 patients with median (interquartile range) age and weight of 78 days (56 to 111) and 5 kg (4.1 to 5.7), respectively, underwent primary repair. After a median (interquartile range) follow-up of 2.3 years (0.1 to 4.6), 18 patients (14%) required a total of 22 reinterventions. Youden's J-index revealed significantly lower risk of intermediate-term reintervention when repaired after 55 days of age (8% for >55 days old versus 31% for ≤55 days of age). Multivariable Cox regression identified age 55 days and younger (hazard ratio [HR] 4.5, 95% confidence interval [CI] 1.6 to 12.8, p = 0.004), valve sparing repair (HR 15.3, 95% CI 1.8 to 128.5, p < 0.001), residual right ventricular outflow tract (RVOT) gradient (HR 1.11, 95% CI 1.1 to 1.2, p < 0.001), and inadequate TPS (HR 21.5, 95% CI 7.4 to 63, p < 0.001) as independent predictors of overall intermediate-term reintervention. Elective repair in patients greater than 55 days of age, irrespective of size of the patient, can be safely performed without any increase in reintervention rates. Both residual peak RVOT gradient and TPS are effective in identifying patients at increased risk of reintervention. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

An auxiliary optimization method for complex public transit route network based on link prediction

NASA Astrophysics Data System (ADS)

Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

2018-02-01

Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

Multiple scattering theory for total skin electron beam design.

PubMed

Antolak, J A; Hogstrom, K R

1998-06-01

The purpose of this manuscript is to describe a method for designing a broad beam of electrons suitable for total skin electron irradiation (TSEI). A theoretical model of a TSEI beam from a linear accelerator with a dual scattering system has been developed. The model uses Fermi-Eyges theory to predict the planar fluence of the electron beam after it has passed through various materials between the source and the treatment plane, which includes scattering foils, monitor chamber, air, and a plastic diffusing plate. Unique to this model is its accounting for removal of the tails of the electron beam profile as it passes through the primary x-ray jaws. A method for calculating the planar fluence profile for an obliquely incident beam is also described. Off-axis beam profiles and percentage depth doses are measured with ion chambers, film, and thermoluminescent dosimeters (TLD). The measured data show that the theoretical model can accurately predict beam energy and planar fluence of the electron beam at normal and oblique incidence. The agreement at oblique angles is not quite as good but is sufficiently accurate to be of predictive value when deciding on the optimal angles for the clinical TSEI beams. The advantage of our calculational approach for designing a TSEI beam is that many different beam configurations can be tested without having to perform time-consuming measurements. Suboptimal configurations can be quickly dismissed, and the predicted optimal solution should be very close to satisfying the clinical specifications.

General Methodology Combining Engineering Optimization of Primary HVAC and R Plants with Decision Analysis Methods--Part II: Uncertainty and Decision Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Wei; Reddy, T. A.; Gurian, Patrick

2007-01-31

A companion paper to Jiang and Reddy that presents a general and computationally efficient methodology for dyanmic scheduling and optimal control of complex primary HVAC&R plants using a deterministic engineering optimization approach.

Imaging in rectal cancer with emphasis on local staging with MRI

PubMed Central

Arya, Supreeta; Das, Deepak; Engineer, Reena; Saklani, Avanish

2015-01-01

Imaging in rectal cancer has a vital role in staging disease, and in selecting and optimizing treatment planning. High-resolution MRI (HR-MRI) is the recommended method of first choice for local staging of rectal cancer for both primary staging and for restaging after preoperative chemoradiation (CT-RT). HR-MRI helps decide between upfront surgery and preoperative CT-RT. It provides high accuracy for prediction of circumferential resection margin at surgery, T category, and nodal status in that order. MRI also helps assess resectability after preoperative CT-RT and decide between sphincter saving or more radical surgery. Accurate technique is crucial for obtaining high-resolution images in the appropriate planes for correct staging. The phased array external coil has replaced the endorectal coil that is no longer recommended. Non-fat suppressed 2D T2-weighted (T2W) sequences in orthogonal planes to the tumor are sufficient for primary staging. Contrast-enhanced MRI is considered inappropriate for both primary staging and restaging. Diffusion-weighted sequence may be of value in restaging. Multidetector CT cannot replace MRI in local staging, but has an important role for evaluating distant metastases. Positron emission tomography-computed tomography (PET/CT) has a limited role in the initial staging of rectal cancer and is reserved for cases with resectable metastatic disease before contemplating surgery. This article briefly reviews the comprehensive role of imaging in rectal cancer, describes the role of MRI in local staging in detail, discusses the optimal MRI technique, and provides a synoptic report for both primary staging and restaging after CT-RT in routine practice. PMID:25969638

Esophageal capsule endoscopy is not the optimal technique to determine the need for primary prophylaxis in patients with cirrhosis

PubMed Central

Krok, Karen L.; Wagennar, Rebecca Rankin; Kantsevoy, Sergey V.

2016-01-01

Introduction Capsule endoscopy has been suggested as a potential alternative to endoscopy for detection of esophagogastric varices and severe portal hypertensive gastropathy (PHG). The aim of the study was to determine whether PillCam esophageal capsule endoscopy could replace endoscopy for screening purposes. Material and methods Sixty-two patients with cirrhosis with no previous variceal bleeding had PillCam capsule endoscopy and video endoscopy performed on the same day. Sensitivity, specificity, and positive and negative predictive values (PPV, NPV) of capsule endoscopy were compared to endoscopy for the presence and severity of esophageal and gastric varices, PHG and the need for primary prophylaxis. Patients’ preference was assessed by a questionnaire. Results Four (6%) patients were unable to swallow the capsule. Sensitivity, specificity, PPV and NPV of capsule endoscopy for detecting any esophageal varices (92%, 50%, 92%, 50%), large varices (55%, 91%, 75%, 80%), variceal red signs (58%, 87%, 69%, 80%), PHG (95%, 50%, 95%, 50%), and the need for primary prophylaxis (91%, 57%, 78%, 80%) were not optimal, with only moderate agreement (κ) between capsule and upper GI endoscopy. Had only a capsule endoscopy been performed, 12 (21.4%) patients would have received inappropriate treatment. Capsule endoscopy also failed to detect (0/13) gastric varices. The majority of patients ranked capsule endoscopy as more convenient (69%) and their preferred (61%) method. Conclusions Despite the preference expressed by patients for capsule endoscopy, we believe that upper GI endoscopy should remain the preferred screening method for primary prophylaxis. PMID:27186182

Predicting impact of multi-paths on phase change in map-based vehicular ad hoc networks

NASA Astrophysics Data System (ADS)

Rahmes, Mark; Lemieux, George; Sonnenberg, Jerome; Chester, David B.

2014-05-01

Dynamic Spectrum Access, which through its ability to adapt the operating frequency of a radio, is widely believed to be a solution to the limited spectrum problem. Mobile Ad Hoc Networks (MANETs) can extend high capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact cognitive radio employs spectrum sensing to facilitate identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We quantify optimal signal detection in map based cognitive radio networks with multiple rapidly varying phase changes and multiple orthogonal signals. Doppler shift occurs due to reflection, scattering, and rapid vehicle movement. Path propagation as well as vehicle movement produces either constructive or destructive interference with the incident wave. Our signal detection algorithms can assist the Doppler spread compensation algorithm by deciding how many phase changes in signals are present in a selected band of interest. Additionally we can populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate Dynamic Spectrum Access. We show how topography can help predict the impact of multi-paths on phase change, as well as about the prediction from dense traffic areas. Utilization of high resolution geospatial data layers in RF propagation analysis is directly applicable.

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

PubMed

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

Emergent Behaviors from a Cellular Automaton Model for Invasive Tumor Growth in Heterogeneous Microenvironments

PubMed Central

Jiao, Yang; Torquato, Salvatore

2011-01-01

Understanding tumor invasion and metastasis is of crucial importance for both fundamental cancer research and clinical practice. In vitro experiments have established that the invasive growth of malignant tumors is characterized by the dendritic invasive branches composed of chains of tumor cells emanating from the primary tumor mass. The preponderance of previous tumor simulations focused on non-invasive (or proliferative) growth. The formation of the invasive cell chains and their interactions with the primary tumor mass and host microenvironment are not well understood. Here, we present a novel cellular automaton (CA) model that enables one to efficiently simulate invasive tumor growth in a heterogeneous host microenvironment. By taking into account a variety of microscopic-scale tumor-host interactions, including the short-range mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the invasive cells and oxygen/nutrient gradient driven cell motions, our CA model predicts a rich spectrum of growth dynamics and emergent behaviors of invasive tumors. Besides robustly reproducing the salient features of dendritic invasive growth, such as least-resistance paths of cells and intrabranch homotype attraction, we also predict nontrivial coupling between the growth dynamics of the primary tumor mass and the invasive cells. In addition, we show that the properties of the host microenvironment can significantly affect tumor morphology and growth dynamics, emphasizing the importance of understanding the tumor-host interaction. The capability of our CA model suggests that sophisticated in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies. PMID:22215996

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

PubMed

Dong, Ling-Bo; Liu, Zhao-Gang; Li, Feng-Ri; Jiang, Li-Chun

2013-09-01

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.

Preoperative serum alkaline phosphatase: a predictive factor for early hypocalcaemia following parathyroidectomy of primary hyperparathyroidism.

PubMed

Sun, Longhao; He, Xianghui; Liu, Tong

2014-01-01

Postoperative hypocalcemia is one of the most common complications following parathyroidectomy for primary hyperparathyroidism (PHPT). The aim of this study was to analyze the predictive value of biochemical parameters as indicators for episodes of hypocalcemia in patients undergoing parathyroidectomy for PHPT. The patients with PHPT who underwent parathyroidectomy between February 2004 and February 2014 were studied retrospectively at a single medical center. The patients were divided into biochemical, clinical, and no postoperative hypocalcemia groups, based on different clinical manifestations. Potential risk factors for postoperative hypocalcemia were identified and investigated by univariate and multivariate Logistic regression analysis. Of the 139 cases, 25 patients (18.0%) were diagnosed with postoperative hypocalcemia according to the traditional criterion. Univariate analysis revealed only alkaline phosphatase (ALP) and the small area under the curve (AUC) of receiver operating characteristics (ROC) curve for ALP demonstrates low accuracy in predicting the occurrence of postoperative hypocalcemia. Based on new criteria, 22 patients were added to the postoperative hypocalcemia group and similar biochemical parameters were compared. The serum ALP was a significant independent risk factor for postoperative hypocalcemia (P = 0.000) and its AUC of ROC curve was 0.783. The optimal cutoff point was 269 U/L and the sensitivity and specificity for prediction were 89.2% and 64.3%, respectively. The risk of postoperative hypocalcemia after parathyroidectomy should be emphasized for patients with typical symptoms of hypocalcemia despite their serum calcium level is in normal or a little higher range. Serum ALP is a predictive factor for the occurrence of postoperative hypocalcemia.

On the Performance of Alternate Conceptual Ecohydrological Models for Streamflow Prediction

NASA Astrophysics Data System (ADS)

Naseem, Bushra; Ajami, Hoori; Cordery, Ian; Sharma, Ashish

2016-04-01

A merging of a lumped conceptual hydrological model with two conceptual dynamic vegetation models is presented to assess the performance of these models for simultaneous simulations of streamflow and leaf area index (LAI). Two conceptual dynamic vegetation models with differing representation of ecological processes are merged with a lumped conceptual hydrological model (HYMOD) to predict catchment scale streamflow and LAI. The merged RR-LAI-I model computes relative leaf biomass based on transpiration rates while the RR-LAI-II model computes above ground green and dead biomass based on net primary productivity and water use efficiency in response to soil moisture dynamics. To assess the performance of these models, daily discharge and 8-day MODIS LAI product for 27 catchments of 90 - 1600km2 in size located in the Murray - Darling Basin in Australia are used. Our results illustrate that when single-objective optimisation was focussed on maximizing the objective function for streamflow or LAI, the other un-calibrated predicted outcome (LAI if streamflow is the focus) was consistently compromised. Thus, single-objective optimization cannot take into account the essence of all processes in the conceptual ecohydrological models. However, multi-objective optimisation showed great strength for streamflow and LAI predictions. Both response outputs were better simulated by RR-LAI-II than RR-LAI-I due to better representation of physical processes such as net primary productivity (NPP) in RR-LAI-II. Our results highlight that simultaneous calibration of streamflow and LAI using a multi-objective algorithm proves to be an attractive tool for improved streamflow predictions.

Optimality Based Dynamic Plant Allocation Model: Predicting Acclimation Response to Climate Change

NASA Astrophysics Data System (ADS)

Srinivasan, V.; Drewry, D.; Kumar, P.; Sivapalan, M.

2009-12-01

Allocation of assimilated carbon to different plant parts determines the future plant status and is important to predict long term (months to years) vegetated land surface fluxes. Plants have the ability to modify their allometry and exhibit plasticity by varying the relative proportions of the structural biomass contained in each of its tissue. The ability of plants to be plastic provides them with the potential to acclimate to changing environmental conditions in order to enhance their probability of survival. Allometry based allocation models and other empirical allocation models do not account for plant plasticity cause by acclimation due to environmental changes. In the absence of a detailed understanding of the various biophysical processes involved in plant growth and development an optimality approach is adopted here to predict carbon allocation in plants. Existing optimality based models of plant growth are either static or involve considerable empiricism. In this work, we adopt an optimality based approach (coupled with limitations on plant plasticity) to predict the dynamic allocation of assimilated carbon to different plant parts. We explore the applicability of this approach using several optimization variables such as net primary productivity, net transpiration, realized growth rate, total end of growing season reproductive biomass etc. We use this approach to predict the dynamic nature of plant acclimation in its allocation of carbon to different plant parts under current and future climate scenarios. This approach is designed as a growth sub-model in the multi-layer canopy plant model (MLCPM) and is used to obtain land surface fluxes and plant properties over the growing season. The framework of this model is such that it retains the generality and can be applied to different types of ecosystems. We test this approach using the data from free air carbon dioxide enrichment (FACE) experiments using soybean crop at the Soy-FACE research site. Our results show that there are significant changes in the allocation patterns of vegetation when subjected to elevated CO2 indicating that our model is able to account for plant plasticity arising from acclimation. Soybeans when grown under elevated CO2, increased their allocation to structural components such as leaves and decreased their allocation to reproductive biomass. This demonstrates that plant acclimation causes lower than expected crop yields when grown under elevated CO2. Our findings can have serious implications in estimating future crop yields under climate change scenarios where it is widely expected that rising CO2 will fully offset losses due to climate change.

Is optimism real?

PubMed

Simmons, Joseph P; Massey, Cade

2012-11-01

Is optimism real, or are optimistic forecasts just cheap talk? To help answer this question, we investigated whether optimistic predictions persist in the face of large incentives to be accurate. We asked National Football League football fans to predict the winner of a single game. Roughly half (the partisans) predicted a game involving their favorite team, and the other half (the neutrals) predicted a game involving 2 teams they were neutral about. Participants were promised either a small incentive ($5) or a large incentive ($50) for correctly predicting the game's winner. Optimism emerged even when incentives were large, as partisans were much more likely than neutrals to predict partisans' favorite teams to win. Strong optimism also emerged among participants whose responses to follow-up questions strongly suggested that they believed the predictions they made. This research supports the claim that optimism is real. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Development of a bedside viable ultrasound protocol to quantify appendicular lean tissue mass.

PubMed

Paris, Michael T; Lafleur, Benoit; Dubin, Joel A; Mourtzakis, Marina

2017-10-01

Ultrasound is a non-invasive and readily available tool that can be prospectively applied at the bedside to assess muscle mass in clinical settings. The four-site protocol, which images two anatomical sites on each quadriceps, may be a viable bedside method, but its ability to predict musculature has not been compared against whole-body reference methods. Our primary objectives were to (i) compare the four-site protocol's ability to predict appendicular lean tissue mass from dual-energy X-ray absorptiometry; (ii) optimize the predictability of the four-site protocol with additional anatomical muscle thicknesses and easily obtained covariates; and (iii) assess the ability of the optimized protocol to identify individuals with low lean tissue mass. This observational cross-sectional study recruited 96 university and community dwelling adults. Participants underwent ultrasound scans for assessment of muscle thickness and whole-body dual-energy X-ray absorptiometry scans for assessment of appendicular lean tissue. Ultrasound protocols included (i) the nine-site protocol, which images nine anterior and posterior muscle groups in supine and prone positions, and (ii) the four-site protocol, which images two anterior sites on each quadriceps muscle group in a supine position. The four-site protocol was strongly associated (R 2  = 0.72) with appendicular lean tissue mass, but Bland-Altman analysis displayed wide limits of agreement (-5.67, 5.67 kg). Incorporating the anterior upper arm muscle thickness, and covariates age and sex, alongside the four-site protocol, improved the association (R 2  = 0.91) with appendicular lean tissue and displayed narrower limits of agreement (-3.18, 3.18 kg). The optimized protocol demonstrated a strong ability to identify low lean tissue mass (area under the curve = 0.89). The four-site protocol can be improved with the addition of the anterior upper arm muscle thickness, sex, and age when predicting appendicular lean tissue mass. This optimized protocol can accurately identify low lean tissue mass, while still being easily applied at the bedside. © 2017 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of the Society on Sarcopenia, Cachexia and Wasting Disorders.

Development of a bedside viable ultrasound protocol to quantify appendicular lean tissue mass

PubMed Central

Paris, Michael T.; Lafleur, Benoit; Dubin, Joel A.

2017-01-01

Abstract Background Ultrasound is a non‐invasive and readily available tool that can be prospectively applied at the bedside to assess muscle mass in clinical settings. The four‐site protocol, which images two anatomical sites on each quadriceps, may be a viable bedside method, but its ability to predict musculature has not been compared against whole‐body reference methods. Our primary objectives were to (i) compare the four‐site protocol's ability to predict appendicular lean tissue mass from dual‐energy X‐ray absorptiometry; (ii) optimize the predictability of the four‐site protocol with additional anatomical muscle thicknesses and easily obtained covariates; and (iii) assess the ability of the optimized protocol to identify individuals with low lean tissue mass. Methods This observational cross‐sectional study recruited 96 university and community dwelling adults. Participants underwent ultrasound scans for assessment of muscle thickness and whole‐body dual‐energy X‐ray absorptiometry scans for assessment of appendicular lean tissue. Ultrasound protocols included (i) the nine‐site protocol, which images nine anterior and posterior muscle groups in supine and prone positions, and (ii) the four‐site protocol, which images two anterior sites on each quadriceps muscle group in a supine position. Results The four‐site protocol was strongly associated (R 2 = 0.72) with appendicular lean tissue mass, but Bland–Altman analysis displayed wide limits of agreement (−5.67, 5.67 kg). Incorporating the anterior upper arm muscle thickness, and covariates age and sex, alongside the four‐site protocol, improved the association (R 2 = 0.91) with appendicular lean tissue and displayed narrower limits of agreement (−3.18, 3.18 kg). The optimized protocol demonstrated a strong ability to identify low lean tissue mass (area under the curve = 0.89). Conclusions The four‐site protocol can be improved with the addition of the anterior upper arm muscle thickness, sex, and age when predicting appendicular lean tissue mass. This optimized protocol can accurately identify low lean tissue mass, while still being easily applied at the bedside. PMID:28722298

Using natural selection and optimization for smarter vegetation models - challenges and opportunities

NASA Astrophysics Data System (ADS)

Franklin, Oskar; Han, Wang; Dieckmann, Ulf; Cramer, Wolfgang; Brännström, Åke; Pietsch, Stephan; Rovenskaya, Elena; Prentice, Iain Colin

2017-04-01

Dynamic global vegetation models (DGVMs) are now indispensable for understanding the biosphere and for estimating the capacity of ecosystems to provide services. The models are continuously developed to include an increasing number of processes and to utilize the growing amounts of observed data becoming available. However, while the versatility of the models is increasing as new processes and variables are added, their accuracy suffers from the accumulation of uncertainty, especially in the absence of overarching principles controlling their concerted behaviour. We have initiated a collaborative working group to address this problem based on a 'missing law' - adaptation and optimization principles rooted in natural selection. Even though this 'missing law' constrains relationships between traits, and therefore can vastly reduce the number of uncertain parameters in ecosystem models, it has rarely been applied to DGVMs. Our recent research have shown that optimization- and trait-based models of gross primary production can be both much simpler and more accurate than current models based on fixed functional types, and that observed plant carbon allocations and distributions of plant functional traits are predictable with eco-evolutionary models. While there are also many other examples of the usefulness of these and other theoretical principles, it is not always straight-forward to make them operational in predictive models. In particular on longer time scales, the representation of functional diversity and the dynamical interactions among individuals and species presents a formidable challenge. Here we will present recent ideas on the use of adaptation and optimization principles in vegetation models, including examples of promising developments, but also limitations of the principles and some key challenges.

Order of Magnitude Sensitivity Increase in X-ray Fluorescence Computed Tomography (XFCT) Imaging With an Optimized Spectro-Spatial Detector Configuration: Theory and Simulation

PubMed Central

Ahmad, Moiz; Bazalova, Magdalena; Xiang, Liangzhong

2014-01-01

The purpose of this study was to increase the sensitivity of XFCT imaging by optimizing the data acquisition geometry for reduced scatter X-rays. The placement of detectors and detector energy window were chosen to minimize scatter X-rays. We performed both theoretical calculations and Monte Carlo simulations of this optimized detector configuration on a mouse-sized phantom containing various gold concentrations. The sensitivity limits were determined for three different X-ray spectra: a monoenergetic source, a Gaussian source, and a conventional X-ray tube source. Scatter X-rays were minimized using a backscatter detector orientation (scatter direction > 110° to the primary X-ray beam). The optimized configuration simultaneously reduced the number of detectors and improved the image signal-to-noise ratio. The sensitivity of the optimized configuration was 10 µg/mL (10 pM) at 2 mGy dose with the mono-energetic source, which is an order of magnitude improvement over the unoptimized configuration (102 pM without the optimization). Similar improvements were seen with the Gaussian spectrum source and conventional X-ray tube source. The optimization improvements were predicted in the theoretical model and also demonstrated in simulations. The sensitivity of XFCT imaging can be enhanced by an order of magnitude with the data acquisition optimization, greatly enhancing the potential of this modality for future use in clinical molecular imaging. PMID:24770916

Order of magnitude sensitivity increase in X-ray Fluorescence Computed Tomography (XFCT) imaging with an optimized spectro-spatial detector configuration: theory and simulation.

PubMed

Ahmad, Moiz; Bazalova, Magdalena; Xiang, Liangzhong; Xing, Lei

2014-05-01

The purpose of this study was to increase the sensitivity of XFCT imaging by optimizing the data acquisition geometry for reduced scatter X-rays. The placement of detectors and detector energy window were chosen to minimize scatter X-rays. We performed both theoretical calculations and Monte Carlo simulations of this optimized detector configuration on a mouse-sized phantom containing various gold concentrations. The sensitivity limits were determined for three different X-ray spectra: a monoenergetic source, a Gaussian source, and a conventional X-ray tube source. Scatter X-rays were minimized using a backscatter detector orientation (scatter direction > 110(°) to the primary X-ray beam). The optimized configuration simultaneously reduced the number of detectors and improved the image signal-to-noise ratio. The sensitivity of the optimized configuration was 10 μg/mL (10 pM) at 2 mGy dose with the mono-energetic source, which is an order of magnitude improvement over the unoptimized configuration (102 pM without the optimization). Similar improvements were seen with the Gaussian spectrum source and conventional X-ray tube source. The optimization improvements were predicted in the theoretical model and also demonstrated in simulations. The sensitivity of XFCT imaging can be enhanced by an order of magnitude with the data acquisition optimization, greatly enhancing the potential of this modality for future use in clinical molecular imaging.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

Early Discharge After Primary Percutaneous Coronary Intervention: The Added Value of N‐Terminal Pro–Brain Natriuretic Peptide to the Zwolle Risk Score

PubMed Central

Schellings, Dirk A. A. M.; Adiyaman, Ahmet; Giannitsis, Evangelos; Hamm, Christian; Suryapranata, Harry; ten Berg, Jurrien M.; Hoorntje, Jan C. A.; van‘t Hof, Arnoud W. J.

2014-01-01

Background The Zwolle Risk Score (ZRS) identifies ST‐elevation myocardial infarction (STEMI) patients treated with primary percutaneous coronary intervention (PPCI) eligible for early discharge. We aimed to investigate whether baseline N‐terminal pro–brain natriuretic peptide (NT‐proBNP) is also able to identify these patients and could improve future risk strategies. Methods and Results PPCI patients included in the Ongoing Tirofiban in Myocardial Infarction Evaluation (On‐TIME) II study were candidates (N=861). We analyzed whether ZRS and baseline NT‐proBNP predicted 30‐day mortality and assessed the occurrence of major adverse cardiac events (MACEs) and major bleeding. Receiver operating characteristic curve analysis was used to assess discriminative accuracy for ZRS, NT‐pro‐BNP, and their combination. After multiple imputation, 845 patients were included. Both ZRS >3 (hazard ratio [HR]=9.42; P<0.001) and log NT‐pro‐BNP (HR=2.61; P<0.001) values were associated with 30‐day mortality. On multivariate analysis, both the ZRS (HR=1.41; 95% confidence interval [CI]=1.27 to 1.56; P<0.001) and log NT‐proBNP (HR=2.09; 95% CI=1.59 to 2.74; P<0.001) independently predicted death at 30 days. The area under the curve for 30‐day mortality for combined ZRS/NT‐proBNP was 0.94 (95% CI=0.90 to 0.99), with optimal predictive values of a ZRS ≥2 and a NT‐proBNP value of ≥200 pg/mL. Using these cut‐off values, 64% of the study population could be identified as very low risk with zero mortality at 30 days follow‐up and low occurrence of MACEs and major bleeding between 48 hours and 10 days (1.3% and 0.6%, respectively). Conclusion Baseline NT‐proBNP identifies a large group of low‐risk patients who may be eligible for early (48‐ to 72‐hour) discharge, whereas optimal predictive accuracy is reached by the combination of both baseline NT‐proBNP and ZRS. PMID:25389283

Determining the Threshold for HbA1c as a Predictor for Adverse Outcomes After Total Joint Arthroplasty: A Multicenter, Retrospective Study.

PubMed

Tarabichi, Majd; Shohat, Noam; Kheir, Michael M; Adelani, Muyibat; Brigati, David; Kearns, Sean M; Patel, Pankajkumar; Clohisy, John C; Higuera, Carlos A; Levine, Brett R; Schwarzkopf, Ran; Parvizi, Javad; Jiranek, William A

2017-09-01

Although HbA1c is commonly used for assessing glycemic control before surgery, there is no consensus regarding its role and the appropriate threshold in predicting adverse outcomes. This study was designed to evaluate the potential link between HbA1c and subsequent periprosthetic joint infection (PJI), with the intention of determining the optimal threshold for HbA1c. This is a multicenter retrospective study, which identified 1645 diabetic patients who underwent primary total joint arthroplasty (1004 knees and 641 hips) between 2001 and 2015. All patients had an HbA1c measured within 3 months of surgery. The primary outcome of interest was a PJI at 1 year based on the Musculoskeletal Infection Society criteria. Secondary outcomes included orthopedic (wound and mechanical complications) and nonorthopedic complications (sepsis, thromboembolism, genitourinary, and cardiovascular complications). A regression analysis was performed to determine the independent influence of HbA1c for predicting PJI. Overall 22 cases of PJI occurred at 1 year (1.3%). HbA1c at a threshold of 7.7 was distinct for predicting PJI (area under the curve, 0.65; 95% confidence interval, 0.51-0.78). Using this threshold, PJI rates increased from 0.8% (11 of 1441) to 5.4% (11 of 204). In the stepwise logistic regression analysis, PJI remained the only variable associated with higher HbA1c (odds ratio, 1.5; confidence interval, 1.2-2.0; P = .0001). There was no association between high HbA1c levels and other complications assessed. High HbA1c levels are associated with an increased risk for PJI. A threshold of 7.7% seems to be more indicative of infection than the commonly used 7% and should perhaps be the goal in preoperative patient optimization. Copyright © 2017 Elsevier Inc. All rights reserved.

Multimodality Tumor Delineation and Predictive Modelling via Fuzzy-Fusion Deformable Models and Biological Potential Functions

NASA Astrophysics Data System (ADS)

Wasserman, Richard Marc

The radiation therapy treatment planning (RTTP) process may be subdivided into three planning stages: gross tumor delineation, clinical target delineation, and modality dependent target definition. The research presented will focus on the first two planning tasks. A gross tumor target delineation methodology is proposed which focuses on the integration of MRI, CT, and PET imaging data towards the generation of a mathematically optimal tumor boundary. The solution to this problem is formulated within a framework integrating concepts from the fields of deformable modelling, region growing, fuzzy logic, and data fusion. The resulting fuzzy fusion algorithm can integrate both edge and region information from multiple medical modalities to delineate optimal regions of pathological tissue content. The subclinical boundaries of an infiltrating neoplasm cannot be determined explicitly via traditional imaging methods and are often defined to extend a fixed distance from the gross tumor boundary. In order to improve the clinical target definition process an estimation technique is proposed via which tumor growth may be modelled and subclinical growth predicted. An in vivo, macroscopic primary brain tumor growth model is presented, which may be fit to each patient undergoing treatment, allowing for the prediction of future growth and consequently the ability to estimate subclinical local invasion. Additionally, the patient specific in vivo tumor model will be of significant utility in multiple diagnostic clinical applications.

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

PubMed

Li, Xiaohong; Blount, Patricia L; Vaughan, Thomas L; Reid, Brian J

2011-02-01

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamp, Florian; Department of Radiation Oncology, Technische Universität München, Klinikum Rechts der Isar, München; Physik-Department, Technische Universität München, Garching

2015-11-01

Purpose: The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Methods and Materials: Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damagemore » simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. Results: We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β){sub X} = 2 Gy. Conclusions: These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization.« less

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra.

PubMed

Kamp, Florian; Cabal, Gonzalo; Mairani, Andrea; Parodi, Katia; Wilkens, Jan J; Carlson, David J

2015-11-01

The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damage simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β)X = 2 Gy. These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimal PGU operation strategy in CHP systems

NASA Astrophysics Data System (ADS)

Yun, Kyungtae

Traditional power plants only utilize about 30 percent of the primary energy that they consume, and the rest of the energy is usually wasted in the process of generating or transmitting electricity. On-site and near-site power generation has been considered by business, labor, and environmental groups to improve the efficiency and the reliability of power generation. Combined heat and power (CHP) systems are a promising alternative to traditional power plants because of the high efficiency and low CO2 emission achieved by recovering waste thermal energy produced during power generation. A CHP operational algorithm designed to optimize operational costs must be relatively simple to implement in practice such as to minimize the computational requirements from the hardware to be installed. This dissertation focuses on the following aspects pertaining the design of a practical CHP operational algorithm designed to minimize the operational costs: (a) real-time CHP operational strategy using a hierarchical optimization algorithm; (b) analytic solutions for cost-optimal power generation unit operation in CHP Systems; (c) modeling of reciprocating internal combustion engines for power generation and heat recovery; (d) an easy to implement, effective, and reliable hourly building load prediction algorithm.

Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa

2016-10-01

In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

Recent advances in the diagnosis and treatment of primary biliary cholangitis

PubMed Central

Huang, Ying-Qiu

2016-01-01

Primary biliary cholangitis (PBC), formerly referred to as primary biliary cirrhosis, is an infrequent progressive intrahepatic cholestatic autoimmune illness that can evolve into hepatic fibrosis, hepatic cirrhosis, hepatic failure, and, in some cases, hepatocellular carcinoma. The disease itself is characterized by T-lymphocyte-mediated chronic non-suppurative destructive cholangitis and elevated serum levels of extremely specific anti-mitochondrial autoantibodies (AMAs). In this article, we will not only review epidemiology, risk factors, natural history, predictive scores, radiologic approaches (e.g., acoustic radiation force impulse imaging, vibration controlled transient elastography, and magnetic resonance elastography), clinical features, serological characteristics covering biochemical markers, immunoglobulins, infections markers, biomarkers, predictive fibrosis marker, specific antibodies (including AMAs such as AMA-M2), anti-nuclear autoantibodies [such as anti-multiple nuclear dot autoantibodies (anti-sp100, PML, NDP52, anti-sp140), anti-rim-like/membranous anti-nuclear autoantibodies (anti-gp210, anti-p62), anti-centromere autoantibodies, and some of the novel autoantibodies], histopathological characteristics of PBC, diagnostic advances, and anti-diastole of PBC. Furthermore, this review emphasizes the recent advances in research of PBC in terms of therapies, including ursodeoxycholic acid, budesonide, methotrexate, obeticholic acid, cyclosporine A, fibrates such as bezafibrate and fenofibrate, rituximab, mesenchymal stem cells transplant, and hepatic transplant. Currently, hepatic transplant remains the only optimal choice with acknowledged treatment efficiency for end-stage PBC patients. PMID:27957241

Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

NASA Astrophysics Data System (ADS)

Palchak, David

Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

Role of Positron Emission Tomography in the Treatment of Occult Disease in Head-and-Neck Cancer: A Modeling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, Mark H., E-mail: markp@u.washington.ed; Smith, Wade P.; Parvathaneni, Upendra

2011-03-15

Purpose: To determine under what conditions positron emission tomography (PET) imaging will be useful in decisions regarding the use of radiotherapy for the treatment of clinically occult lymph node metastases in head-and-neck cancer. Methods and Materials: A decision model of PET imaging and its downstream effects on radiotherapy outcomes was constructed using an influence diagram. This model included the sensitivity and specificity of PET, as well as the type and stage of the primary tumor. These parameters were varied to determine the optimal strategy for imaging and therapy for different clinical situations. Maximum expected utility was the metric by whichmore » different actions were ranked. Results: For primary tumors with a low probability of lymph node metastases, the sensitivity of PET should be maximized, and 50 Gy should be delivered if PET is positive and 0 Gy if negative. As the probability for lymph node metastases increases, PET imaging becomes unnecessary in some situations, and the optimal dose to the lymph nodes increases. The model needed to include the causes of certain health states to predict current clinical practice. Conclusion: The model demonstrated the ability to reproduce expected outcomes for a range of tumors and provided recommendations for different clinical situations. The differences between the optimal policies and current clinical practice are likely due to a disparity between stated clinical decision processes and actual decision making by clinicians.« less

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

PubMed Central

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments. PMID:22563454

Fishing for teratogens: a consortium effort for a harmonized zebrafish developmental toxicology assay.

PubMed

Ball, Jonathan S; Stedman, Donald B; Hillegass, Jedd M; Zhang, Cindy X; Panzica-Kelly, Julie; Coburn, Aleasha; Enright, Brian P; Tornesi, Belen; Amouzadeh, Hamid R; Hetheridge, Malcolm; Gustafson, Anne-Lee; Augustine-Rauch, Karen A

2014-05-01

A consortium of biopharmaceutical companies previously developed an optimized Zebrafish developmental toxicity assay (ZEDTA) where chorionated embryos were exposed to non-proprietary test compounds from 5 to 6 h post fertilization and assessed for morphological integrity at 5 days post fertilization. With the original 20 test compounds, this achieved an overall predictive value for teratogenicity of 88% of mammalian in vivo outcome [Gustafson, A. L., Stedman, D. B., Ball, J., Hillegass, J. M., Flood, A., Zhang, C. X., Panzica-Kelly, J., Cao, J., Coburn, A., Enright, B. P., et al. (2012). Interlaboratory assessment of a harmonized Zebrafish developmental toxicology assay-Progress report on phase I. Reprod. Toxicol. 33, 155-164]. In the second phase of this project, 38 proprietary pharmaceutical compounds from four consortium members were evaluated in two laboratories using the optimized method using either pond-derived or cultivated-strain wild-type Zebrafish embryos at concentrations up to 100μM. Embryo uptake of all compounds was assessed using liquid chromatography-tandem mass spectrometry. Twenty eight of 38 compounds had a confirmed embryo uptake of >5%, and with these compounds the ZEDTA achieved an overall predictive value of 82% and 65% at the two respective laboratories. When low-uptake compounds (≤ 5%) were retested with logarithmic concentrations up to 1000μM, the overall predictivity across all 38 compounds was 79% and 62% respectively, with the first laboratory achieving 74% sensitivity (teratogen detection) and 82% specificity (non-teratogen detection) and the second laboratory achieving 63% sensitivity (teratogen detection) and 62% specificity (non-teratogen detection). Subsequent data analyses showed that technical differences rather than strain differences were the primary contributor to interlaboratory differences in predictivity. Based on these results, the ZEDTA harmonized methodology is currently being used for compound assessment at lead optimization stage of development by 4/5 of the consortium companies.

A simple artificial life model explains irrational behavior in human decision-making.

PubMed

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

Multiobjective hyper heuristic scheme for system design and optimization

NASA Astrophysics Data System (ADS)

Rafique, Amer Farhan

2012-11-01

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer

PubMed Central

Lluch, Ana; Ribelles, Nuria; Anton-Torres, Antonio; Sanchez-Rovira, Pedro; Albanell, Joan; Calvo, Lourdes; García-Asenjo, Jose Antonio Lopez; Palacios, Jose; Chacon, Jose Ignacio; Ruiz, Amparo; De la Haba-Rodriguez, Juan; Segui-Palmer, Miguel A.; Cirauqui, Beatriz; Margeli, Mireia; Plazaola, Arrate; Barnadas, Agusti; Casas, Maribel; Caballero, Rosalia; Carrasco, Eva; Rojo, Federico

2016-01-01

Background. In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial. Patients and Methods. We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal threshold for Ki67 using the receiver-operating characteristic curve method to maximize its predictive value for chemotherapy benefit. We also evaluated the predictive role of the defined Ki67 cutoffs for molecular subtypes defined by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Results. A basal Ki67 cutpoint of 50% predicted pathological complete response (pCR). Patients with Ki67 >50% achieved a pCR rate of 40% (36 of 91) versus a pCR rate of 19% in patients with Ki67 ≤50% (33 of 171) (p = .0004). Ki67 predictive value was especially relevant in ER-HER2− and ER-HER2+ patients (pCR rates of 42% and 64%, respectively, in patients with Ki67 >50% versus 15% and 45%, respectively, in patients with Ki67 ≤50%; p = .0337 and .3238, respectively). Both multivariate analyses confirmed the independent predictive value of the Ki67 cutpoint of 50%. Conclusion. Basal Ki67 proliferation index >50% should be considered an independent predictive factor for pCR reached after neoadjuvant chemotherapy, suggesting that cell proliferation is a phenomenon closely related to chemosensitivity. These findings could help to identify a group of patients with a potentially favorable long-term prognosis. Implications for Practice: The use of basal Ki67 status as a predictive factor of chemotherapy benefit could facilitate the identification of a patient subpopulation with high probability of achieving pathological complete response when treated with primary chemotherapy, and thus with a potentially favorable long-term prognosis. PMID:26786263

Improving discrimination in antepartum depression screening using the Edinburgh Postnatal Depression Scale.

PubMed

Venkatesh, Kartik K; Kaimal, Anjali J; Castro, Victor M; Perlis, Roy H

2017-05-01

Universal screening of pregnant women for postpartum depression has recently been recommended; however, optimal application of depression screening tools in stratifying risk has not been defined. The current study examines new approaches to improve the ability of the Edinburgh Postnatal Depression Scale (EPDS) to stratify risk for postpartum depression, including alternate cut points, use of a continuous measure, and incorporation of other putative risk factors. An observational cohort study of 4939 women screened both antepartum and postpartum with a negative EPDS screen antepartum(i.e. EPDS<10). The primary outcome was a probable postpartum major depressive episode(EPDS cut-off ≥10). Area under the receiver operating characteristics curve(AUC), sensitivity, specificity, and predictive values were calculated. 287 women(5.8%) screened positive for postpartum depression. An antepartum EPDS cut-off<5 optimally identified women with a low risk of postpartum depression with a negative predictive value of 97.6%; however, overall discrimination was modest(AUC 0.66, 95%CI: 0.64-0.69); sensitivity was 78.7%, and specificity was 53.8%, and the positive predictive value was low at 9.5%. The negative predictive values were similar(>95%) at all antepartum EPDS cut-off values from 4 to 8. Discrimination was improved(AUC ranging from 0.70 to 0.73) when the antepartum EPDS was combined with a prior history of major depressive disorder before pregnancy. An inability to assess EPDS subscales and a relatively low prevalence of depression in this cohort. Though an antepartum EPDS cut-off score <5 yielded the greatest discrimination identifying women at low risk for postpartum depression, the negative predictive value was insufficient to substitute for postpartum screening. Copyright © 2017. Published by Elsevier B.V.

Neutrophil to lymphocyte ratio predicts appropriate therapy in idiopathic dilated cardiomyopathy patients with primary prevention implantable cardioverter defibrillator

PubMed Central

Uçar, Fatih M.; Açar, Burak

2017-01-01

Objectives: To investigate whether an inflammatory marker of neutrophil to lymphocyte ratio (NLR) predicts appropriate implantable cardioverter defibrillator (ICD) therapy (shock or anti tachycardia pacing) in idiopathic dilated cardiomyopathy (IDC) patients. Methods: We retrospectively examined IDC patients (mean age: 58.3 ± 11.8 years, 81.5% male) with ICD who admitted to outpatient clinic for pacemaker control at 2 tertiary care hospitals in Ankara and Edirne, Turkey from January 2013-2015. All ICDs were implanted for primary prevention. Hematological and biochemical parameters were measured prior procedure. Results: Over a median follow-up period of 43 months (Range 7-125), 68 (33.1%) patients experienced appropriate ICD therapy. The NLR was increased in patients that received appropriate therapy (4.39 ± 2.94 versus 2.96 ± 1.97, p<0.001). To identify independent risk factors for appropriate therapy, a multivariate linear regression model was conducted and age (β=0.163, p=0.013), fasting glucose (β=0.158, p=0.017), C-reactive protein (CRP) (β=0.289, p<0.001) and NLR (β=0.212, p<0.008) were found to be independent risk factors for appropriate ICD therapy. Conclusions: Before ICD implantation by using NLR and CRP, arrhythmic episodes may be predictable and better antiarrhythmic medical therapy optimization may protect these IDC patients from unwanted events. PMID:28133686

Predicting the apparent viscosity and yield stress of mixtures of primary, secondary and anaerobically digested sewage sludge: Simulating anaerobic digesters.

PubMed

Markis, Flora; Baudez, Jean-Christophe; Parthasarathy, Rajarathinam; Slatter, Paul; Eshtiaghi, Nicky

2016-09-01

Predicting the flow behaviour, most notably, the apparent viscosity and yield stress of sludge mixtures inside the anaerobic digester is essential because it helps optimize the mixing system in digesters. This paper investigates the rheology of sludge mixtures as a function of digested sludge volume fraction. Sludge mixtures exhibited non-Newtonian, shear thinning, yield stress behaviour. The apparent viscosity and yield stress of sludge mixtures prepared at the same total solids concentration was influenced by the interactions within the digested sludge and increased with the volume fraction of digested sludge - highlighted using shear compliance and shear modulus of sludge mixtures. However, when a thickened primary - secondary sludge mixture was mixed with dilute digested sludge, the apparent viscosity and yield stress decreased with increasing the volume fraction of digested sludge. This was caused by the dilution effect leading to a reduction in the hydrodynamic and non-hydrodynamic interactions when dilute digested sludge was added. Correlations were developed to predict the apparent viscosity and yield stress of the mixtures as a function of the digested sludge volume fraction and total solids concentration of the mixtures. The parameters of correlations can be estimated using pH of sludge. The shear and complex modulus were also modelled and they followed an exponential relationship with increasing digested sludge volume fraction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Supportive care for children with cancer. Guidelines of the Childrens Cancer Study Group. The use of nutritional therapy.

PubMed

Lukens, J N

1984-01-01

Nutritional support for children with cancer is predicated on the belief that optimal nutrition promotes tolerance of anti-neoplastic therapy and preserves immunologic responsiveness. The use of nutritional support is based on the assumption that there is effective therapy for the primary disease and that there will be a predictable period of nutritional stress. The most common nutritional problem is posed by the failure of sick children willingly to eat enough to maintain nutritional homeostasis. Supplementation of oral intake with a nutritional formula given by a small-bore nasogastric tube is simple, effective, and economical. If the sum of oral and tolerated nasogastric tube feedings is less than that required for optimal nutrition, unmet needs may be satisfied by nutrients given into a peripheral vein. Total parenteral nutrition, given by central vein, is reserved for situations in which the combination of enteral and peripheral venous alimentation is inadequate.

Predicting the mental health of college students with psychological capital.

