Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

PubMed

Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P

2015-07-01

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders. Published by Elsevier Ireland Ltd.

Advanced propeller noise prediction in the time domain

NASA Technical Reports Server (NTRS)

Farassat, F.; Dunn, M. H.; Spence, P. L.

1992-01-01

The time domain code ASSPIN gives acousticians a powerful technique of advanced propeller noise prediction. Except for nonlinear effects, the code uses exact solutions of the Ffowcs Williams-Hawkings equation with exact blade geometry and kinematics. By including nonaxial inflow, periodic loading noise, and adaptive time steps to accelerate computer execution, the development of this code becomes complete.

Anti-tumour activity of platinum compounds in advanced prostate cancer-a systematic literature review.

PubMed

Hager, S; Ackermann, C J; Joerger, M; Gillessen, S; Omlin, A

2016-06-01

For men with advanced castration-resistant prostate cancer (CRPC), several treatment options are available, including androgen receptor (AR) pathway inhibitors (abiraterone acetate, enzalutamide), taxanes (docetaxel, cabazitaxel) and the radionuclide (radium-223). However, cross-resistance is a clinically relevant problem. Platinum compounds have been tested in a number of clinical trials in molecularly unselected prostate cancer patients. Advances in CRPC molecular profiling have shown that a significant proportion of patients harbour DNA repair defects, which may serve as predictive markers for sensitivity to platinum agents. To systematically identify and analyse clinical trials that have evaluated platinum agents in advanced prostate cancer patients. PubMed was searched to identify published clinical trials of platinum agents in advanced prostate cancer. The PRIMSA statement was followed for the systematic review process. Identified trials are analysed for study design, statistical plan, assessments of anti-tumour activity and the potential value of predictive biomarkers. A total of 163 references were identified by the literature search and 72 publications that met the selection criteria were included in this review; of these 33 used carboplatin, 27 cisplatin, 6 satraplatin, 4 oxaliplatin and 2 other platinum compounds. Overall, anti-tumour activity varies in the range of 10%-40% for objective response and 20%-70% for PSA decline ≥50%. Response seemed highest for the combinations of carboplatin with taxanes or oxaliplatin with gemcitabine. The interpretation of the clinical data is limited by differences in response criteria used and patient populations studied. Platinum compounds have moderate anti-tumour activity in molecularly unselected patients with advanced prostate cancer. Translational evidence of DNA repair deficiency should be leveraged in future studies to select prostate cancer patients most likely to benefit from platinum-based therapy. © The

Levels of circulating soluble receptor activator of NF-κB and interleukins-1 predicting outcome of locally advanced basal cell carcinoma.

PubMed

Lin, Quan; Li, Yan; Zhang, Duo; Jin, Hongjuan

2016-12-01

Decreasing levels of cytokines are associated with better responses to therapies, while increasing levels are related to progression or recurrence and decreased survival. NF-κB's role in the cell cycle and its ubiquity are only stressed out by the evidence for the importance of activation (aberrant activation in the majority of cancers) of both canonical and non-canonical pathways in advanced basal cell carcinomas (aBCCs), a subset of basal cell carcinoma (BCC). NF-κB acts through its canonical, or classical, form activated by interleukin-1 (IL-1), regulates cytoprotective, innate, and adaptive immune responses. However, NF-κB2 often acts through its non-canonical or alternate pathway. During the two-year study period, we selected 21 patients presenting with aBCCs due to delay in accessing medical attention with an advanced form of BCCs (n = 19) and infiltrative BCCs (n = 2). Initial diagnosis of BCCs of head and neck was made clinically and verified by skin biopsy. Venous blood was drawn and serum was obtained. Samples were collected at baseline and every three days thereafter (days 3, 6, 9, etc. until surgery). Antigenes' quantities (cytokines) were determined by ELISA kits. Initially, the mean value of all cytokine subjects was significantly different related to the control group (P <0.05). Changes in serum levels of circulating soluble receptor activator of NF-κB and interleukins-1 (α and β) were observed following the surgery. Changes in serum levels of circulating soluble receptor activator of NF-κB and interleukins-1 (α and β) are evident throughout our study period and a certain regularity in its dynamics is evident as the follow-up period moves away. It was therefore concluded that measurement of these factors might be useful in predicting the overall outcome of patients with aBCCs. This study highlights the systemic effects of aBCCs, but further studies are required on this topic. © The Author(s) 2016.