PubMed

Selvaraj, Priscilla Rose; Bhat, Christine Suniti

2018-06-01

Behavioral health treatment is grounded in the medical model with language of deficits and problems, rather than resources and strengths. With developments in the field of positive psychology, re-focusing on well-being rather than illness is possible. The primary purpose of this study was to examine relationships and predictions that exist between levels of mental health in college students, i.e., flourishing, moderate mental health, and languishing, and psychological capital (PsyCap). For this cross-sectional, exploratory study survey method was used for data collection and for analyses of results a series of descriptive, correlation, ANOVA, and multiple regression analyses were done. Results indicated that developing positive psychological strengths such as hope, efficacy, resilience, and optimism (acronym HERO) within college students significantly increased their positive mental health. Based on the predictive nature of PsyCap, mental health professionals may engage more in creating programs incorporating PsyCap development intervention for college students. Implications for counseling and programmatic services for college students are presented along with suggestions for future research.

Introducing Bayesian thinking to high-throughput screening for false-negative rate estimation.

PubMed

Wei, Xin; Gao, Lin; Zhang, Xiaolei; Qian, Hong; Rowan, Karen; Mark, David; Peng, Zhengwei; Huang, Kuo-Sen

2013-10-01

High-throughput screening (HTS) has been widely used to identify active compounds (hits) that bind to biological targets. Because of cost concerns, the comprehensive screening of millions of compounds is typically conducted without replication. Real hits that fail to exhibit measurable activity in the primary screen due to random experimental errors will be lost as false-negatives. Conceivably, the projected false-negative rate is a parameter that reflects screening quality. Furthermore, it can be used to guide the selection of optimal numbers of compounds for hit confirmation. Therefore, a method that predicts false-negative rates from the primary screening data is extremely valuable. In this article, we describe the implementation of a pilot screen on a representative fraction (1%) of the screening library in order to obtain information about assay variability as well as a preliminary hit activity distribution profile. Using this training data set, we then developed an algorithm based on Bayesian logic and Monte Carlo simulation to estimate the number of true active compounds and potential missed hits from the full library screen. We have applied this strategy to five screening projects. The results demonstrate that this method produces useful predictions on the numbers of false negatives.

Chiral stationary phase optimized selectivity liquid chromatography: A strategy for the separation of chiral isomers.

PubMed

Hegade, Ravindra Suryakant; De Beer, Maarten; Lynen, Frederic

2017-09-15

Chiral Stationary-Phase Optimized Selectivity Liquid Chromatography (SOSLC) is proposed as a tool to optimally separate mixtures of enantiomers on a set of commercially available coupled chiral columns. This approach allows for the prediction of the separation profiles on any possible combination of the chiral stationary phases based on a limited number of preliminary analyses, followed by automated selection of the optimal column combination. Both the isocratic and gradient SOSLC approach were implemented for prediction of the retention times for a mixture of 4 chiral pairs on all possible combinations of the 5 commercial chiral columns. Predictions in isocratic and gradient mode were performed with a commercially available and with an in-house developed Microsoft visual basic algorithm, respectively. Optimal predictions in the isocratic mode required the coupling of 4 columns whereby relative deviations between the predicted and experimental retention times ranged between 2 and 7%. Gradient predictions led to the coupling of 3 chiral columns allowing baseline separation of all solutes, whereby differences between predictions and experiments ranged between 0 and 12%. The methodology is a novel tool allowing optimizing the separation of mixtures of optical isomers. Copyright © 2017 Elsevier B.V. All rights reserved.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Lightweight structure design for supporting plate of primary mirror

NASA Astrophysics Data System (ADS)

Wang, Xiao; Wang, Wei; Liu, Bei; Qu, Yan Jun; Li, Xu Peng

2017-10-01

A topological optimization design for the lightweight technology of supporting plate of the primary mirror is presented in this paper. The supporting plate of the primary mirror is topologically optimized under the condition of determined shape, loads and environment. And the optimal structure is obtained. The diameter of the primary mirror in this paper is 450mm, and the material is SiC1 . It is better to select SiC/Al as the supporting material. Six points of axial relative displacement can be used as constraints in optimization2 . Establishing the supporting plate model and setting up the model parameters. After analyzing the force of the main mirror on the supporting plate, the model is applied with force and constraints. Modal analysis and static analysis of supporting plates are calculated. The continuum structure topological optimization mathematical model is created with the variable-density method. The maximum deformation of the surface of supporting plate under the gravity of the mirror and the first model frequency are assigned to response variable, and the entire volume of supporting structure is converted to object function. The structures before and after optimization are analyzed using the finite element method. Results show that the optimized fundamental frequency increases 29.85Hz and has a less displacement compared with the traditional structure.

TargetM6A: Identifying N6-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

PubMed

Li, Guang-Qing; Liu, Zi; Shen, Hong-Bin; Yu, Dong-Jun

2016-10-01

As one of the most ubiquitous post-transcriptional modifications of RNA, N 6 -methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly important for both basic biomedical research and practical drug development. In this study, we designed a computational-based method, called TargetM6A, to rapidly and accurately target [Formula: see text] sites solely from the primary RNA sequences. Two new features, i.e., position-specific nucleotide/dinucleotide propensities (PSNP/PSDP), are introduced and combined with the traditional nucleotide composition (NC) feature to formulate RNA sequences. The extracted features are further optimized to obtain a much more compact and discriminative feature subset by applying an incremental feature selection (IFS) procedure. Based on the optimized feature subset, we trained TargetM6A on the training dataset with a support vector machine (SVM) as the prediction engine. We compared the proposed TargetM6A method with existing methods for predicting [Formula: see text] sites by performing stringent jackknife tests and independent validation tests on benchmark datasets. The experimental results show that the proposed TargetM6A method outperformed the existing methods for predicting [Formula: see text] sites and remarkably improved the prediction performances, with MCC = 0.526 and AUC = 0.818. We also provided a user-friendly web server for TargetM6A, which is publicly accessible for academic use at http://csbio.njust.edu.cn/bioinf/TargetM6A.

Optimizing Decision Support for Tailored Health Behavior Change Applications.

PubMed

Kukafka, Rita; Jeong, In cheol; Finkelstein, Joseph

2015-01-01

The Tailored Lifestyle Change Decision Aid (TLC DA) system was designed to provide support for a person to make an informed choice about which behavior change to work on when multiple unhealthy behaviors are present. TLC DA can be delivered via web, smartphones and tablets. The system collects a significant amount of information that is used to generate tailored messages to consumers to persuade them in certain healthy lifestyles. One limitation is the necessity to collect vast amounts of information from users who manually enter. By identifying an optimal set of self-reported parameters we will be able to minimize the data entry burden of the app users. The study was to identify primary determinants of health behavior choices made by patients after using the system. Using discriminant analysis an optimal set of predictors was identified. The resulting set included smoking status, smoking cessation success estimate, self-efficacy, body mass index and diet status. Predicting smoking cessation choice was the most accurate, followed by weight management. Physical activity and diet choices were better identified in a combined cluster.

Self-esteem and optimism in men and women infected with HIV.

PubMed

Anderson, E H

2000-01-01

Self-esteem and optimism have been associated with appraisal and outcomes in a variety of situations. The degree to which the contribution of self-esteem and optimism to outcomes over time is accounted for by the differences in threat (primary) or resource (secondary) appraisal has not been established in persons with human immunodeficiency virus (HIV). To examine the longitudinal relationship of personality (self-esteem and optimism) on primary and secondary appraisal and outcomes of well-being, mood, CD4+ T-lymphocyte count, and selected activities. Men (n = 56) and women (n = 42) infected with HIV completed eight self-report measures twice over 18 months. Hierarchical Multiple Regressions were used to examine the relationship of personality variables on appraisals and outcomes. The mediating effects of primary and secondary appraisals were explored. Self-esteem uniquely accounted for 6% of the variance in primary appraisal and 5% in secondary appraisal. Optimism accounted for 8% of the unique variance in secondary appraisal. Primary and secondary appraisal mediated differently between personality and outcome variables. A strong predictor of well-being, mood disturbance, and activity disruption at Time 2 was participants' initial level of these variables. Socioeconomic status was a strong predictor of mood. Self-esteem and optimism are important but different resources for adapting to HIV disease. Strategies for reducing threats and increasing resources associated with HIV may improve an individual's mood and sense of well-being.

Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

NASA Astrophysics Data System (ADS)

Song, Chen; Zhong-Cheng, Wu; Hong, Lv

2018-03-01

Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

PubMed Central

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Medium-term survival after primary angioplasty for myocardial infarction complicated by cardiogenic shock after the age of 75 years.

PubMed

Samadi, A; Le Feuvre, C; Allali, Y; Collet, J-P; Barthélémy, O; Beygui, F; Helft, G; Montalescot, G; Metzger, J-P

2008-03-01

To assess mortality in people > or =75 years of age 6 months after myocardial infarction complicated by cardiogenic shock and treated by angioplasty with complete revascularisation and optimal anti-thrombotic treatment; to compare results to those of younger patients with or without shock and to analyse predictive factors for death. The study is based on 1011 consecutive patients with myocardial infarction admitted for primary angioplasty, subdivided into four groups by age and the presence or absence of cardiogenic shock: group 1 (<75 years of age without shock, n=733), group 2 (<75 years of age with shock, n=49), group 3 (> or =75 years of age without shock, n=208) and group 4 (> or =75 years of age with shock, n=20). These four patient groups were compared for mortality rates and predictive factors for in-hospital and 6 month mortality. In-hospital mortality in groups 1 to 4 was 1.7%, 30.6%, 9.1%, and 70% (p<0.0001) respectively and 6-month mortality was 3.1%, 40%, 16% and 78% (P<0.0001). By univariate analysis renal failure was a predictive factor for death at 6 months in patients without cardiogenic shock (groups 1 and 3), and left ventricular function in patients in group 2. No predictive factors were found in group 4 patients. The independent predictive factors for death at 6 months were: age >75 years of age (P<0.0003), cardiogenic shock (P<0.0001), triple vessel lesions (P<0.01) and creatinine clearance (P=0.004). Mortality after angioplasty remains high in people > or =75 years with cardiogenic shock despite all the advances in the management of myocardial infarction. These disappointing results should encourage us to assess the role of surgical revascularisation and circulatory assistance.

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

PubMed

Storchi, Riccardo; Zippo, Antonio G; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications

USDA-ARS?s Scientific Manuscript database

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for their optimal design. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optim...

Investigation of Primary Mirror Segment's Residual Errors for the Thirty Meter Telescope

NASA Technical Reports Server (NTRS)

Seo, Byoung-Joon; Nissly, Carl; Angeli, George; MacMynowski, Doug; Sigrist, Norbert; Troy, Mitchell; Williams, Eric

2009-01-01

The primary mirror segment aberrations after shape corrections with warping harness have been identified as the single largest error term in the Thirty Meter Telescope (TMT) image quality error budget. In order to better understand the likely errors and how they will impact the telescope performance we have performed detailed simulations. We first generated unwarped primary mirror segment surface shapes that met TMT specifications. Then we used the predicted warping harness influence functions and a Shack-Hartmann wavefront sensor model to determine estimates for the 492 corrected segment surfaces that make up the TMT primary mirror. Surface and control parameters, as well as the number of subapertures were varied to explore the parameter space. The corrected segment shapes were then passed to an optical TMT model built using the Jet Propulsion Laboratory (JPL) developed Modeling and Analysis for Controlled Optical Systems (MACOS) ray-trace simulator. The generated exit pupil wavefront error maps provided RMS wavefront error and image-plane characteristics like the Normalized Point Source Sensitivity (PSSN). The results have been used to optimize the segment shape correction and wavefront sensor designs as well as provide input to the TMT systems engineering error budgets.

Use of manometric temperature measurement (MTM) and SMART freeze dryer technology for development of an optimized freeze-drying cycle.

PubMed

Gieseler, Henning; Kramer, Tony; Pikal, Michael J

2007-12-01

This report provides, for the first time, a summary of experiments using SMART Freeze Dryer technology during a 9 month testing period. A minimum ice sublimation area of about 300 cm(2) for the laboratory freeze dryer, with a chamber volume 107.5 L, was found consistent with data obtained during previous experiments with a smaller freeze dryer (52 L). Good reproducibility was found for cycle design with different type of excipients, formulations, and vials used. SMART primary drying end point estimates were accurate in the majority of the experiments, but showed an over prediction of primary cycle time when the product did not fully achieve steady state conditions before the first MTM measurement was performed. Product resistance data for 5% sucrose mixtures at varying fill depths were very reproducible. Product temperature determined by SMART was typically in good agreement with thermocouple data through about 50% of primary drying time, with significant deviations occurring near the end of primary drying, as expected, but showing a bias much earlier in primary drying for high solid content formulations (16.6% Pfizer product) and polyvinylpyrrolidone (40 kDa) likely due to water "re-adsorption" by the amorphous product during the MTM test. (c) 2007 Wiley-Liss, Inc.

Wood Combustion Behaviour in a Fixed Bed Combustor

NASA Astrophysics Data System (ADS)

Tokit, Ernie Mat; Aziz, Azhar Abdul; Ghazali, Normah Mohd

2010-06-01

Waste wood is used as feedstock for Universiti Teknologi Malaysia's newly-developed two-stage incinerator system. The research goals are to optimize the operation of the thermal system to the primary chamber, to improve its combustion efficiency and to minimize its pollutants formation. The combustion process is evaluated with the variation of fuel's moisture content. For optimum operating condition, where the gasification efficiency is 95.53%, the moisture content of the fuel is best set at 17%; giving outlet operating temperature of 550°C and exhaust gas concentrations with 1213 ppm of CO, 6% of CO2 and 14% of O2 respectively. In line to the experimental work, a computational fluid dynamics software, Fluent is used to simulate the performance of the primary chamber. Here the predicted optimum gasification efficiency stands at 95.49% with CO, CO2 and O2 concentrations as 1301 ppm, 6.5% and 13.5% respectively.

Biomolecular markers of cancer-associated thromboembolism

PubMed Central

Hanna, Diana L.; White, Richard H.; Wun, Ted

2013-01-01

Venous thromboembolism (VTE; deep venous thrombosis and pulmonary embolism) is associated with a poor prognosis in most malignancies and is a major cause of death among cancer patients. Universal anticoagulation for primary thromboprophylaxis in the outpatient setting is precluded by potential bleeding complications, especially without sufficient evidence that all patients would benefit from such prophylaxis. Therefore, appropriately targeting cancer patients for thromboprophylaxis is key to reducing morbidity and perhaps mortality. Predictive biomarkers could aid in identifying patients at high risk for VTE. Possible biomarkers for VTE include C-reactive protein, platelet and leukocyte counts, D-dimer and prothrombin fragment 1+2, procoagulant factor VIII, tissue factor, and soluble P-selectin. Evidence is emerging to support the use of risk assessment models in selecting appropriate candidates for primary thromboprophylaxis in the cancer setting. Further studies are needed to optimize these models and determine utility in reducing morbidity and mortality from cancer-associated thromboembolism. PMID:23522921

Performance of Swashplateless Ultralight Helicopter Rotor with Trailing-edge Flaps for Primary Flight Control

NASA Technical Reports Server (NTRS)

Shen, Jin-Wei; Chopra, Inderjit

2003-01-01

The objective of present study is to evaluate the rotor performance, trailing-edge deflections and actuation requirement of a helicopter rotor with trailing-edge flap system for primary flight control. The swashplateless design is implemented by modifying a two-bladed teetering rotor of an production ultralight helicopter through the use of plain flaps on the blades, and by replacing the pitch link to fixed system control system assembly with a root spring. A comprehensive rotorcraft analysis based on UMARC is carried out to obtain the results for both the swashplateless and a conventional baseline rotor configuration. The predictions show swashplateless configuration achieve superior performance than the conventional rotor attributed from reduction of parasite drag by eliminating swashplate mechanic system. It is indicated that optimal selection of blade pitch index angle, flap location, length, and chord ratio reduces flap deflections and actuation requirements, however, has virtually no effect on rotor performance.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Optimal Methods to Screen Men and Women for Intimate Partner Violence: Results from an Internal Medicine Residency Continuity Clinic

ERIC Educational Resources Information Center

Kapur, Nitin A.; Windish, Donna M.

2011-01-01

Contradictory data exist regarding optimal methods and instruments for intimate partner violence (IPV) screening in primary care settings. The purpose of this study was to determine the optimal method and screening instrument for IPV among men and women in a primary-care resident clinic. We conducted a cross-sectional study at an urban, academic,…

The interrelationship between preoperative anemia and N-terminal pro-B-type natriuretic peptide: the effect on predicting postoperative cardiac outcome in vascular surgery patients.

PubMed

Goei, Dustin; Flu, Willem-Jan; Hoeks, Sanne E; Galal, Wael; Dunkelgrun, Martin; Boersma, Eric; Kuijper, Ruud; van Kuijk, Jan-Peter; Winkel, Tamara A; Schouten, Olaf; Bax, Jeroen J; Poldermans, Don

2009-11-01

N-terminal pro-B-type natriuretic peptide (NT-proBNP) predicts adverse cardiac outcome in patients undergoing vascular surgery. However, several conditions might influence this prognostic value, including anemia. In this study, we evaluated whether anemia confounds the prognostic value of NT-proBNP for predicting cardiac events in patients undergoing vascular surgery. A detailed cardiac history, resting echocardiography, and hemoglobin and NT-proBNP levels were obtained in 666 patients before vascular surgery. Anemia was defined as serum hemoglobin <13 g/dL for men and <12 g/dL for women. Troponin T measurements and 12-lead electrocardiograms were performed on postoperative days 1, 3, 7, and 30 and whenever clinically indicated. The primary end point of the study was the composite of 30-day postoperative cardiovascular death, nonfatal myocardial infarction, and troponin T release. Receiver operating characteristic curve analysis was used to assess the optimal cutoff value of NT-proBNP for the prediction of the composite end point. Multivariable regression analysis was used to assess the additional value of NT-proBNP for the prediction of postoperative cardiac events in nonanemic and anemic patients. Anemia was present in 206 patients (31%) before surgery. Hemoglobin level was inversely related with the NT-proBNP levels (beta coefficient = -2.242; P = 0.025). The optimal predictive cutoff value of NT-proBNP for predicting the composite cardiovascular outcome was 350 pg/mL. After adjustment for clinical cardiac risk factors, both anemia (odds ratio [OR] 1.53; 95% confidence interval [CI]: 1.07-2.99) and increased levels of NT-proBNP (OR 4.09; 95% CI: 2.19-7.64) remained independent predictors for postoperative cardiac events. However, increased levels of NT-proBNP were not predictive for the risk of adverse cardiac events in the subgroup of anemic patients (OR 2.16; 95% CI: 0.90-5.21). Both anemia and NT-proBNP are independently associated with an increased risk for postoperative cardiac events in patients undergoing vascular surgery. NT-proBNP has less predictive value in anemic patients.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

NASA Astrophysics Data System (ADS)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk.

PubMed

Walsh, Colin G; Sharman, Kavya; Hripcsak, George

2017-12-01

Prior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model's reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. To select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Observational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. C-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78-0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862. Choice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result. Copyright © 2017 Elsevier Inc. All rights reserved.

Inclusion of tank configurations as a variable in the cost optimization of branched piped-water networks

NASA Astrophysics Data System (ADS)

Hooda, Nikhil; Damani, Om

2017-06-01

The classic problem of the capital cost optimization of branched piped networks consists of choosing pipe diameters for each pipe in the network from a discrete set of commercially available pipe diameters. Each pipe in the network can consist of multiple segments of differing diameters. Water networks also consist of intermediate tanks that act as buffers between incoming flow from the primary source and the outgoing flow to the demand nodes. The network from the primary source to the tanks is called the primary network, and the network from the tanks to the demand nodes is called the secondary network. During the design stage, the primary and secondary networks are optimized separately, with the tanks acting as demand nodes for the primary network. Typically the choice of tank locations, their elevations, and the set of demand nodes to be served by different tanks is manually made in an ad hoc fashion before any optimization is done. It is desirable therefore to include this tank configuration choice in the cost optimization process itself. In this work, we explain why the choice of tank configuration is important to the design of a network and describe an integer linear program model that integrates the tank configuration to the standard pipe diameter selection problem. In order to aid the designers of piped-water networks, the improved cost optimization formulation is incorporated into our existing network design system called JalTantra.

Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy.

PubMed

Fan, Jiawei; Wang, Jiazhou; Zhang, Zhen; Hu, Weigang

2017-06-01

To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. © 2017 American Association of Physicists in Medicine.

A model for the Pockels effect in distorted liquid crystal blue phases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Castles, F., E-mail: flynn.castles@materials.ox.ac.uk

2015-09-07

Recent experiments have found that a mechanically distorted blue phase can exhibit a primary linear electro-optic (Pockels) effect [F. Castles et al., Nat. Mater. 13, 817 (2014)]. Here, it is shown that flexoelectricity can account for the experimental results and a model, which is based on continuum theory but takes into account the sub-unit-cell structure, is proposed. The model provides a quantitative description of the effect accurate to the nearest order of magnitude and predicts that the Pockels coefficient(s) in an optimally distorted blue phase may be two orders of magnitude larger than in lithium niobate.

A uniform management approach to optimize outcome in fetal growth restriction.

PubMed

Seravalli, Viola; Baschat, Ahmet A

2015-06-01

A uniform approach to the diagnosis and management of fetal growth restriction (FGR) consistently produces better outcome, prevention of unanticipated stillbirth, and appropriate timing of delivery. Early-onset and late-onset FGR represent two distinct clinical phenotypes of placental dysfunction. Management challenges in early-onset FGR revolve around prematurity and coexisting maternal hypertensive disease, whereas in late-onset disease failure of diagnosis or surveillance leading to unanticipated stillbirth is the primary issue. Identifying the surveillance tests that have the highest predictive accuracy for fetal acidemia and establishing the appropriate monitoring interval to detect fetal deterioration is a high priority. Copyright © 2015 Elsevier Inc. All rights reserved.

Psychometric analysis of the Generalized Anxiety Disorder scale (GAD-7) in primary care using modern item response theory.

PubMed

Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd

2017-01-01

The Generalized Anxiety Disorder scale (GAD-7) is one of the most frequently used diagnostic self-report scales for screening, diagnosis and severity assessment of anxiety disorder. Its psychometric properties from the view of the Item Response Theory paradigm have rarely been investigated. We aimed to close this gap by analyzing the GAD-7 within a large sample of primary care patients with respect to its psychometric properties and its implications for scoring using Item Response Theory. Robust, nonparametric statistics were used to check unidimensionality of the GAD-7. A graded response model was fitted using a Bayesian approach. The model fit was evaluated using posterior predictive p-values, item information functions were derived and optimal predictions of anxiety were calculated. The sample included N = 3404 primary care patients (60% female; mean age, 52,2; standard deviation 19.2) The analysis indicated no deviations of the GAD-7 scale from unidimensionality and a decent fit of a graded response model. The commonly suggested ultra-brief measure consisting of the first two items, the GAD-2, was supported by item information analysis. The first four items discriminated better than the last three items with respect to latent anxiety. The information provided by the first four items should be weighted more heavily. Moreover, estimates corresponding to low to moderate levels of anxiety show greater variability. The psychometric validity of the GAD-2 was supported by our analysis.

Psychometric analysis of the Generalized Anxiety Disorder scale (GAD-7) in primary care using modern item response theory

PubMed Central

Shedden-Mora, Meike C.; Löwe, Bernd

2017-01-01

Objective The Generalized Anxiety Disorder scale (GAD-7) is one of the most frequently used diagnostic self-report scales for screening, diagnosis and severity assessment of anxiety disorder. Its psychometric properties from the view of the Item Response Theory paradigm have rarely been investigated. We aimed to close this gap by analyzing the GAD-7 within a large sample of primary care patients with respect to its psychometric properties and its implications for scoring using Item Response Theory. Methods Robust, nonparametric statistics were used to check unidimensionality of the GAD-7. A graded response model was fitted using a Bayesian approach. The model fit was evaluated using posterior predictive p-values, item information functions were derived and optimal predictions of anxiety were calculated. Results The sample included N = 3404 primary care patients (60% female; mean age, 52,2; standard deviation 19.2) The analysis indicated no deviations of the GAD-7 scale from unidimensionality and a decent fit of a graded response model. The commonly suggested ultra-brief measure consisting of the first two items, the GAD-2, was supported by item information analysis. The first four items discriminated better than the last three items with respect to latent anxiety. Conclusion The information provided by the first four items should be weighted more heavily. Moreover, estimates corresponding to low to moderate levels of anxiety show greater variability. The psychometric validity of the GAD-2 was supported by our analysis. PMID:28771530

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Simulating carbon and water fluxes at Arctic and boreal ecosystems in Alaska by optimizing the modified BIOME-BGC with eddy covariance data

NASA Astrophysics Data System (ADS)

Ueyama, M.; Kondo, M.; Ichii, K.; Iwata, H.; Euskirchen, E. S.; Zona, D.; Rocha, A. V.; Harazono, Y.; Nakai, T.; Oechel, W. C.

2013-12-01

To better predict carbon and water cycles in Arctic ecosystems, we modified a process-based ecosystem model, BIOME-BGC, by introducing new processes: change in active layer depth on permafrost and phenology of tundra vegetation. The modified BIOME-BGC was optimized using an optimization method. The model was constrained using gross primary productivity (GPP) and net ecosystem exchange (NEE) at 23 eddy covariance sites in Alaska, and vegetation/soil carbon from a literature survey. The model was used to simulate regional carbon and water fluxes of Alaska from 1900 to 2011. Simulated regional fluxes were validated with upscaled GPP, ecosystem respiration (RE), and NEE based on two methods: (1) a machine learning technique and (2) a top-down model. Our initial simulation suggests that the original BIOME-BGC with default ecophysiological parameters substantially underestimated GPP and RE for tundra and overestimated those fluxes for boreal forests. We will discuss how optimization using the eddy covariance data impacts the historical simulation by comparing the new version of the model with simulated results from the original BIOME-BGC with default ecophysiological parameters. This suggests that the incorporation of the active layer depth and plant phenology processes is important to include when simulating carbon and water fluxes in Arctic ecosystems.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

ERIC Educational Resources Information Center

Mozer, Michael C.; Pashler, Harold; Homaei, Hadjar

2008-01-01

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect…

Fast Appearance Modeling for Automatic Primary Video Object Segmentation.

PubMed

Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong

2016-02-01

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.

Relationship between compliance and persistence with osteoporosis medications and fracture risk in primary health care in France: a retrospective case-control analysis.

PubMed

Cotté, François-Emery; Mercier, Florence; De Pouvourville, Gérard

2008-12-01

Nonadherence to treatment is an important determinant of long-term outcomes in women with osteoporosis. This study was conducted to investigate the association between adherence and osteoporotic fracture risk and to identify optimal thresholds for good compliance and persistence. A secondary objective was to perform a preliminary evaluation of the cost consequences of adherence. This was a retrospective case-control analysis. Data were derived from the Thales prescription database, which contains information on >1.6 million patients in the primary health care setting in France. Cases were women aged >or=50 years who had an osteoporosis-related fracture in 2006. For each case, 5 matched controls were randomly selected. Both compliance and persistence aspects of treatment adherence were examined. Compliance was estimated based on the medication possession ratio (MPR). Persistence was calculated as the time from the initial filling of a prescription for osteoporosis medication until its discontinuation. The mean (SD) MPR was lower in cases compared with controls (58.8% [34.7%] vs 72.1% [28.8%], respectively; P < 0.001). Cases were more likely than controls to discontinue osteoporosis treatment (50.0% vs 25.3%; P < 0.001), yielding a significantly lower proportion of patients who were still persistent at 1 year (34.1% vs 40.9%; P < 0.001). MPR was the best predictor of fracture risk, with an area under the receiver-operating-characteristic curve that was higher than that for persistence (0.59 vs 0.55). The optimal MPR threshold for predicting fracture risk was >or=68.0%. Compared with less-compliant women, women who achieved this threshold had a 51% reduction in fracture risk. The difference in annual drug expenditure between women achieving this threshold and those who did not was approximately euro300. The optimal threshold for persistence with therapy was at least 6 months. Attaining this threshold was associated with a 28% reduction in fracture risk compared with less-persistent women. In this study, better treatment adherence was associated with a greater reduction in fracture risk. Compliance appeared to predict fracture risk better than did persistence.

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method

PubMed Central

Yu, Huihui; Chen, Yingyi; Hassan, ShahbazGul; Li, Daoliang

2016-01-01

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds. PMID:27270206

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method.

PubMed

Yu, Huihui; Chen, Yingyi; Hassan, ShahbazGul; Li, Daoliang

2016-06-08

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds.

Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.

PubMed

Vuong, Kylie; Armstrong, Bruce K; Weiderpass, Elisabete; Lund, Eiliv; Adami, Hans-Olov; Veierod, Marit B; Barrett, Jennifer H; Davies, John R; Bishop, D Timothy; Whiteman, David C; Olsen, Catherine M; Hopper, John L; Mann, Graham J; Cust, Anne E; McGeechan, Kevin

2016-08-01

Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk. The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.

The missing biology in land carbon models (Invited)

NASA Astrophysics Data System (ADS)

Prentice, I. C.; Cornwell, W.; Dong, N.; Maire, V.; Wang, H.; Wright, I.

2013-12-01

Models of terrestrial carbon cycling give divergent results, and recent developments - notably the inclusion of nitrogen-carbon cycle coupling - have apparently made matters worse. More extensive benchmarking of models would be highly desirable, but is not a panacea. Problems with current models include overparameterization (assigning separate sets of parameter values for each plant functional type can easily obscure more fundamental model limitations), and the widespread persistence of incorrect paradigms to describe plant responses to environment. Next-generation models require a more sound basis in observations and theory. A possible way forward will be outlined. It will be shown how the principle of optimization by natural selection can yield testable, general hypotheses about plant function. A specific optimality hypothesis about the control of CO2 drawdown versus water loss by leaves will be shown to yield global and quantitatively verifable predictions of plant behaviour as demonstrated in field gas-exchange measurements across species from different environments, and in the global pattern of stable carbon isotope discrimination by plants. Combined with the co-limitation hypothesis for the control of photosynthetic capacity and an economic approach to the costs of nutrient acquisition, this hypothesis provides a potential foundation for a comprehensive predictive understanding of the controls of primary production on land.

Three-Dimensional Nacelle Aeroacoustics Code With Application to Impedance Education

NASA Technical Reports Server (NTRS)

Watson, Willie R.

2000-01-01

A three-dimensional nacelle acoustics code that accounts for uniform mean flow and variable surface impedance liners is developed. The code is linked to a commercial version of the NASA-developed General Purpose Solver (for solution of linear systems of equations) in order to obtain the capability to study high frequency waves that may require millions of grid points for resolution. Detailed, single-processor statistics for the performance of the solver in rigid and soft-wall ducts are presented. Over the range of frequencies of current interest in nacelle liner research, noise attenuation levels predicted from the code were in excellent agreement with those predicted from mode theory. The equation solver is memory efficient, requiring only a small fraction of the memory available on modern computers. As an application, the code is combined with an optimization algorithm and used to reduce the impedance spectrum of a ceramic liner. The primary problem with using the code to perform optimization studies at frequencies above I1kHz is the excessive CPU time (a major portion of which is matrix assembly). The research recommends that research be directed toward development of a rapid sparse assembler and exploitation of the multiprocessor capability of the solver to further reduce CPU time.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Optimizing the identification of patients with axial spondyloarthritis in primary care--the case for a two-step strategy combining the most relevant clinical items with HLA B27.

PubMed

Braun, Annalina; Gnann, Holger; Saracbasi, Ertan; Grifka, Joachim; Kiltz, Uta; Letschert, Katrin; Braun, Jürgen

2013-08-01

The combination of clinical items suggestive of inflammatory back pain has proved useful for early identification of patients with axial SpA (axSpA) in primary care. However, whether HLA B27 contributes to that is unclear, and published recommendations have advised against it. In this study, we reanalysed data of that trial in relation to the HLA B27 results. Consecutive patients <45 years old (n = 950) with back pain (BP) >2 months presenting to 143 orthopaedists were referred to 36 rheumatologists who made the diagnosis. The predictive value of HLA B27 (n = 298) alone and in combination, including modelling and a two-step strategy, was calculated. Among all patients (mean age 36 years, 52% female, median duration of BP 32 months), 107 had axSpA (36%). Using a simple model, HLA B27 alone performed better than all combinations of clinical items and adding it did not improve likelihood ratios (LRs). Using modelling, two-phase strategies were analysed. Additional items were only relevant in the HLA B27-negative group: improvement by movement, buttock pain and psoriasis. Combining this information revealed the presumably best strategy to predict axSpA in primary care: more than one of these items or HLA B27 need to be present (sensitivity 80.4%, specificity 75.4%, LR+ 3.27 and LR- 0.26). This is the first study to show that patients with axSpA are more reliably identified in primary care by a strategy that includes HLA B27. Because of the two-step approach, the test needs to be performed in only about half of patients with chronic BP.

Optimizing the Primary Prevention of Type-2 Diabetes in Primary Health Care

ClinicalTrials.gov

2017-08-18

Interprofessional Relations; Primary Health Care/Organization & Administration; Diabetes Mellitus, Type 2/Prevention & Control; Primary Prevention/Methods; Risk Reduction Behavior; Randomized Controlled Trial; Life Style

Self-management support for chronic pain in primary care: a cross-sectional study of patient experiences and nursing roles.

PubMed

Lukewich, Julia; Mann, Elizabeth; VanDenKerkhof, Elizabeth; Tranmer, Joan

2015-11-01

The aim of this study was to describe chronic pain self-management from the perspective of individuals living with chronic pain in the context of primary care nursing. Self-management is a key chronic pain treatment modality and support for self-managing chronic pain is mainly provided in the context of primary care. Although nurses are optimally suited to facilitate self-management in primary care, there is a need to explore opportunities for optimizing their roles. Two cross-sectional studies. The Chronic Pain Self-Management Survey was conducted in 2011-2012 to explore the epidemiology and self-management of chronic pain in Canadian adults. The questionnaire was distributed to 1504 individuals in Ontario. In 2011, the Primary Care Nursing Roles Survey was distributed to 1911 primary care nurses in Ontario to explore their roles and to determine the extent to which chronic disease management strategies, including support for self-management, were implemented in primary care. Few respondents to the pain survey identified nurses as being the 'most helpful' facilitator of self-management while physicians were most commonly cited. Seventy-six per cent of respondents used medication to manage their chronic pain. Few respondents to the nursing survey worked in practices with specific programmes for individuals with chronic pain. Individuals with chronic pain identified barriers and facilitators to self-managing their pain and nurses identified barriers and facilitators to optimizing their role in primary care. There are several opportunities for primary care practices to facilitate self-management of chronic pain, including the optimization of the primary care nursing role. © 2015 John Wiley & Sons Ltd.

A predictive control framework for optimal energy extraction of wind farms

NASA Astrophysics Data System (ADS)

Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.

2016-09-01

This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

PubMed

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

Co-Optimization of CO 2-EOR and Storage Processes in Mature Oil Reservoirs

DOE PAGES

Ampomah, William; Balch, Robert S.; Grigg, Reid B.; ...

2016-08-02

This article presents an optimization methodology for CO 2 enhanced oil recovery in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO 2 flood and for optimizing both oil production and CO 2 storage in the Farnsworth Unit (FWU), Ochiltree County, Texas. A geological framework model constructed from geophysical, geological, and engineering data acquired from the FWU was the basis for all reservoir simulations and the optimization method. An equation of state was calibrated with laboratory fluid analyses and subsequently used to predict the thermodynamic minimum miscible pressure (MMP).more » Initial history calibrations of primary, secondary and tertiary recovery were conducted as the basis for the study. After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). The PRSM utilized an objective function that maximized both oil recovery and CO 2 storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO 2 purchase, gas recycle and addition of infill wells and/or patterns. The PRSM proxy model was ‘trained’ or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO 2 storage. The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO 2 storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO 2 storage within partially depleted oil reservoirs such as FWU. Lastly, this study may serve as a benchmark for potential CO 2–EOR projects in the Anadarko basin and/or geologically similar basins throughout the world.« less

Co-Optimization of CO 2-EOR and Storage Processes in Mature Oil Reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ampomah, William; Balch, Robert S.; Grigg, Reid B.

This article presents an optimization methodology for CO 2 enhanced oil recovery in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO 2 flood and for optimizing both oil production and CO 2 storage in the Farnsworth Unit (FWU), Ochiltree County, Texas. A geological framework model constructed from geophysical, geological, and engineering data acquired from the FWU was the basis for all reservoir simulations and the optimization method. An equation of state was calibrated with laboratory fluid analyses and subsequently used to predict the thermodynamic minimum miscible pressure (MMP).more » Initial history calibrations of primary, secondary and tertiary recovery were conducted as the basis for the study. After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). The PRSM utilized an objective function that maximized both oil recovery and CO 2 storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO 2 purchase, gas recycle and addition of infill wells and/or patterns. The PRSM proxy model was ‘trained’ or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO 2 storage. The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO 2 storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO 2 storage within partially depleted oil reservoirs such as FWU. Lastly, this study may serve as a benchmark for potential CO 2–EOR projects in the Anadarko basin and/or geologically similar basins throughout the world.« less

An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations

PubMed Central

Kish, Nicole E.; Helmuth, Brian; Wethey, David S.

2016-01-01

Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, Mytilus californianus, using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change. PMID:27729979

Patient-specific dosimetric endpoints based treatment plan quality control in radiotherapy.

PubMed

Song, Ting; Staub, David; Chen, Mingli; Lu, Weiguo; Tian, Zhen; Jia, Xun; Li, Yongbao; Zhou, Linghong; Jiang, Steve B; Gu, Xuejun

2015-11-07

In intensity modulated radiotherapy (IMRT), the optimal plan for each patient is specific due to unique patient anatomy. To achieve such a plan, patient-specific dosimetric goals reflecting each patient's unique anatomy should be defined and adopted in the treatment planning procedure for plan quality control. This study is to develop such a personalized treatment plan quality control tool by predicting patient-specific dosimetric endpoints (DEs). The incorporation of patient specific DEs is realized by a multi-OAR geometry-dosimetry model, capable of predicting optimal DEs based on the individual patient's geometry. The overall quality of a treatment plan is then judged with a numerical treatment plan quality indicator and characterized as optimal or suboptimal. Taking advantage of clinically available prostate volumetric modulated arc therapy (VMAT) treatment plans, we built and evaluated our proposed plan quality control tool. Using our developed tool, six of twenty evaluated plans were identified as sub-optimal plans. After plan re-optimization, these suboptimal plans achieved better OAR dose sparing without sacrificing the PTV coverage, and the dosimetric endpoints of the re-optimized plans agreed well with the model predicted values, which validate the predictability of the proposed tool. In conclusion, the developed tool is able to accurately predict optimally achievable DEs of multiple OARs, identify suboptimal plans, and guide plan optimization. It is a useful tool for achieving patient-specific treatment plan quality control.

Predicting recreational fishing use of offshore petroleum platforms in the Central Gulf of Mexico

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gordon, W.R. Jr.

1987-01-01

This study is based on the premise that properly sited artificial reefs for optimal human recreational use, a predictive model based upon the marine travel patterns and behavior of marine recreational fishermen, is needed. This research used data gathered from a previous study that addressed the recreational fishing use of offshore oil and gas structures (Ditton and Auyong 1984); on-site data were also collected. The primary research objective was to generate a predictive model that can be applied to artificial-reef development efforts elsewhere. This study investigated the recreational-user patterns of selected petroleum platforms structures in the Central Gulf of Mexico.more » The petroleum structures offshore from the Louisiana coastline provide a unique research tool. Although intended to facilitate the exploration and recovery of hydrocarbons, petroleum platforms also serve as defacto artificial reefs, providing habitat for numerous species of fish and other marine life. Petroleum platforms were found to be the principal fishing destinations within the study area. On-site findings reveal that marine recreational fishermen were as mobile on water, as they are on land. On-site findings were used to assist in the development of a predictive model.« less

Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice.

PubMed

Howes, Andrew; Duggan, Geoffrey B; Kalidindi, Kiran; Tseng, Yuan-Chi; Lewis, Richard L

2016-07-01

It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. The experiments use a no-choice/choice utility learning paradigm where the no-choice phase is used to elicit a profile of each participant's performance across the strategy space and the choice phase is used to test predicted choices within this space. They show that the majority of individuals select strategies that are boundedly optimal. Further, individual differences in what people choose to do are successfully predicted by the analysis. Two issues are discussed: (a) the performance of the minority of participants who did not find boundedly optimal adaptations, and (b) the possibility that individuals anticipate what, with practice, will become a bounded optimal strategy, rather than what is boundedly optimal during training. Copyright © 2015 Cognitive Science Society, Inc.

Average optimal DPOAE primary tone levels in normal-hearing adults.

PubMed

Marcrum, Steven C; Kummer, Peter; Kreitmayer, Christoph; Steffens, Thomas

2016-01-01

Despite great progress towards optimizing DPOAE primary tone characteristics, factors such as stimulus and intra-subject emission variability have not been addressed. The purpose of this study was to identify optimal primary tone level relationships when these sources of variability were acknowledged, and to identify any influences of test frequency. Following coupler-based measurements assessing primary tone level stability, two experiments were conducted. In experiment 1, DPOAE test-retest reliability without probe replacement was measured for f2 = 1-6 kHz with L1 = L2 = 65 dB SPL. In experiment 2, optimal L1-L2 relationships were identified for f2 = 1-6 kHz. For 20 ≤ L2 ≤ 75 dB SPL, L1 was varied 15 dB SPL above and below the recommendation of L1 = 0.4 L2 + 39 [dB SPL]. Eleven normal-hearing adults participated in experiment 1. Thirty normal-hearing adults participated in experiment 2. Stimulus variability did not exceed 0.1 dB SPL. DPOAE reliability testing revealed an across-frequency mean standard error of measurement of 0.52 dB SPL. The average optimal L1-L2 relationship was described by L1 = 0.49 L2 + 41 [dB SPL]. A significant effect of frequency was identified for 6 kHz. Including relevant sources of variability improves internal validity of a primary tone level optimization formula.

Optimization of lightweight structure and supporting bipod flexure for a space mirror.

PubMed

Chen, Yi-Cheng; Huang, Bo-Kai; You, Zhen-Ting; Chan, Chia-Yen; Huang, Ting-Ming

2016-12-20

This article presents an optimization process for integrated optomechanical design. The proposed optimization process for integrated optomechanical design comprises computer-aided drafting, finite element analysis (FEA), optomechanical transfer codes, and an optimization solver. The FEA was conducted to determine mirror surface deformation; then, deformed surface nodal data were transferred into Zernike polynomials through MATLAB optomechanical transfer codes to calculate the resulting optical path difference (OPD) and optical aberrations. To achieve an optimum design, the optimization iterations of the FEA, optomechanical transfer codes, and optimization solver were automatically connected through a self-developed Tcl script. Two examples of optimization design were illustrated in this research, namely, an optimum lightweight design of a Zerodur primary mirror with an outer diameter of 566 mm that is used in a spaceborne telescope and an optimum bipod flexure design that supports the optimum lightweight primary mirror. Finally, optimum designs were successfully accomplished in both examples, achieving a minimum peak-to-valley (PV) value for the OPD of the deformed optical surface. The simulated optimization results showed that (1) the lightweight ratio of the primary mirror increased from 56% to 66%; and (2) the PV value of the mirror supported by optimum bipod flexures in the horizontal position effectively decreased from 228 to 61 nm.

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Establishment of an immortalized mouse dermal papilla cell strain with optimized culture strategy.

PubMed

Guo, Haiying; Xing, Yizhan; Zhang, Yiming; He, Long; Deng, Fang; Ma, Xiaogen; Li, Yuhong

2018-01-01

Dermal papilla (DP) plays important roles in hair follicle regeneration. Long-term culture of mouse DP cells can provide enough cells for research and application of DP cells. We optimized the culture strategy for DP cells from three dimensions: stepwise dissection, collagen I coating, and optimized culture medium. Based on the optimized culture strategy, we immortalized primary DP cells with SV40 large T antigen, and established several immortalized DP cell strains. By comparing molecular expression and morphologic characteristics with primary DP cells, we found one cell strain named iDP6 was similar with primary DP cells. Further identifications illustrate that iDP6 expresses FGF7 and α-SMA, and has activity of alkaline phosphatase. During the process of characterization of immortalized DP cell strains, we also found that cells in DP were heterogeneous. We successfully optimized culture strategy for DP cells, and established an immortalized DP cell strain suitable for research and application of DP cells.

Establishment of an immortalized mouse dermal papilla cell strain with optimized culture strategy

PubMed Central

Zhang, Yiming; He, Long; Deng, Fang; Ma, Xiaogen

2018-01-01

Dermal papilla (DP) plays important roles in hair follicle regeneration. Long-term culture of mouse DP cells can provide enough cells for research and application of DP cells. We optimized the culture strategy for DP cells from three dimensions: stepwise dissection, collagen I coating, and optimized culture medium. Based on the optimized culture strategy, we immortalized primary DP cells with SV40 large T antigen, and established several immortalized DP cell strains. By comparing molecular expression and morphologic characteristics with primary DP cells, we found one cell strain named iDP6 was similar with primary DP cells. Further identifications illustrate that iDP6 expresses FGF7 and α-SMA, and has activity of alkaline phosphatase. During the process of characterization of immortalized DP cell strains, we also found that cells in DP were heterogeneous. We successfully optimized culture strategy for DP cells, and established an immortalized DP cell strain suitable for research and application of DP cells. PMID:29383288

Streamflow Prediction based on Chaos Theory

NASA Astrophysics Data System (ADS)

Li, X.; Wang, X.; Babovic, V. M.

2015-12-01

Chaos theory is a popular method in hydrologic time series prediction. Local model (LM) based on this theory utilizes time-delay embedding to reconstruct the phase-space diagram. For this method, its efficacy is dependent on the embedding parameters, i.e. embedding dimension, time lag, and nearest neighbor number. The optimal estimation of these parameters is thus critical to the application of Local model. However, these embedding parameters are conventionally estimated using Average Mutual Information (AMI) and False Nearest Neighbors (FNN) separately. This may leads to local optimization and thus has limitation to its prediction accuracy. Considering about these limitation, this paper applies a local model combined with simulated annealing (SA) to find the global optimization of embedding parameters. It is also compared with another global optimization approach of Genetic Algorithm (GA). These proposed hybrid methods are applied in daily and monthly streamflow time series for examination. The results show that global optimization can contribute to the local model to provide more accurate prediction results compared with local optimization. The LM combined with SA shows more advantages in terms of its computational efficiency. The proposed scheme here can also be applied to other fields such as prediction of hydro-climatic time series, error correction, etc.

Optimism as a predictor of the effects of laboratory-induced stress on fears and hope.

PubMed

Kimhi, Shaul; Eshel, Yohanan; Shahar, Eldad

2013-01-01

The objective of the current study is to explore optimism as a predictor of personal and collective fear, as well as hope, following laboratory-induced stress. Students (N = 107; 74 female, 33 male) were assigned randomly to either the experimental (stress--political violence video clip) or the control group (no-stress--nature video clip). Questionnaires of fear and hope were administered immediately after the experiment (Time 1) and 3 weeks later (Time 2). Structural equation modeling indicated the following: (a) Optimism significantly predicted both fear and hope in the stress group at Time 1, but not in the no-stress group. (b) Optimism predicted hope but not fear at Time 2 in the stress group. (c) Hope at Time 1 significantly predicted hope at Time 2, in both the stress and the no-stress groups. (d) Gender did not predict significantly fear at Time 1 in the stress group, despite a significant difference between genders. This study supports previous studies indicating that optimism plays an important role in people's coping with stress. However, based on our research the data raise the question of whether optimism, by itself, or environmental stress, by itself, may accurately predict stress response.

Can HbA1c replace OGTT for the diagnosis of diabetes mellitus among Chinese patients with impaired fasting glucose?

PubMed

Yu, Esther Y T; Wong, Carlos K H; Ho, S Y; Wong, Samuel Y S; Lam, Cindy L K

2015-12-01

HbA1c ≥ 6.5% has been recommended as a diagnostic criterion for the detection of diabetes mellitus (DM) since 2010 because of its convenience, stability and significant correlation with diabetic complications. Nevertheless, the accuracy of HbA1c compared to glucose-based diagnostic criteria varies among subjects of different ethnicity and risk profile. This study aimed to evaluate the accuracy of HbA1c for diagnosing DM compared to the diagnosis by oral glucose tolerance test (OGTT) and the optimal HbA1c level to diagnose DM in primary care Chinese patients with impaired fasting glucose (IFG). A cross-sectional study was carried out in three public primary care clinics in Hong Kong. About 1128 Chinese adults with IFG (i.e. FG level between 5.6 and 6.9 mmol/l in the past 18 months) were recruited to receive paired OGTT and HbA1c tests. Sensitivities and specificities of HbA1c at different threshold levels for predicting DM compared to the diagnosis by OGTT were evaluated. A receiver operating characteristic (ROC) curve was used to determine the optimal cut-off level. Among the 1128 subjects (mean age 64.2±8.9 year, 48.8% male), 229 (20.3%) were diagnosed to have DM by OGTT. The sensitivity and specificity of HbA1c ≥6.5% were 33.2% and 93.5%, respectively, for predicting DM diagnosed by OGTT. The area under the ROC curve was 0.770, indicating HbA1c had fair discriminatory power. The optimal cut-off threshold of HbA1c was 6.3% for discriminating DM from non-DM, with sensitivity and specificity of 56.3% and 85.5%, respectively. HbA1c ≥ 5.6% has the highest sensitivity and negative predictive value of 96.1% and 94.5%, respectively. HbA1c ≥ 6.5% is highly specific in identifying people with DM, but it may miss the majority (66.8%) of the DM cases. An HbA1c threshold of <5.6% is more appropriate to be used for the exclusion of DM. OGTT should be performed for the confirmation of DM among Chinese patients with IFG who have an HbA1c between 5.6% and 6.4%. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Concerted One-Electron Two-Proton Transfer Processes in Models Inspired by the Tyr-His Couple of Photosystem II

DOE PAGES

Huynh, Mioy T.; Mora, S. Jimena; Villalba, Matias; ...

2017-05-09

Nature employs a TyrZ-His pair as a redox relay that couples proton transfer to the redox process between P680 and the water oxidizing catalyst in photosystem II. Artificial redox relays composed of different benzimidazole–phenol dyads (benzimidazole models His and phenol models Tyr) with substituents designed to simulate the hydrogen bond network surrounding the TyrZ-His pair have been prepared. Furthermore, when the benzimidazole substituents are strong proton acceptors such as primary or tertiary amines, theory predicts that a concerted two proton transfer process associated with the electrochemical oxidation of the phenol will take place. Furthermore, theory predicts a decrease in themore » redox potential of the phenol by ~300 mV and a small kinetic isotope effect (KIE). Indeed, electrochemical, spectroelectrochemical, and KIE experimental data are consistent with these predictions. Our results were obtained by using theory to guide the rational design of artificial systems and have implications for managing proton activity to optimize efficiency at energy conversion sites involving water oxidation and reduction.« less

A multidimensional model of the effect of gravity on the spatial orientation of the monkey

NASA Technical Reports Server (NTRS)

Merfeld, D. M.; Young, L. R.; Oman, C. M.; Shelhamer, M. J.

1993-01-01

A "sensory conflict" model of spatial orientation was developed. This mathematical model was based on concepts derived from observer theory, optimal observer theory, and the mathematical properties of coordinate rotations. The primary hypothesis is that the central nervous system of the squirrel monkey incorporates information about body dynamics and sensory dynamics to develop an internal model. The output of this central model (expected sensory afference) is compared to the actual sensory afference, with the difference defined as "sensory conflict." The sensory conflict information is, in turn, used to drive central estimates of angular velocity ("velocity storage"), gravity ("gravity storage"), and linear acceleration ("acceleration storage") toward more accurate values. The model successfully predicts "velocity storage" during rotation about an earth-vertical axis. The model also successfully predicts that the time constant of the horizontal vestibulo-ocular reflex is reduced and that the axis of eye rotation shifts toward alignment with gravity following postrotatory tilt. Finally, the model predicts the bias, modulation, and decay components that have been observed during off-vertical axis rotations (OVAR).

Influence of monitoring data selection for optimization of a steady state multimedia model on the magnitude and nature of the model prediction bias.

PubMed

Kim, Hee Seok; Lee, Dong Soo

2017-11-01

SimpleBox is an important multimedia model used to estimate the predicted environmental concentration for screening-level exposure assessment. The main objectives were (i) to quantitatively assess how the magnitude and nature of prediction bias of SimpleBox vary with the selection of observed concentration data set for optimization and (ii) to present the prediction performance of the optimized SimpleBox. The optimization was conducted using a total of 9604 observed multimedia data for 42 chemicals of four groups (i.e., polychlorinated dibenzo-p-dioxins/furans (PCDDs/Fs), polybrominated diphenyl ethers (PBDEs), phthalates, and polycyclic aromatic hydrocarbons (PAHs)). The model performance was assessed based on the magnitude and skewness of prediction bias. Monitoring data selection in terms of number of data and kind of chemicals plays a significant role in optimization of the model. The coverage of the physicochemical properties was found to be very important to reduce the prediction bias. This suggests that selection of observed data should be made such that the physicochemical property (such as vapor pressure, octanol-water partition coefficient, octanol-air partition coefficient, and Henry's law constant) range of the selected chemical groups be as wide as possible. With optimization, about 55%, 90%, and 98% of the total number of the observed concentration ratios were predicted within factors of three, 10, and 30, respectively, with negligible skewness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Xylem formation can be modeled statistically as a function of primary growth and cambium activity.

PubMed

Huang, Jian-Guo; Deslauriers, Annie; Rossi, Sergio

2014-08-01

Primary (budburst, foliage and shoot) growth and secondary (cambium and xylem) growth of plants play a vital role in sequestering atmospheric carbon. However, their potential relationships have never been mathematically quantified and the underlying physiological mechanisms are unclear. We monitored primary and secondary growth in Picea mariana and Abies balsamea on a weekly basis from 2010 to 2013 at four sites over an altitudinal gradient (25-900 m) in the eastern Canadian boreal forest. We determined the timings of onset and termination through the fitted functions and their first derivative. We quantified the potential relationships between primary growth and secondary growth using the mixed-effects model. We found that xylem formation of boreal conifers can be modeled as a function of cambium activity, bud phenology, and shoot and needle growth, as well as species- and site-specific factors. Our model reveals that there may be an optimal mechanism to simultaneously allocate the photosynthetic products and stored nonstructural carbon to growth of different organs at different times in the growing season. This mathematical link can bridge phenological modeling, forest ecosystem productivity and carbon cycle modeling, which will certainly contribute to an improved prediction of ecosystem productivity and carbon equilibrium. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

Secondary mediation and regression analyses of the PTClinResNet database: determining causal relationships among the International Classification of Functioning, Disability and Health levels for four physical therapy intervention trials.

PubMed

Mulroy, Sara J; Winstein, Carolee J; Kulig, Kornelia; Beneck, George J; Fowler, Eileen G; DeMuth, Sharon K; Sullivan, Katherine J; Brown, David A; Lane, Christianne J

2011-12-01

Each of the 4 randomized clinical trials (RCTs) hosted by the Physical Therapy Clinical Research Network (PTClinResNet) targeted a different disability group (low back disorder in the Muscle-Specific Strength Training Effectiveness After Lumbar Microdiskectomy [MUSSEL] trial, chronic spinal cord injury in the Strengthening and Optimal Movements for Painful Shoulders in Chronic Spinal Cord Injury [STOMPS] trial, adult stroke in the Strength Training Effectiveness Post-Stroke [STEPS] trial, and pediatric cerebral palsy in the Pediatric Endurance and Limb Strengthening [PEDALS] trial for children with spastic diplegic cerebral palsy) and tested the effectiveness of a muscle-specific or functional activity-based intervention on primary outcomes that captured pain (STOMPS, MUSSEL) or locomotor function (STEPS, PEDALS). The focus of these secondary analyses was to determine causal relationships among outcomes across levels of the International Classification of Functioning, Disability and Health (ICF) framework for the 4 RCTs. With the database from PTClinResNet, we used 2 separate secondary statistical approaches-mediation analysis for the MUSSEL and STOMPS trials and regression analysis for the STEPS and PEDALS trials-to test relationships among muscle performance, primary outcomes (pain related and locomotor related), activity and participation measures, and overall quality of life. Predictive models were stronger for the 2 studies with pain-related primary outcomes. Change in muscle performance mediated or predicted reductions in pain for the MUSSEL and STOMPS trials and, to some extent, walking speed for the STEPS trial. Changes in primary outcome variables were significantly related to changes in activity and participation variables for all 4 trials. Improvement in activity and participation outcomes mediated or predicted increases in overall quality of life for the 3 trials with adult populations. Variables included in the statistical models were limited to those measured in the 4 RCTs. It is possible that other variables also mediated or predicted the changes in outcomes. The relatively small sample size in the PEDALS trial limited statistical power for those analyses. Evaluating the mediators or predictors of change between each ICF level and for 2 fundamentally different outcome variables (pain versus walking) provided insights into the complexities inherent across 4 prevalent disability groups.

Towards Carbon-Neutral CO2 Conversion to Hydrocarbons.

PubMed

Mattia, Davide; Jones, Matthew D; O'Byrne, Justin P; Griffiths, Owen G; Owen, Rhodri E; Sackville, Emma; McManus, Marcelle; Plucinski, Pawel

2015-12-07

With fossil fuels still predicted to contribute close to 80 % of the primary energy consumption by 2040, methods to limit further CO2 emissions in the atmosphere are urgently needed to avoid the catastrophic scenarios associated with global warming. In parallel with improvements in energy efficiency and CO2 storage, the conversion of CO2 has emerged as a complementary route with significant potential. In this work we present the direct thermo-catalytic conversion of CO2 to hydrocarbons using a novel iron nanoparticle-carbon nanotube (Fe@CNT) catalyst. We adopted a holistic and systematic approach to CO2 conversion by integrating process optimization-identifying reaction conditions to maximize conversion and selectivity towards long chain hydrocarbons and/or short olefins-with catalyst optimization through the addition of promoters. The result is the production of valuable hydrocarbons in a manner that can approach carbon neutrality under realistic industrial process conditions. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dispositional optimism as predictor of outcome in short- and long-term psychotherapy.

PubMed

Heinonen, Erkki; Heiskanen, Tiia; Lindfors, Olavi; Härkäpää, Kristiina; Knekt, Paul

2017-09-01

Dispositional optimism predicts various beneficial outcomes in somatic health and treatment, but has been little studied in psychotherapy. This study investigated whether an optimistic disposition differentially predicts patients' ability to benefit from short-term versus long-term psychotherapy. A total of 326 adult outpatients with mood and/or anxiety disorder were randomized into short-term (solution-focused or short-term psychodynamic) or long-term psychodynamic therapy and followed up for 3 years. Dispositional optimism was assessed by patients at baseline with the self-rated Life Orientation Test (LOT) questionnaire. Outcome was assessed at baseline and seven times during the follow-up, in terms of depressive (BDI, HDRS), anxiety (SCL-90-ANX, HARS), and general psychiatric symptoms (SCL-90-GSI), all seven follow-up points including patients' self-reports and three including interview-based measures. Lower dispositional optimism predicted faster symptom reduction in short-term than in long-term psychotherapy. Higher optimism predicted equally rapid and eventually greater benefits in long-term, as compared to short-term, psychotherapy. Weaker optimism appeared to predict sustenance of problems early in long-term therapy. Stronger optimism seems to best facilitate engaging in and benefiting from a long-term therapy process. Closer research might clarify the psychological processes responsible for these effects and help fine-tune both briefer and longer interventions to optimize treatment effectiveness for particular patients and their psychological qualities. Weaker dispositional optimism does not appear to inhibit brief therapy from effecting symptomatic recovery. Patients with weaker optimism do not seem to gain added benefits from long-term therapy, but instead may be susceptible to prolonged psychiatric symptoms in the early stages of long-term therapy. © 2016 The British Psychological Society.

Recent Results on "Approximations to Optimal Alarm Systems for Anomaly Detection"

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2009-01-01

An optimal alarm system and its approximations may use Kalman filtering for univariate linear dynamic systems driven by Gaussian noise to provide a layer of predictive capability. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. An optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability in this particular scenario.

Optimization of global model composed of radial basis functions using the term-ranking approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, Peng; Tao, Chao, E-mail: taochao@nju.edu.cn; Liu, Xiao-Jun

2014-03-15

A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.

Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Optimal 3D culture of primary articular chondrocytes for use in the rotating wall vessel bioreactor.

PubMed

Mellor, Liliana F; Baker, Travis L; Brown, Raquel J; Catlin, Lindsey W; Oxford, Julia Thom

2014-08-01

Reliable culturing methods for primary articular chondrocytes are essential to study the effects of loading and unloading on joint tissue at the cellular level. Due to the limited proliferation capacity of primary chondrocytes and their tendency to dedifferentiate in conventional culture conditions, long-term culturing conditions of primary chondrocytes can be challenging. The goal of this study was to develop a suspension culturing technique that not only would retain the cellular morphology, but also maintain the gene expression characteristics of primary articular chondrocytes. Three-dimensional culturing methods were compared and optimized for primary articular chondrocytes in the rotating wall vessel bioreactor, which changes the mechanical culture conditions to provide a form of suspension culture optimized for low shear and turbulence. We performed gene expression analysis and morphological characterization of cells cultured in alginate beads, Cytopore-2 microcarriers, primary monolayer culture, and passaged monolayer cultures using reverse transcription-PCR and laser scanning confocal microscopy. Primary chondrocytes grown on Cytopore-2 microcarriers maintained the phenotypical morphology and gene expression pattern observed in primary bovine articular chondrocytes, and retained these characteristics for up to 9 d. Our results provide a novel and alternative culturing technique for primary chondrocytes suitable for studies that require suspension such as those using the rotating wall vessel bioreactor. In addition, we provide an alternative culturing technique for primary chondrocytes that can impact future mechanistic studies of osteoarthritis progression, treatments for cartilage damage and repair, and cartilage tissue engineering.

Counteracting Obstacles with Optimistic Predictions

ERIC Educational Resources Information Center

Zhang, Ying; Fishbach, Ayelet

2010-01-01

This research tested for counteractive optimism: a self-control strategy of generating optimistic predictions of future goal attainment in order to overcome anticipated obstacles in goal pursuit. In support of the counteractive optimism model, participants in 5 studies predicted better performance, more time invested in goal activities, and lower…

Predictive optimal control of sewer networks using CORAL tool: application to Riera Blanca catchment in Barcelona.

PubMed

Puig, V; Cembrano, G; Romera, J; Quevedo, J; Aznar, B; Ramón, G; Cabot, J

2009-01-01

This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control.

Urine albumin/creatinine ratio, high sensitivity C-reactive protein and N-terminal pro brain natriuretic peptide--three new cardiovascular risk markers--do they improve risk prediction and influence treatment?

PubMed

Olsen, Michael H; Sehestedt, Thomas; Lyngbaek, Stig; Hansen, Tine W; Rasmussen, Susanne; Wachtell, Kristian; Torp-Pedersen, Christian; Hildebrandt, Per R; Ibsen, Hans

2010-01-01

In order to prioritize limited health resources in a time of increasing demands optimal cardiovascular risk stratification is essential. We tested the additive prognostic value of 3 relatively new, but established cardiovascular risk markers: N-terminal pro brain natriuretic peptide (Nt-proBNP), related to hemodynamic cardiovascular risk factors, high sensitivity C-reactive protein (hsCRP), related to metabolic cardiovascular risk factors and urine albumin/creatinine ratio (UACR), related to hemodynamic as well as metabolic risk factors. In healthy subjects with a 10-year risk of cardiovascular death lower than 5% based on HeartScore and therefore not eligible for primary prevention, the actual 10-year risk of cardiovascular death exceeded 5% in a small subgroup of subjects with UACR higher than the 95-percentile of approximately 1.6 mg/mmol. Combined use of high UACR or high hsCRP identified a larger subgroup of 16% with high cardiovascular risk in which primary prevention may be advised despite low-moderate cardiovascular risk based on HeartScore. Furthermore, combined use of high UACR or high Nt-proBNP in subjects with known cardiovascular disease or diabetes identified a large subgroup of 48% with extremely high cardiovascular risk who should be referred for specialist care to optimize treatment.

Hierarchical random walks in trace fossils and the origin of optimal search behavior

PubMed Central

Sims, David W.; Reynolds, Andrew M.; Humphries, Nicolas E.; Southall, Emily J.; Wearmouth, Victoria J.; Metcalfe, Brett; Twitchett, Richard J.

2014-01-01

Efficient searching is crucial for timely location of food and other resources. Recent studies show that diverse living animals use a theoretically optimal scale-free random search for sparse resources known as a Lévy walk, but little is known of the origins and evolution of foraging behavior and the search strategies of extinct organisms. Here, using simulations of self-avoiding trace fossil trails, we show that randomly introduced strophotaxis (U-turns)—initiated by obstructions such as self-trail avoidance or innate cueing—leads to random looping patterns with clustering across increasing scales that is consistent with the presence of Lévy walks. This predicts that optimal Lévy searches may emerge from simple behaviors observed in fossil trails. We then analyzed fossilized trails of benthic marine organisms by using a novel path analysis technique and find the first evidence, to our knowledge, of Lévy-like search strategies in extinct animals. Our results show that simple search behaviors of extinct animals in heterogeneous environments give rise to hierarchically nested Brownian walk clusters that converge to optimal Lévy patterns. Primary productivity collapse and large-scale food scarcity characterizing mass extinctions evident in the fossil record may have triggered adaptation of optimal Lévy-like searches. The findings suggest that Lévy-like behavior has been used by foragers since at least the Eocene but may have a more ancient origin, which might explain recent widespread observations of such patterns among modern taxa. PMID:25024221

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Lactate clearance as the predictor of outcome in pediatric septic shock.

PubMed

Choudhary, Richa; Sitaraman, Sadasivan; Choudhary, Anita

2017-01-01

Septic shock can rapidly evolve into multiple system organ failure and death. In the recent years, hyperlactatemia has been found to be a risk factor for mortality in critically ill adults. To evaluate the predictive value of lactate clearance and to determine the optimal cut-off value for predicting outcome in children with septic shock. A prospective observational study was performed on children with septic shock admitted to pediatric Intensive Care Unit (PICU). Serial lactate levels were measured at PICU admission, 24 and 48 h later. Lactate clearance, percent decrease in lactate level in 24 h, was calculated. The primary outcome measure was survival or nonsurvival at the end of hospital stay. We performed receiver operating characteristic analyses to calculate optimal cut-off values. The mean lactate levels at admission were significantly higher in the nonsurvivors than survivors, 5.12 ± 3.51 versus 3.13 ± 1.71 mmol/L ( P = 0.0001). The cut-off for lactate level at admission for the best prediction of mortality was determined as ≥4 mmol/L (odds ratio 5.4; 95% confidence interval [CI] =2.45-12.09). Mean lactate clearance was significantly higher in survivors than nonsurvivors (17.9 ± 39.9 vs. -23.2 ± 62.7; P < 0.0001). A lactate clearance rate of <10% at 24 h had a sensitivity and specificity of 78.7% and 72.2%, respectively and a positive predictive value of 83.1% for death. Failure to achieve a lactate clearance of more than 10% was associated with greater risk of mortality (likelihood ratio + 2.83; 95% CI = 1.82-4.41). Serial lactate levels can be used to predict outcome in pediatric septic shock. A 24 h lactate clearance cut-off of <10% is a predictor of in-hospital mortality in such patients.

Effective heating of magnetic nanoparticle aggregates for in vivo nano-theranostic hyperthermia.

PubMed

Wang, Chencai; Hsu, Chao-Hsiung; Li, Zhao; Hwang, Lian-Pin; Lin, Ying-Chih; Chou, Pi-Tai; Lin, Yung-Ya

2017-01-01

Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe 3 O 4 and cobalt ferrite CoFe 2 O 4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10-20 nm. This happens to be an important size range for MR cancer nano-theranostics, as it exhibits the highest efficacy against both primary and metastatic tumors in vivo. Our studies show that a relatively 20%-25% smaller magnetic nanoparticle diameter should be chosen to reach the maximal heating efficiency in comparison with the optimal size predicted by previous models.

Effective heating of magnetic nanoparticle aggregates for in vivo nano-theranostic hyperthermia

PubMed Central

Wang, Chencai; Hsu, Chao-Hsiung; Li, Zhao; Hwang, Lian-Pin; Lin, Ying-Chih; Chou, Pi-Tai; Lin, Yung-Ya

2017-01-01

Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe3O4 and cobalt ferrite CoFe2O4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10–20 nm. This happens to be an important size range for MR cancer nano-theranostics, as it exhibits the highest efficacy against both primary and metastatic tumors in vivo. Our studies show that a relatively 20%–25% smaller magnetic nanoparticle diameter should be chosen to reach the maximal heating efficiency in comparison with the optimal size predicted by previous models. PMID:28894366

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation.

PubMed

Dudek, Christian-Alexander; Dannheim, Henning; Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de.

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation

PubMed Central

Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de. PMID:28750104

Optimal two-phase sampling design for comparing accuracies of two binary classification rules.

PubMed

Xu, Huiping; Hui, Siu L; Grannis, Shaun

2014-02-10

In this paper, we consider the design for comparing the performance of two binary classification rules, for example, two record linkage algorithms or two screening tests. Statistical methods are well developed for comparing these accuracy measures when the gold standard is available for every unit in the sample, or in a two-phase study when the gold standard is ascertained only in the second phase in a subsample using a fixed sampling scheme. However, these methods do not attempt to optimize the sampling scheme to minimize the variance of the estimators of interest. In comparing the performance of two classification rules, the parameters of primary interest are the difference in sensitivities, specificities, and positive predictive values. We derived the analytic variance formulas for these parameter estimates and used them to obtain the optimal sampling design. The efficiency of the optimal sampling design is evaluated through an empirical investigation that compares the optimal sampling with simple random sampling and with proportional allocation. Results of the empirical study show that the optimal sampling design is similar for estimating the difference in sensitivities and in specificities, and both achieve a substantial amount of variance reduction with an over-sample of subjects with discordant results and under-sample of subjects with concordant results. A heuristic rule is recommended when there is no prior knowledge of individual sensitivities and specificities, or the prevalence of the true positive findings in the study population. The optimal sampling is applied to a real-world example in record linkage to evaluate the difference in classification accuracy of two matching algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

Germination parameterization and development of an after-ripening thermal-time model for primary dormancy release of Lithospermum arvense seeds.

PubMed

Chantre, Guillermo R; Batlla, Diego; Sabbatini, Mario R; Orioli, Gustavo

2009-06-01

Models based on thermal-time approaches have been a useful tool for characterizing and predicting seed germination and dormancy release in relation to time and temperature. The aims of the present work were to evaluate the relative accuracy of different thermal-time approaches for the description of germination in Lithospermum arvense and to develop an after-ripening thermal-time model for predicting seed dormancy release. Seeds were dry-stored at constant temperatures of 5, 15 or 24 degrees C for up to 210 d. After different storage periods, batches of 50 seeds were incubated at eight constant temperature regimes of 5, 8, 10, 13, 15, 17, 20 or 25 degrees C. Experimentally obtained cumulative-germination curves were analysed using a non-linear regression procedure to obtain optimal population thermal parameters for L. arvense. Changes in these parameters were described as a function of after-ripening thermal-time and storage temperature. The most accurate approach for simulating the thermal-germination response of L. arvense was achieved by assuming a normal distribution of both base and maximum germination temperatures. The results contradict the widely accepted assumption of a single T(b) value for the entire seed population. The after-ripening process was characterized by a progressive increase in the mean maximum germination temperature and a reduction in the thermal-time requirements for germination at sub-optimal temperatures. The after-ripening thermal-time model developed here gave an acceptable description of the observed field emergence patterns, thus indicating its usefulness as a predictive tool to enhance weed management tactics.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

PubMed

Wang, Lingle; Wu, Yujie; Deng, Yuqing; Kim, Byungchan; Pierce, Levi; Krilov, Goran; Lupyan, Dmitry; Robinson, Shaughnessy; Dahlgren, Markus K; Greenwood, Jeremy; Romero, Donna L; Masse, Craig; Knight, Jennifer L; Steinbrecher, Thomas; Beuming, Thijs; Damm, Wolfgang; Harder, Ed; Sherman, Woody; Brewer, Mark; Wester, Ron; Murcko, Mark; Frye, Leah; Farid, Ramy; Lin, Teng; Mobley, David L; Jorgensen, William L; Berne, Bruce J; Friesner, Richard A; Abel, Robert

2015-02-25

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Optimization of the water chemistry of the primary coolant at nuclear power plants with VVER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barmin, L. F.; Kruglova, T. K.; Sinitsyn, V. P.

2005-01-15

Results of the use of automatic hydrogen-content meter for controlling the parameter of 'hydrogen' in the primary coolant circuit of the Kola nuclear power plant are presented. It is shown that the correlation between the 'hydrogen' parameter in the coolant and the 'hydrazine' parameter in the makeup water can be used for controlling the water chemistry of the primary coolant system, which should make it possible to optimize the water chemistry at different power levels.

Is Optimism Real?

ERIC Educational Resources Information Center

Simmons, Joseph P.; Massey, Cade

2012-01-01

Is optimism real, or are optimistic forecasts just cheap talk? To help answer this question, we investigated whether optimistic predictions persist in the face of large incentives to be accurate. We asked National Football League football fans to predict the winner of a single game. Roughly half (the partisans) predicted a game involving their…

Optimizing finite element predictions of local subchondral bone structural stiffness using neural network-derived density-modulus relationships for proximal tibial subchondral cortical and trabecular bone.

PubMed

Nazemi, S Majid; Amini, Morteza; Kontulainen, Saija A; Milner, Jaques S; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D

2017-01-01

Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local structural stiffness at the proximal tibial subchondral surface. Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting stiffness. Finite element modeling predicted 81% of experimental stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5g/cm 3 density. In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral structural stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reliability of a Bayesian network to predict an elevated aldosterone-to-renin ratio.

PubMed

Ducher, Michel; Mounier-Véhier, Claire; Lantelme, Pierre; Vaisse, Bernard; Baguet, Jean-Philippe; Fauvel, Jean-Pierre

2015-05-01

Resistant hypertension is common, mainly idiopathic, but sometimes related to primary aldosteronism. Thus, most hypertension specialists recommend screening for primary aldosteronism. To optimize the selection of patients whose aldosterone-to-renin ratio (ARR) is elevated from simple clinical and biological characteristics. Data from consecutive patients referred between 1 June 2008 and 30 May 2009 were collected retrospectively from five French 'European excellence hypertension centres' institutional registers. Patients were included if they had at least one of: onset of hypertension before age 40 years, resistant hypertension, history of hypokalaemia, efficient treatment by spironolactone, and potassium supplementation. An ARR>32 ng/L and aldosterone>160 ng/L in patients treated without agents altering the renin-angiotensin system was considered as elevated. Bayesian network and stepwise logistic regression were used to predict an elevated ARR. Of 334 patients, 89 were excluded (31 for incomplete data, 32 for taking agents that alter the renin-angiotensin system and 26 for other reasons). Among 245 included patients, 110 had an elevated ARR. Sensitivity reached 100% or 63.3% using Bayesian network or logistic regression, respectively, and specificity reached 89.6% or 67.2%, respectively. The area under the receiver-operating-characteristic curve obtained with the Bayesian network was significantly higher than that obtained by stepwise regression (0.93±0.02 vs. 0.70±0.03; P<0.001). In hypertension centres, Bayesian network efficiently detected patients with an elevated ARR. An external validation study is required before use in primary clinical settings. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Impact of database quality in knowledge-based treatment planning for prostate cancer.

PubMed

Wall, Phillip D H; Carver, Robert L; Fontenot, Jonas D

2018-03-13

This article investigates dose-volume prediction improvements in a common knowledge-based planning (KBP) method using a Pareto plan database compared with using a conventional, clinical plan database. Two plan databases were created using retrospective, anonymized data of 124 volumetric modulated arc therapy (VMAT) prostate cancer patients. The clinical plan database (CPD) contained planning data from each patient's clinically treated VMAT plan, which were manually optimized by various planners. The multicriteria optimization database (MCOD) contained Pareto-optimal plan data from VMAT plans created using a standardized multicriteria optimization protocol. Overlap volume histograms, incorporating fractional organ at risk volumes only within the treatment fields, were computed for each patient and used to match new patient anatomy to similar database patients. For each database patient, CPD and MCOD KBP predictions were generated for D 10 , D 30 , D 50 , D 65 , and D 80 of the bladder and rectum in a leave-one-out manner. Prediction achievability was evaluated through a replanning study on a subset of 31 randomly selected database patients using the best KBP predictions, regardless of plan database origin, as planning goals. MCOD predictions were significantly lower than CPD predictions for all 5 bladder dose-volumes and rectum D 50 (P = .004) and D 65 (P < .001), whereas CPD predictions for rectum D 10 (P = .005) and D 30 (P < .001) were significantly less than MCOD predictions. KBP predictions were statistically achievable in the replans for all predicted dose-volumes, excluding D 10 of bladder (P = .03) and rectum (P = .04). Compared with clinical plans, replans showed significant average reductions in D mean for bladder (7.8 Gy; P < .001) and rectum (9.4 Gy; P < .001), while maintaining statistically similar planning target volume, femoral head, and penile bulb dose. KBP dose-volume predictions derived from Pareto plans were more optimal overall than those resulting from manually optimized clinical plans, which significantly improved KBP-assisted plan quality. This work investigates how the plan quality of knowledge databases affects the performance and achievability of dose-volume predictions from a common knowledge-based planning approach for prostate cancer. Bladder and rectum dose-volume predictions derived from a database of standardized Pareto-optimal plans were compared with those derived from clinical plans manually designed by various planners. Dose-volume predictions from the Pareto plan database were significantly lower overall than those from the clinical plan database, without compromising achievability. Copyright © 2018 Elsevier Inc. All rights reserved.

Improving care coordination in the specialty referral process between primary and specialty care.

PubMed

Lin, Caroline Y

2012-01-01

There is growing evidence of sub-optimal care coordination in the US. Care coordination includes the specialty referral process, which involves referral decision-making and information transfer between primary and specialty care. This article summarizes the evidence of sub-optimal care coordination in this process, as well as potential strategies to improve it.

Optimizing DER Participation in Inertial and Primary-Frequency Response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhao, Changhong; Guggilam, Swaroop

This paper develops an approach to enable the optimal participation of distributed energy resources (DERs) in inertial and primary-frequency response alongside conventional synchronous generators. Leveraging a reduced-order model description of frequency dynamics, DERs' synthetic inertias and droop coefficients are designed to meet time-domain performance objectives of frequency overshoot and steady-state regulation. Furthermore, an optimization-based method centered around classical economic dispatch is developed to ensure that DERs share the power injections for inertial- and primary-frequency response in proportion to their power ratings. Simulations for a modified New England test-case system composed of ten synchronous generators and six instances of the IEEEmore » 37-node test feeder with frequency-responsive DERs validate the design strategy.« less

A Computational Approach to Model Vascular Adaptation During Chronic Hemodialysis: Shape Optimization as a Substitute for Growth Modeling

NASA Astrophysics Data System (ADS)

Mahmoudzadeh Akherat, S. M. Javid; Boghosian, Michael; Cassel, Kevin; Hammes, Mary

2015-11-01

End-stage-renal disease patients depend on successful long-term hemodialysis via vascular access, commonly facilitated via a Brachiocephalic Fistula (BCF). The primary cause of BCF failure is Cephalic Arch Stenosis (CAS). It is believed that low Wall Shear Stress (WSS) regions, which occur because of the high flow rates through the natural bend in the cephalic vein, create hemodynamic circumstances that trigger the onset and development of Intimal Hyperplasia (IH) and subsequent CAS. IH is hypothesized to be a natural effort to reshape the vessel, aiming to bring the WSS values back to a physiologically acceptable range. We seek to explore the correlation between regions of low WSS and subsequent IH and CAS in patient-specific geometries. By utilizing a shape optimization framework, a method is proposed to predict cardiovascular adaptation that could potentially be an alternative to vascular growth and remodeling. Based on an objective functional that seeks to alter the vessel shape in such a way as to readjust the WSS to be within the normal physiological range, CFD and shape optimization are then coupled to investigate whether the optimal shape evolution is correlated with actual patient-specific geometries thereafter. Supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01 DK90769).

An MEG signature corresponding to an axiomatic model of reward prediction error.

PubMed

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

Renormalization group invariance and optimal QCD renormalization scale-setting: a key issues review.

PubMed

Wu, Xing-Gang; Ma, Yang; Wang, Sheng-Quan; Fu, Hai-Bing; Ma, Hong-Hao; Brodsky, Stanley J; Mojaza, Matin

2015-12-01

A valid prediction for a physical observable from quantum field theory should be independent of the choice of renormalization scheme--this is the primary requirement of renormalization group invariance (RGI). Satisfying scheme invariance is a challenging problem for perturbative QCD (pQCD), since a truncated perturbation series does not automatically satisfy the requirements of the renormalization group. In a previous review, we provided a general introduction to the various scale setting approaches suggested in the literature. As a step forward, in the present review, we present a discussion in depth of two well-established scale-setting methods based on RGI. One is the 'principle of maximum conformality' (PMC) in which the terms associated with the β-function are absorbed into the scale of the running coupling at each perturbative order; its predictions are scheme and scale independent at every finite order. The other approach is the 'principle of minimum sensitivity' (PMS), which is based on local RGI; the PMS approach determines the optimal renormalization scale by requiring the slope of the approximant of an observable to vanish. In this paper, we present a detailed comparison of the PMC and PMS procedures by analyzing two physical observables R(e+e-) and [Formula: see text] up to four-loop order in pQCD. At the four-loop level, the PMC and PMS predictions for both observables agree within small errors with those of conventional scale setting assuming a physically-motivated scale, and each prediction shows small scale dependences. However, the convergence of the pQCD series at high orders, behaves quite differently: the PMC displays the best pQCD convergence since it eliminates divergent renormalon terms; in contrast, the convergence of the PMS prediction is questionable, often even worse than the conventional prediction based on an arbitrary guess for the renormalization scale. PMC predictions also have the property that any residual dependence on the choice of initial scale is highly suppressed even for low-order predictions. Thus the PMC, based on the standard RGI, has a rigorous foundation; it eliminates an unnecessary systematic error for high precision pQCD predictions and can be widely applied to virtually all high-energy hadronic processes, including multi-scale problems.

Renormalization group invariance and optimal QCD renormalization scale-setting: a key issues review

NASA Astrophysics Data System (ADS)

Wu, Xing-Gang; Ma, Yang; Wang, Sheng-Quan; Fu, Hai-Bing; Ma, Hong-Hao; Brodsky, Stanley J.; Mojaza, Matin

2015-12-01

A valid prediction for a physical observable from quantum field theory should be independent of the choice of renormalization scheme—this is the primary requirement of renormalization group invariance (RGI). Satisfying scheme invariance is a challenging problem for perturbative QCD (pQCD), since a truncated perturbation series does not automatically satisfy the requirements of the renormalization group. In a previous review, we provided a general introduction to the various scale setting approaches suggested in the literature. As a step forward, in the present review, we present a discussion in depth of two well-established scale-setting methods based on RGI. One is the ‘principle of maximum conformality’ (PMC) in which the terms associated with the β-function are absorbed into the scale of the running coupling at each perturbative order; its predictions are scheme and scale independent at every finite order. The other approach is the ‘principle of minimum sensitivity’ (PMS), which is based on local RGI; the PMS approach determines the optimal renormalization scale by requiring the slope of the approximant of an observable to vanish. In this paper, we present a detailed comparison of the PMC and PMS procedures by analyzing two physical observables R e+e- and Γ(H\\to b\\bar{b}) up to four-loop order in pQCD. At the four-loop level, the PMC and PMS predictions for both observables agree within small errors with those of conventional scale setting assuming a physically-motivated scale, and each prediction shows small scale dependences. However, the convergence of the pQCD series at high orders, behaves quite differently: the PMC displays the best pQCD convergence since it eliminates divergent renormalon terms; in contrast, the convergence of the PMS prediction is questionable, often even worse than the conventional prediction based on an arbitrary guess for the renormalization scale. PMC predictions also have the property that any residual dependence on the choice of initial scale is highly suppressed even for low-order predictions. Thus the PMC, based on the standard RGI, has a rigorous foundation; it eliminates an unnecessary systematic error for high precision pQCD predictions and can be widely applied to virtually all high-energy hadronic processes, including multi-scale problems.

Optimal Prediction in the Retina and Natural Motion Statistics

NASA Astrophysics Data System (ADS)

Salisbury, Jared M.; Palmer, Stephanie E.

2016-03-01

Almost all behaviors involve making predictions. Whether an organism is trying to catch prey, avoid predators, or simply move through a complex environment, the organism uses the data it collects through its senses to guide its actions by extracting from these data information about the future state of the world. A key aspect of the prediction problem is that not all features of the past sensory input have predictive power, and representing all features of the external sensory world is prohibitively costly both due to space and metabolic constraints. This leads to the hypothesis that neural systems are optimized for prediction. Here we describe theoretical and computational efforts to define and quantify the efficient representation of the predictive information by the brain. Another important feature of the prediction problem is that the physics of the world is diverse enough to contain a wide range of possible statistical ensembles, yet not all inputs are probable. Thus, the brain might not be a generalized predictive machine; it might have evolved to specifically solve the prediction problems most common in the natural environment. This paper summarizes recent results on predictive coding and optimal predictive information in the retina and suggests approaches for quantifying prediction in response to natural motion. Basic statistics of natural movies reveal that general patterns of spatiotemporal correlation are present across a wide range of scenes, though individual differences in motion type may be important for optimal processing of motion in a given ecological niche.

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib

PubMed Central

Sobotta, Svantje; Raue, Andreas; Huang, Xiaoyun; Vanlier, Joep; Jünger, Anja; Bohl, Sebastian; Albrecht, Ute; Hahnel, Maximilian J.; Wolf, Stephanie; Mueller, Nikola S.; D'Alessandro, Lorenza A.; Mueller-Bohl, Stephanie; Boehm, Martin E.; Lucarelli, Philippe; Bonefas, Sandra; Damm, Georg; Seehofer, Daniel; Lehmann, Wolf D.; Rose-John, Stefan; van der Hoeven, Frank; Gretz, Norbert; Theis, Fabian J.; Ehlting, Christian; Bode, Johannes G.; Timmer, Jens; Schilling, Marcel; Klingmüller, Ursula

2017-01-01

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines. PMID:29062282

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib.

PubMed

Sobotta, Svantje; Raue, Andreas; Huang, Xiaoyun; Vanlier, Joep; Jünger, Anja; Bohl, Sebastian; Albrecht, Ute; Hahnel, Maximilian J; Wolf, Stephanie; Mueller, Nikola S; D'Alessandro, Lorenza A; Mueller-Bohl, Stephanie; Boehm, Martin E; Lucarelli, Philippe; Bonefas, Sandra; Damm, Georg; Seehofer, Daniel; Lehmann, Wolf D; Rose-John, Stefan; van der Hoeven, Frank; Gretz, Norbert; Theis, Fabian J; Ehlting, Christian; Bode, Johannes G; Timmer, Jens; Schilling, Marcel; Klingmüller, Ursula

2017-01-01

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.

Identical anthropometric characteristics of impaired fasting glucose combined with impaired glucose tolerance and newly diagnosed type 2 diabetes: anthropometric indicators to predict hyperglycaemia in a community-based prospective cohort study in southwest China

PubMed Central

Zhang, Fang; Wan, Qin; Cao, Hongyi; Tang, Lizhi; Li, Daigang; Lü, Qingguo; Yan, Zhe; Li, Jing; Yang, Qiu; Zhang, Yuwei; Tong, Nanwei

2018-01-01

Objectives To assess the anthropometric characteristics of normoglycaemic individuals who subsequently developed hyperglycaemia, and to evaluate the validity of these measures to predict prediabetes and diabetes. Design A community-based prospective cohort study. Participants In total, 1885 residents with euglycaemia at baseline from six communities were enrolled. Setting Sichuan, southwest China. Primary outcome measures The incidences of prediabetes and diabetes were the primary outcomes. Methods The waist-to-height ratio (WHtR), body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) of all participants were measured at baseline and during follow-up. A 75 g glucose oral glucose tolerance test was conducted at each survey. Results During a median of 3.00 (IQR: 2.92–4.17) years follow-up, the cumulative incidence of isolated impaired fasting glucose (IFG), isolated impaired glucose tolerance (IGT), IFG combined with IGT (IFG+IGT), and newly diagnosed diabetes mellitus (NDDM) were 8.44%, 18.14%, 8.06% and 13.79%, respectively. WHtR, BMI, WC and WHR were significantly different among subjects who subsequently progressed to isolated IFG or IGT, IFG+IGT or NDDM (p<0.05). The anthropometric characteristics of IFG+IGT subjects were similar to those of the NDDM population (p>0.005). All the baseline anthropometric measurements were useful for the prediction of future prediabetes and NDDM (p<0.05). The optimal thresholds for the four measurements were calculated for the prediction of hyperglycaemia, with a WHtR value of 0.52 performing best to identify isolated IFG or IGT, IFG+IGT and NDDM. Conclusions Anthropometric measures, especially WHtR, could be used to predict hyperglycaemia 3 years in advance. Distinct from isolated IFG and IGT, the individuals who developed combined IFG+IGT had identical anthropometric profiles to those who progressed to NDDM. PMID:29743321

The influence of sarcopenia on survival and surgical complications in ovarian cancer patients undergoing primary debulking surgery.

PubMed

Rutten, I J G; Ubachs, J; Kruitwagen, R F P M; van Dijk, D P J; Beets-Tan, R G H; Massuger, L F A G; Olde Damink, S W M; Van Gorp, T

2017-04-01

Sarcopenia, severe skeletal muscle loss, has been identified as a prognostic factor in various malignancies. This study aims to investigate whether sarcopenia is associated with overall survival (OS) and surgical complications in patients with advanced ovarian cancer undergoing primary debulking surgery (PDS). Ovarian cancer patients (n = 216) treated with PDS were enrolled retrospectively. Total skeletal muscle surface area was measured on axial computed tomography at the level of the third lumbar vertebra. Optimum stratification was used to find the optimal skeletal muscle index cut-off to define sarcopenia (≤38.73 cm 2 /m 2 ). Cox-regression and Kaplan-Meier analysis were used to analyse the relationship between sarcopenia and OS. The effect of sarcopenia on the development of major surgical complications was studied with logistic regression. Kaplan-Meier analysis showed a significant survival disadvantage for patients with sarcopenia compared to patients without sarcopenia (p = 0.010). Sarcopenia univariably predicted OS (HR 1.536 (95% CI 1.105-2.134), p = 0.011) but was not significant in multivariable Cox-regression analysis (HR 1.362 (95% CI 0.968-1.916), p = 0.076). Significant predictors for OS in multivariable Cox-regression analysis were complete PDS, treatment in a specialised centre and the development of major complications. Sarcopenia was not predictive of major complications. Sarcopenia was not predictive of OS or major complications in ovarian cancer patients undergoing primary debulking surgery. However a strong trend towards a survival disadvantage for patients with sarcopenia was seen. Future prospective studies should focus on interventions to prevent or reverse sarcopenia and possibly increase ovarian cancer survival. Complete cytoreduction remains the strongest predictor of ovarian cancer survival. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Measuring the influence of a mental health training module on the therapeutic optimism of advanced nurse practitioner students in the United Kingdom.

PubMed

Hemingway, Steve; Rogers, Melanie; Elsom, Stephen

2014-03-01

To evaluate the influence of a mental health training module on the therapeutic optimism of advanced nurse practitioner (ANP) students in primary care (family practice). Three cohorts of ANPs who undertook a Mental Health Problems in Primary Care Module as part of their MSc ANP (primary care) run by the University of Huddersfield completed the Elsom Therapeutic Optimism Scale (ETOS), in a pre- and postformat. The ETOS is a 10-item, self-administered scale, which has been used to evaluate therapeutic optimism previously in mental health professionals. All three cohorts who completed the scale showed an improvement in their therapeutic optimism scores. With stigma having such a detrimental effect for people diagnosed with a mental health problem, ANPs who are more mental health literate facilitated by education and training in turn facilitates them to have the skills and confidence to engage and inspire hope for the person diagnosed with mental health problems. ©2013 The Author(s) ©2013 American Association of Nurse Practitioners.

Less than Optimal Parenting Strategies Predict Maternal Low-Level Depression beyond that of Child Transgressions

ERIC Educational Resources Information Center

Lagace-Seguin, Daniel G.; d'Entremont, Marc-Robert L.

2006-01-01

The relationship between less than optimal parenting styles, child transgressions and maternal depression were examined. It was predicted that variations in parenting styles would predict maternal depression over and above child transgressions. The present study involved approximately 68 children, their mothers and their preschool teachers.…

ERic Acute StrokE Recanalization: A study using predictive analytics to assess a new device for mechanical thrombectomy.

PubMed

Siemonsen, Susanne; Forkert, Nils D; Bernhardt, Martina; Thomalla, Götz; Bendszus, Martin; Fiehler, Jens

2017-08-01

Aim and hypothesis Using a new study design, we investigate whether next-generation mechanical thrombectomy devices improve clinical outcomes in ischemic stroke patients. We hypothesize that this new methodology is superior to intravenous tissue plasminogen activator therapy alone. Methods and design ERic Acute StrokE Recanalization is an investigator-initiated prospective single-arm, multicenter, controlled, open label study to compare the safety and effectiveness of a new recanalization device and distal access catheter in acute ischemic stroke patients with symptoms attributable to acute ischemic stroke and vessel occlusion of the internal cerebral artery or middle cerebral artery. Study outcome The primary effectiveness endpoint is the volume of saved tissue. Volume of saved tissue is defined as difference of the actual infarct volume and the brain volume that is predicted to develop infarction by using an optimized high-level machine learning model that is trained on data from a historical cohort treated with IV tissue plasminogen activator. Sample size estimates Based on own preliminary data, 45 patients fulfilling all inclusion criteria need to complete the study to show an efficacy >38% with a power of 80% and a one-sided alpha error risk of 0.05 (based on a one sample t-test). Discussion ERic Acute StrokE Recanalization is the first prospective study in interventional stroke therapy to use predictive analytics as primary and secondary endpoint. Such trial design cannot replace randomized controlled trials with clinical endpoints. However, ERic Acute StrokE Recanalization could serve as an exemplary trial design for evaluating nonpivotal neurovascular interventions.

A Lymph Node Ratio of 10% Is Predictive of Survival in Stage III Colon Cancer: A French Regional Study

PubMed Central

Sabbagh, Charles; Mauvais, François; Cosse, Cyril; Rebibo, Lionel; Joly, Jean-Paul; Dromer, Didier; Aubert, Christine; Carton, Sophie; Dron, Bernard; Dadamessi, Innocenti; Maes, Bernard; Perrier, Guillaume; Manaouil, David; Fontaine, Jean-François; Gozy, Michel; Panis, Xavier; Foncelle, Pierre Henri; de Fresnoy, Hugues; Leroux, Fabien; Vaneslander, Pierre; Ghighi, Caroline; Regimbeau, Jean-Marc

2014-01-01

Lymph node ratio (LNR) (positive lymph nodes/sampled lymph nodes) is predictive of survival in colon cancer. The aim of the present study was to validate the LNR as a prognostic factor and to determine the optimum LNR cutoff for distinguishing between “good prognosis” and “poor prognosis” colon cancer patients. From January 2003 to December 2007, patients with TNM stage III colon cancer operated on with at least of 3 years of follow-up and not lost to follow-up were included in this retrospective study. The two primary endpoints were 3-year overall survival (OS) and disease-free survival (DFS) as a function of the LNR groups and the cutoff. One hundred seventy-eight patients were included. There was no correlation between the LNR group and 3-year OS (P = 0.06) and a significant correlation between the LNR group and 3-year DFS (P = 0.03). The optimal LNR cutoff of 10% was significantly correlated with 3-year OS (P = 0.02) and DFS (P = 0.02). The LNR was not an accurate prognostic factor when fewer than 12 lymph nodes were sampled. Clarification and simplification of the LNR classification are prerequisites for use of this system in randomized control trials. An LNR of 10% appears to be the optimal cutoff. PMID:25058763

A lymph node ratio of 10% is predictive of survival in stage III colon cancer: a French regional study.

PubMed

Sabbagh, Charles; Mauvais, François; Cosse, Cyril; Rebibo, Lionel; Joly, Jean-Paul; Dromer, Didier; Aubert, Christine; Carton, Sophie; Dron, Bernard; Dadamessi, Innocenti; Maes, Bernard; Perrier, Guillaume; Manaouil, David; Fontaine, Jean-François; Gozy, Michel; Panis, Xavier; Foncelle, Pierre Henri; de Fresnoy, Hugues; Leroux, Fabien; Vaneslander, Pierre; Ghighi, Caroline; Regimbeau, Jean-Marc

2014-01-01

Lymph node ratio (LNR) (positive lymph nodes/sampled lymph nodes) is predictive of survival in colon cancer. The aim of the present study was to validate the LNR as a prognostic factor and to determine the optimum LNR cutoff for distinguishing between "good prognosis" and "poor prognosis" colon cancer patients. From January 2003 to December 2007, patients with TNM stage III colon cancer operated on with at least of 3 years of follow-up and not lost to follow-up were included in this retrospective study. The two primary endpoints were 3-year overall survival (OS) and disease-free survival (DFS) as a function of the LNR groups and the cutoff. One hundred seventy-eight patients were included. There was no correlation between the LNR group and 3-year OS (P=0.06) and a significant correlation between the LNR group and 3-year DFS (P=0.03). The optimal LNR cutoff of 10% was significantly correlated with 3-year OS (P=0.02) and DFS (P=0.02). The LNR was not an accurate prognostic factor when fewer than 12 lymph nodes were sampled. Clarification and simplification of the LNR classification are prerequisites for use of this system in randomized control trials. An LNR of 10% appears to be the optimal cutoff.

Essays on the economics of natural gas pipelines

NASA Astrophysics Data System (ADS)

Oliver, Matthew E.

The natural gas pipeline transportation industry is comprised of a primary market and a secondary market. In the primary market, pipelines sell 'firm' transport capacity contracts to gas traders, local distribution companies, and other parties. The (per unit) secondary market value of transport is rarely comparable to the regulated primary market two-part tariff. When and where available capacity in the secondary market is scarce, its value can far exceed the primary market tariffs paid by firm contract holders, generating scarcity rents. The following essays demonstrate that this phenomenon has predictable effects on natural gas spot prices, firm capacity reservations, the pipeline's capacity construction and expansion decisions, and the economic welfare of producers and consumers at the market hubs connected by the pipeline. Chapter 1 provides a theoretical framework for understanding how pipeline congestion affects natural gas spot prices within the context of the current regulatory environment, and empirically quantifies this effect over a specific regional pipeline network. As available pipeline capacity over a given route connecting two hubs becomes scarce, the spot prices for gas at the hubs are driven apart---a phenomenon indicative of some market friction that inhibits the ability of spot price arbitrage to fully integrate the two prices, undermining economic efficiency. The theoretical component of Chapter 1 illuminates a potential source of this friction: the deregulated structure of the secondary market for gas transportation services. To support and quantify the predictions of the theoretical model, the empirical component demonstrates that the effect of congestion on the secondary market value of transport---the key factor in driving apart spot prices---can be quite strong. Coefficient estimates indicate that dramatic increases in transport costs are likely to result from marginal increases in congestion. This result has important implications because upward pressure on the demand for pipeline transport is imminent, owing to the recent surge in available natural gas reserve estimates and the expected growth in consumption demand over the foreseeable future. Chapter 2 derives optimality conditions for capacity and two-part tariff structure in the primary market, when demand for the shipping service in the secondary market is stochastic but stationary. Based on their individual demand distributions, the overall demand distribution, and the two-part tariff structure, natural gas traders reserve firm capacity contracts over a given transportation route served by a single pipeline. The traders' individual demands sum to the aggregate demand for primary market capacity reservations over the route. The aggregate capacity reservation demand function then feeds into the pipeline's profit-maximization problem, which for comparison is analyzed under three alternative regulatory regimes: unregulated monopoly, Ramsey second-best solution, and rate-of-return regulation. For each case, the optimality conditions are parameterized and solved numerically. Results demonstrate that optimal capacity under rate-of-return regulation is lower than what would occur under a Ramsey second-best solution, exacerbating the congestion issue discussed in Chapter 1, and ultimately reducing overall social welfare. Chapter 3 examines a natural gas trader's willingness to contract expanded capacity over a given pipeline route, when demand in the secondary market is stochastic and increasing over time. A discrete time and scale framework provides the template for analyzing the trader's behavior and solving for his optimal expansion contracting strategy through time. Willingness to contract in any period hinges on the trade-off between the value of the option to contract expanded capacity (now or in a future period), and the 'spread option' value of utilizing contracted capacity to ship gas. The rate-of-return regulated primary market two-part tariff and the unregulated secondary market value of transport each affect these option values, but the latter provides a strong incentive to the trader to both delay and suppress his willingness to contract expanded capacity relative to the demand for gas shipping services. As a result, the pipeline is chronically congested. Relating this to the results of Chapters 1 and 2, there are likely to be strong welfare effects associated with this behavior. (Abstract shortened by UMI.)

Integrating prediction, provenance, and optimization into high energy workflows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schram, M.; Bansal, V.; Friese, R. D.

We propose a novel approach for efficient execution of workflows on distributed resources. The key components of this framework include: performance modeling to quantitatively predict workflow component behavior; optimization-based scheduling such as choosing an optimal subset of resources to meet demand and assignment of tasks to resources; distributed I/O optimizations such as prefetching; and provenance methods for collecting performance data. In preliminary results, these techniques improve throughput on a small Belle II workflow by 20%.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

NASA Astrophysics Data System (ADS)

Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

2018-03-01

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

Role of muscle pulleys in producing eye position-dependence in the angular vestibuloocular reflex: a model-based study

NASA Technical Reports Server (NTRS)

Thurtell, M. J.; Kunin, M.; Raphan, T.; Wall, C. C. (Principal Investigator)

2000-01-01

It is well established that the head and eye velocity axes do not always align during compensatory vestibular slow phases. It has been shown that the eye velocity axis systematically tilts away from the head velocity axis in a manner that is dependent on eye-in-head position. The mechanisms responsible for producing these axis tilts are unclear. In this model-based study, we aimed to determine whether muscle pulleys could be involved in bringing about these phenomena. The model presented incorporates semicircular canals, central vestibular pathways, and an ocular motor plant with pulleys. The pulleys were modeled so that they brought about a rotation of the torque axes of the extraocular muscles that was a fraction of the angle of eye deviation from primary position. The degree to which the pulleys rotated the torque axes was altered by means of a pulley coefficient. Model input was head velocity and initial eye position data from passive and active yaw head impulses with fixation at 0 degrees, 20 degrees up and 20 degrees down, obtained from a previous experiment. The optimal pulley coefficient required to fit the data was determined by calculating the mean square error between data and model predictions of torsional eye velocity. For active head impulses, the optimal pulley coefficient varied considerably between subjects. The median optimal pulley coefficient was found to be 0.5, the pulley coefficient required for producing saccades that perfectly obey Listing's law when using a two-dimensional saccadic pulse signal. The model predicted the direction of the axis tilts observed in response to passive head impulses from 50 ms after onset. During passive head impulses, the median optimal pulley coefficient was found to be 0.21, when roll gain was fixed at 0.7. The model did not accurately predict the alignment of the eye and head velocity axes that was observed early in the response to passive head impulses. We found that this alignment could be well predicted if the roll gain of the angular vestibuloocular reflex was modified during the initial period of the response, while pulley coefficient was maintained at 0.5. Hence a roll gain modification allows stabilization of the retinal image without requiring a change in the pulley effect. Our results therefore indicate that the eye position-dependent velocity axis tilts could arise due to the effects of the pulleys and that a roll gain modification in the central vestibular structures may be responsible for countering the pulley effect.

Neural Network Prediction of New Aircraft Design Coefficients

NASA Technical Reports Server (NTRS)

Norgaard, Magnus; Jorgensen, Charles C.; Ross, James C.

1997-01-01

This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using a hybrid neural network optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, and flap schedules. For validation, the tool was tested on a 55% scale model of the USAF/NASA Subsonic High Alpha Research Concept aircraft (SHARC). Four different networks were trained to predict coefficients of lift, drag, moment of inertia, and lift drag ratio (C(sub L), C(sub D), C(sub M), and L/D) from angle of attack and flap settings. The latter network was then used to determine an overall optimal flap setting and for finding optimal flap schedules.

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

NASA Astrophysics Data System (ADS)

Sun, Xinyao; Wang, Xue; Wu, Jiangwei; Liu, Youda

2014-05-01

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Happiness as a motivator: positive affect predicts primary control striving for career and educational goals.

PubMed

Haase, Claudia M; Poulin, Michael J; Heckhausen, Jutta

2012-08-01

What motivates individuals to invest time and effort and overcome obstacles (i.e., strive for primary control) when pursuing important goals? We propose that positive affect predicts primary control striving for career and educational goals, and we explore the mediating role of control beliefs. In Study 1, positive affect predicted primary control striving for career goals in a two-wave longitudinal study of a U.S. sample. In Study 2, positive affect predicted primary control striving for career and educational goals and objective career outcomes in a six-wave longitudinal study of a German sample. Control beliefs partially mediated the longitudinal associations with primary control striving. Thus, when individuals experience positive affect, they become more motivated to invest time and effort, and overcome obstacles when pursuing their goals, in part because they believe they have more control over attaining their goals.

Habitus and Flow in Primary School Musical Practice: Relations between Family Musical Cultural Capital, Optimal Experience and Music Participation

ERIC Educational Resources Information Center

Valenzuela, Rafael; Codina, Nuria

2014-01-01

Based on Bourdieu's idea that cultural capital is strongly related to family context, we describe the relations between family musical cultural capital and optimal experience during compulsory primary school musical practice. We analyse whether children from families with higher levels of musical cultural capital, and specifically with regard to…

Optimism and recovery after acute coronary syndrome: a clinical cohort study.

PubMed

Ronaldson, Amy; Molloy, Gerard J; Wikman, Anna; Poole, Lydia; Kaski, Juan-Carlos; Steptoe, Andrew

2015-04-01

Optimism is associated with reduced cardiovascular mortality, but its impact on recovery after acute coronary syndrome (ACS) is poorly understood. We hypothesized that greater optimism would lead to more effective physical and emotional adaptation after ACS and would buffer the impact of persistent depressive symptoms on clinical outcomes. This prospective observational clinical study took place in an urban general hospital and involved 369 patients admitted with a documented ACS. Optimism was assessed with a standardized questionnaire. The main outcomes were physical health status, depressive symptoms, smoking, physical activity, and fruit and vegetable consumption measured 12 months after ACS, and composite major adverse cardiac events (cardiovascular death, readmission with reinfarction or unstable angina, and coronary artery bypass graft surgery) assessed over an average of 45.7 months. We found that optimism predicted better physical health status 12 months after ACS independently of baseline physical health, age, sex, ethnicity, social deprivation, and clinical risk factors (B = 0.65, 95% confidence interval [CI] = 0.10-1.20). Greater optimism also predicted reduced risk of depressive symptoms (odds ratio = 0.82, 95% CI = 0.74-0.90), more smoking cessation, and more fruit and vegetable consumption at 12 months. Persistent depressive symptoms 12 months after ACS predicted major adverse cardiac events over subsequent years (odds ratio = 2.56, 95% CI = 1.16-5.67), but only among individuals low in optimism (optimism × depression interaction; p = .014). Optimism predicts better physical and emotional health after ACS. Measuring optimism may help identify individuals at risk. Pessimistic outlooks can be modified, potentially leading to improved recovery after major cardiac events.

Performing aggressive code optimization with an ability to rollback changes made by the aggressive optimizations

DOEpatents

Gschwind, Michael K

2013-07-23

Mechanisms for aggressively optimizing computer code are provided. With these mechanisms, a compiler determines an optimization to apply to a portion of source code and determines if the optimization as applied to the portion of source code will result in unsafe optimized code that introduces a new source of exceptions being generated by the optimized code. In response to a determination that the optimization is an unsafe optimization, the compiler generates an aggressively compiled code version, in which the unsafe optimization is applied, and a conservatively compiled code version in which the unsafe optimization is not applied. The compiler stores both versions and provides them for execution. Mechanisms are provided for switching between these versions during execution in the event of a failure of the aggressively compiled code version. Moreover, predictive mechanisms are provided for predicting whether such a failure is likely.

Optimal Futility Interim Design: A Predictive Probability of Success Approach with Time-to-Event Endpoint.

PubMed

Tang, Zhongwen

2015-01-01

An analytical way to compute predictive probability of success (PPOS) together with credible interval at interim analysis (IA) is developed for big clinical trials with time-to-event endpoints. The method takes account of the fixed data up to IA, the amount of uncertainty in future data, and uncertainty about parameters. Predictive power is a special type of PPOS. The result is confirmed by simulation. An optimal design is proposed by finding optimal combination of analysis time and futility cutoff based on some PPOS criteria.

Perceptual precision of passive body tilt is consistent with statistically optimal cue integration

PubMed Central

Karmali, Faisal; Nicoucar, Keyvan; Merfeld, Daniel M.

2017-01-01

When making perceptual decisions, humans have been shown to optimally integrate independent noisy multisensory information, matching maximum-likelihood (ML) limits. Such ML estimators provide a theoretic limit to perceptual precision (i.e., minimal thresholds). However, how the brain combines two interacting (i.e., not independent) sensory cues remains an open question. To study the precision achieved when combining interacting sensory signals, we measured perceptual roll tilt and roll rotation thresholds between 0 and 5 Hz in six normal human subjects. Primary results show that roll tilt thresholds between 0.2 and 0.5 Hz were significantly lower than predicted by a ML estimator that includes only vestibular contributions that do not interact. In this paper, we show how other cues (e.g., somatosensation) and an internal representation of sensory and body dynamics might independently contribute to the observed performance enhancement. In short, a Kalman filter was combined with an ML estimator to match human performance, whereas the potential contribution of nonvestibular cues was assessed using published bilateral loss patient data. Our results show that a Kalman filter model including previously proven canal-otolith interactions alone (without nonvestibular cues) can explain the observed performance enhancements as can a model that includes nonvestibular contributions. NEW & NOTEWORTHY We found that human whole body self-motion direction-recognition thresholds measured during dynamic roll tilts were significantly lower than those predicted by a conventional maximum-likelihood weighting of the roll angular velocity and quasistatic roll tilt cues. Here, we show that two models can each match this “apparent” better-than-optimal performance: 1) inclusion of a somatosensory contribution and 2) inclusion of a dynamic sensory interaction between canal and otolith cues via a Kalman filter model. PMID:28179477

Predictability of a Coupled Model of ENSO Using Singular Vector Analysis: Optimal Growth and Forecast Skill.

NASA Astrophysics Data System (ADS)

Xue, Yan

The optimal growth and its relationship with the forecast skill of the Zebiak and Cane model are studied using a simple statistical model best fit to the original nonlinear model and local linear tangent models about idealized climatic states (the mean background and ENSO cycles in a long model run), and the actual forecast states, including two sets of runs using two different initialization procedures. The seasonally varying Markov model best fit to a suite of 3-year forecasts in a reduced EOF space (18 EOFs) fits the original nonlinear model reasonably well and has comparable or better forecast skill. The initial error growth in a linear evolution operator A is governed by the eigenvalues of A^{T}A, and the square roots of eigenvalues and eigenvectors of A^{T}A are named singular values and singular vectors. One dominant growing singular vector is found, and the optimal 6 month growth rate is largest for a (boreal) spring start and smallest for a fall start. Most of the variation in the optimal growth rate of the two forecasts is seasonal, attributable to the seasonal variations in the mean background, except that in the cold events it is substantially suppressed. It is found that the mean background (zero anomaly) is the most unstable state, and the "forecast IC states" are more unstable than the "coupled model states". One dominant growing singular vector is found, characterized by north-south and east -west dipoles, convergent winds on the equator in the eastern Pacific and a deepened thermocline in the whole equatorial belt. This singular vector is insensitive to initial time and optimization time, but its final pattern is a strong function of initial states. The ENSO system is inherently unpredictable for the dominant singular vector can amplify 5-fold to 24-fold in 6 months and evolve into the large scales characteristic of ENSO. However, the inherent ENSO predictability is only a secondary factor, while the mismatches between the model and data is a primary factor controlling the current forecast skill.

Optimization and Modeling of Noise Reduction for Turbulent Jets with Induced Asymmetry

NASA Astrophysics Data System (ADS)

Rostamimonjezi, Sara

This project relates to the development of next-generation high-speed aircraft that are efficient and environmentally compliant. The emphasis of the research is on reducing noise from high-performance engines that will power these aircraft. A strong component of engine noise is jet mixing noise that comes from the turbulent mixing process between the high-speed exhaust flow of the engine and the atmosphere. The fan flow deflection method (FFD) suppresses jet noise by deflecting the fan stream downward, by a few degrees, with respect to the core stream. This reduces the convective Mach number of the primary shear layer and turbulent kinetic energy in the downward direction and therefore reduces the noise emitted towards the ground. The redistribution of the fan stream is achieved with inserting airfoil-shaped vanes inside the fan duct. Aerodynamic optimization of FFD has been done by Dr. Juntao Xiong using a computational fluid dynamics code to maximize reduction of noise perceived by the community while minimizing aerodynamic losses. The optimal vane airfoils are used in a parametric experimental study of 50 4-vane deflector configurations. The vane chord length, angle of attack, and azimuthal location are the parameters studied in acoustic optimization. The best vane configuration yields a reduction in cumulative (downward + sideline) effective perceived noise level (EPNL) of 5.3 dB. The optimization study underscores the sensitivity of FFD to deflector parameters and the need for careful design in the practical implementation of this noise reduction approach. An analytical model based on Reynolds Averaged Navier Stokes (RANS) and acoustic analogy is developed to predict the spectral changes from a known baseline in the direction of peak emission. A generalized form for space-time correlation is introduced that allows shapes beyond the traditional exponential forms. Azimuthal directivity based on the wavepacket model of jet noise is integrated with the acoustic analogy model. A physics-based definition of convective Mach number is proposed. The predicted noise reduction is in reasonable agreement with the experiments. The study underscores the importance of a proper definition of convective Mach number when modeling noise in the direction of peak emission.

Home | BEopt

Science.gov Websites

BEopt - Building Energy Optimization BEopt NREL - National Renewable Energy Laboratory Primary Energy Optimization) software provides capabilities to evaluate residential building designs and identify sequential search optimization technique used by BEopt: Finds minimum-cost building designs at different

Low abundance of the matrix arm of complex I in mitochondria predicts longevity in mice

PubMed Central

Miwa, Satomi; Jow, Howsun; Baty, Karen; Johnson, Amy; Czapiewski, Rafal; Saretzki, Gabriele; Treumann, Achim; von Zglinicki, Thomas

2014-01-01

Mitochondrial function is an important determinant of the ageing process; however, the mitochondrial properties that enable longevity are not well understood. Here we show that optimal assembly of mitochondrial complex I predicts longevity in mice. Using an unbiased high-coverage high-confidence approach, we demonstrate that electron transport chain proteins, especially the matrix arm subunits of complex I, are decreased in young long-living mice, which is associated with improved complex I assembly, higher complex I-linked state 3 oxygen consumption rates and decreased superoxide production, whereas the opposite is seen in old mice. Disruption of complex I assembly reduces oxidative metabolism with concomitant increase in mitochondrial superoxide production. This is rescued by knockdown of the mitochondrial chaperone, prohibitin. Disrupted complex I assembly causes premature senescence in primary cells. We propose that lower abundance of free catalytic complex I components supports complex I assembly, efficacy of substrate utilization and minimal ROS production, enabling enhanced longevity. PMID:24815183

Kindergarten predictors of second versus eighth grade reading comprehension impairments.

PubMed

Adlof, Suzanne M; Catts, Hugh W; Lee, Jaehoon

2010-01-01

Multiple studies have shown that kindergarten measures of phonological awareness and alphabet knowledge are good predictors of reading achievement in the primary grades. However, less attention has been given to the early predictors of later reading achievement. This study used a modified best-subsets variable-selection technique to examine kindergarten predictors of early versus later reading comprehension impairments. Participants included 433 children involved in a longitudinal study of language and reading development. The kindergarten test battery assessed various language skills in addition to phonological awareness, alphabet knowledge, naming speed, and nonverbal cognitive ability. Reading comprehension was assessed in second and eighth grades. Results indicated that different combinations of variables were required to optimally predict second versus eighth grade reading impairments. Although some variables effectively predicted reading impairments in both grades, their relative contributions shifted over time. These results are discussed in light of the changing nature of reading comprehension over time. Further research will help to improve the early identification of later reading disabilities.

The bias of the log power spectrum for discrete surveys

NASA Astrophysics Data System (ADS)

Repp, Andrew; Szapudi, István

2018-03-01

A primary goal of galaxy surveys is to tighten constraints on cosmological parameters, and the power spectrum P(k) is the standard means of doing so. However, at translinear scales P(k) is blind to much of these surveys' information - information which the log density power spectrum recovers. For discrete fields (such as the galaxy density), A* denotes the statistic analogous to the log density: A* is a `sufficient statistic' in that its power spectrum (and mean) capture virtually all of a discrete survey's information. However, the power spectrum of A* is biased with respect to the corresponding log spectrum for continuous fields, and to use P_{A^*}(k) to constrain the values of cosmological parameters, we require some means of predicting this bias. Here, we present a prescription for doing so; for Euclid-like surveys (with cubical cells 16h-1 Mpc across) our bias prescription's error is less than 3 per cent. This prediction will facilitate optimal utilization of the information in future galaxy surveys.

Stationary-phase optimized selectivity liquid chromatography: development of a linear gradient prediction algorithm.

PubMed

De Beer, Maarten; Lynen, Fréderic; Chen, Kai; Ferguson, Paul; Hanna-Brown, Melissa; Sandra, Pat

2010-03-01

Stationary-phase optimized selectivity liquid chromatography (SOS-LC) is a tool in reversed-phase LC (RP-LC) to optimize the selectivity for a given separation by combining stationary phases in a multisegment column. The presently (commercially) available SOS-LC optimization procedure and algorithm are only applicable to isocratic analyses. Step gradient SOS-LC has been developed, but this is still not very elegant for the analysis of complex mixtures composed of components covering a broad hydrophobicity range. A linear gradient prediction algorithm has been developed allowing one to apply SOS-LC as a generic RP-LC optimization method. The algorithm allows operation in isocratic, stepwise, and linear gradient run modes. The features of SOS-LC in the linear gradient mode are demonstrated by means of a mixture of 13 steroids, whereby baseline separation is predicted and experimentally demonstrated.

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Sootblowing optimization for improved boiler performance

DOEpatents

James, John Robert; McDermott, John; Piche, Stephen; Pickard, Fred; Parikh, Neel J.

2012-12-25

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

Sootblowing optimization for improved boiler performance

DOEpatents

James, John Robert; McDermott, John; Piche, Stephen; Pickard, Fred; Parikh, Neel J

2013-07-30

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

Comparing spatial regression to random forests for large ...

EPA Pesticide Factsheets

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

Development and validation of a nomogram to estimate the pretest probability of cancer in Chinese patients with solid solitary pulmonary nodules: A multi-institutional study.

PubMed

She, Yunlang; Zhao, Lilan; Dai, Chenyang; Ren, Yijiu; Jiang, Gening; Xie, Huikang; Zhu, Huiyuan; Sun, Xiwen; Yang, Ping; Chen, Yongbing; Shi, Shunbin; Shi, Weirong; Yu, Bing; Xie, Dong; Chen, Chang

2017-11-01

To develop and validate a nomogram to estimate the pretest probability of malignancy in Chinese patients with solid solitary pulmonary nodule (SPN). A primary cohort of 1798 patients with pathologically confirmed solid SPNs after surgery was retrospectively studied at five institutions from January 2014 to December 2015. A nomogram based on independent prediction factors of malignant solid SPN was developed. Predictive performance also was evaluated using the calibration curve and the area under the receiver operating characteristic curve (AUC). The mean age of the cohort was 58.9 ± 10.7 years. In univariate and multivariate analysis, age; history of cancer; the log base 10 transformations of serum carcinoembryonic antigen value; nodule diameter; the presence of spiculation, pleural indentation, and calcification remained the predictive factors of malignancy. A nomogram was developed, and the AUC value (0.85; 95%CI, 0.83-0.88) was significantly higher than other three models. The calibration cure showed optimal agreement between the malignant probability as predicted by nomogram and the actual probability. We developed and validated a nomogram that can estimate the pretest probability of malignant solid SPNs, which can assist clinical physicians to select and interpret the results of subsequent diagnostic tests. © 2017 Wiley Periodicals, Inc.

Optimal design criteria - prediction vs. parameter estimation

NASA Astrophysics Data System (ADS)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Age-Related Differences in Goals: Testing Predictions from Selection, Optimization, and Compensation Theory and Socioemotional Selectivity Theory

ERIC Educational Resources Information Center

Penningroth, Suzanna L.; Scott, Walter D.

2012-01-01

Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two…

Improved detection of hereditary haemochromatosis.

PubMed

Ogilvie, Catherine; Gaffney, Dairena; Murray, Heather; Kerry, Andrew; Haig, Caroline; Spooner, Richard; Fitzsimons, Edward J

2015-03-01

There is high prevalence of hereditary haemochromatosis (HH) in North European populations, yet the diagnosis is often delayed or missed in primary care. Primary care physicians frequently request serum ferritin (SF) estimation but appear uncertain as how to investigate patients with raised SF values. Our aim was to develop a laboratory algorithm with high predictive value for the diagnosis of HH in patients from primary care with raised SF values. Transferrin saturation (Tsat) was measured on SF samples sent from primary care; 1657 male and 2077 female patients age ≥ 30 years with SF ≥ 200 μg/L. HFE genotyping was performed on all 878 male and 867 female patients with Tsat >30%. This study identified 402 (206 men; 196 women) C282Y carriers and 132 (58 men; 74 women) C282Y homozygotes. Optimal limits for combined SF and Tsat values for HH recognition were established. The detection rate for homozygous C282Y HH for male patients with both SF ≥ 300 μg/L and Tsat >50% was 18.8% (52/272) and 16.3% (68/415) for female patients with both SF ≥ 200 μg/L and Tsat >40%. The large number of SF requests received from primary care should be used as a resource to improve the diagnosis of HH in areas of high prevalence. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Ternary isocratic mobile phase optimization utilizing resolution Design Space based on retention time and peak width modeling.

PubMed

Kawabe, Takefumi; Tomitsuka, Toshiaki; Kajiro, Toshi; Kishi, Naoyuki; Toyo'oka, Toshimasa

2013-01-18

An optimization procedure of ternary isocratic mobile phase composition in the HPLC method using a statistical prediction model and visualization technique is described. In this report, two prediction models were first evaluated to obtain reliable prediction results. The retention time prediction model was constructed by modification from past respectable knowledge of retention modeling against ternary solvent strength changes. An excellent correlation between observed and predicted retention time was given in various kinds of pharmaceutical compounds by the multiple regression modeling of solvent strength parameters. The peak width of half height prediction model employed polynomial fitting of the retention time, because a linear relationship between the peak width of half height and the retention time was not obtained even after taking into account the contribution of the extra-column effect based on a moment method. Accurate prediction results were able to be obtained by such model, showing mostly over 0.99 value of correlation coefficient between observed and predicted peak width of half height. Then, a procedure to visualize a resolution Design Space was tried as the secondary challenge. An artificial neural network method was performed to link directly between ternary solvent strength parameters and predicted resolution, which were determined by accurate prediction results of retention time and a peak width of half height, and to visualize appropriate ternary mobile phase compositions as a range of resolution over 1.5 on the contour profile. By using mixtures of similar pharmaceutical compounds in case studies, we verified a possibility of prediction to find the optimal range of condition. Observed chromatographic results on the optimal condition mostly matched with the prediction and the average of difference between observed and predicted resolution were approximately 0.3. This means that enough accuracy for prediction could be achieved by the proposed procedure. Consequently, the procedure to search the optimal range of ternary solvent strength achieving an appropriate separation is provided by using the resolution Design Space based on accurate prediction. Copyright © 2012 Elsevier B.V. All rights reserved.

A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy.

PubMed

Xiong, Dapeng; Zeng, Jianyang; Gong, Haipeng

2017-09-01

Residue-residue contacts are of great value for protein structure prediction, since contact information, especially from those long-range residue pairs, can significantly reduce the complexity of conformational sampling for protein structure prediction in practice. Despite progresses in the past decade on protein targets with abundant homologous sequences, accurate contact prediction for proteins with limited sequence information is still far from satisfaction. Methodologies for these hard targets still need further improvement. We presented a computational program DeepConPred, which includes a pipeline of two novel deep-learning-based methods (DeepCCon and DeepRCon) as well as a contact refinement step, to improve the prediction of long-range residue contacts from primary sequences. When compared with previous prediction approaches, our framework employed an effective scheme to identify optimal and important features for contact prediction, and was only trained with coevolutionary information derived from a limited number of homologous sequences to ensure robustness and usefulness for hard targets. Independent tests showed that 59.33%/49.97%, 64.39%/54.01% and 70.00%/59.81% of the top L/5, top L/10 and top 5 predictions were correct for CASP10/CASP11 proteins, respectively. In general, our algorithm ranked as one of the best methods for CASP targets. All source data and codes are available at http://166.111.152.91/Downloads.html . hgong@tsinghua.edu.cn or zengjy321@tsinghua.edu.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Large-scale linear programs in planning and prediction.

DOT National Transportation Integrated Search

2017-06-01

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

Conceptualizing and measuring illness self-concept: a comparison with self-esteem and optimism in predicting fibromyalgia adjustment.

PubMed

Morea, Jessica M; Friend, Ronald; Bennett, Robert M

2008-12-01

Illness self-concept (ISC), or the extent to which individuals are consumed by their illness, was theoretically described and evaluated with the Illness Self-Concept Scale (ISCS), a new 23-item scale, to predict adjustment in fibromyalgia. To establish convergent and discriminant validity, illness self-concept was compared to self-esteem and optimism in predicting health status, illness intrusiveness, depression, and life satisfaction. The ISCS demonstrated good reliability (alpha = .94; test-retest r = .80) and was a strong predictor of outcomes, even after controlling for optimism or self-esteem. The ISCS predicted unique variance in health-related outcomes; optimism and self-esteem did not, providing construct validation. Illness self-concept may play a significant role in coping with fibromyalgia and may prove useful in the evaluation of other chronic illnesses. (c) 2008 Wiley Periodicals, Inc.

Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation dissipation theorem

NASA Astrophysics Data System (ADS)

Frank, T. D.; Patanarapeelert, K.; Beek, P. J.

2008-05-01

We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted.

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Understanding adolescent response to a technology-based depression prevention program.

PubMed

Gladstone, Tracy; Marko-Holguin, Monika; Henry, Jordan; Fogel, Joshua; Diehl, Anne; Van Voorhees, Benjamin W

2014-01-01

Guided by the Behavioral Vaccine Theory of prevention, this study uses a no-control group design to examine intervention variables that predict favorable changes in depressive symptoms at 6- to 8-week follow-up in at-risk adolescents who participated in a primary care, Internet-based prevention program. Participants included 83 adolescents from primary care settings ages 14 to 21 (M = 17.5, SD = 2.04), 56.2% female, with 41% non-White. Participants completed self-report measures, met with a physician, and then completed a 14-module Internet intervention targeting the prevention of depression. Linear regression models indicated that several intervention factors (duration on website in days, the strength of the relationship with the physician, perceptions of ease of use, and the perceived relevance of the material presented) were significantly associated with greater reductions in depressive symptoms from baseline to follow-up. Automatic negative thoughts significantly mediated the relation between change in depressive symptoms scores and both duration of use and physician relationship. Several intervention variables predicted favorable changes in depressive symptom scores among adolescents who participated in an Internet-based prevention program, and the strength of two of these variables was mediated by automatic negative thoughts. These findings support the importance of cognitive factors in preventing adolescent depression and suggest that modifiable aspects of technology-based intervention experience and relationships should be considered in optimizing intervention design.

Optimization of a Lunar Pallet Lander Reinforcement Structure Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Burt, Adam

2014-01-01

In this paper, a unique system level spacecraft design optimization will be presented. A Genetic Algorithm is used to design the global pattern of the reinforcing structure, while a gradient routine is used to adequately stiffen the sub-structure. The system level structural design includes determining the optimal physical location (and number) of reinforcing beams of a lunar pallet lander deck structure. Design of the substructure includes determining placement of secondary stiffeners and the number of rivets required for assembly.. In this optimization, several considerations are taken into account. The primary objective was to raise the primary natural frequencies of the structure such that the Pallet Lander primary structure does not significantly couple with the launch vehicle. A secondary objective is to determine how to properly stiffen the reinforcing beams so that the beam web resists the shear buckling load imparted by the spacecraft components mounted to the pallet lander deck during launch and landing. A third objective is that the calculated stress does not exceed the allowable strength of the material. These design requirements must be met while, minimizing the overall mass of the spacecraft. The final paper will discuss how the optimization was implemented as well as the results. While driven by optimization algorithms, the primary purpose of this effort was to demonstrate the capability of genetic algorithms to enable design automation in the preliminary design cycle. By developing a routine that can automatically generate designs through the use of Finite Element Analysis, considerable design efficiencies, both in time and overall product, can be obtained over more traditional brute force design methods.

Pattern of tumour growth of the primary colon cancer predicts long-term outcome after resection of liver metastases.

PubMed

Spelt, Lidewij; Sasor, Agata; Ansari, Daniel; Andersson, Roland

2016-10-01

To identify significant predictive factors for overall survival (OS) and disease-free survival (DFS) after liver resection for colon cancer metastases, with special focus on features of the primary colon cancer, such as lymph node ratio (LNR), vascular invasion, and perineural invasion. Patients operated for colonic cancer liver metastases between 2006 and 2014 were included. Details on patient characteristics, the primary colon cancer operation and metastatic disease were collected. Multivariate analysis was performed to select predictive variables for OS and DFS. Median OS and DFS were 67 and 20 months, respectively. 1-, 3- and 5-year OS were 97, 76, and 52%. 1-, 3- and 5-year DFS were 65, 42, and 37%. Multivariate analysis showed LNR to be an independent predictive factor for DFS but not for OS. Other identified predictive factors were vascular and perineural invasion of the primary colon cancer, size of the largest metastasis and severe complications after liver surgery for OS, and perineural invasion, number of liver metastases and preoperative CEA-level for DFS. Traditional N-stage was also considered to be an independent predictive factor for DFS in a separate multivariate analysis. LNR and perineural invasion of the primary colon cancer can be used as a prognostic variable for DFS after a concomitant liver resection for colon cancer metastases. Vascular and perineural invasion of the primary colon cancer are predictive for OS.

Experimental characterization of recurrent ovarian immature teratoma cells after optimal surgery.

PubMed

Tanaka, Tetsuji; Toujima, Saori; Utsunomiya, Tomoko; Yukawa, Kazunori; Umesaki, Naohiko

2008-07-01

Minimal optimal surgery without chemotherapy is often performed for patients with ovarian immature teratoma, which frequently occurs in young women who hope for future pregnancies. If tumors recur after the operation, anticancer drug chemotherapy is often administered, although few studies have highlighted differences between the recurrent and the primary tumor cells. Therefore, we have established experimental animal models of recurrent ovarian immature teratoma cells after optimal surgery and characterized the anticancer drug sensitivity and antigenicity of the recurrent tumors. Surgically-excised tumor cells of a grade II ovarian immature teratoma were cultured in vitro and transplanted into nude mice to establish stable cell lines. Differential drug sensitivity and antigenicity of the tumor cells were compared between the primary and the nude mouse tumors. Nude mouse tumor cells showed a normal 46XX karyotype. Cultured primary cells showed a remarkably high sensitivity to paclitaxel, docetaxel, adriamycin and pirarubicin, compared to peritoneal cancer cells obtained from a patient with ovarian adenocarcinomatous peritonitis. The drug sensitivity of teratoma cells to 5-fluorouracil, bleomycin or peplomycin was also significantly higher. However, there was no significant difference in sensitivity to platinum drugs between the primary teratoma and the peritoneal adenocarcinoma cells. As for nude mouse tumor cells, sensitivity to 12 anticancer drugs was significantly lower than that of the primary tumor cells, while there was little difference in sensitivity to carboplatin or peplomycin between the primary and nude mouse tumor cells. Flow cytometry showed that the expression of smooth muscle actin (SMA) significantly decreased in nude mouse tumor cells when compared to cultured primary cells. In conclusion, ovarian immature teratomas with normal karyotypes have a malignant potential to recur after minimal surgery. During nude mouse transplantation, SMA-overexpressing cells appeared to be selectively excluded and nude mouse tumor cells were less sensitive to the majority of anticancer drugs than the primary tumor cells. These results indicate that after optimal surgery for ovarian immature teratoma, recurrent cells can be more resistant to anticancer drugs than the primary tumors. Therefore, it is likely that adjuvant chemotherapy lowers the risk of ovarian immature teratomas recurring after optimal surgery. BEP and PBV regimens are frequently given to teratoma patients. However, paclitaxel/carboplatin or docetaxel/carboplatin, which are the most effective chemotherapy treatments for epithelial ovarian cancer patients, are considered to be an alternative regimen, especially in the prevention of reproductive toxicity.

International Primary Care Respiratory Group (IPCRG) Guidelines: management of asthma.

PubMed

van der Molen, Thys; Østrem, Anders; Stallberg, Bjorn; Østergaard, Marianne Stubbe; Singh, Raj B

2006-02-01

Worldwide, most patients with asthma are treated in primary care. Optimal primary care management of asthma is therefore of considerable importance. This IPCRG Guideline paper on the management of asthma in primary care is fully consistent with GINA guidelines. It is split into two sections, the first on the management of adults and schoolchildren, and the second on the management of pre-school children. It highlights the treatment goals for asthma and gives an overview of optimal management including the topics which should be covered by the primary care health professional when educating a patient about asthma. It covers the classification of the disease, the stepwise approach to pharmacologic therapy, disease monitoring, the management of exacerbations, and the identification of patients at risk of asthma death.

Effectiveness Modelling and Economic Evaluation of Primary HPV Screening for Cervical Cancer Prevention in New Zealand

PubMed Central

Lew, Jie-Bin; Simms, Kate; Smith, Megan; Lewis, Hazel; Neal, Harold; Canfell, Karen

2016-01-01

Background New Zealand (NZ) is considering transitioning from 3-yearly cervical cytology screening in women 20–69 years (current practice) to primary HPV screening. We evaluated HPV-based screening in both HPV-unvaccinated women and cohorts offered HPV vaccination in New Zealand (vaccination coverage ~50%). Methods A complex model of HPV transmission, vaccination, cervical screening, and invasive cervical cancer was extensively validated against national population-based datasets. Sixteen potential strategies for HPV screening were considered. Results Most primary HPV strategies were more effective than current practice, for both unvaccinated women and cohorts offered vaccination. The optimal strategy for both groups was 5-yearly HPV screening in women aged 25–69 years with partial genotyping for HPV 16/18 and referral to colposcopy, and cytological triage of other oncogenic types. This is predicted to reduce cervical cancer incidence and mortality by a further 12–16% and to save 4–13% annually in program costs (excluding overheads). The findings are sensitive to assumptions about future adherence to initiating screening at 25 years. Conclusion Primary HPV screening with partial genotyping would be more effective and less costly than the current cytology-based screening program, in both unvaccinated women and cohorts offered vaccination. These findings have been considered in a review of cervical screening in NZ. PMID:27187495

Robotic lower limb prosthesis design through simultaneous computer optimizations of human and prosthesis costs

NASA Astrophysics Data System (ADS)

Handford, Matthew L.; Srinivasan, Manoj

2016-02-01

Robotic lower limb prostheses can improve the quality of life for amputees. Development of such devices, currently dominated by long prototyping periods, could be sped up by predictive simulations. In contrast to some amputee simulations which track experimentally determined non-amputee walking kinematics, here, we explicitly model the human-prosthesis interaction to produce a prediction of the user’s walking kinematics. We obtain simulations of an amputee using an ankle-foot prosthesis by simultaneously optimizing human movements and prosthesis actuation, minimizing a weighted sum of human metabolic and prosthesis costs. The resulting Pareto optimal solutions predict that increasing prosthesis energy cost, decreasing prosthesis mass, and allowing asymmetric gaits all decrease human metabolic rate for a given speed and alter human kinematics. The metabolic rates increase monotonically with speed. Remarkably, by performing an analogous optimization for a non-amputee human, we predict that an amputee walking with an appropriately optimized robotic prosthesis can have a lower metabolic cost - even lower than assuming that the non-amputee’s ankle torques are cost-free.

The Effects of General Social Support and Social Support for Racial Discrimination on African American Women’s Well-Being

PubMed Central

Seawell, Asani H.; Cutrona, Carolyn E.; Russell, Daniel W.

2012-01-01

The present longitudinal study examined the role of general and tailored social support in mitigating the deleterious impact of racial discrimination on depressive symptoms and optimism in a large sample of African American women. Participants were 590 African American women who completed measures assessing racial discrimination, general social support, tailored social support for racial discrimination, depressive symptoms, and optimism at two time points (2001–2002 and 2003–2004). Our results indicated that higher levels of general and tailored social support predicted optimism one year later; changes in both types of support also predicted changes in optimism over time. Although initial levels of neither measure of social support predicted depressive symptoms over time, changes in tailored support predicted changes in depressive symptoms. We also sought to determine whether general and tailored social support “buffer” or diminish the negative effects of racial discrimination on depressive symptoms and optimism. Our results revealed a classic buffering effect of tailored social support, but not general support on depressive symptoms for women experiencing high levels of discrimination. PMID:24443614

Optimization-based scatter estimation using primary modulation for computed tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yi; Ma, Jingchen; Zhao, Jun, E-mail: junzhao

Purpose: Scatter reduces the image quality in computed tomography (CT), but scatter correction remains a challenge. A previously proposed primary modulation method simultaneously obtains the primary and scatter in a single scan. However, separating the scatter and primary in primary modulation is challenging because it is an underdetermined problem. In this study, an optimization-based scatter estimation (OSE) algorithm is proposed to estimate and correct scatter. Methods: In the concept of primary modulation, the primary is modulated, but the scatter remains smooth by inserting a modulator between the x-ray source and the object. In the proposed algorithm, an objective function ismore » designed for separating the scatter and primary. Prior knowledge is incorporated in the optimization-based framework to improve the accuracy of the estimation: (1) the primary is always positive; (2) the primary is locally smooth and the scatter is smooth; (3) the location of penumbra can be determined; and (4) the scatter-contaminated data provide knowledge about which part is smooth. Results: The simulation study shows that the edge-preserving weighting in OSE improves the estimation accuracy near the object boundary. Simulation study also demonstrates that OSE outperforms the two existing primary modulation algorithms for most regions of interest in terms of the CT number accuracy and noise. The proposed method was tested on a clinical cone beam CT, demonstrating that OSE corrects the scatter even when the modulator is not accurately registered. Conclusions: The proposed OSE algorithm improves the robustness and accuracy in scatter estimation and correction. This method is promising for scatter correction of various kinds of x-ray imaging modalities, such as x-ray radiography, cone beam CT, and the fourth-generation CT.« less

Prediction of primary somatosensory neuron activity during active tactile exploration

PubMed Central

Campagner, Dario; Evans, Mathew Hywel; Bale, Michael Ross; Erskine, Andrew; Petersen, Rasmus Strange

2016-01-01

Primary sensory neurons form the interface between world and brain. Their function is well-understood during passive stimulation but, under natural behaving conditions, sense organs are under active, motor control. In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration, we recorded from primary mechanosensory neurons of awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously measured both whisker motion and forces with high-speed videography. Using Generalised Linear Models, we found that primary neuron responses were poorly predicted by whisker angle, but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion. These results are in apparent contrast to previous studies of passive stimulation, but could be reconciled by differences in the kinematics-force relationship between active and passive conditions. Thus, simple statistical models can predict rich neural activity elicited by natural, exploratory behaviour involving active movement of sense organs. DOI: http://dx.doi.org/10.7554/eLife.10696.001 PMID:26880559

Predictive Analytics for Coordinated Optimization in Distribution Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui

This talk will present NREL's work on developing predictive analytics that enables the optimal coordination of all the available resources in distribution systems to achieve the control objectives of system operators. Two projects will be presented. One focuses on developing short-term state forecasting-based optimal voltage regulation in distribution systems; and the other one focuses on actively engaging electricity consumers to benefit distribution system operations.

Travel optimization by foraging bumblebees through readjustments of traplines after discovery of new feeding locations.

PubMed

Lihoreau, Mathieu; Chittka, Lars; Raine, Nigel E

2010-12-01

Animals collecting resources that replenish over time often visit patches in predictable sequences called traplines. Despite the widespread nature of this strategy, we still know little about how spatial memory develops and guides individuals toward suitable routes. Here, we investigate whether flower visitation sequences by bumblebees Bombus terrestris simply reflect the order in which flowers were discovered or whether they result from more complex navigational strategies enabling bees to optimize their foraging routes. We analyzed bee flight movements in an array of four artificial flowers maximizing interfloral distances. Starting from a single patch, we sequentially added three new patches so that if bees visited them in the order in which they originally encountered flowers, they would follow a long (suboptimal) route. Bees' tendency to visit patches in their discovery order decreased with experience. Instead, they optimized their flight distances by rearranging flower visitation sequences. This resulted in the development of a primary route (trapline) and two or three less frequently used secondary routes. Bees consistently used these routes after overnight breaks while occasionally exploring novel possibilities. We discuss how maintaining some level of route flexibility could allow traplining animals to cope with dynamic routing problems, analogous to the well-known traveling salesman problem.

Optimization design of wireless charging system for autonomous robots based on magnetic resonance coupling

NASA Astrophysics Data System (ADS)

Wang, Junhua; Hu, Meilin; Cai, Changsong; Lin, Zhongzheng; Li, Liang; Fang, Zhijian

2018-05-01

Wireless charging is the key technology to realize real autonomy of mobile robots. As the core part of wireless power transfer system, coupling mechanism including coupling coils and compensation topology is analyzed and optimized through simulations, to achieve stable and practical wireless charging suitable for ordinary robots. Multi-layer coil structure, especially double-layer coil is explored and selected to greatly enhance coupling performance, while shape of ferrite shielding goes through distributed optimization to guarantee coil fault tolerance and cost effectiveness. On the basis of optimized coils, primary compensation topology is analyzed to adopt composite LCL compensation, to stabilize operations of the primary side under variations of mutual inductance. Experimental results show the optimized system does make sense for wireless charging application for robots based on magnetic resonance coupling, to realize long-term autonomy of robots.

Predicting power-optimal kinematics of avian wings

PubMed Central

Parslew, Ben

2015-01-01

A theoretical model of avian flight is developed which simulates wing motion through a class of methods known as predictive simulation. This approach uses numerical optimization to predict power-optimal kinematics of avian wings in hover, cruise, climb and descent. The wing dynamics capture both aerodynamic and inertial loads. The model is used to simulate the flight of the pigeon, Columba livia, and the results are compared with previous experimental measurements. In cruise, the model unearths a vast range of kinematic modes that are capable of generating the required forces for flight. The most efficient mode uses a near-vertical stroke–plane and a flexed-wing upstroke, similar to kinematics recorded experimentally. In hover, the model predicts that the power-optimal mode uses an extended-wing upstroke, similar to hummingbirds. In flexing their wings, pigeons are predicted to consume 20% more power than if they kept their wings full extended, implying that the typical kinematics used by pigeons in hover are suboptimal. Predictions of climbing flight suggest that the most energy-efficient way to reach a given altitude is to climb as steeply as possible, subjected to the availability of power. PMID:25392398

Reduction and prediction of N2O emission from an Anoxic/Oxic wastewater treatment plant upon DO control and model simulation.

PubMed

Sun, Shichang; Bao, Zhiyuan; Li, Ruoyu; Sun, Dezhi; Geng, Haihong; Huang, Xiaofei; Lin, Junhao; Zhang, Peixin; Ma, Rui; Fang, Lin; Zhang, Xianghua; Zhao, Xuxin

2017-11-01

In order to make a better understanding of the characteristics of N 2 O emission in A/O wastewater treatment plant, full-scale and pilot-scale experiments were carried out and a back propagation artificial neural network model based on the experimental data was constructed to make a precise prediction of N 2 O emission. Results showed that, N 2 O flux from different units followed a descending order: aerated grit tank>oxic zone≫anoxic zone>final clarifier>primary clarifier, but 99.4% of the total emission of N 2 O (1.60% of N-load) was monitored from the oxic zone due to its big surface area. A proper DO control could reduce N 2 O emission down to 0.21% of N-load in A/O process, and a two-hidden-layers back propagation model with an optimized structure of 4:3:9:1 could achieve a good simulation of N 2 O emission, which provided a new method for the prediction of N 2 O emission during wastewater treatment. Copyright © 2017. Published by Elsevier Ltd.

Unrealistic optimism in advice taking: A computational account.

PubMed

Leong, Yuan Chang; Zaki, Jamil

2018-02-01

Expert advisors often make surprisingly inaccurate predictions about the future, yet people heed their suggestions nonetheless. Here we provide a novel, computational account of this unrealistic optimism in advice taking. Across 3 studies, participants observed as advisors predicted the performance of a stock. Advisors varied in their accuracy, performing reliably above, at, or below chance. Despite repeated feedback, participants exhibited inflated perceptions of advisors' accuracy, and reliably "bet" on advisors' predictions more than their performance warranted. Participants' decisions tightly tracked a computational model that makes 2 assumptions: (a) people hold optimistic initial expectations about advisors, and (b) people preferentially incorporate information that adheres to their expectations when learning about advisors. Consistent with model predictions, explicitly manipulating participants' initial expectations altered their optimism bias and subsequent advice-taking. With well-calibrated initial expectations, participants no longer exhibited an optimism bias. We then explored crowdsourced ratings as a strategy to curb unrealistic optimism in advisors. Star ratings for each advisor were collected from an initial group of participants, which were then shown to a second group of participants. Instead of calibrating expectations, these ratings propagated and exaggerated the unrealistic optimism. Our results provide a computational account of the cognitive processes underlying inflated perceptions of expertise, and explore the boundary conditions under which they occur. We discuss the adaptive value of this optimism bias, and how our account can be extended to explain unrealistic optimism in other domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Oral and dental management for head and neck cancer patients treated by chemotherapy and radiotherapy.

PubMed

McCaul, Lorna K

2012-03-01

The incidence of head and neck cancer is rising. The attendant oral complications of cancer management make oral health maintenance a lifelong challenge for these patients. Holistic management in the context of a core multidisciplinary team is essential in optimizing outcomes. Predicting the risk of adverse oral outcomes is difficult. Effective communication between healthcare professionals in the core and extended teams and with the patient is essential. Primary care dental teams will be involved in the long-term management of oral care for head and cancer patients. A broad understanding of the management of head and neck cancer, consequences of treatment and the need for good communication is key to good quality patient care.

Departures From Optimality When Pursuing Multiple Approach or Avoidance Goals

PubMed Central

2016-01-01

This article examines how people depart from optimality during multiple-goal pursuit. The authors operationalized optimality using dynamic programming, which is a mathematical model used to calculate expected value in multistage decisions. Drawing on prospect theory, they predicted that people are risk-averse when pursuing approach goals and are therefore more likely to prioritize the goal in the best position than the dynamic programming model suggests is optimal. The authors predicted that people are risk-seeking when pursuing avoidance goals and are therefore more likely to prioritize the goal in the worst position than is optimal. These predictions were supported by results from an experimental paradigm in which participants made a series of prioritization decisions while pursuing either 2 approach or 2 avoidance goals. This research demonstrates the usefulness of using decision-making theories and normative models to understand multiple-goal pursuit. PMID:26963081

Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.

PubMed

Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen

2016-07-01

This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

PubMed Central

Wang, Jie-Sheng; Han, Shuang

2015-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034

An integrated prediction and optimization model of biogas production system at a wastewater treatment facility.

PubMed

Akbaş, Halil; Bilgen, Bilge; Turhan, Aykut Melih

2015-11-01

This study proposes an integrated prediction and optimization model by using multi-layer perceptron neural network and particle swarm optimization techniques. Three different objective functions are formulated. The first one is the maximization of methane percentage with single output. The second one is the maximization of biogas production with single output. The last one is the maximization of biogas quality and biogas production with two outputs. Methane percentage, carbon dioxide percentage, and other contents' percentage are used as the biogas quality criteria. Based on the formulated models and data from a wastewater treatment facility, optimal values of input variables and their corresponding maximum output values are found out for each model. It is expected that the application of the integrated prediction and optimization models increases the biogas production and biogas quality, and contributes to the quantity of electricity production at the wastewater treatment facility. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimism and Recovery After Acute Coronary Syndrome: A Clinical Cohort Study

PubMed Central

Ronaldson, Amy; Molloy, Gerard J.; Wikman, Anna; Poole, Lydia; Kaski, Juan-Carlos; Steptoe, Andrew

2015-01-01

ABSTRACT Objective Optimism is associated with reduced cardiovascular mortality, but its impact on recovery after acute coronary syndrome (ACS) is poorly understood. We hypothesized that greater optimism would lead to more effective physical and emotional adaptation after ACS and would buffer the impact of persistent depressive symptoms on clinical outcomes. Methods This prospective observational clinical study took place in an urban general hospital and involved 369 patients admitted with a documented ACS. Optimism was assessed with a standardized questionnaire. The main outcomes were physical health status, depressive symptoms, smoking, physical activity, and fruit and vegetable consumption measured 12 months after ACS, and composite major adverse cardiac events (cardiovascular death, readmission with reinfarction or unstable angina, and coronary artery bypass graft surgery) assessed over an average of 45.7 months. Results We found that optimism predicted better physical health status 12 months after ACS independently of baseline physical health, age, sex, ethnicity, social deprivation, and clinical risk factors (B = 0.65, 95% confidence interval [CI] = 0.10–1.20). Greater optimism also predicted reduced risk of depressive symptoms (odds ratio = 0.82, 95% CI = 0.74–0.90), more smoking cessation, and more fruit and vegetable consumption at 12 months. Persistent depressive symptoms 12 months after ACS predicted major adverse cardiac events over subsequent years (odds ratio = 2.56, 95% CI = 1.16–5.67), but only among individuals low in optimism (optimism × depression interaction; p = .014). Conclusions Optimism predicts better physical and emotional health after ACS. Measuring optimism may help identify individuals at risk. Pessimistic outlooks can be modified, potentially leading to improved recovery after major cardiac events. PMID:25738438

Optimal secondary source position in exterior spherical acoustical holophony

NASA Astrophysics Data System (ADS)

Pasqual, A. M.; Martin, V.

2012-02-01

Exterior spherical acoustical holophony is a branch of spatial audio reproduction that deals with the rendering of a given free-field radiation pattern (the primary field) by using a compact spherical loudspeaker array (the secondary source). More precisely, the primary field is known on a spherical surface surrounding the primary and secondary sources and, since the acoustic fields are described in spherical coordinates, they are naturally subjected to spherical harmonic analysis. Besides, the inverse problem of deriving optimal driving signals from a known primary field is ill-posed because the secondary source cannot radiate high-order spherical harmonics efficiently, especially in the low-frequency range. As a consequence, a standard least-squares solution will overload the transducers if the primary field contains such harmonics. Here, this is avoided by discarding the strongly decaying spherical waves, which are identified through inspection of the radiation efficiency curves of the secondary source. However, such an unavoidable regularization procedure increases the least-squares error, which also depends on the position of the secondary source. This paper deals with the above-mentioned questions in the context of far-field directivity reproduction at low and medium frequencies. In particular, an optimal secondary source position is sought, which leads to the lowest reproduction error in the least-squares sense without overloading the transducers. In order to address this issue, a regularization quality factor is introduced to evaluate the amount of regularization required. It is shown that the optimal position improves significantly the holophonic reconstruction and maximizes the regularization quality factor (minimizes the amount of regularization), which is the main general contribution of this paper. Therefore, this factor can also be used as a cost function to obtain the optimal secondary source position.

Germination parameterization and development of an after-ripening thermal-time model for primary dormancy release of Lithospermum arvense seeds

PubMed Central

Chantre, Guillermo R.; Batlla, Diego; Sabbatini, Mario R.; Orioli, Gustavo

2009-01-01

Background and Aims Models based on thermal-time approaches have been a useful tool for characterizing and predicting seed germination and dormancy release in relation to time and temperature. The aims of the present work were to evaluate the relative accuracy of different thermal-time approaches for the description of germination in Lithospermum arvense and to develop an after-ripening thermal-time model for predicting seed dormancy release. Methods Seeds were dry-stored at constant temperatures of 5, 15 or 24 °C for up to 210 d. After different storage periods, batches of 50 seeds were incubated at eight constant temperature regimes of 5, 8, 10, 13, 15, 17, 20 or 25 °C. Experimentally obtained cumulative-germination curves were analysed using a non-linear regression procedure to obtain optimal population thermal parameters for L. arvense. Changes in these parameters were described as a function of after-ripening thermal-time and storage temperature. Key Results The most accurate approach for simulating the thermal-germination response of L. arvense was achieved by assuming a normal distribution of both base and maximum germination temperatures. The results contradict the widely accepted assumption of a single Tb value for the entire seed population. The after-ripening process was characterized by a progressive increase in the mean maximum germination temperature and a reduction in the thermal-time requirements for germination at sub-optimal temperatures. Conclusions The after-ripening thermal-time model developed here gave an acceptable description of the observed field emergence patterns, thus indicating its usefulness as a predictive tool to enhance weed management tactics. PMID:19332426

Population-based V3 genotypic tropism assay: a retrospective analysis using screening samples from the A4001029 and MOTIVATE studies.

PubMed

McGovern, Rachel A; Thielen, Alexander; Mo, Theresa; Dong, Winnie; Woods, Conan K; Chapman, Douglass; Lewis, Marilyn; James, Ian; Heera, Jayvant; Valdez, Hernan; Harrigan, P Richard

2010-10-23

The MOTIVATE-1 and 2 studies compared maraviroc (MVC) along with optimized background therapy (OBT) vs. placebo along with OBT in treatment-experienced patients screened as having R5-HIV (original Monogram Trofile). A subset screened with non-R5 HIV were treated with MVC or placebo along with OBT in a sister safety trial, A4001029. This analysis retrospectively examined the performance of population-based sequence analysis of HIV-1 env V3-loop to predict coreceptor tropism. Triplicate V3-loop sequences were generated using stored screening plasma samples and data was processed using custom software ('ReCall'), blinded to clinical response. Tropism was inferred using geno2pheno ('g2p'; 5% false positive rate). Primary outcomes were viral load changes after starting maraviroc; and concordance with prior screening Trofile results. Genotype and Trofile results were available for 1164 individuals with virological outcome data (N = 169 non-R5 by Trofile). Compared with Trofile, V3 genotyping had a specificity of 92.6% and a sensitivity of 67.4% for detecting non-R5 virus. However, when compared with clinical outcome, virological responses were consistently similar between Trofile and V3 genotype at weeks 8 and 24 following the initiation of therapy for patients categorized as R5. Despite differences in sensitivity for predicting non-R5 HIV, week 8 and 24 week virological responses were similar in this treatment-experienced population. These findings suggest the potential utility of V3 genotyping as an accessible assay to select patients who may benefit from maraviroc treatment. Optimization of the predictive tropism algorithm may lead to further improvement in the clinical utility of HIV genotypic tropism assays.

Admission Systolic Blood Pressure Predicts the Number of Blood Pressure Medications at Discharge in Patients With Primary Intracerebral Hemorrhage.

PubMed

Khawaja, Ayaz M; Shiue, Harn; Boehme, Amelia K; Albright, Karen C; Venkatraman, Anand; Kumar, Gyanendra; Lyerly, Michael J; Hays-Shapshak, Angela; Mirza, Maira; Gropen, Toby I; Harrigan, Mark R

2018-03-01

Control of systolic blood pressure (SBP) after primary intracerebral hemorrhage improves outcomes. Factors determining the number of blood pressure medications (BPM) required for goal SBP<160 mm Hg at discharge are unknown. We hypothesized that higher admission-SBPs require a greater number of BPM for goal discharge-SBP<160 mm Hg, and investigated factors influencing this goal. We conducted a retrospective review of 288 patients who presented with primary intracerebral hemorrhage. Admission-SBP was obtained. Primary outcome was the number of BPM at discharge. Comparison was made between patients presenting with and without a history of hypertension, and patients discharged on <3 and ≥3 BPM. Patients with hypertension history had a higher median admission-SBP compared with those without (180 vs. 157 mm Hg, P=0.0001). In total, 133 of 288 (46.2%) patients were discharged on <3 BPM; 155/288 (53.8%) were discharged on ≥3 BPM. Hypertension history (P<0.0001) and admission-SBP (P<0.0001) predicted the number of BPM at discharge. In patients without hypertension history, every 10 mm Hg increase in SBP resulted in an absolute increase of 0.5 BPM at discharge (P=0.0011), whereas in those with hypertension, the absolute increase was 1.3 BPM (P=0.0012). In comparison with patients discharged on <3 BPM, patients discharged on ≥3 BPM were more likely to have a higher median admission-SBP, be younger in age, belong to the African-American race, have a history of diabetes, have higher median admission-National Institutes of Health Stroke Scale and modified Rankin Scale of 4 to 5 at discharge. An understanding of the factors influencing BPM at discharge may help clinicians better optimize blood pressure control both before and after discharge.

Contralateral suppression of aldosterone at adrenal venous sampling predicts hyperkalemia following adrenalectomy for primary aldosteronism.

PubMed

Shariq, Omair A; Bancos, Irina; Cronin, Patricia A; Farley, David R; Richards, Melanie L; Thompson, Geoffrey B; Young, William F; McKenzie, Travis J

2018-01-01

We aimed to determine whether a greater degree of contralateral suppression of aldosterone secretion at adrenal venous sampling predicted the development of postoperative hyperkalemia after unilateral adrenalectomy for primary aldosteronism. A retrospective analysis of patients undergoing unilateral adrenalectomy for primary aldosteronism between 2004-2015 was performed. Clinical and biochemical parameters of patients who developed hyperkalemia (≥5.2 mmol/L) after unilateral adreanlectomy were compared with those who remained normokalemic. The contralateral suppression index was defined as the aldosterone-to-cortisol ratio from the nondominant adrenal vein divided by the aldosterone-to-cortisol ratio from the external iliac vein. Of 192 patients who met criteria for inclusion, 12 (6.3%) developed hyperkalemia (median serum potassium 5.5 mmol/L, range 5.2-6.2 mmol/L), with a median time to onset of 13.5 days (range 7-55 days). Five patients had transiently increased serum potassium concentrations that normalized spontaneously. Four patients received mineralocorticoid replacement therapy with fludrocortisone. On univariate analysis, hyperkalemic patients had slightly greater preoperative serum creatinine levels (1.2 vs 1.0 mg/dL, P = .01), higher postoperative creatinine (1.3 vs 1.0 mg/dL, P = .02), lesser median contralateral suppression index (0.14 vs 0.27, P = .03), and larger adenomas (1.9 vs 1.4 cm, P = .02). On multivariable logistic regression, the contralateral suppression index remained the only significant predictor of postoperative hyperkalemia (P = .04) with an optimal cut-off of <0.47. Hyperkalemia after unilateral adrenalectomy for primary aldosteronism is uncommon and usually transient, but may require mineralocorticoid supplementation. Patients with a contralateral suppression index of <0.47 require meticulous follow-up and monitoring of serum potassium concentrations after unilateral adrenalectomy. Copyright © 2017 Elsevier Inc. All rights reserved.

Population PKPD modeling of BACE1 inhibitor-induced reduction in Aβ levels in vivo and correlation to in vitro potency in primary cortical neurons from mouse and guinea pig.

PubMed

Janson, Juliette; Eketjäll, Susanna; Tunblad, Karin; Jeppsson, Fredrik; Von Berg, Stefan; Niva, Camilla; Radesäter, Ann-Cathrin; Fälting, Johanna; Visser, Sandra A G

2014-03-01

The aims were to quantify the in vivo time-course between the oral dose, the plasma and brain exposure and the inhibitory effect on Amyloid β (Aβ) in brain and cerebrospinal fluid, and to establish the correlation between in vitro and in vivo potency of novel β-secretase (BACE1) inhibitors. BACE1-mediated inhibition of Aβ was quantified in in vivo dose- and/or time-response studies and in vitro in SH-SY5Y cells, N2A cells, and primary cortical neurons (PCN). An indirect response model with inhibition on Aβ production rate was used to estimate unbound in vivo IC 50 in a population pharmacokinetic-pharmacodynamic modeling approach. Estimated in vivo inhibitory potencies varied between 1 and 1,000 nM. The turnover half-life of Aβ40 in brain was predicted to be 0.5 h in mouse and 1 h in guinea pig. An excellent correlation between PCN and in vivo potency was observed. Moreover, a strong correlation in potency was found between human SH-SY5Y cells and mouse PCN, being 4.5-fold larger in SH-SY5Y cells. The strong in vivo-in vitro correlation increased the confidence in using human cell lines for screening and optimization of BACE1 inhibitors. This can optimize the design and reduce the number of preclinical in vivo effect studies.

A Complete Procedure for Predicting and Improving the Performance of HAWT's

NASA Astrophysics Data System (ADS)

Al-Abadi, Ali; Ertunç, Özgür; Sittig, Florian; Delgado, Antonio

2014-06-01

A complete procedure for predicting and improving the performance of the horizontal axis wind turbine (HAWT) has been developed. The first process is predicting the power extracted by the turbine and the derived rotor torque, which should be identical to that of the drive unit. The BEM method and a developed post-stall treatment for resolving stall-regulated HAWT is incorporated in the prediction. For that, a modified stall-regulated prediction model, which can predict the HAWT performance over the operating range of oncoming wind velocity, is derived from existing models. The model involves radius and chord, which has made it more general in applications for predicting the performance of different scales and rotor shapes of HAWTs. The second process is modifying the rotor shape by an optimization process, which can be applied to any existing HAWT, to improve its performance. A gradient- based optimization is used for adjusting the chord and twist angle distribution of the rotor blade to increase the extraction of the power while keeping the drive torque constant, thus the same drive unit can be kept. The final process is testing the modified turbine to predict its enhanced performance. The procedure is applied to NREL phase-VI 10kW as a baseline turbine. The study has proven the applicability of the developed model in predicting the performance of the baseline as well as the optimized turbine. In addition, the optimization method has shown that the power coefficient can be increased while keeping same design rotational speed.

Prediction uncertainty and optimal experimental design for learning dynamical systems.

PubMed

Letham, Benjamin; Letham, Portia A; Rudin, Cynthia; Browne, Edward P

2016-06-01

Dynamical systems are frequently used to model biological systems. When these models are fit to data, it is necessary to ascertain the uncertainty in the model fit. Here, we present prediction deviation, a metric of uncertainty that determines the extent to which observed data have constrained the model's predictions. This is accomplished by solving an optimization problem that searches for a pair of models that each provides a good fit for the observed data, yet has maximally different predictions. We develop a method for estimating a priori the impact that additional experiments would have on the prediction deviation, allowing the experimenter to design a set of experiments that would most reduce uncertainty. We use prediction deviation to assess uncertainty in a model of interferon-alpha inhibition of viral infection, and to select a sequence of experiments that reduces this uncertainty. Finally, we prove a theoretical result which shows that prediction deviation provides bounds on the trajectories of the underlying true model. These results show that prediction deviation is a meaningful metric of uncertainty that can be used for optimal experimental design.

The association between plasma big endothelin-1 levels at admission and long-term outcomes in patients with atrial fibrillation.

PubMed

Wu, Shuang; Yang, Yan-Min; Zhu, Jun; Ren, Jia-Meng; Wang, Juan; Zhang, Han; Shao, Xing-Hui

2018-05-01

The prognostic role of big endothelin-1 (ET-1) in atrial fibrillation (AF) is unclear. We aimed to assess its predictive value in patients with AF. A total of 716 AF patients were enrolled and divided into two groups based on the optimal cut-off value of big ET-1 in predicting all-cause mortality. The primary outcomes were all-cause mortality and major adverse events (MAEs). Cox regression analysis and net reclassification improvement (NRI) analysis were performed to assess the predictive value of big ET-1 on outcomes. With the optimal cut-off value of 0.55 pmol/L, 326 patients were classified into the high big ET-1 levels group. Cardiac dysfunction and left atrial dilation were factors related to high big ET-1 levels. During a median follow-up of 3 years, patients with big ET-1 ≥ 0.55 pmol/L had notably higher risk of all-cause death (44.8% vs. 11.5%, p < 0.001), MAEs (51.8% vs. 17.4%, p < 0.001), cardiovascular death, major bleeding, and tended to have higher thromboembolic risk. After adjusting for confounding factors, high big ET-1 level was an independent predictor of all-cause mortality (hazard ratio (HR) 2.11, 95% confidence interval (CI) 1.46-3.05; p < 0.001), MAEs (HR 2.05, 95% CI 1.50-2.80; p = 0.001), and cardiovascular death (HR 2.44, 95% CI 1.52-3.93; p < 0.001). NRI analysis showed that big ET-1 allowed a significant improvement of 0.32 in the accuracy of predicting the risk of both all-cause mortality and MAEs. Elevated big ET-1 levels is an independent predictor of long-term all-cause mortality, MAEs, and cardiovascular death in patients with AF. Copyright © 2018 Elsevier B.V. All rights reserved.

Predictors of survival and ability to wean from short-term mechanical circulatory support device following acute myocardial infarction complicated by cardiogenic shock.

PubMed

Garan, A Reshad; Eckhardt, Christina; Takeda, Koji; Topkara, Veli K; Clerkin, Kevin; Fried, Justin; Masoumi, Amirali; Demmer, Ryan T; Trinh, Pauline; Yuzefpolskaya, Melana; Naka, Yoshifumi; Burkhoff, Dan; Kirtane, Ajay; Colombo, Paolo C; Takayama, Hiroo

2017-11-01

Cardiogenic shock following acute myocardial infarction (AMI-CS) portends a poor prognosis. Short-term mechanical circulatory support devices (MCSDs) provide hemodynamic support for patients with cardiogenic shock but predictors of survival and the ability to wean from short-term MCSDs remain largely unknown. All patients > 18 years old treated at our institution with extra-corporeal membrane oxygenation or short-term surgical ventricular assist device for AMI-CS were studied. We collected acute myocardial infarction details with demographic and hemodynamic variables. Primary outcomes were survival to discharge and recovery from MCSD (i.e. survival without heart replacement therapy including durable ventricular assist device or heart transplant). One hundred and twenty-four patients received extra-corporeal membrane oxygenation or short-term surgical ventricular assist device following acute myocardial infarction from 2007 to 2016; 89 received extra-corporeal membrane oxygenation and 35 short-term ventricular assist device. Fifty-five (44.4%) died in the hospital and 69 (55.6%) survived to discharge. Twenty-six (37.7%) required heart replacement therapy (four transplant, 22 durable ventricular assist device) and 43 (62.3%) were discharged without heart replacement therapy. Age and cardiac index at MCSD implantation were predictors of survival to discharge; patients over 60 years with cardiac index <1.5 l/min per m 2 had a low likelihood of survival. The angiographic result after revascularization predicted recovery from MCSD (odds ratio 9.00, 95% confidence interval 2.45-32.99, p=0.001), but 50% of those optimally revascularized still required heart replacement therapy. Cardiac index predicted recovery from MCSD among this group (odds ratio 4.06, 95% confidence interval 1.45-11.55, p=0.009). Among AMI-CS patients requiring short-term MCSDs, age and cardiac index predict survival to discharge. Angiographic result and cardiac index predict ventricular recovery but 50% of those optimally revascularized still required heart replacement therapy.

Optimism and Pessimism in Social Context: An Interpersonal Perspective on Resilience and Risk

PubMed Central

Smith, Timothy W.; Ruiz, John M.; Cundiff, Jenny M.; Baron, Kelly G.; Nealey-Moore, Jill B.

2016-01-01

Using the interpersonal perspective, we examined social correlates of dispositional optimism. In Study 1, optimism and pessimism were associated with warm-dominant and hostile-submissive interpersonal styles, respectively, across four samples, and had expected associations with social support and interpersonal stressors. In 300 married couples, Study 2 replicated these findings regarding interpersonal styles, using self-reports and spouse ratings. Optimism-pessimism also had significant actor and partner associations with marital quality. In Study 3 (120 couples), husbands’ and wives’ optimism predicted increases in their own marital adjustment over time, and husbands’ optimism predicted increases in wives’ marital adjustment. Thus, the interpersonal perspective is a useful integrative framework for examining social processes that could contribute to associations of optimism-pessimism with physical health and emotional adjustment. PMID:27840458

Aircraft Trajectories Computation-Prediction-Control. Volume 1 (La Trajectoire de l’Avion Calcul-Prediction-Controle)

DTIC Science & Technology

1990-03-01

knowledge covering problems of this type is called calculus of variations or optimal control theory (Refs. 1-8). As stated before, appli - cations occur...to the optimality conditions and the feasibility equations of Problem (GP), respectively. Clearly, after the transformation (26) is applied , the...trajectories, the primal sequential gradient-restoration algorithm (PSGRA) is applied to compute optimal trajectories for aeroassisted orbital transfer

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

NASA Astrophysics Data System (ADS)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

PubMed Central

Rayan, Anwar; Raiyn, Jamal

2017-01-01

Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab. PMID:29121120

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity.

PubMed

Rayan, Anwar; Raiyn, Jamal; Falah, Mizied

2017-01-01

Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of in silico techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.

Model-Based Battery Management Systems: From Theory to Practice

NASA Astrophysics Data System (ADS)

Pathak, Manan

Lithium-ion batteries are now extensively being used as the primary storage source. Capacity and power fade, and slow recharging times are key issues that restrict its use in many applications. Battery management systems are critical to address these issues, along with ensuring its safety. This dissertation focuses on exploring various control strategies using detailed physics-based electrochemical models developed previously for lithium-ion batteries, which could be used in advanced battery management systems. Optimal charging profiles for minimizing capacity fade based on SEI-layer formation are derived and the benefits of using such control strategies are shown by experimentally testing them on a 16 Ah NMC-based pouch cell. This dissertation also explores different time-discretization strategies for non-linear models, which gives an improved order of convergence for optimal control problems. Lastly, this dissertation also explores a physics-based model for predicting the linear impedance of a battery, and develops a freeware that is extremely robust and computationally fast. Such a code could be used for estimating transport, kinetic and material properties of the battery based on the linear impedance spectra.

Study on the flow in the pipelines of the support system of circulating fluidized bed

NASA Astrophysics Data System (ADS)

Meng, L.; Yang, J.; Zhou, L. J.; Wang, Z. W.; Zhuang, X. H.

2013-12-01

In the support system of Circulating Fluidized Bed (Below referred to as CFB) of thermal power plant, the pipelines of primary wind are used for transporting the cold air to the boiler, which is important in controlling and combustion effect. The pipeline design will greatly affect the energy loss of the system, and accordingly affect the thermal power plant economic benefits and production environment. Three-dimensional numerical simulation is carried out for the pipeline internal flow field of a thermal power plant in this paper. Firstly three turbulence models were compared and the results showed that the SST k-ω model converged better and the energy losses predicted were closer to the experimental results. The influence of the pipeline design form on the flow characteristics are analysed, then the optimization designs of the pipeline are proposed according to the energy loss distribution of the flow field, in order to reduce energy loss and improve the efficiency of tunnel. The optimization plan turned out to be efficacious; about 36% of the pressure loss is reduced.

The use of laminar tube flow in the study of hydrodynamic and chemical influences on polymer flocculation of Escherichia coli.

PubMed

Whittington, P N; George, N

1992-08-05

The optimization of microbial flocculation for subsequent biomass separation must relate the floc properties to separation process criteria. The effects of flocculant type, dose, and hydrodynamic conditions on floc formation in laminar tube flow were determined for an Escherichia coli system. Combined with an on-line aggregation sensor, this technique allows the flocculation process to be rapidly optimized. This is important, because interbatch variation in fermentation broth has consequences for flocculation control and subsequent downstream processing. Changing tube diameter and length while maintaining a constant flow rate allowed independent study of the effects of shear and time on the flocculation rate and floc characteristics. Tube flow at higher shear rates increased the rate and completeness of flocculation, but reduced the maximum floc size attained. The mechanism for this size limitation does not appear to be fracture or erosion of existing flocs. Rearrangement of particles within the flocs appears to be most likely. The Camp number predicted the extent of flocculation obtained in terms of the reduction in primary particle number, but not in terms of floc size.

Prediction of Dementia in Primary Care Patients

PubMed Central

Jessen, Frank; Wiese, Birgitt; Bickel, Horst; Eiffländer-Gorfer, Sandra; Fuchs, Angela; Kaduszkiewicz, Hanna; Köhler, Mirjam; Luck, Tobias; Mösch, Edelgard; Pentzek, Michael; Riedel-Heller, Steffi G.; Wagner, Michael; Weyerer, Siegfried; Maier, Wolfgang; van den Bussche, Hendrik

2011-01-01

Background Current approaches for AD prediction are based on biomarkers, which are however of restricted availability in primary care. AD prediction tools for primary care are therefore needed. We present a prediction score based on information that can be obtained in the primary care setting. Methodology/Principal Findings We performed a longitudinal cohort study in 3.055 non-demented individuals above 75 years recruited via primary care chart registries (Study on Aging, Cognition and Dementia, AgeCoDe). After the baseline investigation we performed three follow-up investigations at 18 months intervals with incident dementia as the primary outcome. The best set of predictors was extracted from the baseline variables in one randomly selected half of the sample. This set included age, subjective memory impairment, performance on delayed verbal recall and verbal fluency, on the Mini-Mental-State-Examination, and on an instrumental activities of daily living scale. These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD. The score was applied to the second half of the sample (test cohort). Here, the prediction accuracy was 0.79. With a cut-off of at least 80% sensitivity in the first cohort, 79.6% sensitivity, 66.4% specificity, 14.7% positive predictive value (PPV) and 97.8% negative predictive value of (NPV) for AD were achieved in the test cohort. At a cut-off for a high risk population (5% of individuals with the highest risk score in the first cohort) the PPV for AD was 39.1% (52% for any dementia) in the test cohort. Conclusions The prediction score has useful prediction accuracy. It can define individuals (1) sensitively for low cost-low risk interventions, or (2) more specific and with increased PPV for measures of prevention with greater costs or risks. As it is independent of technical aids, it may be used within large scale prevention programs. PMID:21364746

Prediction of dementia in primary care patients.

PubMed

Jessen, Frank; Wiese, Birgitt; Bickel, Horst; Eiffländer-Gorfer, Sandra; Fuchs, Angela; Kaduszkiewicz, Hanna; Köhler, Mirjam; Luck, Tobias; Mösch, Edelgard; Pentzek, Michael; Riedel-Heller, Steffi G; Wagner, Michael; Weyerer, Siegfried; Maier, Wolfgang; van den Bussche, Hendrik

2011-02-18

Current approaches for AD prediction are based on biomarkers, which are however of restricted availability in primary care. AD prediction tools for primary care are therefore needed. We present a prediction score based on information that can be obtained in the primary care setting. We performed a longitudinal cohort study in 3.055 non-demented individuals above 75 years recruited via primary care chart registries (Study on Aging, Cognition and Dementia, AgeCoDe). After the baseline investigation we performed three follow-up investigations at 18 months intervals with incident dementia as the primary outcome. The best set of predictors was extracted from the baseline variables in one randomly selected half of the sample. This set included age, subjective memory impairment, performance on delayed verbal recall and verbal fluency, on the Mini-Mental-State-Examination, and on an instrumental activities of daily living scale. These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD. The score was applied to the second half of the sample (test cohort). Here, the prediction accuracy was 0.79. With a cut-off of at least 80% sensitivity in the first cohort, 79.6% sensitivity, 66.4% specificity, 14.7% positive predictive value (PPV) and 97.8% negative predictive value of (NPV) for AD were achieved in the test cohort. At a cut-off for a high risk population (5% of individuals with the highest risk score in the first cohort) the PPV for AD was 39.1% (52% for any dementia) in the test cohort. The prediction score has useful prediction accuracy. It can define individuals (1) sensitively for low cost-low risk interventions, or (2) more specific and with increased PPV for measures of prevention with greater costs or risks. As it is independent of technical aids, it may be used within large scale prevention programs.

Optimization of the in silico designed Kemp eliminase KE70 by computational design and directed evolution

PubMed Central

Khersonsky, Olga; Röthlisberger, Daniela; Wollacott, Andrew M.; Murphy, Paul; Dym, Orly; Albeck, Shira; Kiss, Gert; Houk, K. N.; Baker, David; Tawfik, Dan S.

2013-01-01

Although de novo computational enzyme design has been shown to be feasible, the field is still in its infancy: the kinetic parameters of designed enzymes are still orders of magnitude lower than those of naturally occurring ones. Nonetheless, designed enzymes can be improved by directed evolution, as recently exemplified for the designed Kemp eliminase KE07. Random mutagenesis and screening resulted in variants with >200-fold higher catalytic efficiency, and provided insights about features missing in the designed enzyme. Here we describe the optimization of KE70, another designed Kemp eliminase. Amino acid substitutions predicted to improve catalysis in design calculations involving extensive backbone sampling were individually tested. Those proven beneficial were combinatorially incorporated into the originally designed KE70 along with random mutations, and the resulting libraries were screened for improved eliminase activity. Nine rounds of mutation and selection resulted in >400-fold improvement in the catalytic efficiency of the original KE70 design, reflected in both higher kcat and lower KM values, with the best variants exhibiting kcat/KM values of >5x104 s−1M−1. The optimized KE70 variants were characterized structurally and biochemically providing insights into the origins of the improvements in catalysis. Three primary contributions were identified: first, the reshaping of the active site cavity to achieve tighter substrate binding; second, the fine-tuning of the electrostatics around the catalytic His-Asp dyad; and third, stabilization of the active-site dyad in a conformation optimal for catalysis. PMID:21277311

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection.

PubMed

Zawbaa, Hossam M; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

2016-01-01

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres—Focus on Feature Selection

PubMed Central

Zawbaa, Hossam M.; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

2016-01-01

Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven. PMID:27315205

Perforated Peptic Ulcer Repair: Factors Predicting Conversion in Laparoscopy and Postoperative Septic Complications.

PubMed

Muller, Markus K; Wrann, Simon; Widmer, Jeannette; Klasen, Jennifer; Weber, Markus; Hahnloser, Dieter

2016-09-01

The surgical treatment for perforated peptic ulcers can be safely performed laparoscopically. The aim of the study was to define simple predictive factors for conversion and septic complications. This retrospective case-control study analyzed patients treated with either laparoscopic surgery or laparotomy for perforated peptic ulcers. A total of 71 patients were analyzed. Laparoscopically operated patients had a shorter hospital stay (13.7 vs. 15.1 days). In an intention-to-treat analysis, patients with conversion to open surgery (analyzed as subgroup from laparoscopic approach group) showed no prolonged hospital stay (15.3 days) compared to patients with a primary open approach. Complication and mortality rates were not different between the groups. The statistical analysis identified four intraoperative risk factors for conversion: Mannheim peritonitis index (MPI) > 21 (p = 0.02), generalized peritonitis (p = 0.04), adhesions, and perforations located in a region other than the duodenal anterior wall. We found seven predictive factors for septic complications: age >70 (p = 0.02), cardiopulmonary disease (p = 0.04), ASA > 3 (p = 0.002), CRP > 100 (p = 0.005), duration of symptoms >24 h (p = 0.02), MPI > 21(p = 0.008), and generalized peritonitis (p = 0.02). Our data suggest that a primary laparoscopic approach has no disadvantages. Factors necessitating conversions emerged during the procedure inhibiting a preoperative selection. Factors suggesting imminent septic complications can be assessed preoperatively. An assessment of the proposed parameters may help optimize the management of possible septic complications.

Prediction of chemical biodegradability using support vector classifier optimized with differential evolution.

PubMed

Cao, Qi; Leung, K M

2014-09-22

Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

[Application of an artificial neural network in the design of sustained-release dosage forms].

PubMed

Wei, X H; Wu, J J; Liang, W Q

2001-09-01

To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets. The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output. The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles. The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

PubMed Central

Walter, Jonathan P.; Kinney, Allison L.; Banks, Scott A.; D'Lima, Darryl D.; Besier, Thor F.; Lloyd, David G.; Fregly, Benjamin J.

2014-01-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values. PMID:24402438

Muscle synergies may improve optimization prediction of knee contact forces during walking.

PubMed

Walter, Jonathan P; Kinney, Allison L; Banks, Scott A; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Fregly, Benjamin J

2014-02-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

Impact of Ice Morphology on Design Space of Pharmaceutical Freeze-Drying.

PubMed

Goshima, Hiroshika; Do, Gabsoo; Nakagawa, Kyuya

2016-06-01

It has been known that the sublimation kinetics of a freeze-drying product is affected by its internal ice crystal microstructures. This article demonstrates the impact of the ice morphologies of a frozen formulation in a vial on the design space for the primary drying of a pharmaceutical freeze-drying process. Cross-sectional images of frozen sucrose-bovine serum albumin aqueous solutions were optically observed and digital pictures were acquired. Binary images were obtained from the optical data to extract the geometrical parameters (i.e., ice crystal size and tortuosity) that relate to the mass-transfer resistance of water vapor during the primary drying step. A mathematical model was used to simulate the primary drying kinetics and provided the design space for the process. The simulation results predicted that the geometrical parameters of frozen solutions significantly affect the design space, with large and less tortuous ice morphologies resulting in wide design spaces and vice versa. The optimal applicable drying conditions are influenced by the ice morphologies. Therefore, owing to the spatial distributions of the geometrical parameters of a product, the boundary curves of the design space are variable and could be tuned by controlling the ice morphologies. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

The importance of personality and parental styles on optimism in adolescents.

PubMed

Zanon, Cristian; Bastianello, Micheline Roat; Pacico, Juliana Cerentini; Hutz, Claudio Simon

2014-01-01

Some studies have suggested that personality factors are important to optimism development. Others have emphasized that family relations are relevant variables to optimism. This study aimed to evaluate the importance of parenting styles to optimism controlling for the variance accounted for by personality factors. Participants were 344 Brazilian high school students (44% male) with mean age of 16.2 years (SD = 1) who answered personality, optimism, responsiveness and demandingness scales. Hierarchical regression analyses were conducted having personality factors (in the first step) and maternal and paternal parenting styles, and demandingness and responsiveness (in the second step) as predictive variables and optimism as the criterion. Personality factors, especially neuroticism (β = -.34, p < .01), extraversion (β = .26, p < .01) and agreeableness (β = .16, p < .01), accounted for 34% of the optimism variance and insignificant variance was predicted exclusively by parental styles (1%). These findings suggest that personality is more important to optimism development than parental styles.

Pair-Wise and Many-Body Dispersive Interactions Coupled to an Optimally Tuned Range-Separated Hybrid Functional.

PubMed

Agrawal, Piyush; Tkatchenko, Alexandre; Kronik, Leeor

2013-08-13

We propose a nonempirical, pair-wise or many-body dispersion-corrected, optimally tuned range-separated hybrid functional. This functional retains the advantages of the optimal-tuning approach in the prediction of the electronic structure. At the same time, it gains accuracy in the prediction of binding energies for dispersively bound systems, as demonstrated on the S22 and S66 benchmark sets of weakly bound dimers.

Optimizing a Sensor Network with Data from Hazard Mapping Demonstrated in a Heavy-Vehicle Manufacturing Facility.

PubMed

Berman, Jesse D; Peters, Thomas M; Koehler, Kirsten A

2018-05-28

To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards. Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance. The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs. Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

PubMed

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

PubMed Central

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055

Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.

PubMed

Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam

2016-03-01

A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

Computational optimization and biological evolution.

PubMed

Goryanin, Igor

2010-10-01

Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

Prediction of protein-protein interaction network using a multi-objective optimization approach.

PubMed

Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

2016-06-01

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

Analysis of Spatial Autocorrelation for Optimal Observation Network in Korea

NASA Astrophysics Data System (ADS)

Park, S.; Lee, S.; Lee, E.; Park, S. K.

2016-12-01

Many studies for improving prediction of high-impact weather have been implemented, such as THORPEX (The Observing System Research and Predictability Experiment), FASTEX (Fronts and Atlantic Storm-Track Experiment), NORPEX (North Pacific Experiment), WSR/NOAA (Winter Storm Reconnaissance), and DOTSTAR (Dropwindsonde Observations for Typhoon Surveillance near the TAiwan Region). One of most important objectives in these studies is to find effects of observation on forecast, and to establish optimal observation network. However, there are lack of such studies on Korea, although Korean peninsula exhibits a highly complex terrain so it is difficult to predict its weather phenomena. Through building the future optimal observation network, it is necessary to increase utilization of numerical weather prediction and improve monitoring·tracking·prediction skills of high-impact weather in Korea. Therefore, we will perform preliminary study to understand the spatial scale for an expansion of observation system through Spatial Autocorrelation (SAC) analysis. In additions, we will develop a testbed system to design an optimal observation network. Analysis is conducted with Automatic Weather System (AWS) rainfall data, global upper air grid observation (i.e., temperature, pressure, humidity), Himawari satellite data (i.e., water vapor) during 2013-2015 of Korea. This study will provide a guideline to construct observation network for not only improving weather prediction skill but also cost-effectiveness.

Results of an integrated structure/control law design sensitivity analysis

NASA Technical Reports Server (NTRS)

Gilbert, Michael G.

1989-01-01

A design sensitivity analysis method for Linear Quadratic Cost, Gaussian (LQG) optimal control laws, which predicts change in the optimal control law due to changes in fixed problem parameters using analytical sensitivity equations is discussed. Numerical results of a design sensitivity analysis for a realistic aeroservoelastic aircraft example are presented. In this example, the sensitivity of the optimally controlled aircraft's response to various problem formulation and physical aircraft parameters is determined. These results are used to predict the aircraft's new optimally controlled response if the parameter was to have some other nominal value during the control law design process. The sensitivity results are validated by recomputing the optimal control law for discrete variations in parameters, computing the new actual aircraft response, and comparing with the predicted response. These results show an improvement in sensitivity accuracy for integrated design purposes over methods which do not include changes in the optimal control law. Use of the analytical LQG sensitivity expressions is also shown to be more efficient than finite difference methods for the computation of the equivalent sensitivity information.

Economic optimization of operations for hybrid energy systems under variable markets

DOE PAGES

Chen, Jen; Garcia, Humberto E.

2016-05-21

We prosed a hybrid energy systems (HES) which is an important element to enable increasing penetration of clean energy. Our paper investigates the operations flexibility of HES, and develops a methodology for operations optimization for maximizing economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. In order to compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulationmore » results of two specific HES configurations are included to illustrate the proposed methodology and computational capability. These results demonstrate the economic viability of HES under proposed operations optimizer, suggesting the diversion of energy for alternative energy output while participating in the ancillary service market. Economic advantages of such operations optimizer and associated flexible operations are illustrated by comparing the economic performance of flexible operations against that of constant operations. Sensitivity analysis with respect to market variability and prediction error, are also performed.« less

Economic optimization of operations for hybrid energy systems under variable markets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Jen; Garcia, Humberto E.

We prosed a hybrid energy systems (HES) which is an important element to enable increasing penetration of clean energy. Our paper investigates the operations flexibility of HES, and develops a methodology for operations optimization for maximizing economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. In order to compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulationmore » results of two specific HES configurations are included to illustrate the proposed methodology and computational capability. These results demonstrate the economic viability of HES under proposed operations optimizer, suggesting the diversion of energy for alternative energy output while participating in the ancillary service market. Economic advantages of such operations optimizer and associated flexible operations are illustrated by comparing the economic performance of flexible operations against that of constant operations. Sensitivity analysis with respect to market variability and prediction error, are also performed.« less

Strontium-90 Biokinetics from Simulated Wound Intakes in Non-human Primates Compared with Combined Model Predictions from National Council on Radiation Protection and Measurements Report 156 and International Commission on Radiological Protection Publication 67.

PubMed

Allen, Mark B; Brey, Richard R; Gesell, Thomas; Derryberry, Dewayne; Poudel, Deepesh

2016-01-01

This study had a goal to evaluate the predictive capabilities of the National Council on Radiation Protection and Measurements (NCRP) wound model coupled to the International Commission on Radiological Protection (ICRP) systemic model for 90Sr-contaminated wounds using non-human primate data. Studies were conducted on 13 macaque (Macaca mulatta) monkeys, each receiving one-time intramuscular injections of 90Sr solution. Urine and feces samples were collected up to 28 d post-injection and analyzed for 90Sr activity. Integrated Modules for Bioassay Analysis (IMBA) software was configured with default NCRP and ICRP model transfer coefficients to calculate predicted 90Sr intake via the wound based on the radioactivity measured in bioassay samples. The default parameters of the combined models produced adequate fits of the bioassay data, but maximum likelihood predictions of intake were overestimated by a factor of 1.0 to 2.9 when bioassay data were used as predictors. Skeletal retention was also over-predicted, suggesting an underestimation of the excretion fraction. Bayesian statistics and Monte Carlo sampling were applied using IMBA to vary the default parameters, producing updated transfer coefficients for individual monkeys that improved model fit and predicted intake and skeletal retention. The geometric means of the optimized transfer rates for the 11 cases were computed, and these optimized sample population parameters were tested on two independent monkey cases and on the 11 monkeys from which the optimized parameters were derived. The optimized model parameters did not improve the model fit in most cases, and the predicted skeletal activity produced improvements in three of the 11 cases. The optimized parameters improved the predicted intake in all cases but still over-predicted the intake by an average of 50%. The results suggest that the modified transfer rates were not always an improvement over the default NCRP and ICRP model values.

Study of chromatic adaptation using memory color matches, Part II: colored illuminants.

PubMed

Smet, Kevin A G; Zhai, Qiyan; Luo, Ming R; Hanselaer, Peter

2017-04-03

In a previous paper, 12 corresponding color data sets were derived for 4 neutral illuminants using the long-term memory colours of five familiar objects. The data were used to test several linear (one-step and two-step von Kries, RLAB) and nonlinear (Hunt and Nayatani) chromatic adaptation transforms (CAT). This paper extends that study to a total of 156 corresponding color sets by including 9 more colored illuminants: 2 with low and 2 with high correlated color temperatures as well as 5 representing high chroma adaptive conditions. As in the previous study, a two-step von Kries transform whereby the degree of adaptation D is optimized to minimize the DEu'v' prediction errors outperformed all other tested models for both memory color and literature corresponding color sets, whereby prediction errors were lower for the memory color set. Most of the transforms tested, except the two- and one-step von Kries models with optimized D, showed large errors for corresponding color subsets that contained non-neutral adaptive conditions as all of them tended to overestimate the effective degree of adaptation in this study. An analysis of the impact of the sensor space primaries in which the adaptation is performed was found to have little impact compared to that of model choice. Finally, the effective degree of adaptation for the 13 illumination conditions (4 neutral + 9 colored) was successfully modelled using a bivariate Gaussian in a Macleod-Boyton like chromaticity diagram.

Recognition, prevention, and proactive management of hypoglycemia in patients with type 1 diabetes mellitus.

PubMed

Unger, Jeff; Parkin, Christopher

2011-07-01

Hypoglycemia is the key barrier that prevents patients from optimizing glycemic control with the use of pharmacotherapeutic interventions. Optimal glycemic control for patients with type 1 diabetes (T1DM) includes methods that provide glucose-regulated physiologic insulin replacement or secretion in association with glucose monitoring methods designed to predict and prevent acute extreme changes in glycemic variability. Patients with T1DM experience an average of 2 episodes of symptomatic hypoglycemia each week and at least 1 episode of severe, disabling hypoglycemia annually. Asymptomatic hypoglycemia is common, as shown in studies using continuous glucose monitoring (CGM). Episodes of hypoglycemia (symptomatic and asymptomatic) impair counterregulatory defenses against subsequent events, resulting in the inability to respond to and recover from serious hypoglycemia. This defective counterregulation is known as hypoglycemic-associated autonomic failure. When patients are prescribed a more intensive medication regimen or reinforcing lifestyle interventions, such as medical nutrition therapy and exercise therapy, providers should also assess their ability to proactively identify and manage hypoglycemia. Although self-monitoring of blood glucose regimens, such as pre- and post-meal and periodic middle-of-the-night glucose testing, can help predict the risk of developing hypoglycemia, CGM technology allows patients to receive real-time notification of impending events either through preset alarms or simply by looking at the device display. This review explores the utility of initiating CGM within the primary care setting for patients at high risk for developing hypoglycemia.

A wire length minimization approach to ocular dominance patterns in mammalian visual cortex

NASA Astrophysics Data System (ADS)

Chklovskii, Dmitri B.; Koulakov, Alexei A.

2000-09-01

The primary visual area (V1) of the mammalian brain is a thin sheet of neurons. Because each neuron is dominated by either right or left eye one can treat V1 as a binary mixture of neurons. The spatial arrangement of neurons dominated by different eyes is known as the ocular dominance (OD) pattern. We propose a theory for OD patterns based on the premise that they are evolutionary adaptations to minimize the length of intra-cortical connections. Thus, the existing OD patterns are obtained by solving a wire length minimization problem. We divide all the neurons into two classes: right- and left-eye dominated. We find that if the number of connections of each neuron with the neurons of the same class differs from that with the other class, the segregation of neurons into monocular regions indeed reduces the wire length. The shape of the regions depends on the relative number of neurons in the two classes. If both classes are equally represented we find that the optimal OD pattern consists of alternating stripes. If one class is less numerous than the other, the optimal OD pattern consists of patches of the underrepresented (ipsilateral) eye dominated neurons surrounded by the neurons of the other class. We predict the transition from stripes to patches when the fraction of neurons dominated by the ipsilateral eye is about 40%. This prediction agrees with the data in macaque and Cebus monkeys. Our theory can be applied to other binary cortical systems.

Selective reinforcement of a 2m-class lightweight mirror for horizontal beam optical testing

NASA Astrophysics Data System (ADS)

Besuner, R. W.; Chow, K. P.; Kendrick, S. E.; Streetman, S.

2008-07-01

Optical testing of large mirrors for space telescopes can be challenging and complex. Demanding optical requirements necessitate both precise mirror figure and accurate prediction of zero gravity shape. Mass and packaging constraints require mirrors to be lightweighted and optically fast. Reliability and low mass imply simple mounting schemes, with basic kinematic mounts preferable to active figure control or whiffle trees. Ground testing should introduce as little uncertainty as possible, ideally employing flight mounts without offloaders. Testing mirrors with their optical axes horizontal can result in less distortion than in the vertical orientation, though distortion will increase with mirror speed. Finite element modeling and optimization tools help specify selective reinforcement of the mirror structure to minimize wavefront errors in a one gravity test, while staying within mass budgets and meeting other requirements. While low distortions are necessary, an important additional criterion is that designs are tolerant to imperfect positioning of the mounts relative to the neutral surface of the mirror substrate. In this paper, we explore selective reinforcement of a 2-meter class, f/1.25 primary mirror for the proposed SNAP space telescope. We specify designs optimized for various mount radial locations both with and without backup mount locations. Reinforced designs are predicted to have surface distortions in the horizontal beam test low enough to perform optical testing on the ground, on flight mounts, and without offloaders. Importantly, the required accuracy of mount locations is on the order of millimeters rather than tenths of millimeters.

Improving Models for Coseismic And Postseismic Deformation from the 2002 Denali, Alaska Earthquake

NASA Astrophysics Data System (ADS)

Harper, H.; Freymueller, J. T.

2016-12-01

Given the multi-decadal temporal scale of postseismic deformation, predictions of previous models for postseismic deformation resulting from the 2002 Denali Fault earthquake (M 7.9) do not agree with longer-term observations. In revising the past postseismic models with what is now over a decade of data, the first step is revisiting coseismic displacements and slip distribution of the earthquake. Advances in processing allow us to better constrain coseismic displacement estimates, which affect slip distribution predictions in modeling. Additionally, an updated slip model structure from a homogeneous model to a layered model rectifies previous inconsistencies between coseismic and postseismic models. Previous studies have shown that two primary processes contribute to postseismic deformation: afterslip, which decays with a short time constant; and viscoelastic relaxation, which decays with a longer time constant. We fit continuous postseismic GPS time series with three different relaxation models: 1) logarithmic decay + exponential decay, 2) log + exp + exp, and 3) log + log + exp. A grid search is used to minimize total model WRSS, and we find optimal relaxation times of: 1) 0.125 years (log) and 21.67 years (exp); 2) 0.14 years (log), 0.68 years (exp), and 28.33 years (exp); 3) 0.055 years (log), 14.44 years (log), and 22.22 years (exp). While there is not a one-to-one correspondence between a particular decay constant and a mechanism, the optimization of these constants allows us to model the future timeseries and constrain the contribution of different postseismic processes.

Optimizing protocols for risk prediction in asymptomatic carotid stenosis using embolic signal detection: the Asymptomatic Carotid Emboli Study.

PubMed

King, Alice; Shipley, Martin; Markus, Hugh

2011-10-01

Improved methods are required to identify patients with asymptomatic carotid stenosis at high risk for stroke. The Asymptomatic Carotid Emboli Study recently showed embolic signals (ES) detected by transcranial Doppler on 2 recordings that lasted 1-hour independently predict 2-year stroke risk. ES detection is time-consuming, and whether similar predictive information could be obtained from simpler recording protocols is unknown. In a predefined secondary analysis of Asymptomatic Carotid Emboli Study, we looked at the temporal variation of ES. We determined the predictive yield associated with different recording protocols and with the use of a higher threshold to indicate increased risk (≥2 ES). To compare the different recording protocols, sensitivity and specificity analyses were performed using analysis of receiver-operator characteristic curves. Of 477 patients, 467 had baseline recordings adequate for analysis; 77 of these had ES on 1 or both of the 2 recordings. ES status on the 2 recordings was significantly associated (P<0.0001), but there was poor agreement between ES positivity on the 2 recordings (κ=0.266). For the primary outcome of ipsilateral stroke or transient ischemic attack, the use of 2 baseline recordings lasting 1 hour had greater predictive accuracy than either the first baseline recording alone (P=0.0005), a single 30-minute (P<0.0001) recording, or 2 recordings lasting 30 minutes (P<0.0001). For the outcome of ipsilateral stroke alone, two recordings lasting 1 hour had greater predictive accuracy when compared to all other recording protocols (all P<0.0001). Our analysis demonstrates the relative predictive yield of different recording protocols that can be used in application of the technique in clinical practice. Two baseline recordings lasting 1 hour as used in Asymptomatic Carotid Emboli Study gave the best risk prediction.

Subtype Diagnosis of Primary Aldosteronism: Is Adrenal Vein Sampling Always Necessary?

PubMed Central

Buffolo, Fabrizio; Monticone, Silvia; Williams, Tracy A.; Rossato, Denis; Burrello, Jacopo; Tetti, Martina; Veglio, Franco; Mulatero, Paolo

2017-01-01

Aldosterone producing adenoma and bilateral adrenal hyperplasia are the two most common subtypes of primary aldosteronism (PA) that require targeted and distinct therapeutic approaches: unilateral adrenalectomy or lifelong medical therapy with mineralocorticoid receptor antagonists. According to the 2016 Endocrine Society Guideline, adrenal venous sampling (AVS) is the gold standard test to distinguish between unilateral and bilateral aldosterone overproduction and therefore, to safely refer patients with PA to surgery. Despite significant advances in the optimization of the AVS procedure and the interpretation of hormonal data, a standardized protocol across centers is still lacking. Alternative methods are sought to either localize an aldosterone producing adenoma or to predict the presence of unilateral disease and thereby substantially reduce the number of patients with PA who proceed to AVS. In this review, we summarize the recent advances in subtyping PA for the diagnosis of unilateral and bilateral disease. We focus on the developments in the AVS procedure, the interpretation criteria, and comparisons of the performance of AVS with the alternative methods that are currently available. PMID:28420172

Topography-based Flood Planning and Optimization Capability Development Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Judi, David R.; Tasseff, Byron A.; Bent, Russell W.

2014-02-26

Globally, water-related disasters are among the most frequent and costly natural hazards. Flooding inflicts catastrophic damage on critical infrastructure and population, resulting in substantial economic and social costs. NISAC is developing LeveeSim, a suite of nonlinear and network optimization models, to predict optimal barrier placement to protect critical regions and infrastructure during flood events. LeveeSim currently includes a high-performance flood model to simulate overland flow, as well as a network optimization model to predict optimal barrier placement during a flood event. The LeveeSim suite models the effects of flooding in predefined regions. By manipulating a domain’s underlying topography, developers alteredmore » flood propagation to reduce detrimental effects in areas of interest. This numerical altering of a domain’s topography is analogous to building levees, placing sandbags, etc. To induce optimal changes in topography, NISAC used a novel application of an optimization algorithm to minimize flooding effects in regions of interest. To develop LeveeSim, NISAC constructed and coupled hydrodynamic and optimization algorithms. NISAC first implemented its existing flood modeling software to use massively parallel graphics processing units (GPUs), which allowed for the simulation of larger domains and longer timescales. NISAC then implemented a network optimization model to predict optimal barrier placement based on output from flood simulations. As proof of concept, NISAC developed five simple test scenarios, and optimized topographic solutions were compared with intuitive solutions. Finally, as an early validation example, barrier placement was optimized to protect an arbitrary region in a simulation of the historic Taum Sauk dam breach.« less

Depression in primary TKA and higher medical comorbidities in revision TKA are associated with suboptimal subjective improvement in knee function.

PubMed

Singh, Jasvinder A; Lewallen, David G

2014-04-11

To characterize whether medical comorbidities, depression and anxiety predict patient-reported functional improvement after total knee arthroplasty (TKA). We analyzed the prospectively collected data from the Mayo Clinic Total Joint Registry for patients who underwent primary or revision TKA between 1993-2005. Using multivariable-adjusted logistic regression analyses, we examined whether medical comorbidities, depression and anxiety were associated with patient-reported subjective improvement in knee function 2- or 5-years after primary or revision TKA. Odds ratios (OR), along with 95% confidence intervals (CI) and p-value are presented. We studied 7,139 primary TKAs at 2- and 4,234 at 5-years; and, 1,533 revision TKAs at 2-years and 881 at 5-years. In multivariable-adjusted analyses, we found that depression was associated with significantly lower odds of 0.5 (95% confidence interval [CI]: 0.3 to 0.9; p = 0.02) of 'much better' knee functional status (relative to same or worse status) 2 years after primary TKA. Higher Deyo-Charlson index was significantly associated with lower odds of 0.5 (95% CI: 0.2 to 1.0; p = 0.05) of 'much better' knee functional status after revision TKA for every 5-point increase in score. Depression in primary TKA and higher medical comorbidity in revision TKA cohorts were associated with suboptimal improvement in index knee function. It remains to be seen whether strategies focused at optimization of medical comorbidities and depression pre- and peri-operatively may help to improve TKA outcomes. Study limitations include non-response bias and the use of diagnostic codes, which may be associated with under-diagnosis of conditions.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

PubMed

Cournia, Zoe; Allen, Bryce; Sherman, Woody

2017-12-26

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

A stimulus-dependent spike threshold is an optimal neural coder

PubMed Central

Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

2015-01-01

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.

PubMed

Sasaki, Satoshi; Comber, Alexis J; Suzuki, Hiroshi; Brunsdon, Chris

2010-01-28

Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations. Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared. The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.

Optimal speeds for walking and running, and walking on a moving walkway.

PubMed

Srinivasan, Manoj

2009-06-01

Many aspects of steady human locomotion are thought to be constrained by a tendency to minimize the expenditure of metabolic cost. This paper has three parts related to the theme of energetic optimality: (1) a brief review of energetic optimality in legged locomotion, (2) an examination of the notion of optimal locomotion speed, and (3) an analysis of walking on moving walkways, such as those found in some airports. First, I describe two possible connotations of the term "optimal locomotion speed:" that which minimizes the total metabolic cost per unit distance and that which minimizes the net cost per unit distance (total minus resting cost). Minimizing the total cost per distance gives the maximum range speed and is a much better predictor of the speeds at which people and horses prefer to walk naturally. Minimizing the net cost per distance is equivalent to minimizing the total daily energy intake given an idealized modern lifestyle that requires one to walk a given distance every day--but it is not a good predictor of animals' walking speeds. Next, I critique the notion that there is no energy-optimal speed for running, making use of some recent experiments and a review of past literature. Finally, I consider the problem of predicting the speeds at which people walk on moving walkways--such as those found in some airports. I present two substantially different theories to make predictions. The first theory, minimizing total energy per distance, predicts that for a range of low walkway speeds, the optimal absolute speed of travel will be greater--but the speed relative to the walkway smaller--than the optimal walking speed on stationary ground. At higher walkway speeds, this theory predicts that the person will stand still. The second theory is based on the assumption that the human optimally reconciles the sensory conflict between the forward speed that the eye sees and the walking speed that the legs feel and tries to equate the best estimate of the forward speed to the naturally preferred speed. This sensory conflict theory also predicts that people would walk slower than usual relative to the walkway yet move faster than usual relative to the ground. These predictions agree qualitatively with available experimental observations, but there are quantitative differences.

Prediction of pilot opinion ratings using an optimal pilot model. [of aircraft handling qualities in multiaxis tasks

NASA Technical Reports Server (NTRS)

Hess, R. A.

1977-01-01

A brief review of some of the more pertinent applications of analytical pilot models to the prediction of aircraft handling qualities is undertaken. The relative ease with which multiloop piloting tasks can be modeled via the optimal control formulation makes the use of optimal pilot models particularly attractive for handling qualities research. To this end, a rating hypothesis is introduced which relates the numerical pilot opinion rating assigned to a particular vehicle and task to the numerical value of the index of performance resulting from an optimal pilot modeling procedure as applied to that vehicle and task. This hypothesis is tested using data from piloted simulations and is shown to be reasonable. An example concerning a helicopter landing approach is introduced to outline the predictive capability of the rating hypothesis in multiaxis piloting tasks.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Two routes toward optimism: how agentic and communal themes in autobiographical memories guide optimism for the future.

PubMed

Austin, Adrienne; Costabile, Kristi

2017-11-01

Autobiographical memories are particularly adaptive because they function not only to preserve the past, but also to direct our future thoughts and behaviours. Two studies were conducted to examine how communal and agentic themes of positive autobiographical memories differentially predicted the route from autobiographical memories to optimism for the future. Across two studies, results revealed that the degree to which participants focused on communal themes in their autobiographical memories predicted their experience of nostalgia. In turn, the experience of nostalgia increased participants' levels of self-esteem and in turn, optimism for the future. By contrast, the degree to which participants focused on agentic themes in their memories predicted self-esteem and optimism, operating outside the experience of nostalgia. These effects remained even after controlling for self-focused attention. Together, these studies provide greater understanding of the interrelations among autobiographical memory, self-concept, and time, and demonstrate how agency and communion operate to influence perceptions of one's future when thinking about the past.

Comparison of primary optics in amonix CPV arrays

NASA Astrophysics Data System (ADS)

Nayak, Aditya; Kinsey, Geoffrey S.; Liu, Mingguo; Bagienski, William; Garboushian, Vahan

2012-10-01

The Amonix CPV system utilizes an acrylic Fresnel lens Primary Optical Element (POE) and a reflective Secondary Optical Element (SOE). Improvements in the optical design have contributed to more than 10% increase in rated power last year. In order to further optimize the optical power path, Amonix is looking at various trade-offs in optics, including, concentration, optical materials, reliability, and cost. A comparison of optical materials used for manufacturing the primary optical element and optical design trade off's used to maximize power output will be presented. Optimization of the power path has led to the demonstration of a module lens-area efficiency of 35% in outdoor testing at Amonix.

Optimization of a new mathematical model for bacterial growth

USDA-ARS?s Scientific Manuscript database

The objective of this research is to optimize a new mathematical equation as a primary model to describe the growth of bacteria under constant temperature conditions. An optimization algorithm was used in combination with a numerical (Runge-Kutta) method to solve the differential form of the new gr...

A New Approach for Identifying Patients with Undiagnosed Chronic Obstructive Pulmonary Disease

PubMed Central

Mannino, David; Leidy, Nancy Kline; Malley, Karen G.; Bacci, Elizabeth D.; Barr, R. Graham; Bowler, Russ P.; Han, MeiLan K.; Houfek, Julia F.; Make, Barry; Meldrum, Catherine A.; Rennard, Stephen; Thomashow, Byron; Walsh, John; Yawn, Barbara P.

2017-01-01

Rationale: Chronic obstructive pulmonary disease (COPD) is often unrecognized and untreated. Objectives: To develop a method for identifying undiagnosed COPD requiring treatment with currently available therapies (FEV1 <60% predicted and/or exacerbation risk). Methods: We conducted a multisite, cross-sectional, case-control study in U.S. pulmonary and primary care clinics that recruited subjects from primary care settings. Cases were patients with COPD and at least one exacerbation in the past year or FEV1 less than 60% of predicted without exacerbation in the past year. Control subjects were persons with no COPD or with mild COPD (FEV1 ≥60% predicted, no exacerbation in the past year). In random forests analyses, we identified the smallest set of questions plus peak expiratory flow (PEF) with optimal sensitivity (SN) and specificity (SP). Measurements and Main Results: PEF and spirometry were recorded in 186 cases and 160 control subjects. The mean (SD) age of the sample population was 62.7 (10.1) years; 55% were female; 86% were white; and 16% had never smoked. The mean FEV1 percent predicted for cases was 42.5% (14.2%); for control subjects, it was 82.5% (15.7%). A five-item questionnaire, CAPTURE (COPD Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk), was used to assess exposure, breathing problems, tiring easily, and acute respiratory illnesses. CAPTURE exhibited an SN of 95.7% and an SP of 44.4% for differentiating cases from all control subjects, and an SN of 95.7% and an SP of 67.8% for differentiating cases from no-COPD control subjects. The PEF (males, <350 L/min; females, <250 L/min) SN and SP were 88.0% and 77.5%, respectively, for differentiating cases from all control subjects, and they were 88.0% and 90.8%, respectively, for distinguishing cases from no-COPD control subjects. The CAPTURE plus PEF exhibited improved SN and SP for all cases versus all control subjects (89.7% and 78.1%, respectively) and for all cases versus no-COPD control subjects (89.7% and 93.1%, respectively). Conclusions: CAPTURE with PEF can identify patients with COPD who would benefit from currently available therapy and require further diagnostic evaluation. Clinical trial registered with clinicaltrials.gov (NCT01880177). PMID:27783539

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less

Development of Predictive Energy Management Strategies for Hybrid Electric Vehicles

NASA Astrophysics Data System (ADS)

Baker, David

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods.

Researches of fruit quality prediction model based on near infrared spectrum

NASA Astrophysics Data System (ADS)

Shen, Yulin; Li, Lian

2018-04-01

With the improvement in standards for food quality and safety, people pay more attention to the internal quality of fruits, therefore the measurement of fruit internal quality is increasingly imperative. In general, nondestructive soluble solid content (SSC) and total acid content (TAC) analysis of fruits is vital and effective for quality measurement in global fresh produce markets, so in this paper, we aim at establishing a novel fruit internal quality prediction model based on SSC and TAC for Near Infrared Spectrum. Firstly, the model of fruit quality prediction based on PCA + BP neural network, PCA + GRNN network, PCA + BP adaboost strong classifier, PCA + ELM and PCA + LS_SVM classifier are designed and implemented respectively; then, in the NSCT domain, the median filter and the SavitzkyGolay filter are used to preprocess the spectral signal, Kennard-Stone algorithm is used to automatically select the training samples and test samples; thirdly, we achieve the optimal models by comparing 15 kinds of prediction model based on the theory of multi-classifier competition mechanism, specifically, the non-parametric estimation is introduced to measure the effectiveness of proposed model, the reliability and variance of nonparametric estimation evaluation of each prediction model to evaluate the prediction result, while the estimated value and confidence interval regard as a reference, the experimental results demonstrate that this model can better achieve the optimal evaluation of the internal quality of fruit; finally, we employ cat swarm optimization to optimize two optimal models above obtained from nonparametric estimation, empirical testing indicates that the proposed method can provide more accurate and effective results than other forecasting methods.

Stepped Care to Optimize Pain care Effectiveness (SCOPE) trial study design and sample characteristics.

PubMed

Kroenke, Kurt; Krebs, Erin; Wu, Jingwei; Bair, Matthew J; Damush, Teresa; Chumbler, Neale; York, Tish; Weitlauf, Sharon; McCalley, Stephanie; Evans, Erica; Barnd, Jeffrey; Yu, Zhangsheng

2013-03-01

Pain is the most common physical symptom in primary care, accounting for an enormous burden in terms of patient suffering, quality of life, work and social disability, and health care and societal costs. Although collaborative care interventions are well-established for conditions such as depression, fewer systems-based interventions have been tested for chronic pain. This paper describes the study design and baseline characteristics of the enrolled sample for the Stepped Care to Optimize Pain care Effectiveness (SCOPE) study, a randomized clinical effectiveness trial conducted in five primary care clinics. SCOPE has enrolled 250 primary care veterans with persistent (3 months or longer) musculoskeletal pain of moderate severity and randomized them to either the stepped care intervention or usual care control group. Using a telemedicine collaborative care approach, the intervention couples automated symptom monitoring with a telephone-based, nurse care manager/physician pain specialist team to treat pain. The goal is to optimize analgesic management using a stepped care approach to drug selection, symptom monitoring, dose adjustment, and switching or adding medications. All subjects undergo comprehensive outcome assessments at baseline, 1, 3, 6 and 12 months by interviewers blinded to treatment group. The primary outcome is pain severity/disability, and secondary outcomes include pain beliefs and behaviors, psychological functioning, health-related quality of life and treatment satisfaction. Innovations of SCOPE include optimized analgesic management (including a stepped care approach, opioid risk stratification, and criteria-based medication adjustment), automated monitoring, and centralized care management that can cover multiple primary care practices. Published by Elsevier Inc.

The WOMEN study: what is the optimal method for ischemia evaluation in women? A multi-center, prospective, randomized study to establish the optimal method for detection of coronary artery disease (CAD) risk in women at an intermediate-high pretest likelihood of CAD: study design.

PubMed

Mieres, Jennifer H; Shaw, Leslee J; Hendel, Robert C; Heller, Gary V

2009-01-01

Coronary artery disease remains the leading cause of morbidity and mortality in women. The optimal non-invasive test for evaluation of ischemic heart disease in women is unknown. Although current guidelines support the choice of the exercise tolerance test (ETT) as a first line test for women with a normal baseline ECG and adequate exercise capabilities, supportive data for this recommendation are controversial. The what is the optimal method for ischemia evaluation in women? (WOMEN) study was designed to determine the optimal non-invasive strategy for CAD risk detection of intermediate and high risk women presenting with chest pain or equivalent symptoms suggestive of ischemic heart disease. The study will prospectively compare the 2-year event rates in women capable of performing exercise treadmill testing or Tc-99 m tetrofosmin SPECT myocardial perfusion imaging (MPI). The study will enroll women presenting for the evaluation of chest pain or anginal equivalent symptoms who are capable of performing >5 METs of exercise while at intermediate-high pretest risk for ischemic heart disease who will be randomized to either ETT testing alone or with Tc-99 m tetrofosmin SPECT MPI. The null hypothesis for this project is that the exercise ECG has the same negative predictive value for risk detection as gated myocardial perfusion SPECT in women. The primary aim is to compare 2-year cardiac event rates in women randomized to SPECT MPI to those randomized to ETT. The WOMEN study seeks to provide objective information for guidelines for the evaluation of symptomatic women with an intermediate-high likelihood for CAD.

Integrated design and manufacturing for the high speed civil transport (a combined aerodynamics/propulsion optimization study)

NASA Technical Reports Server (NTRS)

Baecher, Juergen; Bandte, Oliver; DeLaurentis, Dan; Lewis, Kemper; Sicilia, Jose; Soboleski, Craig

1995-01-01

This report documents the efforts of a Georgia Tech High Speed Civil Transport (HSCT) aerospace student design team in completing a design methodology demonstration under NASA's Advanced Design Program (ADP). Aerodynamic and propulsion analyses are integrated into the synthesis code FLOPS in order to improve its prediction accuracy. Executing the integrated product and process development (IPPD) methodology proposed at the Aerospace Systems Design Laboratory (ASDL), an improved sizing process is described followed by a combined aero-propulsion optimization, where the objective function, average yield per revenue passenger mile ($/RPM), is constrained by flight stability, noise, approach speed, and field length restrictions. Primary goals include successful demonstration of the application of the response surface methodolgy (RSM) to parameter design, introduction to higher fidelity disciplinary analysis than normally feasible at the conceptual and early preliminary level, and investigations of relationships between aerodynamic and propulsion design parameters and their effect on the objective function, $/RPM. A unique approach to aircraft synthesis is developed in which statistical methods, specifically design of experiments and the RSM, are used to more efficiently search the design space for optimum configurations. In particular, two uses of these techniques are demonstrated. First, response model equations are formed which represent complex analysis in the form of a regression polynomial. Next, a second regression equation is constructed, not for modeling purposes, but instead for the purpose of optimization at the system level. Such an optimization problem with the given tools normally would be difficult due to the need for hard connections between the various complex codes involved. The statistical methodology presents an alternative and is demonstrated via an example of aerodynamic modeling and planform optimization for a HSCT.

Building Energy Modeling and Control Methods for Optimization and Renewables Integration

NASA Astrophysics Data System (ADS)

Burger, Eric M.

This dissertation presents techniques for the numerical modeling and control of building systems, with an emphasis on thermostatically controlled loads. The primary objective of this work is to address technical challenges related to the management of energy use in commercial and residential buildings. This work is motivated by the need to enhance the performance of building systems and by the potential for aggregated loads to perform load following and regulation ancillary services, thereby enabling the further adoption of intermittent renewable energy generation technologies. To increase the generalizability of the techniques, an emphasis is placed on recursive and adaptive methods which minimize the need for customization to specific buildings and applications. The techniques presented in this dissertation can be divided into two general categories: modeling and control. Modeling techniques encompass the processing of data streams from sensors and the training of numerical models. These models enable us to predict the energy use of a building and of sub-systems, such as a heating, ventilation, and air conditioning (HVAC) unit. Specifically, we first present an ensemble learning method for the short-term forecasting of total electricity demand in buildings. As the deployment of intermittent renewable energy resources continues to rise, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. Second, we present a recursive parameter estimation technique for identifying a thermostatically controlled load (TCL) model that is non-linear in the parameters. For TCLs to perform demand response services in real-time markets, online methods for parameter estimation are needed. Third, we develop a piecewise linear thermal model of a residential building and train the model using data collected from a custom-built thermostat. This model is capable of approximating unmodeled dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.

PubMed

Hamilton, C A; Miller, A; Casablanca, Y; Horowitz, N S; Rungruang, B; Krivak, T C; Richard, S D; Rodriguez, N; Birrer, M J; Backes, F J; Geller, M A; Quinn, M; Goodheart, M J; Mutch, D G; Kavanagh, J J; Maxwell, G L; Bookman, M A

2018-02-01

To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study

PubMed Central

Hamilton, C. A.; Miller, A.; Casablanca, Y.; Horowitz, N. S.; Rungruang, B.; Krivak, T. C.; Richard, S. D.; Rodriguez, N.; Birrer, M.J.; Backes, F.J.; Geller, M.A.; Quinn, M.; Goodheart, M.J.; Mutch, D.G.; Kavanagh, J.J.; Maxwell, G. L.; Bookman, M. A.

2018-01-01

Objective To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Methods Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10 years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). Results The analysis dataset included 3,010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. Conclusions The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. PMID:29195926

Optimal design of a piezoelectric transducer for exciting guided wave ultrasound in rails

NASA Astrophysics Data System (ADS)

Ramatlo, Dineo A.; Wilke, Daniel N.; Loveday, Philip W.

2017-02-01

An existing Ultrasonic Broken Rail Detection System installed in South Africa on a heavy duty railway line is currently being upgraded to include defect detection and location. To accomplish this, an ultrasonic piezoelectric transducer to strongly excite a guided wave mode with energy concentrated in the web (web mode) of a rail is required. A previous study demonstrated that the recently developed SAFE-3D (Semi-Analytical Finite Element - 3 Dimensional) method can effectively predict the guided waves excited by a resonant piezoelectric transducer. In this study, the SAFE-3D model is used in the design optimization of a rail web transducer. A bound-constrained optimization problem was formulated to maximize the energy transmitted by the transducer in the web mode when driven by a pre-defined excitation signal. Dimensions of the transducer components were selected as the three design variables. A Latin hypercube sampled design of experiments that required a total of 500 SAFE-3D analyses in the design space was employed in a response surface-based optimization approach. The Nelder-Mead optimization algorithm was then used to find an optimal transducer design on the constructed response surface. The radial basis function response surface was first verified by comparing a number of predicted responses against the computed SAFE-3D responses. The performance of the optimal transducer predicted by the optimization algorithm on the response surface was also verified to be sufficiently accurate using SAFE-3D. The computational advantages of SAFE-3D in optimal transducer design are noteworthy as more than 500 analyses were performed. The optimal design was then manufactured and experimental measurements were used to validate the predicted performance. The adopted design method has demonstrated the capability to automate the design of transducers for a particular rail cross-section and frequency range.

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

PubMed

Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

2016-02-01

To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

Fine Time Course Expression Analysis Identifies Cascades of Activation and Repression and Maps a Putative Regulator of Mammalian Sex Determination

PubMed Central

Looger, Loren L.; Ohler, Uwe; Capel, Blanche

2013-01-01

In vertebrates, primary sex determination refers to the decision within a bipotential organ precursor to differentiate as a testis or ovary. Bifurcation of organ fate begins between embryonic day (E) 11.0–E12.0 in mice and likely involves a dynamic transcription network that is poorly understood. To elucidate the first steps of sexual fate specification, we profiled the XX and XY gonad transcriptomes at fine granularity during this period and resolved cascades of gene activation and repression. C57BL/6J (B6) XY gonads showed a consistent ∼5-hour delay in the activation of most male pathway genes and repression of female pathway genes relative to 129S1/SvImJ, which likely explains the sensitivity of the B6 strain to male-to-female sex reversal. Using this fine time course data, we predicted novel regulatory genes underlying expression QTLs (eQTLs) mapped in a previous study. To test predictions, we developed an in vitro gonad primary cell assay and optimized a lentivirus-based shRNA delivery method to silence candidate genes and quantify effects on putative targets. We provide strong evidence that Lmo4 (Lim-domain only 4) is a novel regulator of sex determination upstream of SF1 (Nr5a1), Sox9, Fgf9, and Col9a3. This approach can be readily applied to identify regulatory interactions in other systems. PMID:23874228

Final Report: Connecting genomic capabilities to physiology and response: Systems biology of the widespread alga Micromonas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Worden, Alexandra Z.; Callister, Stephen; Stuart, Joshua

Increased stratification, less mixing and reduced nutrient concentrations in marine surface waters are predicted under a number of climate-change scenarios. These conditions are considered favorable for tiny photosynthetic algae (picophytoplankton), shaping their role in mediating future CO2 conditions. One possibility is that picophytoplankton such as Micromonas that have broad geographical ranges will more successfully adapt to changing environmental conditions. However, their capacity to thrive under the multi-factorial impacts of low pH, low nutrients, increasing temperature and changes in community composition is not known. Here, we developed the dual-Micromonas model system, which entailed generating optimized genomic information for two Micromonas speciesmore » and developing a highperformance chemostat system in which both CO2 and nutrients could be consistently manipulated. This system is now fully operational. Project results are available in several publications will others are still in the analysis phase. Overall, our results show that Micromonas primary production will likely decrease under predicted future climate conditions. Furthermore, our studies on Micromonas provide new insights to the land plant ancestor, including the discovery of conserved signaling mechanisms (known to be essential to plant development) as well as the discovery of widespread chemical-sensing molecular switches. Collectively, this research highlights Micromonas as an important new model green alga for understanding plant gene networks and evolution as well as for investigating perturbation effects on marine primary production.« less

The Relative Roles of Passive Surface Forces and Active Ion Transport in the Modulation of Airway Surface Liquid Volume and Composition

PubMed Central

Tarran, Robert; Grubb, Barbara R.; Gatzy, John T.; Davis, C. William; Boucher, Richard C.

2001-01-01

Two hypotheses have been proposed recently that offer different views on the role of airway surface liquid (ASL) in lung defense. The “compositional” hypothesis predicts that ASL [NaCl] is kept low (<50 mM) by passive forces to permit antimicrobial factors to act as a chemical defense. The “volume” hypothesis predicts that ASL volume (height) is regulated isotonically by active ion transport to maintain efficient mechanical mucus clearance as the primary form of lung defense. To compare these hypotheses, we searched for roles for: (1) passive forces (surface tension, ciliary tip capillarity, Donnan, and nonionic osmolytes) in the regulation of ASL composition; and (2) active ion transport in ASL volume regulation. In primary human tracheobronchial cultures, we found no evidence that a low [NaCl] ASL could be produced by passive forces, or that nonionic osmolytes contributed substantially to ASL osmolality. Instead, we found that active ion transport regulated ASL volume (height), and that feedback existed between the ASL and airway epithelia to govern the rate of ion transport and volume absorption. The mucus layer acted as a “reservoir” to buffer periciliary liquid layer height (7 μm) at a level optimal for mucus transport by donating or accepting liquid to or from the periciliary liquid layer, respectively. These data favor the active ion transport/volume model hypothesis to describe ASL physiology. PMID:11479349

What is the best strategy for investigating abnormal liver function tests in primary care? Implications from a prospective study.

PubMed

Lilford, Richard J; Bentham, Louise M; Armstrong, Matthew J; Neuberger, James; Girling, Alan J

2013-06-20

Evaluation of predictive value of liver function tests (LFTs) for the detection of liver-related disease in primary care. A prospective observational study. 11 UK primary care practices. Patients (n=1290) with an abnormal eight-panel LFT (but no previously diagnosed liver disease). Patients were investigated by recording clinical features, and repeating LFTs, specific tests for individual liver diseases, and abdominal ultrasound scan. Patients were characterised as having: hepatocellular disease; biliary disease; tumours of the hepato-biliary system and none of the above. The relationship between LFT results and disease categories was evaluated by stepwise regression and logistic discrimination, with adjustment for demographic and clinical factors. True and False Positives generated by all possible LFT combinations were compared with a view towards optimising the choice of analytes in the routine LFT panel. Regression methods showed that alanine aminotransferase (ALT) was associated with hepatocellular disease (32 patients), while alkaline phosphatase (ALP) was associated with biliary disease (12 patients) and tumours of the hepatobiliary system (9 patients). A restricted panel of ALT and ALP was an efficient choice of analytes, comparing favourably with the complete panel of eight analytes, provided that 48 False Positives can be tolerated to obtain one additional True Positive. Repeating a complete panel in response to an abnormal reading is not the optimal strategy. The LFT panel can be restricted to ALT and ALP when the purpose of testing is to exclude liver disease in primary care.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

[Optimal extraction of effective constituents from Aralia elata by central composite design and response surface methodology].

PubMed

Lv, Shao-Wa; Liu, Dong; Hu, Pan-Pan; Ye, Xu-Yan; Xiao, Hong-Bin; Kuang, Hai-Xue

2010-03-01

To optimize the process of extracting effective constituents from Aralia elata by response surface methodology. The independent variables were ethanol concentration, reflux time and solvent fold, the dependent variable was extraction rate of total saponins in Aralia elata. Linear or no-linear mathematic models were used to estimate the relationship between independent and dependent variables. Response surface methodology was used to optimize the process of extraction. The prediction was carried out through comparing the observed and predicted values. Regression coefficient of binomial fitting complex model was as high as 0.9617, the optimum conditions of extraction process were 70% ethanol, 2.5 hours for reflux, 20-fold solvent and 3 times for extraction. The bias between observed and predicted values was -2.41%. It shows the optimum model is highly predictive.

Effects of number of training generations on genomic prediction for various traits in a layer chicken population.

PubMed

Weng, Ziqing; Wolc, Anna; Shen, Xia; Fernando, Rohan L; Dekkers, Jack C M; Arango, Jesus; Settar, Petek; Fulton, Janet E; O'Sullivan, Neil P; Garrick, Dorian J

2016-03-19

Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

[Validation of the abbreviated Zarit scales for measuring burden syndrome in the primary caregiver of an elderly patient].

PubMed

Vélez Lopera, Johana María; Berbesí Fernández, Dedsy; Cardona Arango, Doris; Segura Cardona, Angela; Ordóñez Molina, Jaime

2012-07-01

To determine which abbreviated Zarit Scale (ZS) better evaluates the burden of the caregiver of an elderly patient in Medellin, Colombia. Validation study. Primary Care setting in the city of Medellin. Primary caregiver of dependent elderly patients over 65 years old. Sensitivity, specificity, positive predictive value, and negative predictive value for the different abbreviated Zarit scales, plus performing a reliability analysis using the Cronbach Alpha coefficient. The abbreviated scales obtained a sensitivity of between 36.84 and 81.58%, specificity between 95.99 and 100%, positive predictive values between 71.05 and 100%, and negative predictive values of between 91.64 and 97.42%. The scale that better determined caregiver burden in Primary Care was the Bedard Screening scale, with a sensitivity of 81.58%, a specificity of 96.35% and positive and negative predictive values of 75.61% and 97.42%, respectively. Copyright © 2010 Elsevier España, S.L. All rights reserved.

A preoperative low cancer antigen 125 level (≤25.8 mg/dl) is a useful criterion to determine the optimal timing of interval debulking surgery following neoadjuvant chemotherapy in epithelial ovarian cancer.

PubMed

Morimoto, Akemi; Nagao, Shoji; Kogiku, Ai; Yamamoto, Kasumi; Miwa, Maiko; Wakahashi, Senn; Ichida, Kotaro; Sudo, Tamotsu; Yamaguchi, Satoshi; Fujiwara, Kiyoshi

2016-06-01

The purpose of this study is to investigate the clinical characteristics to determine the optimal timing of interval debulking surgery following neoadjuvant chemotherapy in patients with advanced epithelial ovarian cancer. We reviewed the charts of women with advanced epithelial ovarian cancer, fallopian tube cancer or primary peritoneal cancer who underwent interval debulking surgery following neoadjuvant chemotherapy at our cancer center from April 2006 to April 2014. There were 139 patients, including 91 with ovarian cancer [International Federation of Gynecology and Obstetrics (FIGO) Stage IIIc in 56 and IV in 35], two with fallopian tube cancers (FIGO Stage IV, both) and 46 with primary peritoneal cancer (FIGO Stage IIIc in 27 and IV in 19). After 3-6 cycles (median, 4 cycles) of platinum-based chemotherapy, interval debulking surgery was performed. Sixty-seven patients (48.2%) achieved complete resection of all macroscopic disease, while 72 did not. More patients with cancer antigen 125 levels ≤25.8 mg/dl at pre-interval debulking surgery achieved complete resection than those with higher cancer antigen 125 levels (84.7 vs. 21.3%; P< 0.0001). Patients with no ascites at pre-interval debulking surgery also achieved a higher complete resection rate (63.5 vs. 34.1%; P< 0.0001). Moreover, most patients (86.7%) with cancer antigen 125 levels ≤25.8 mg/dl and no ascites at pre-interval debulking surgery achieved complete resection. A low cancer antigen 125 level of ≤25.8 mg/dl and the absence of ascites at pre-interval debulking surgery are major predictive factors for complete resection during interval debulking surgery and present useful criteria to determine the optimal timing of interval debulking surgery. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Conversion-to-open in laparoscopic appendectomy: A cohort analysis of risk factors and outcomes.

PubMed

Finnerty, Brendan M; Wu, Xian; Giambrone, Gregory P; Gaber-Baylis, Licia K; Zabih, Ramin; Bhat, Akshay; Zarnegar, Rasa; Pomp, Alfons; Fleischut, Peter; Afaneh, Cheguevara

2017-04-01

Identifying risk factors for conversion from laparoscopic to open appendectomy could select patients who may benefit from primary open appendectomy. We aimed to develop a predictive scoring model for conversion from laparoscopic to open based on pre-operative patient characteristics. A retrospective review of the State Inpatient Database (2007-2011) was performed using derivation (N = 71,617) and validation (N = 143,235) cohorts of adults ≥ 18 years with acute appendicitis treated by laparoscopic-only (LA), conversion from laparoscopic to open (CA), or primary open (OA) appendectomy. Pre-operative variables independently associated with CA were identified and reported as odds ratios (OR) with 95% confidence intervals (CI). A weighted integer-based scoring model to predict CA was designed based on pre-operative variable ORs, and complications between operative subgroups were compared. Independent predictors of CA in the derivation cohort were age ≥40 (OR 1.67; CI 1.55-1.80), male sex (OR 1.25; CI 1.17-1.34), black race (OR 1.46; CI 1.28-1.66), diabetes (OR 1.47; CI 1.31-1.65), obesity (OR 1.56; CI 1.40-1.74), and acute appendicitis with abscess or peritonitis (OR 7.00; CI 6.51-7.53). In the validation cohort, the CA predictive scoring model had an optimal cutoff score of 4 (range 0-9). The risk of conversion-to-open was ≤5% for a score <4, compared to 10-25% for a score ≥4. On composite outcomes analysis controlling for all pre-operative variables, CA had a higher likelihood of infectious/inflammatory (OR 1.44; CI 1.31-1.58), hematologic (OR 1.31; CI 1.17-1.46), and renal (OR 1.22; CI 1.06-1.39) complications compared to OA. Additionally, CA had a higher likelihood of infectious/inflammatory, respiratory, cardiovascular, hematologic, and renal complications compared to LA. CA patients have an unfavorable complication profile compared to OA. The predictors identified in this scoring model could help select for patients who may benefit from primary open appendectomy. Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Simple optimized Brenner potential for thermodynamic properties of diamond

NASA Astrophysics Data System (ADS)

Liu, F.; Tang, Q. H.; Shang, B. S.; Wang, T. C.

2012-02-01

We have examined the commonly used Brenner potentials in the context of the thermodynamic properties of diamond. A simple optimized Brenner potential is proposed that provides very good predictions of the thermodynamic properties of diamond. It is shown that, compared to the experimental data, the lattice wave theory of molecular dynamics (LWT) with this optimized Brenner potential can accurately predict the temperature dependence of specific heat, lattice constant, Grüneisen parameters and coefficient of thermal expansion (CTE) of diamond.

Stepped care for depression is easy to recommend, but harder to implement: results of an explorative study within primary care in the Netherlands.

PubMed

Hermens, Marleen L M; Muntingh, Anna; Franx, Gerdien; van Splunteren, Peter T; Nuyen, Jasper

2014-01-09

Depression is a common mental disorder with a high burden of disease which is mainly treated in primary care. It is unclear to what extent stepped care principles are applied in routine primary care. The first aim of this explorative study was to examine the gap between routine primary depression care and optimal care, as formulated in the depression guidelines. The second aim was to explore the facilitators and barriers that affect the provision of optimal care. Optimal care was operationalised by indicators covering the entire continuum of depression care: from prevention to chronic depression. Routine care was investigated by interviewing general practitioners (GPs) individually and together with other mental health care providers about the depression care they delivered collaboratively. Qualitative analysis of transcripts was performed using thematic coding. Additionally, the GPs completed a self-report questionnaire. Six GPs and 22 other (mostly primary) mental health care providers participated. The GPs and their primary care colleagues embraced a general stepped care approach. They offered psycho-education and counselling to mildly depressed patients. When the treatment effects were not satisfactory or patients were more severely depressed, the GPs offered, or referred to, psychotherapy or pharmacotherapy. Patients with a complex and severe depressive disorder were directly referred to specialised mental health care. However, GPs relied on their clinical judgment and rarely used instruments to assess and monitor the severity of depressive symptoms. Structured, evidence based interventions such as self-management and e-health were rarely offered to patients with depressive symptoms. Specific psychological interventions for relapse prevention or for chronically depressed patients were not available. A wide range of influencing factors for the provision of optimal depression care were put forward. Close collaboration with other mental health care professionals was considered an important factor for improvement by nearly all GPs. The management of depression in primary care seems in line with stepped care principles, although it can be improved by applying more elements of a stepped care approach. Collaboration between GPs and mental health care providers in primary care and secondary care should be enhanced.

Optimized Non-Obstructive Particle Damping (NOPD) Treatment for Composite Honeycomb Structures

NASA Technical Reports Server (NTRS)

Panossian, H.

2008-01-01

Non-Obstructive Particle Damping (NOPD) technology is a passive vibration damping approach whereby metallic or non-metallic particles in spherical or irregular shapes, of heavy or light consistency, and even liquid particles are placed inside cavities or attached to structures by an appropriate means at strategic locations, to absorb vibration energy. The objective of the work described herein is the development of a design optimization procedure and discussion of test results for such a NOPD treatment on honeycomb (HC) composite structures, based on finite element modeling (FEM) analyses, optimization and tests. Modeling and predictions were performed and tests were carried out to correlate the test data with the FEM. The optimization procedure consisted of defining a global objective function, using finite difference methods, to determine the optimal values of the design variables through quadratic linear programming. The optimization process was carried out by targeting the highest dynamic displacements of several vibration modes of the structure and finding an optimal treatment configuration that will minimize them. An optimal design was thus derived and laboratory tests were conducted to evaluate its performance under different vibration environments. Three honeycomb composite beams, with Nomex core and aluminum face sheets, empty (untreated), uniformly treated with NOPD, and optimally treated with NOPD, according to the analytically predicted optimal design configuration, were tested in the laboratory. It is shown that the beam with optimal treatment has the lowest response amplitude. Described below are results of modal vibration tests and FEM analyses from predictions of the modal characteristics of honeycomb beams under zero, 50% uniform treatment and an optimal NOPD treatment design configuration and verification with test data.

Reactive transport modeling of CO2 mineral sequestration in basaltic rocks

NASA Astrophysics Data System (ADS)

Aradottir, E. S.; Sonnenthal, E. L.; Bjornsson, G.; Jonsson, H.

2011-12-01

CO2 mineral sequestration in basalt may provide a long lasting, thermodynamically stable, and environmentally benign solution to reduce greenhouse gases in the atmosphere. Multi-dimensional, field scale, reactive transport models of this process have been developed with a focus on the CarbFix pilot CO2 injection in Iceland. An extensive natural analog literature review was conducted in order to identify the primary and secondary minerals associated with water-basalt interaction at low and elevated CO2 conditions. Based on these findings, an internally consistent thermodynamic database describing the mineral reactions of interest was developed and validated. Hydrological properties of field scale mass transport models were properly defined by calibration to field data using iTOUGH2. Reactive chemistry was coupled to the models and TOUGHREACT used for running predictive simulations carried out with the objective of optimizing long-term management of injection sites, to quantify the amount of CO2 that can be mineralized, and to identify secondary minerals that compete with carbonates for cations leached from the primary rock. Calibration of field data from the CarbFix reservoir resulted in a horizontal permeability for lava flows of 300 mD and a vertical permeability of 1700 mD. Active matrix porosity was estimated to be 8.5%. The CarbFix numerical models were a valuable engineering tool for designing optimal injection and production schemes aimed at increasing groundwater flow. Reactive transport simulations confirm dissolution of primary basaltic minerals as well as carbonate formation, and thus indicate in situ CO2 mineral sequestration in basalts to be a viable option. Furthermore, the simulations imply that clay minerals are most likely to compete with magnesite-siderite solid solutions for Mg and Fe leached from primary minerals, whereas zeolites compete with calcite for dissolved Ca. In the case of the CarbFix pilot injection, which involves a continuous injection of 1,100 tons CO2 in total for 6 months, the basalt hosted reservoir was estimated to have a 100% sequestering efficiency after 10 years. In the case of an upscaled 10 year long injection of 40,000 tons per year, sequestering efficiency of the same reservoir was estimated to be about 10% after 100 years. However, sequestering efficiency in the latter case has every potential of increasing substantially with time due to the vast amount of primary basaltic minerals in the reservoir.

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE PAGES

Li, Mingjie; Zhou, Ping; Wang, Hong; ...

2017-09-19

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Mingjie; Zhou, Ping; Wang, Hong

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Ion Thruster Discharge Performance Per Magnetic Field Topography

NASA Technical Reports Server (NTRS)

Wirz, Richard E.; Goebel, Dan

2006-01-01

DC-ION is a detailed computational model for predicting the plasma characteristics of rain-cusp ion thrusters. The advanced magnetic field meshing algorithm used by DC-ION allows precise treatment of the secondary electron flow. This capability allows self-consistent estimates of plasma potential that improves the overall consistency of the results of the discharge model described in Reference [refJPC05mod1]. Plasma potential estimates allow the model to predict the onset of plasma instabilities, and important shortcoming of the previous model for optimizing the design of discharge chambers. A magnetic field mesh simplifies the plasma flow calculations, for both the ions and the secondary electrons, and significantly reduces numerical diffusion that can occur with meshes not aligned with the magnetic field. Comparing the results of this model to experimental data shows that the behavior of the primary electrons, and the precise manner of their confinement, dictates the fundamental efficiency of ring-cusp. This correlation is evident in simulations of the conventionally sized NSTAR thruster (30 cm diameter) and the miniature MiXI thruster (3 cm diameter).

Improving Earth/Prediction Models to Improve Network Processing

NASA Astrophysics Data System (ADS)

Wagner, G. S.

2017-12-01

The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.

Global positive expectancies in adolescence and health-related behaviours: Longitudinal models of latent growth and cross-lagged effects

PubMed Central

Carvajal, Scott C.

2015-01-01

Constructs representative of global positive expectancies (GPE) such as dispositional optimism and hope have been theoretically and empirically linked to many positive mental and physical health outcomes. However such expectancies’ health implications for adolescents, as well as their trajectory over time, are less well understood than for adult populations. This study tested whether GPE predict the key indicators of adolescents’ future physical health status, their health-related behaviours. A prospective longitudinal study design was employed whereby a diverse population-based cohort (N = 744; mean age at baseline = 12) completed three surveys over approximately 18 months. Rigorous tests of causal predominance and reciprocal effects were conducted through latent growth and cross-panel structural equation models. Results showed GPE systematically decreased during the course of the study, yet higher initial levels of GPE predicted less alcohol drinking, healthier food choice and greater physical activity over time. GPE’s protective relationships towards health protective behaviours (vs. health risk behaviours that also included tobacco smoking) appear more independent from depressive symptomatology, and the primary findings were robust across socio-demographic groups. PMID:22149606

Efficient operation scheduling for adsorption chillers using predictive optimization-based control methods

NASA Astrophysics Data System (ADS)

Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz

2017-10-01

Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.

Predictive optimized adaptive PSS in a single machine infinite bus.

PubMed

Milla, Freddy; Duarte-Mermoud, Manuel A

2016-07-01

Power System Stabilizer (PSS) devices are responsible for providing a damping torque component to generators for reducing fluctuations in the system caused by small perturbations. A Predictive Optimized Adaptive PSS (POA-PSS) to improve the oscillations in a Single Machine Infinite Bus (SMIB) power system is discussed in this paper. POA-PSS provides the optimal design parameters for the classic PSS using an optimization predictive algorithm, which adapts to changes in the inputs of the system. This approach is part of small signal stability analysis, which uses equations in an incremental form around an operating point. Simulation studies on the SMIB power system illustrate that the proposed POA-PSS approach has better performance than the classical PSS. In addition, the effort in the control action of the POA-PSS is much less than that of other approaches considered for comparison. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimism and positive and negative feelings in parents of young children with developmental delay.

PubMed

Kurtz-Nelson, E; McIntyre, L L

2017-07-01

Parents' positive and negative feelings about their young children influence both parenting behaviour and child problem behaviour. Research has not previously examined factors that contribute to positive and negative feelings in parents of young children with developmental delay (DD). The present study sought to examine whether optimism, a known protective factor for parents of children with DD, was predictive of positive and negative feelings for these parents. Data were collected from 119 parents of preschool-aged children with developmental delay. Two separate hierarchical linear regression analyses were conducted to determine if optimism significantly predicted positive feelings and negative feelings and whether optimism moderated relations between parenting stress and parent feelings. Increased optimism was found to predict increased positive feelings and decreased negative feelings after controlling for child problem behaviour and parenting stress. In addition, optimism was found to moderate the relation between parenting stress and positive feelings. Results suggest that optimism may impact how parents perceive their children with DD. Future research should examine how positive and negative feelings impact positive parenting behaviour and the trajectory of problem behaviour specifically for children with DD. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

PubMed

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Probabilistic framework for product design optimization and risk management

NASA Astrophysics Data System (ADS)

Keski-Rahkonen, J. K.

2018-05-01

Probabilistic methods have gradually gained ground within engineering practices but currently it is still the industry standard to use deterministic safety margin approaches to dimensioning components and qualitative methods to manage product risks. These methods are suitable for baseline design work but quantitative risk management and product reliability optimization require more advanced predictive approaches. Ample research has been published on how to predict failure probabilities for mechanical components and furthermore to optimize reliability through life cycle cost analysis. This paper reviews the literature for existing methods and tries to harness their best features and simplify the process to be applicable in practical engineering work. Recommended process applies Monte Carlo method on top of load-resistance models to estimate failure probabilities. Furthermore, it adds on existing literature by introducing a practical framework to use probabilistic models in quantitative risk management and product life cycle costs optimization. The main focus is on mechanical failure modes due to the well-developed methods used to predict these types of failures. However, the same framework can be applied on any type of failure mode as long as predictive models can be developed.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets.

PubMed

Feinstein, Wei P; Brylinski, Michal

2015-01-01

Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated. In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite. A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins. This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size. Graphical AbstractWe developed a procedure to optimize the box size in molecular docking calculations. Left panel shows the predicted binding pose of NADP (green sticks) compared to the experimental complex structure of human aldose reductase (blue sticks) using a default protocol. Right panel shows the docking accuracy using an optimized box size.

Optimal plant nitrogen use improves model representation of vegetation response to elevated CO2

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Kern, Melanie; Engel, Jan; Zaehle, Sönke

2017-04-01

Existing global vegetation models often cannot accurately represent observed ecosystem behaviour under transient conditions such as elevated atmospheric CO2, a problem that can be attributed to an inflexibility in model representation of plant responses. Plant optimality concepts have been proposed as a solution to this problem as they offer a way to represent plastic plant responses in complex models. Here we present a novel, next generation vegetation model which includes optimal nitrogen allocation to and within the canopy as well as optimal biomass allocation between above- and belowground components in response to nutrient and water availability. The underlying hypothesis is that plants adjust their use of nitrogen in response to environmental conditions and nutrient availability in order to maximise biomass growth. We show that for two FACE (Free Air CO2 enrichment) experiments, the Duke forest and Oak Ridge forest sites, the model can better predict vegetation responses over the duration of the experiment when optimal processes are included. Specifically, under elevated CO2 conditions, the model predicts a lower optimal leaf N concentration as well as increased biomass allocation to fine roots, which, combined with a redistribution of leaf N between the Rubisco and chlorophyll components, leads to a continued NPP response under high CO2, where models with a fixed canopy stoichiometry predict a quick onset of N limitation.Existing global vegetation models often cannot accurately represent observed ecosystem behaviour under transient conditions such as elevated atmospheric CO2, a problem that can be attributed to an inflexibility in model representation of plant responses. Plant optimality concepts have been proposed as a solution to this problem as they offer a way to represent plastic plant responses in complex models. Here we present a novel, next generation vegetation model which includes optimal nitrogen allocation to and within the canopy as well as optimal biomass allocation between above- and belowground components in response to nutrient and water availability. The underlying hypothesis is that plants adjust their use of nitrogen in response to environmental conditions and nutrient availability in order to maximise biomass growth. We show that for two FACE (Free Air CO2 enrichment) experiments, the Duke forest and Oak Ridge forest sites, the model can better predict vegetation responses over the duration of the experiment when optimal processes are included. Specifically, under elevated CO2 conditions, the model predicts a lower optimal leaf N concentration as well as increased biomass allocation to fine roots, which, combined with a redistribution of leaf N between the Rubisco and chlorophyll components, leads to a continued NPP response under high CO2, where models with a fixed canopy stoichiometry predict a quick onset of N limitation.

The optimization study on the tool wear of carbide cutting tool during milling Carbon Fibre Reinforced (CFRP) using Response Surface Methodology (RSM)

NASA Astrophysics Data System (ADS)

Nor Khairusshima, M. K.; Hafiz Zakwan, B. Muhammad; Suhaily, M.; Sharifah, I. S. S.; Shaffiar, N. M.; Rashid, M. A. N.

2018-01-01

Carbon Fibre Reinforced Plastic (CFRP) composite has become one of famous materials in industry, such as automotive, aeronautics, aerospace and aircraft. CFRP is attractive due to its properties, which promising better strength and high specification of mechanical properties other than its high resistance to corrosion. Other than being abrasive material due to the carbon nature, CFRP is an anisotropic material, which the knowledge of machining metal and steel cannot be applied during machining CFRP. The improper technique and parameters used to machine CFRP may result in high tool wear. This paper is to study the tool wear of 8 mm diameter carbide cutting tool during milling CFRP. To predict the suitable cutting parameters within range of 3500-6220 (rev/min), 200-245 (mm/min), and 0.4-1.8 (mm) for cutting speed, speed, feed rate and depth of cut respectively, which produce optimized result (less tool wear), Response Surface Methodology (RSM) has been used. Based on the developed mathematical model, feed rate was identified as the primary significant item that influenced tool wear. The optimized cutting parameters are cutting speed, feed and depth of cut of 3500 rev/min, 200 mm/min and 0.5 mm, respectively, with tool wear of 0.0267 mm. It is also can be observed that as the cutting speed and feed rate increased the tool wear is increasing.

Discovery of metabolic signatures for predicting whole organism toxicology.

PubMed

Hines, Adam; Staff, Fred J; Widdows, John; Compton, Russell M; Falciani, Francesco; Viant, Mark R

2010-06-01

Toxicological studies in sentinel organisms frequently use biomarkers to assess biological effect. Development of "omic" technologies has enhanced biomarker discovery at the molecular level, providing signatures unique to toxicant mode-of-action (MOA). However, these signatures often lack relevance to organismal responses, such as growth or reproduction, limiting their value for environmental monitoring. Our primary objective was to discover metabolic signatures in chemically exposed organisms that can predict physiological toxicity. Marine mussels (Mytilus edulis) were exposed for 7 days to 12 and 50 microg/l copper and 50 and 350 microg/l pentachlorophenol (PCP), toxicants with unique MOAs. Physiological responses comprised an established measure of organism energetic fitness, scope for growth (SFG). Metabolic fingerprints were measured in the same individuals using nuclear magnetic resonance-based metabolomics. Metabolic signatures predictive of SFG were sought using optimal variable selection strategies and multivariate regression and then tested upon independently field-sampled mussels from rural and industrialized sites. Copper and PCP induced rational metabolic and physiological changes. Measured and predicted SFG were highly correlated for copper (r(2) = 0.55, P = 2.82 x 10(-7)) and PCP (r(2) = 0.66, P = 3.20 x 10(-6)). Predictive metabolites included methionine and arginine/phosphoarginine for copper and allantoin, valine, and methionine for PCP. When tested on field-sampled animals, metabolic signatures predicted considerably reduced fitness of mussels from the contaminated (SFG = 6.0 J/h/g) versus rural (SFG = 15.2 J/h/g) site. We report the first successful discovery of metabolic signatures in chemically exposed environmental organisms that inform on molecular MOA and that can predict physiological toxicity. This could have far-reaching implications for monitoring impacts on environmental health.

Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills.

PubMed

Azadi, Sama; Amiri, Hamid; Rakhshandehroo, G Reza

2016-09-01

Waste burial in uncontrolled landfills can cause serious environmental damages and unpleasant consequences. Leachates produced in landfills have the potential to contaminate soil and groundwater resources. Leachate management is one of the major issues with respect to landfills environmental impacts. Improper design of landfills can lead to leachate spread in the environment, and hence, engineered landfills are required to have leachate monitoring programs. The high cost of such programs may be greatly reduced and cost efficiency of the program may be optimized if one can predict leachate contamination level and foresee management and treatment strategies. The aim of this study is to develop two expert systems consisting of Artificial Neural Network (ANN) and Principal Component Analysis-M5P (PCA-M5P) models to predict Chemical Oxygen Demand (COD) load in leachates produced in lab-scale landfills. Measured data from three landfill lysimeters, including rainfall depth, number of days after waste deposition, thickness of top and bottom Compacted Clay Liners (CCLs), and thickness of top cover over the lysimeter, were utilized to develop, train, validate, and test the expert systems and predict the leachate COD load. Statistical analysis of the prediction results showed that both models possess good prediction ability with a slight superiority for ANN over PCA-M5P. Based on test datasets, the mean absolute percentage error for ANN and PCA-M5P models were 4% and 12%, respectively, and the correlation coefficient for both models was greater than 0.98. Developed models may be used as a rough estimate for leachate COD load prediction in primary landfill designs, where the effect of a top and/or bottom liner is disputed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design and methodology of the COACH-2 (Comparative study on guideline adherence and patient compliance in heart failure patients) study: HF clinics versus primary care in stable patients on optimal therapy.

PubMed

Luttik, M L A; Brons, M; Jaarsma, T; Hillege, H L; Hoes, A; de Jong, R; Linssen, G; Lok, D J; Berger, M; van Veldhuisen, D J

2012-08-01

Since the number of heart failure (HF) patients is still growing and long-term treatment of HF patients is necessary, it is important to initiate effective ways for structural involvement of primary care services in HF management programs. However, evidence on whether and when patients can be referred back to be managed in primary care is lacking. To determine whether long-term patient management in primary care, after initial optimisation of pharmacological and non-pharmacological treatment in a specialised HF clinic, is equally effective as long-term management in a specialised HF clinic in terms of guideline adherence and patient compliance. The study is designed as a randomised, controlled, non-inferiority trial. Two-hundred patients will be randomly assigned to be managed and followed in primary care or in a HFclinic. Patients are eligible to participate if they are (1) clinically stable, (2) optimally up-titrated on medication (according to ESC guidelines) and, (3) have received optimal education and counselling on pre-specified issues regarding HF and its treatment. Furthermore, close cooperation between secondary and primary care in terms of back referral to or consultation of the HF clinic will be provided.The primary outcome will be prescriber adherence and patient compliance with medication after 12 months. Secondary outcomes measures will be readmission rate, mortality, quality of life and patient compliance with other lifestyle changes. The results of the study will add to the understanding of the role of primary care and HF clinics in the long-term follow-up of HF patients.

Prediction-Correction Algorithms for Time-Varying Constrained Optimization

DOE PAGES

Simonetto, Andrea; Dall'Anese, Emiliano

2017-07-26

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

Optimal temperature for malaria transmission is dramaticallylower than previously predicted

USGS Publications Warehouse

Mordecai, Eerin A.; Paaijmans, Krijin P.; Johnson, Leah R.; Balzer, Christian; Ben-Horin, Tal; de Moor, Emily; McNally, Amy; Pawar, Samraat; Ryan, Sadie J.; Smith, Thomas C.; Lafferty, Kevin D.

2013-01-01

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission.

Optimal temperature for malaria transmission is dramatically lower than previously predicted

USGS Publications Warehouse

Mordecai, Erin A.; Paaijmans, Krijn P.; Johnson, Leah R.; Balzer, Christian; Ben-Horin, Tal; de Moor, Emily; McNally, Amy; Pawar, Samraat; Ryan, Sadie J.; Smith, Thomas C.; Lafferty, Kevin D.

2013-01-01

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission.

Combining Simulation and Optimization Models for Hardwood Lumber Production

Treesearch

G.A. Mendoza; R.J. Meimban; W.G. Luppold; Philip A. Araman

1991-01-01

Published literature contains a number of optimization and simulation models dealing with the primary processing of hardwood and softwood logs. Simulation models have been developed primarily as descriptive models for characterizing the general operations and performance of a sawmill. Optimization models, on the other hand, were developed mainly as analytical tools for...

Turbine Performance Optimization Task Status

NASA Technical Reports Server (NTRS)

Griffin, Lisa W.; Turner, James E. (Technical Monitor)

2001-01-01

Capability to optimize for turbine performance and accurately predict unsteady loads will allow for increased reliability, Isp, and thrust-to-weight. The development of a fast, accurate aerodynamic design, analysis, and optimization system is required.

Do Dispositional Pessimism and Optimism Predict Ambulatory Blood Pressure During Schooldays and Nights in Adolescents?

PubMed Central

Räikkönen, Katri; Matthews, Karen A.

2010-01-01

We tested the hypotheses that (1) high pessimism and low optimism (LOT-R overall and subscale scores) would predict high ambulatory blood pressure (ABP) level and 24-hour load (percentage of ABP values exceeding the pediatric 95th percentile) among healthy Black and White adolescents (n = 201; 14–16 yrs) across 2 consecutive school days and (2) that the relationships for the pessimism and optimism subscales would show nonlinear effects. The hypotheses were confirmed for pessimism but not for optimism. The results suggest that high pessimism may have different effects than low optimism on ABP and that even moderate levels of pessimism may effect blood pressure regulation. These results suggest that optimism and pessimism are not the opposite poles on a single continuum but ought to be treated as separate constructs. PMID:18399951

Optimization of Peripheral Vision.

DTIC Science & Technology

1986-11-01

2 References * . . . . . . . . . . . . . . . . . . . . . . . 3 2.* ANATOMICAL FOUNDATIONS OF THE TWO SUBSYSTEMS Primary visualp...athways .......... 6 Retinal layers . . . . . . . . . . . .... ....... 7 Nerves, tracts, and midbrain structures . . . . . . . . . 7 Primary cortaara...perhaps the ambient system can be used as a second primary source of information. One reason that its use in this respect is limited is that the

Added value of delayed computed tomography angiography in primary intracranial hemorrhage and hematoma size for predicting spot sign.

PubMed

Wu, Te Chang; Chen, Tai Yuan; Shiue, Yow Ling; Chen, Jeon Hor; Hsieh, Tsyh-Jyi; Ko, Ching Chung; Lin, Ching Po

2018-04-01

Background The computed tomography angiography (CTA) spot sign represents active contrast extravasation within acute primary intracerebral hemorrhage (ICH) and is an independent predictor of hematoma expansion (HE) and poor clinical outcomes. The spot sign could be detected on first-pass CTA (fpCTA) or delayed CTA (dCTA). Purpose To investigate the additional benefits of dCTA spot sign in primary ICH and hematoma size for predicting spot sign. Material and Methods This is a retrospective study of 100 patients who underwent non-contrast CT (NCCT) and CTA within 24 h of onset of primary ICH. The presence of spot sign on fpCTA or dCTA, and hematoma size on NCCT were recorded. The spot sign on fpCTA or dCTA for predicting significant HE, in-hospital mortality, and poor clinical outcomes (mRS ≥ 4) are calculated. The hematoma size for prediction of CTA spot sign was also analyzed. Results Only the spot sign on dCTA could predict high risk of significant HE and poor clinical outcomes as on fpCTA ( P < 0.05). With dCTA, there is increased sensitivity and negative predictive value (NPV) for predicting significant HE, in-hospital mortality, and poor clinical outcomes. The XY value (product of the two maximum perpendicular axial dimensions) is the best predictor (area under the curve [AUC] = 0.82) for predicting spot sign on fpCTA or dCTA in the absence of intraventricular and subarachnoid hemorrhage. Conclusion This study clarifies that dCTA imaging could improve predictive performance of CTA in primary ICH. Furthermore, the XY value is the best predictor for CTA spot sign.

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

NASA Astrophysics Data System (ADS)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.

Formal optimization of hovering performance using free wake lifting surface theory

NASA Technical Reports Server (NTRS)

Chung, S. Y.

1986-01-01

Free wake techniques for performance prediction and optimization of hovering rotor are discussed. The influence functions due to vortex ring, vortex cylinder, and source or vortex sheets are presented. The vortex core sizes of rotor wake vortices are calculated and their importance is discussed. Lifting body theory for finite thickness body is developed for pressure calculation, and hence performance prediction of hovering rotors. Numerical optimization technique based on free wake lifting line theory is presented and discussed. It is demonstrated that formal optimization can be used with the implicit and nonlinear objective or cost function such as the performance of hovering rotors as used in this report.

Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN

NASA Astrophysics Data System (ADS)

Peter, Josephine; Doloi, B.; Bhattacharyya, B.

2011-01-01

The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actual experimental observations.

Attention to sound improves auditory reliability in audio-tactile spatial optimal integration.

PubMed

Vercillo, Tiziana; Gori, Monica

2015-01-01

The role of attention on multisensory processing is still poorly understood. In particular, it is unclear whether directing attention toward a sensory cue dynamically reweights cue reliability during integration of multiple sensory signals. In this study, we investigated the impact of attention in combining audio-tactile signals in an optimal fashion. We used the Maximum Likelihood Estimation (MLE) model to predict audio-tactile spatial localization on the body surface. We developed a new audio-tactile device composed by several small units, each one consisting of a speaker and a tactile vibrator independently controllable by external software. We tested participants in an attentional and a non-attentional condition. In the attentional experiment, participants performed a dual task paradigm: they were required to evaluate the duration of a sound while performing an audio-tactile spatial task. Three unisensory or multisensory stimuli, conflictual or not conflictual sounds and vibrations arranged along the horizontal axis, were presented sequentially. In the primary task participants had to evaluate in a space bisection task the position of the second stimulus (the probe) with respect to the others (the standards). In the secondary task they had to report occasionally changes in duration of the second auditory stimulus. In the non-attentional task participants had only to perform the primary task (space bisection). Our results showed an enhanced auditory precision (and auditory weights) in the auditory attentional condition with respect to the control non-attentional condition. The results of this study support the idea that modality-specific attention modulates multisensory integration.

Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

NASA Astrophysics Data System (ADS)

Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

2012-03-01

This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

Nontangent, Developed Contour Bulkheads for a Single-Stage Launch Vehicle

NASA Technical Reports Server (NTRS)

Wu, K. Chauncey; Lepsch, Roger A., Jr.

2000-01-01

Dry weights for single-stage launch vehicles that incorporate nontangent, developed contour bulkheads are estimated and compared to a baseline vehicle with 1.414 aspect ratio ellipsoidal bulkheads. Weights, volumes, and heights of optimized bulkhead designs are computed using a preliminary design bulkhead analysis code. The dry weights of vehicles that incorporate the optimized bulkheads are predicted using a vehicle weights and sizing code. Two optimization approaches are employed. A structural-level method, where the vehicle's three major bulkhead regions are optimized separately and then incorporated into a model for computation of the vehicle dry weight, predicts a reduction of4365 lb (2.2 %) from the 200,679-lb baseline vehicle dry weight. In the second, vehicle-level, approach, the vehicle dry weight is the objective function for the optimization. For the vehicle-level analysis, modified bulkhead designs are analyzed and incorporated into the weights model for computation of a dry weight. The optimizer simultaneously manipulates design variables for all three bulkheads to reduce the dry weight. The vehicle-level analysis predicts a dry weight reduction of 5129 lb, a 2.6% reduction from the baseline weight. Based on these results, nontangent, developed contour bulkheads may provide substantial weight savings for single stage vehicles.

The Prediction of the Gas Utilization Ratio Based on TS Fuzzy Neural Network and Particle Swarm Optimization

PubMed Central

Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Baoyong

2018-01-01

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control. PMID:29461469

The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization.

PubMed

Zhang, Sen; Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Baoyong

2018-02-20

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.

The Primary Care Computer Simulation: Optimal Primary Care Manager Empanelment.

DTIC Science & Technology

1997-05-01

explored in which a team consisted of two providers, two nurses, and a nurse aide . Each team had a specific exam room assigned to them. Additionally, a...team consisting of one provider, one nurse, and one nurse aide was simulated. The model also examined the effects of adding two exam rooms. The study...minutes. The optimal solution, which reduced patient time to below 90 minutes, was the mix of one provider, a nurse, and a nurse aide in which each

Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB.

PubMed

Lee, Leng-Feng; Umberger, Brian R

2016-01-01

Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1-2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This should allow researchers to more readily use predictive simulation as a tool to address clinical conditions that limit human mobility.

Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB

PubMed Central

Lee, Leng-Feng

2016-01-01

Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1–2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This should allow researchers to more readily use predictive simulation as a tool to address clinical conditions that limit human mobility. PMID:26835184

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

PubMed Central

Jehee, Janneke F. M.; Ballard, Dana H.

2009-01-01

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. PMID:19412529

Empirically Optimized Flow Cytometric Immunoassay Validates Ambient Analyte Theory

PubMed Central

Parpia, Zaheer A.; Kelso, David M.

2010-01-01

Ekins’ ambient analyte theory predicts, counter intuitively, that an immunoassay’s limit of detection can be improved by reducing the amount of capture antibody. In addition, it also anticipates that results should be insensitive to the volume of sample as well as the amount of capture antibody added. The objective of this study is to empirically validate all of the performance characteristics predicted by Ekins’ theory. Flow cytometric analysis was used to detect binding between a fluorescent ligand and capture microparticles since it can directly measure fractional occupancy, the primary response variable in ambient analyte theory. After experimentally determining ambient analyte conditions, comparisons were carried out between ambient and non-ambient assays in terms of their signal strengths, limits of detection, and their sensitivity to variations in reaction volume and number of particles. The critical number of binding sites required for an assay to be in the ambient analyte region was estimated to be 0.1VKd. As predicted, such assays exhibited superior signal/noise levels and limits of detection; and were not affected by variations in sample volume and number of binding sites. When the signal detected measures fractional occupancy, ambient analyte theory is an excellent guide to developing assays with superior performance characteristics. PMID:20152793

MARS Science Laboratory Post-Landing Location Estimation Using Post2 Trajectory Simulation

NASA Technical Reports Server (NTRS)

Davis, J. L.; Shidner, Jeremy D.; Way, David W.

2013-01-01

The Mars Science Laboratory (MSL) Curiosity rover landed safely on Mars August 5th, 2012 at 10:32 PDT, Earth Received Time. Immediately following touchdown confirmation, best estimates of position were calculated to assist in determining official MSL locations during entry, descent and landing (EDL). Additionally, estimated balance mass impact locations were provided and used to assess how predicted locations compared to actual locations. For MSL, the Program to Optimize Simulated Trajectories II (POST2) was the primary trajectory simulation tool used to predict and assess EDL performance from cruise stage separation through rover touchdown and descent stage impact. This POST2 simulation was used during MSL operations for EDL trajectory analyses in support of maneuver decisions and imaging MSL during EDL. This paper presents the simulation methodology used and results of pre/post-landing MSL location estimates and associated imagery from Mars Reconnaissance Orbiter s (MRO) High Resolution Imaging Science Experiment (HiRISE) camera. To generate these estimates, the MSL POST2 simulation nominal and Monte Carlo data, flight telemetry from onboard navigation, relay orbiter positions from MRO and Mars Odyssey and HiRISE generated digital elevation models (DEM) were utilized. A comparison of predicted rover and balance mass location estimations against actual locations are also presented.

Ruling out coronary artery disease in primary care: development and validation of a simple prediction rule.

PubMed

Bösner, Stefan; Haasenritter, Jörg; Becker, Annette; Karatolios, Konstantinos; Vaucher, Paul; Gencer, Baris; Herzig, Lilli; Heinzel-Gutenbrunner, Monika; Schaefer, Juergen R; Abu Hani, Maren; Keller, Heidi; Sönnichsen, Andreas C; Baum, Erika; Donner-Banzhoff, Norbert

2010-09-07

Chest pain can be caused by various conditions, with life-threatening cardiac disease being of greatest concern. Prediction scores to rule out coronary artery disease have been developed for use in emergency settings. We developed and validated a simple prediction rule for use in primary care. We conducted a cross-sectional diagnostic study in 74 primary care practices in Germany. Primary care physicians recruited all consecutive patients who presented with chest pain (n = 1249) and recorded symptoms and findings for each patient (derivation cohort). An independent expert panel reviewed follow-up data obtained at six weeks and six months on symptoms, investigations, hospital admissions and medications to determine the presence or absence of coronary artery disease. Adjusted odds ratios of relevant variables were used to develop a prediction rule. We calculated measures of diagnostic accuracy for different cut-off values for the prediction scores using data derived from another prospective primary care study (validation cohort). The prediction rule contained five determinants (age/sex, known vascular disease, patient assumes pain is of cardiac origin, pain is worse during exercise, and pain is not reproducible by palpation), with the score ranging from 0 to 5 points. The area under the curve (receiver operating characteristic curve) was 0.87 (95% confidence interval [CI] 0.83-0.91) for the derivation cohort and 0.90 (95% CI 0.87-0.93) for the validation cohort. The best overall discrimination was with a cut-off value of 3 (positive result 3-5 points; negative result

Optimal medical treatment versus carotid endarterectomy: the rationale and design of the Aggressive Medical Treatment Evaluation for Asymptomatic Carotid Artery Stenosis (AMTEC) study.

PubMed

Kolos, Igor; Loukianov, Mikhail; Dupik, Nikolay; Boytsov, Sergey; Deev, Alexandr

2015-02-01

Carotid endarterectomy and medical therapy (aspirin) were shown superior to medical therapy alone for asymptomatic (≥ 60%) carotid stenosis. The role of modern medical therapy (statins, antihypertensive treatment, and aspirin) in the treatment of such patients is undefined. Establishing the safety, efficacy, and durability of optimal medical therapy and lifestyle modification requires rigorous comparison with carotid endarterectomy in asymptomatic patients. The objective is to compare the efficacy of carotid endarterectomy + optimal medical therapy versus optimal medical therapy alone in patients with asymptomatic (70-79%) extracranial carotid stenosis. The Aggressive Medical Treatment Evaluation for Asymptomatic Carotid Artery Stenosis study is a prospective, randomized, parallel, two-arm, multicenter trial. Primary end-points will be analyzed using standard time-to-event statistical modeling with adjustment for major baseline covariates. The primary analysis is on an intent-to-treat basis. The primary outcome is nonfatal stroke, nonfatal myocardial infarction, and death during follow-up of up to five-years, and the secondary outcome includes death from any cause and stroke. © 2013 The Authors. International Journal of Stroke © 2013 World Stroke Organization.

Prediction of Brugia malayi antigenic peptides: candidates for synthetic vaccine design against lymphatic filariasis.

PubMed

Gomase, Virendra S; Chitlange, Nikhilkumar R; Changbhale, Smruti S; Kale, Karbhari V

2013-08-01

Brugia malayi is a threadlike nematode cause's swelling of lymphatic organs, condition well known as lymphatic filariasis; till date no invention made to effectively address lymphatic filariasis. In this analysis we a have predicted suitable antigenic peptides from Brugia malayi antigen protein for peptide vaccine design against lymphatic filariasis based on cross protection phenomenon as, an ample immune response can be generated with a single protein subunit. We found MHC class II binding peptides of Brugia malayi antigen protein are important determinant against the diseased condition. The analysis shows Brugia malayi antigen protein having 505 amino acids, which shows 497 nonamers. In this assay, we have predicted MHC-I binding peptides for 8mer_H2_Db (optimal score- 15.966), 9mer_H2_Db (optimal score- 15.595), 10mer_H2_Db (optimal score- 19.405), 11mer_H2_Dballeles (optimal score- 23.801). We also predicted the SVM based MHCII-IAb nonamers, 51-FQQIDPLDA, 442-FAAIACLVH, 206-YLNPFGHQF, 167-WYVIMAACY, 367-YAMIVIRLL, 434- LVITTAANF, 176-LDSYCLWKP, 435-VITTAANFA, 364-WPGYAMIVI (optimal score- 13.963); MHCII-IAd nonamers, 52-QQIDPLDAE, 171-MAACYLDSY, 239-QWRSVILCN, 168-YVIMAACYL, 3-QYLSVHSLS, 322-EILLHAKVV, 417- LGIIASFVS, 396-KAIFLAHFG, 167-WYVIMAACY, 269-LALHCINVI, 93-FINKAAPKQ, 259-NCIIVLKAF, 79- QGVLLIIPR, 22-TILQRSQAI, 63-RGFVYGNVS, 109-NISSLAFET,(optimal score- 16.748); and MHCII-IAg7 nonamers 171-MAACYLDSY, 73-KIVNGAQGV, 259-NCIIVLKAF, 209-PFGHQFSFE, 102-SCDTLLKNI, 25-QRSQAIRIV, 444- AIACLVHLF, 88-SLVNGFINK, 252-FPRHQLLNC, 471-RFVLANDNE, 52-QQIDPLDAE, 469-HRRFVLAND, 457- SNRHYFLAD, 362-KSWPGYAMI, 476-NDNEGEDFE, 370-IVIRLLQAL (optimal score- 19.847) which represents potential binders from Brugia malayi antigen protein. The method integrates prediction of MHC class I binding proteasomal C-terminal cleavage peptides and Eighteen potential antigenic peptides at average propensity 1.063 having highest local hydrophilicity. Thus a small antigen fragment can induce immune response against whole antigen. This approach can be applied for designing subunit and synthetic peptide vaccines.

SU-F-T-342: Dosimetric Constraint Prediction Guided Automatic Mulit-Objective Optimization for Intensity Modulated Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, T; Zhou, L; Li, Y

Purpose: For intensity modulated radiotherapy, the plan optimization is time consuming with difficulties of selecting objectives and constraints, and their relative weights. A fast and automatic multi-objective optimization algorithm with abilities to predict optimal constraints and manager their trade-offs can help to solve this problem. Our purpose is to develop such a framework and algorithm for a general inverse planning. Methods: There are three main components contained in this proposed multi-objective optimization framework: prediction of initial dosimetric constraints, further adjustment of constraints and plan optimization. We firstly use our previously developed in-house geometry-dosimetry correlation model to predict the optimal patient-specificmore » dosimetric endpoints, and treat them as initial dosimetric constraints. Secondly, we build an endpoint(organ) priority list and a constraint adjustment rule to repeatedly tune these constraints from their initial values, until every single endpoint has no room for further improvement. Lastly, we implement a voxel-independent based FMO algorithm for optimization. During the optimization, a model for tuning these voxel weighting factors respecting to constraints is created. For framework and algorithm evaluation, we randomly selected 20 IMRT prostate cases from the clinic and compared them with our automatic generated plans, in both the efficiency and plan quality. Results: For each evaluated plan, the proposed multi-objective framework could run fluently and automatically. The voxel weighting factor iteration time varied from 10 to 30 under an updated constraint, and the constraint tuning time varied from 20 to 30 for every case until no more stricter constraint is allowed. The average total costing time for the whole optimization procedure is ∼30mins. By comparing the DVHs, better OAR dose sparing could be observed in automatic generated plan, for 13 out of the 20 cases, while others are with competitive results. Conclusion: We have successfully developed a fast and automatic multi-objective optimization for intensity modulated radiotherapy. This work is supported by the National Natural Science Foundation of China (No: 81571771)« less

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Role of optimization criterion in static asymmetric analysis of lumbar spine load.

PubMed

Daniel, Matej

2011-10-01

A common method for load estimation in biomechanics is the inverse dynamics optimization, where the muscle activation pattern is found by minimizing or maximizing the optimization criterion. It has been shown that various optimization criteria predict remarkably similar muscle activation pattern and intra-articular contact forces during leg motion. The aim of this paper is to study the effect of the choice of optimization criterion on L4/L5 loading during static asymmetric loading. Upright standing with weight in one stretched arm was taken as a representative position. Musculoskeletal model of lumbar spine model was created from CT images of Visible Human Project. Several criteria were tested based on the minimization of muscle forces, muscle stresses, and spinal load. All criteria provide the same level of lumbar spine loading (difference is below 25%), except the criterion of minimum lumbar shear force which predicts unrealistically high spinal load and should not be considered further. Estimated spinal load and predicted muscle force activation pattern are in accordance with the intradiscal pressure measurements and EMG measurements. The L4/L5 spine loads 1312 N, 1674 N, and 1993 N were predicted for mass of weight in hand 2, 5, and 8 kg, respectively using criterion of mininum muscle stress cubed. As the optimization criteria do not considerably affect the spinal load, their choice is not critical in further clinical or ergonomic studies and computationally simpler criterion can be used.

Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

NASA Astrophysics Data System (ADS)

Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

2013-03-01

Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

COPS: Large-scale nonlinearly constrained optimization problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bondarenko, A.S.; Bortz, D.M.; More, J.J.

2000-02-10

The authors have started the development of COPS, a collection of large-scale nonlinearly Constrained Optimization Problems. The primary purpose of this collection is to provide difficult test cases for optimization software. Problems in the current version of the collection come from fluid dynamics, population dynamics, optimal design, and optimal control. For each problem they provide a short description of the problem, notes on the formulation of the problem, and results of computational experiments with general optimization solvers. They currently have results for DONLP2, LANCELOT, MINOS, SNOPT, and LOQO.

Optimal apparent damping as a function of the bandwidth of an array of vibration absorbers.

PubMed

Vignola, Joseph; Glean, Aldo; Judge, John; Ryan, Teresa

2013-08-01

The transient response of a resonant structure can be altered by the attachment of one or more substantially smaller resonators. Considered here is a coupled array of damped harmonic oscillators whose resonant frequencies are distributed across a frequency band that encompasses the natural frequency of the primary structure. Vibration energy introduced to the primary structure, which has little to no intrinsic damping, is transferred into and trapped by the attached array. It is shown that, when the properties of the array are optimized to reduce the settling time of the primary structure's transient response, the apparent damping is approximately proportional to the bandwidth of the array (the span of resonant frequencies of the attached oscillators). Numerical simulations were conducted using an unconstrained nonlinear minimization algorithm to find system parameters that result in the fastest settling time. This minimization was conducted for a range of system characteristics including the overall bandwidth of the array, the ratio of the total array mass to that of the primary structure, and the distributions of mass, stiffness, and damping among the array elements. This paper reports optimal values of these parameters and demonstrates that the resulting minimum settling time decreases with increasing bandwidth.

Exact and Optimal Quantum Mechanics/Molecular Mechanics Boundaries.

PubMed

Sun, Qiming; Chan, Garnet Kin-Lic

2014-09-09

Motivated by recent work in density matrix embedding theory, we define exact link orbitals that capture all quantum mechanical (QM) effects across arbitrary quantum mechanics/molecular mechanics (QM/MM) boundaries. Exact link orbitals are rigorously defined from the full QM solution, and their number is equal to the number of orbitals in the primary QM region. Truncating the exact set yields a smaller set of link orbitals optimal with respect to reproducing the primary region density matrix. We use the optimal link orbitals to obtain insight into the limits of QM/MM boundary treatments. We further analyze the popular general hybrid orbital (GHO) QM/MM boundary across a test suite of molecules. We find that GHOs are often good proxies for the most important optimal link orbital, although there is little detailed correlation between the detailed GHO composition and optimal link orbital valence weights. The optimal theory shows that anions and cations cannot be described by a single link orbital. However, expanding to include the second most important optimal link orbital in the boundary recovers an accurate description. The second optimal link orbital takes the chemically intuitive form of a donor or acceptor orbital for charge redistribution, suggesting that optimal link orbitals can be used as interpretative tools for electron transfer. We further find that two optimal link orbitals are also sufficient for boundaries that cut across double bonds. Finally, we suggest how to construct "approximately" optimal link orbitals for practical QM/MM calculations.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

PubMed

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.

Liner Optimization Studies Using the Ducted Fan Noise Prediction Code TBIEM3D

NASA Technical Reports Server (NTRS)

Dunn, M. H.; Farassat, F.

1998-01-01

In this paper we demonstrate the usefulness of the ducted fan noise prediction code TBIEM3D as a liner optimization design tool. Boundary conditions on the interior duct wall allow for hard walls or a locally reacting liner with axially segmented, circumferentially uniform impedance. Two liner optimization studies are considered in which farfield noise attenuation due to the presence of a liner is maximized by adjusting the liner impedance. In the first example, the dependence of optimal liner impedance on frequency and liner length is examined. Results show that both the optimal impedance and attenuation levels are significantly influenced by liner length and frequency. In the second example, TBIEM3D is used to compare radiated sound pressure levels between optimal and non-optimal liner cases at conditions designed to simulate take-off. It is shown that significant noise reduction is achieved for most of the sound field by selecting the optimal or near optimal liner impedance. Our results also indicate that there is relatively large region of the impedance plane over which optimal or near optimal liner behavior is attainable. This is an important conclusion for the designer since there are variations in liner characteristics due to manufacturing imprecisions.

Information-theoretic approach to interactive learning

NASA Astrophysics Data System (ADS)

Still, S.

2009-01-01

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.

Optimal foraging, not biogenetic law, predicts spider orb web allometry.

PubMed

Gregorič, Matjaž; Kiesbüy, Heine C; Lebrón, Shakira G Quiñones; Rozman, Alenka; Agnarsson, Ingi; Kuntner, Matjaž

2013-03-01

The biogenetic law posits that the ontogeny of an organism recapitulates the pattern of evolutionary changes. Morphological evidence has offered some support for, but also considerable evidence against, the hypothesis. However, biogenetic law in behavior remains underexplored. As physical manifestation of behavior, spider webs offer an interesting model for the study of ontogenetic behavioral changes. In orb-weaving spiders, web symmetry often gets distorted through ontogeny, and these changes have been interpreted to reflect the biogenetic law. Here, we test the biogenetic law hypothesis against the alternative, the optimal foraging hypothesis, by studying the allometry in Leucauge venusta orb webs. These webs range in inclination from vertical through tilted to horizontal; biogenetic law predicts that allometry relates to ontogenetic stage, whereas optimal foraging predicts that allometry relates to gravity. Specifically, pronounced asymmetry should only be seen in vertical webs under optimal foraging theory. We show that, through ontogeny, vertical webs in L. venusta become more asymmetrical in contrast to tilted and horizontal webs. Biogenetic law thus cannot explain L. venusta web allometry, but our results instead support optimization of foraging area in response to spider size.

Optimized endogenous post-stratification in forest inventories

Treesearch

Paul L. Patterson

2012-01-01

An example of endogenous post-stratification is the use of remote sensing data with a sample of ground data to build a logistic regression model to predict the probability that a plot is forested and using the predicted probabilities to form categories for post-stratification. An optimized endogenous post-stratified estimator of the proportion of forest has been...

Improving Environmental Model Calibration and Prediction

DTIC Science & Technology

2011-01-18

REPORT Final Report - Improving Environmental Model Calibration and Prediction 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: First, we have continued to...develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies...toward practical hybrid optimization tools for environmental models. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 18-01-2011 13

Guidelines 13 and 14—Prediction uncertainty

USGS Publications Warehouse

Hill, Mary C.; Tiedeman, Claire

2005-01-01

An advantage of using optimization for model development and calibration is that optimization provides methods for evaluating and quantifying prediction uncertainty. Both deterministic and statistical methods can be used. Guideline 13 discusses using regression and post-audits, which we classify as deterministic methods. Guideline 14 discusses inferential statistics and Monte Carlo methods, which we classify as statistical methods.

Discerning the role of optimism in persuasion: the valence-enhancement hypothesis.

PubMed

Geers, Andrew L; Handley, Ian M; McLarney, Amber R

2003-09-01

The valence-enhancement hypothesis argues that because of their active coping strategies, optimists are especially likely to elaborate on valenced information that is of high personal relevance. The hypothesis predicts that as a result, optimists will be more persuaded by personally relevant positive messages and less persuaded by personally relevant negative messages than pessimists. It also predicts that when the message is not personally relevant, optimism and persuasion will not be related in this manner. The results of 3 studies support these predictions and supply evidence against several alternative hypotheses. The possibility that the observed effects are not due to optimism but to the confounding influence of 7 additional variables is also addressed and ruled out. Implications are discussed.

Manipulating Public Expectations; Pre- and Postprimary Statements in the '76 Campaign.

ERIC Educational Resources Information Center

Freshley, Dwight L.

Predicting the outcome of a primary election gives a candidate more exposure to the press, gives him or her a chance to predict modestly and then look better than the prediction, and helps create interest in the election and thereby increase voter turnout. During the 1976 Presidential primaries, most candidates adhered to the classic rule to make…

Do Different Young Plantation-Grown Species Require Different Biomass Models?

Treesearch

Bryce E. Schlaegel; Harvey E. Kennedy

1985-01-01

Sweetgum and water oak trees sampled from a plantation over 7 years were used to test whether primary tree component (bole wood, bole bark, limb wood, limb bark, and leaves) predictions could be summed to estimate total bole, total limb, and total tree values. Estimations by summing primary component predictions were not significantly different from predictions for the...

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Arab, Mohammad M.; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

2017-01-01

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation. PMID:29163583

WHO/INRUD patient care and facility-specific drug use indicators at primary health care centres in Eastern province, Saudi Arabia.

PubMed

El Mahalli, A A; Akl, O A M; Al-Dawood, S F; Al-Nehab, A A; Al-Kubaish, H A; Al-Saeed, S; Elkahky, A A A; Salem, A M A A

2012-11-01

This study aimed to measure the performance of primary health care centres in Eastern province, Saudi Arabia, using the WHO/International Network of Rational Use of Drugs patient care and facility-specific drug use indicators. In a cross-sectional study, 10 health centres were selected using systematic random sampling. A total of 300 patients were interviewed while visiting the centre from January to March 2011 and 10 pharmacists from the same centres were interviewed. Average consultation time was 7.3 min (optimal > or = 30 min), percentage of drugs adequately labelled was 10% (optimal 100%) and patient's knowledge of correct dosage was 79.3% (optimal 100%). The percentage of key drugs in stock was only 59.2% (optimal 100%). An overall index of rational facility-specific drug use was calculated and applied to rank the health centres for benchmarking.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE PAGES

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik; ...

2017-07-25

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fukada, Junichi, E-mail: fukada@rad.med.keio.ac.jp; Shigematsu, Naoyuki; Takeuchi, Hiroya

Purpose: We investigated clinical and treatment-related factors as predictors of symptomatic pericardial effusion in esophageal cancer patients after concurrent chemoradiation therapy. Methods and Materials: We reviewed 214 consecutive primary esophageal cancer patients treated with concurrent chemoradiation therapy between 2001 and 2010 in our institute. Pericardial effusion was detected on follow-up computed tomography. Symptomatic effusion was defined as effusion ≥grade 3 according to Common Terminology Criteria for Adverse Events v4.0 criteria. Percent volume irradiated with 5 to 65 Gy (V5-V65) and mean dose to the pericardium were evaluated employing dose-volume histograms. To evaluate dosimetry for patients treated with two-dimensional planning inmore » the earlier period (2001-2005), computed tomography data at diagnosis were transferred to a treatment planning system to reconstruct three-dimensional plans without modification. Optimal dosimetric thresholds for symptomatic pericardial effusion were calculated by receiver operating characteristic curves. Associating clinical and treatment-related risk factors for symptomatic pericardial effusion were detected by univariate and multivariate analyses. Results: The median follow-up was 29 (range, 6-121) months for eligible 167 patients. Symptomatic pericardial effusion was observed in 14 (8.4%) patients. Dosimetric analyses revealed average values of V30 to V45 for the pericardium and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those with asymptomatic pericardial effusion (P<.05). Pericardial V5 to V55 and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those without pericardial effusion (P<.001). Mean pericardial doses of 36.5 Gy and V45 of 58% were selected as optimal cutoff values for predicting symptomatic pericardial effusion. Multivariate analysis identified mean pericardial dose as the strongest risk factor for symptomatic pericardial effusion. Conclusions: Dose-volume thresholds for the pericardium facilitate predicting symptomatic pericardial effusion. Mean pericardial dose was selected based not only on the optimal dose-volume threshold but also on the most significant risk factor for symptomatic pericardial effusion.« less

Slowing Translation between Protein Domains by Increasing Affinity between mRNAs and the Ribosomal Anti-Shine-Dalgarno Sequence Improves Solubility.

PubMed

Vasquez, Kevin A; Hatridge, Taylor A; Curtis, Nicholas C; Contreras, Lydia M

2016-02-19

Recent studies have demonstrated that effective protein production requires coordination of multiple cotranslational cellular processes, which are heavily affected by translation timing. Until recently, protein engineering has focused on codon optimization to maximize protein production rates, mostly considering the effect of tRNA abundance. However, as it relates to complex multidomain proteins, it has been hypothesized that strategic translational pauses between domains and between distinct individual structural motifs can prevent interactions between nascent chain fragments that generate kinetically trapped misfolded peptides and thereby enhance protein yields. In this study, we introduce synthetic transient pauses between structural domains in a heterologous model protein based on designed patterns of affinity between the mRNA and the anti-Shine-Dalgarno (aSD) sequence on the ribosome. We demonstrate that optimizing translation attenuation at domain boundaries can predictably affect solubility patterns in bacteria. Exploration of the affinity space showed that modifying less than 1% of the nucleotides (on a small 12 amino acid linker) can vary soluble protein yields up to ∼7-fold without altering the primary sequence of the protein. In the context of longer linkers, where a larger number of distinct structural motifs can fold outside the ribosome, optimal synonymous codon variations resulted in an additional 2.1-fold increase in solubility, relative to that of nonoptimized linkers of the same length. While rational construction of 54 linkers of various affinities showed a significant correlation between protein solubility and predicted affinity, only weaker correlations were observed between tRNA abundance and protein solubility. We also demonstrate that naturally occurring high-affinity clusters are present between structural domains of β-galactosidase, one of Escherichia coli's largest native proteins. Interdomain ribosomal affinity is an important factor that has not previously been explored in the context of protein engineering.

Symptomatic pericardial effusion after chemoradiation therapy in esophageal cancer patients.

PubMed

Fukada, Junichi; Shigematsu, Naoyuki; Takeuchi, Hiroya; Ohashi, Toshio; Saikawa, Yoshiro; Takaishi, Hiromasa; Hanada, Takashi; Shiraishi, Yutaka; Kitagawa, Yuko; Fukuda, Keiichi

2013-11-01

We investigated clinical and treatment-related factors as predictors of symptomatic pericardial effusion in esophageal cancer patients after concurrent chemoradiation therapy. We reviewed 214 consecutive primary esophageal cancer patients treated with concurrent chemoradiation therapy between 2001 and 2010 in our institute. Pericardial effusion was detected on follow-up computed tomography. Symptomatic effusion was defined as effusion ≥grade 3 according to Common Terminology Criteria for Adverse Events v4.0 criteria. Percent volume irradiated with 5 to 65 Gy (V5-V65) and mean dose to the pericardium were evaluated employing dose-volume histograms. To evaluate dosimetry for patients treated with two-dimensional planning in the earlier period (2001-2005), computed tomography data at diagnosis were transferred to a treatment planning system to reconstruct three-dimensional plans without modification. Optimal dosimetric thresholds for symptomatic pericardial effusion were calculated by receiver operating characteristic curves. Associating clinical and treatment-related risk factors for symptomatic pericardial effusion were detected by univariate and multivariate analyses. The median follow-up was 29 (range, 6-121) months for eligible 167 patients. Symptomatic pericardial effusion was observed in 14 (8.4%) patients. Dosimetric analyses revealed average values of V30 to V45 for the pericardium and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those with asymptomatic pericardial effusion (P<.05). Pericardial V5 to V55 and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those without pericardial effusion (P<.001). Mean pericardial doses of 36.5 Gy and V45 of 58% were selected as optimal cutoff values for predicting symptomatic pericardial effusion. Multivariate analysis identified mean pericardial dose as the strongest risk factor for symptomatic pericardial effusion. Dose-volume thresholds for the pericardium facilitate predicting symptomatic pericardial effusion. Mean pericardial dose was selected based not only on the optimal dose-volume threshold but also on the most significant risk factor for symptomatic pericardial effusion. Copyright © 2013 Elsevier Inc. All rights reserved.

A New Version of Optimism for Education

ERIC Educational Resources Information Center

Bojesen, Emile

2018-01-01

The primary purpose of this paper is to outline the conceptual means by which it is possible to be optimistic about education. To provide this outline I turn to Ian Hunter and David Blacker, after a brief introduction to Nietzsche's conceptions of optimism and pessimism, to show why certain forms of optimism in education are either intellectually…

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

PubMed Central

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-01-01

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

PubMed

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-05-11

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Multiple Target Laser Designator (MTLD)

DTIC Science & Technology

2007-03-01

Optimized Liquid Crystal Scanning Element Optimize the Nonimaging Predictive Algorithm for Target Ranging, Tracking, and Position Estimation...commercial potential. 3.0 PROGRESS THIS QUARTER 3.1 Optimization of Nonimaging Holographic Antenna for Target Tracking and Position Estimation (Task 6) In

A Pharmacist-Physician Collaboration to Optimize Benzodiazepine Use for Anxiety and Sleep Symptom Control in Primary Care.

PubMed

Furbish, Shannon M L; Kroehl, Miranda E; Loeb, Danielle F; Lam, Huong Mindy; Lewis, Carmen L; Nelson, Jennifer; Chow, Zeta; Trinkley, Katy E

2017-08-01

Benzodiazepines are prescribed inappropriately in up to 40% of outpatients. The purpose of this study is to describe a collaborative team-based care model in which clinical pharmacists work with primary care providers (PCPs) to improve the safe use of benzodiazepines for anxiety and sleep disorders and to assess the preliminary results of the impact of the clinical service on patient outcomes. Adult patients were eligible if they received care from the academic primary care clinic, were prescribed a benzodiazepine chronically, and were not pregnant or managed by psychiatry. Outcomes included baseline PCP confidence and knowledge of appropriate benzodiazepine use, patient symptom severity, and medication changes. Twenty-five of 57 PCPs responded to the survey. PCPs reported greater confidence in diagnosing and treating generalized anxiety and panic disorders than sleep disorder and had variable knowledge of appropriate benzodiazepine prescribing. Twenty-nine patients had at least 1 visit. Over 44 total patient visits, 59% resulted in the addition or optimization of a nonbenzodiazepine medication and 46% resulted in the discontinuation or optimization of a benzodiazepine. Generalized anxiety symptom severity scores significantly improved (-2.0; 95% confidence interval (CI): -3.57 to -0.43). Collaborative team-based models that include clinical pharmacists in primary care can assist in optimizing high-risk benzodiazepine use. Although these findings suggest improvements in safe medication use and symptoms, additional studies are needed to confirm these preliminary results.

Model parameter-related optimal perturbations and their contributions to El Niño prediction errors

NASA Astrophysics Data System (ADS)

Tao, Ling-Jiang; Gao, Chuan; Zhang, Rong-Hua

2018-04-01

Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling ({α _τ } ), and the other involves the thermocline effect on the SST ({α _{Te}} ). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the {α _τ } component is mainly concentrated in the central equatorial Pacific, and the {α _{Te}} component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Niño- or La Niña-like error evolution, resulting in an El Niño-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and to the thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.

An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China.

PubMed

Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2014-06-01

In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.

Relative value of physical examination, mammography, and breast sonography in evaluating the size of the primary tumor and regional lymph node metastases in women receiving neoadjuvant chemotherapy for locally advanced breast carcinoma.

PubMed

Herrada, J; Iyer, R B; Atkinson, E N; Sneige, N; Buzdar, A U; Hortobagyi, G N

1997-09-01

The purpose of this study was to correlate physical examination and sonographic and mammographic measurements of breast tumors and regional lymph nodes with pathological findings and to evaluate the effect of neoadjuvant chemotherapy on clinical Tumor-Node-Metastasis stage by noninvasive methods. This was a retrospective analysis of 100 patients with locally advanced breast cancer registered and treated in prospective trials of neoadjuvant chemotherapy. All patients received four cycles of a doxorubicin-containing regimen and had noninvasive evaluation of the primary tumor and regional lymph nodes before and after neoadjuvant chemotherapy by physical examination, sonography, and mammography and underwent breast surgery and axillary dissection within 5 weeks after completion of neoadjuvant chemotherapy. The correlations between clinical and pathological measurements were determined by Spearman rank correlation analysis. A proportional odds model was used to examine predictive values. Eighty-three patients had both a clinically detectable primary tumor and lymph node metastases. Sixty-four patients had a decrease in Tumor-Node-Metastasis stage after chemotherapy. For 54% of patients, there was concordance in clinical response between the primary tumor and lymph node compartment; for the rest, results were discordant. Physical examination correlated best with pathological findings in the measurement of the primary tumor (P = 0.0003), whereas sonography was the most accurate predictor of size for axillary lymph nodes (P = 0.0005). The combination of physical examination and mammography worked best for assessment of the primary tumor (P = 0.003), whereas combining physical examination with sonography gave optimal evaluation of regional lymph nodes (P = 0.0001). In conclusion, physical examination is the best noninvasive predictor of the real size of locally advanced primary breast cancer, whereas sonography correlates better with the real dimensions of axillary lymph nodes. The combination of physical examination with either mammography or sonography significantly improves the accuracy of noninvasive assessment of tumor dimensions.

Variation in nuclear size and PD-L2 positivity correlate with aggressive chromophobe renal cell carcinoma.

PubMed

Mostafa, Mohamed E; Abdelkader, Amrou; Kuroda, Naoto; Pérez-Montiel, Delia; Banerjee, Anjishnu; Hes, Ondrej; Iczkowski, Kenneth A

2018-06-01

Chromophobe renal cell carcinoma (CRCC) is not amenable to International Society for Urologic Pathology-endorsed nucleolar grading. Novel grading approaches were proposed, but the rarity of adverse pathology hampers their discriminatory value. We investigate simple linear micrometer measurements and a proposed immunostain in CRCCs. 32 patients' CRCCs were studied: 12 adverse cases (stage pT3, recurrence, or metastasis), 15 controls (stage ≤pT2, no recurrence or metastasis after >3 years), and 8 metastases (3 were paired with primary adverse cases). The ratio of greatest dimensions of largest and smallest nuclei, in each of 5 "worst" high-power fields, excluding those with degenerative features, was designated variation in nuclear size (VNS). Percent multinucleate cells (PMC) were also counted. Mouse anti PD-L2 monoclonal antibody immunostaining was performed. Mean VNS measured in adverse primary and control primary tumors were 3.7 ± 0.5 and 2.4 ± 0.4 respectively (P < .001), and 3.4 ± 0.4 for metastases (P < .001). Optimal VNS cut-off was 2.5, with sensitivity and specificity 0.85 and 0.81, respectively. PMCs were 6.0 ± 3.0 for adverse group, 5.7 ± 2.7 for controls, and 4.1 ± 1.6 for metastases (P = NS). PD-L2 could not discriminate adverse versus good primary tumors (χ 2 1.6, P = .2), but was higher in metastases (χ 2 6.9, P < .01), or metastases plus adverse primary tumors (χ 2 4.8, P = .03), compared to good-pathology primary tumors. In conclusion, VNS is an easily obtained measurement that can predict adverse behavior of chromophobe RCC, and may impart value for needle biopsy reporting and the choice of active surveillance. PD-L2 was elevated in metastases but was less useful for primary tumors. Copyright © 2018 Elsevier Inc. All rights reserved.

Validity of the AUDIT-C screen for at-risk drinking among students utilizing university primary care.

PubMed

Campbell, Clare E; Maisto, Stephen A

2018-03-22

Research is needed to establish the psychometric properties of brief screens in university primary care settings. This study aimed to assess the construct validity of one such screen, the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C), for detecting at-risk drinking among students who have utilized on-campus primary care. 389 students recently seen in university primary care completed a confidential online survey in December 2014. Bivariate correlations between the AUDIT-C and measures of alcohol consumption and negative drinking consequences provided concurrent evidence for construct validity. Receiver Operating Characteristic curve analyses determined optimal cut-off scores for at-risk drinking. The AUDIT-C significantly correlated with measures of alcohol consumption and negative drinking consequences (p < .001). Analyses support optimal AUDIT-C cut-off scores of 5 for females and 7 for males. The AUDIT-C is a valid screen for at-risk drinking among students who utilize university primary care.

[Levers in Primary Health Care - Identifying Strategic Success Factors for Improved Primary Care in Upper Austria].

PubMed

Kriegel, J; Rebhandl, E; Reckwitz, N; Hockl, W

2016-12-01

Current and projected general practitioner (GP) and primary care in Austria shows structural and process inadequacies in the quality as well as assurance of healthcare supply. The aim is therefore to develop solution- and patient-oriented measures that take patient-related requirements and medical perspectives into account. Using an effect matrix, subjective expert and user priorities were ascertained, cause and effect relationships were examined, and an expanded circle of success for the optimization of GP and primary care in Upper Austria was developed. Through this, the relevant levers for target-oriented development and optimization of the complex system of GP and primary care in Upper Austria were identified; these are training to become general practitioners, entrepreneurs as well as management and coordination. It is necessary to further adapt the identified levers conceptually and operationally in a targeted approach. This is to be achieved by means of the primary health care (PHC) concept as well as management tools and information and communication technologies (ICT) associated with it. © Georg Thieme Verlag KG Stuttgart · New York.

Cationic amino acid based lipids as effective nonviral gene delivery vectors for primary cultured neurons.

PubMed

Aoshima, Yumiko; Hokama, Ryosuke; Sou, Keitaro; Sarker, Satya Ranjan; Iida, Kabuto; Nakamura, Hideki; Inoue, Takafumi; Takeoka, Shinji

2013-12-18

The delivery of specific genes into neurons offers a potent approach for treatment of diseases as well as for the study of neuronal cell biology. Here we investigated the capabilities of cationic amino acid based lipid assemblies to act as nonviral gene delivery vectors in primary cultured neurons. An arginine-based lipid, Arg-C3-Glu2C14, and a lysine-based lipid, Lys-C3-Glu2C14, with two different types of counterion, chloride ion (Cl-) and trifluoroacetic acid (TFA-), were shown to successfully mediate transfection of primary cultured neurons with plasmid DNA encoding green fluorescent protein. Among four types of lipids, we optimized their conditions such as the lipid-to-DNA ratio and the amount of pDNA and conducted a cytotoxicity assay at the same time. Overall, Arg-C3-Glu2C14 with TFA- induced a rate of transfection in primary cultured neurons higher than that of Lys-C3-Glu2C14 using an optimal weight ratio of lipid-to-plasmid DNA of 1. Moreover, it was suggested that Arg-C3-Glu2C14 with TFA- showed the optimized value higher than that of Lipofectamine2000 in experimental conditions. Thus, Arg-C3-Glu2C14 with TFA- is a promising candidate as a reliable transfection reagent for primary cultured neurons with a relatively low cytotoxicity.

Cationic Amino Acid Based Lipids as Effective Nonviral Gene Delivery Vectors for Primary Cultured Neurons

PubMed Central

2013-01-01

The delivery of specific genes into neurons offers a potent approach for treatment of diseases as well as for the study of neuronal cell biology. Here we investigated the capabilities of cationic amino acid based lipid assemblies to act as nonviral gene delivery vectors in primary cultured neurons. An arginine-based lipid, Arg-C3-Glu2C14, and a lysine-based lipid, Lys-C3-Glu2C14, with two different types of counterion, chloride ion (Cl–) and trifluoroacetic acid (TFA–), were shown to successfully mediate transfection of primary cultured neurons with plasmid DNA encoding green fluorescent protein. Among four types of lipids, we optimized their conditions such as the lipid-to-DNA ratio and the amount of pDNA and conducted a cytotoxicity assay at the same time. Overall, Arg-C3-Glu2C14 with TFA– induced a rate of transfection in primary cultured neurons higher than that of Lys-C3-Glu2C14 using an optimal weight ratio of lipid-to-plasmid DNA of 1. Moreover, it was suggested that Arg-C3-Glu2C14 with TFA– showed the optimized value higher than that of Lipofectamine2000 in experimental conditions. Thus, Arg-C3-Glu2C14 with TFA– is a promising candidate as a reliable transfection reagent for primary cultured neurons with a relatively low cytotoxicity. PMID:24087930

Thermal optimum design for tracking primary mirror of Space Telescope

NASA Astrophysics Data System (ADS)

Pan, Hai-jun; Ruan, Ping; Li, Fu; Wang, Hong-Wei

2011-08-01

In the conventional method, the structural parameters of primary mirror are usually optimized just by the requirement of mechanical performance. Because the influences of structural parameters on thermal stability are not taken fully into account in this simple method, the lightweight optimum design of primary mirror usually brings the bad thermal stability, especially in the complex environment. In order to obtain better thermal stability, a new method about structure-thermal optimum design of tracking primary mirror is discussed. During the optimum process, both the lightweight ratio and thermal stability will be taken into account. The structure-thermal optimum is introduced into the analysis process and commenced after lightweight design as the secondary optimum. Using the engineering analysis of software ANSYS, a parameter finite element analysis (FEA) model of mirror is built. On the premise of appropriate lightweight ratio, the RMS of structure-thermal deformation of mirror surface and lightweight ratio are assigned to be state variables, and the maximal RMS of temperature gradient load to be object variable. The results show that certain structural parameters of tracking primary mirror have different influences on mechanical performance and thermal stability, even they are opposite. By structure-thermal optimizing, the optimized mirror model discussed in this paper has better thermal stability than the old one under the same thermal loads, which can drastically reduce difficulty in thermal control.

Absolute Stability Analysis of a Phase Plane Controlled Spacecraft

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; Plummer, Michael; Bedrossian, Nazareth; Hall, Charles; Jackson, Mark; Spanos, Pol

2010-01-01

Many aerospace attitude control systems utilize phase plane control schemes that include nonlinear elements such as dead zone and ideal relay. To evaluate phase plane control robustness, stability margin prediction methods must be developed. Absolute stability is extended to predict stability margins and to define an abort condition. A constrained optimization approach is also used to design flex filters for roll control. The design goal is to optimize vehicle tracking performance while maintaining adequate stability margins. Absolute stability is shown to provide satisfactory stability constraints for the optimization.

Development and validation of spray models for investigating diesel engine combustion and emissions

NASA Astrophysics Data System (ADS)

Som, Sibendu

Diesel engines intrinsically generate NOx and particulate matter which need to be reduced significantly in order to comply with the increasingly stringent regulations worldwide. This motivates the diesel engine manufacturers to gain fundamental understanding of the spray and combustion processes so as to optimize these processes and reduce engine emissions. Strategies being investigated to reduce engine's raw emissions include advancements in fuel injection systems, efficient nozzle orifice design, injection and combustion control strategies, exhaust gas recirculation, use of alternative fuels such as biodiesel etc. This thesis explores several of these approaches (such as nozzle orifice design, injection control strategy, and biodiesel use) by performing computer modeling of diesel engine processes. Fuel atomization characteristics are known to have a significant effect on the combustion and emission processes in diesel engines. Primary fuel atomization is induced by aerodynamics in the near nozzle region as well as cavitation and turbulence from the injector nozzle. The breakup models that are currently used in diesel engine simulations generally consider aerodynamically induced breakup using the Kelvin-Helmholtz (KH) instability model, but do not account for inner nozzle flow effects. An improved primary breakup (KH-ACT) model incorporating cavitation and turbulence effects along with aerodynamically induced breakup is developed and incorporated in the computational fluid dynamics code CONVERGE. The spray simulations using KH-ACT model are "quasi-dynamically" coupled with inner nozzle flow (using FLUENT) computations. This presents a novel tool to capture the influence of inner nozzle flow effects such as cavitation and turbulence on spray, combustion, and emission processes. Extensive validation is performed against the non-evaporating spray data from Argonne National Laboratory. Performance of the KH and KH-ACT models is compared against the evaporating and combusting data from Sandia National Laboratory. The KH-ACT model is observed to provide better predictions for spray dispersion, axial velocity decay, sauter mean diameter, and liquid and lift-off length interplay which is attributed to the enhanced primary breakup predicted by this model. In addition, experimentally observed trends with changing nozzle conicity could only be captured by the KH-ACT model. Results further indicate that the combustion under diesel engine conditions is characterized by a double-flame structure with a rich premixed reaction zone near the flame stabilization region and a non-premixed reaction zone further downstream. Finally, the differences in inner nozzle flow and spray characteristics of petrodiesel and biodiesel are quantified. The improved modeling capability developed in this work can be used for extensive diesel engine simulations to further optimize injection, spray, combustion, and emission processes.

Predicting the Onset of Anxiety Syndromes at 12 Months in Primary Care Attendees. The PredictA-Spain Study

PubMed Central

Moreno-Peral, Patricia; Luna, Juan de Dios; Marston, Louise; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Muñoz-Bravo, Carlos; Bellón, Juan Ángel

2014-01-01

Background There are no risk algorithms for the onset of anxiety syndromes at 12 months in primary care. We aimed to develop and validate internally a risk algorithm to predict the onset of anxiety syndromes at 12 months. Methods A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multilevel logistic regression and inverse probability weighting to build the risk algorithm. Our main outcome was generalized anxiety, panic and other non-specific anxiety syndromes as measured by the Primary Care Evaluation of Mental Disorders, Patient Health Questionnaire (PRIME-MD-PHQ). We recruited 3,564 adult primary care attendees without anxiety syndromes from 174 family physicians and 32 health centers in 6 Spanish provinces. Results The cumulative 12-month incidence of anxiety syndromes was 12.2%. The predictA-Spain risk algorithm included the following predictors of anxiety syndromes: province; sex (female); younger age; taking medicines for anxiety, depression or stress; worse physical and mental quality of life (SF-12); dissatisfaction with paid and unpaid work; perception of financial strain; and the interactions sex*age, sex*perception of financial strain, and age*dissatisfaction with paid work. The C-index was 0.80 (95% confidence interval = 0.78–0.83) and the Hedges' g = 1.17 (95% confidence interval = 1.04–1.29). The Copas shrinkage factor was 0.98 and calibration plots showed an accurate goodness of fit. Conclusions The predictA-Spain risk algorithm is valid to predict anxiety syndromes at 12 months. Although external validation is required, the predictA-Spain is available for use as a predictive tool in the prevention of anxiety syndromes in primary care. PMID:25184313

Trophic Strategies of Unicellular Plankton.

PubMed

Chakraborty, Subhendu; Nielsen, Lasse Tor; Andersen, Ken H

2017-04-01

Unicellular plankton employ trophic strategies ranging from pure photoautotrophs over mixotrophy to obligate heterotrophs (phagotrophs), with cell sizes from 10 -8 to 1 μg C. A full understanding of how trophic strategy and cell size depend on resource environment and predation is lacking. To this end, we develop and calibrate a trait-based model for unicellular planktonic organisms characterized by four traits: cell size and investments in phototrophy, nutrient uptake, and phagotrophy. We use the model to predict how optimal trophic strategies depend on cell size under various environmental conditions, including seasonal succession. We identify two mixotrophic strategies: generalist mixotrophs investing in all three investment traits and obligate mixotrophs investing only in phototrophy and phagotrophy. We formulate two conjectures: (1) most cells are limited by organic carbon; however, small unicellulars are colimited by organic carbon and nutrients, and only large photoautotrophs and smaller mixotrophs are nutrient limited; (2) trophic strategy is bottom-up selected by the environment, while optimal size is top-down selected by predation. The focus on cell size and trophic strategies facilitates general insights into the strategies of a broad class of organisms in the size range from micrometers to millimeters that dominate the primary and secondary production of the world's oceans.

Multiphase porous media modelling: A novel approach to predicting food processing performance.

PubMed

Khan, Md Imran H; Joardder, M U H; Kumar, Chandan; Karim, M A

2018-03-04

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

A CFD model for biomass fast pyrolysis in fluidized-bed reactors

NASA Astrophysics Data System (ADS)

Xue, Qingluan; Heindel, T. J.; Fox, R. O.

2010-11-01

A numerical study is conducted to evaluate the performance and optimal operating conditions of fluidized-bed reactors for fast pyrolysis of biomass to bio-oil. A comprehensive CFD model, coupling a pyrolysis kinetic model with a detailed hydrodynamics model, is developed. A lumped kinetic model is applied to describe the pyrolysis of biomass particles. Variable particle porosity is used to account for the evolution of particle physical properties. The kinetic scheme includes primary decomposition and secondary cracking of tar. Biomass is composed of reference components: cellulose, hemicellulose, and lignin. Products are categorized into groups: gaseous, tar vapor, and solid char. The particle kinetic processes and their interaction with the reactive gas phase are modeled with a multi-fluid model derived from the kinetic theory of granular flow. The gas, sand and biomass constitute three continuum phases coupled by the interphase source terms. The model is applied to investigate the effect of operating conditions on the tar yield in a fluidized-bed reactor. The influence of various parameters on tar yield, including operating temperature and others are investigated. Predicted optimal conditions for tar yield and scale-up of the reactor are discussed.