Levels of circulating soluble receptor activator of NF-κB and interleukins-1 predicting outcome of locally advanced basal cell carcinoma

PubMed Central

Lin, Quan; Li, Yan; Zhang, Duo; Jin, Hongjuan

2016-01-01

Decreasing levels of cytokines are associated with better responses to therapies, while increasing levels are related to progression or recurrence and decreased survival. NF-κB’s role in the cell cycle and its ubiquity are only stressed out by the evidence for the importance of activation (aberrant activation in the majority of cancers) of both canonical and non-canonical pathways in advanced basal cell carcinomas (aBCCs), a subset of basal cell carcinoma (BCC). NF-κB acts through its canonical, or classical, form activated by interleukin-1 (IL-1), regulates cytoprotective, innate, and adaptive immune responses. However, NF-κB2 often acts through its non-canonical or alternate pathway. During the two-year study period, we selected 21 patients presenting with aBCCs due to delay in accessing medical attention with an advanced form of BCCs (n = 19) and infiltrative BCCs (n = 2). Initial diagnosis of BCCs of head and neck was made clinically and verified by skin biopsy. Venous blood was drawn and serum was obtained. Samples were collected at baseline and every three days thereafter (days 3, 6, 9, etc. until surgery). Antigenes’ quantities (cytokines) were determined by ELISA kits. Initially, the mean value of all cytokine subjects was significantly different related to the control group (P <0.05). Changes in serum levels of circulating soluble receptor activator of NF-κB and interleukins-1 (α and β) were observed following the surgery. Changes in serum levels of circulating soluble receptor activator of NF-κB and interleukins-1 (α and β) are evident throughout our study period and a certain regularity in its dynamics is evident as the follow-up period moves away. It was therefore concluded that measurement of these factors might be useful in predicting the overall outcome of patients with aBCCs. This study highlights the systemic effects of aBCCs, but further studies are required on this topic. PMID:27760847

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.

PubMed

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J; Kim, Doh Kwan

2018-04-01

Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

PubMed

Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

2009-09-01

Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P < .001 and P=.019, respectively). Reclassifying the SHFM-predicted risk with use of the echocardiography-added model resulted in improved prognostic separation. Addition of standard echocardiographic variables to the SHFM results in significant improvement in risk prediction for patients with advanced HF.

Physical Activity in Advanced Age: Physical Activity, Function, and Mortality in Advanced Age: A Longitudinal Follow Up (LiLACS NZ).

PubMed

Mace Firebaugh, Casey; Moyes, Simon; Jatrana, Santosh; Rolleston, Anna; Kerse, Ngaire

2018-01-18

The relationship between physical activity, function, and mortality is not established in advanced age. Physical activity, function, and mortality were followed in a cohort of Māori and non-Māori adults living in advanced age for a period of six years. Generalised Linear regression models were used to analyse the association between physical activity and NEADL while Kaplan-Meier survival analysis, and Cox-proportional hazard models were used to assess the association between the physical activity and mortality. The Hazard Ratio for mortality for those in the least active physical activity quartile was 4.1 for Māori and 1.8 for non- Māori compared to the most active physical activity quartile. There was an inverse relationship between physical activity and mortality, with lower hazard ratios for mortality at all levels of physical activity. Higher levels of physical activity were associated with lower mortality and higher functional status in advanced aged adults.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data

PubMed Central

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J.; Kim, Doh Kwan

2018-01-01

Objective Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. Methods The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Results Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. Conclusion These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events. PMID:29614852

Circulating CD147 predicts mortality in advanced hepatocellular carcinoma.

PubMed

Lee, Aimei; Rode, Anthony; Nicoll, Amanda; Maczurek, Annette E; Lim, Lucy; Lim, Seok; Angus, Peter; Kronborg, Ian; Arachchi, Niranjan; Gorelik, Alexandra; Liew, Danny; Warner, Fiona J; McCaughan, Geoffrey W; McLennan, Susan V; Shackel, Nicholas A

2016-02-01

The glycoprotein CD147 has a role in tumor progression, is readily detectable in the circulation, and is abundantly expressed in hepatocellular carcinoma (HCC). Advanced HCC patients are a heterogeneous group with some individuals having dismal survival. The aim of this study was to examine circulating soluble CD147 levels as a prognostic marker in HCC patients. CD147 was measured in 277 patients (110 HCC, 115 chronic liver disease, and 52 non-liver disease). Clinical data included etiology, tumor progression, Barcelona Clinic Liver Cancer (BCLC) stage, and treatment response. Patients with HCC were stratified into two groups based upon the 75th percentile of CD147 levels (24 ng/mL). CD147 in HCC correlated inversely with poor survival (P = 0.031). Increased CD147 predicted poor survival in BCLC stages C and D (P = 0.045), and CD147 levels >24 ng/mL predicted a significantly diminished 90-day and 180-day survival time (hazard ratio [HR] = 6.1; 95% confidence interval [CI]: 2.1-63.2; P = 0.0045 and HR = 2.8; 95% CI: 1.2-12.6; P = 0.028, respectively). In BCLC stage C, CD147 predicted prognosis; levels >24 ng/mL were associated with a median survival of 1.5 months compared with 6.5 months with CD147 levels ≤24 ng/mL (P = 0.03). CD147 also identified patients with a poor prognosis independent from treatment frequency, modality, and tumor size. Circulating CD147 is an independent marker of survival in advanced HCC. CD147 requires further evaluation as a potential new prognostic measure in HCC to identify patients with advanced disease who have a poor prognosis. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

DNA Methyltransferase Activity Assays: Advances and Challenges

PubMed Central

Poh, Wan Jun; Wee, Cayden Pang Pee; Gao, Zhiqiang

2016-01-01

DNA methyltransferases (MTases), a family of enzymes that catalyse the methylation of DNA, have a profound effect on gene regulation. A large body of evidence has indicated that DNA MTase is potentially a predictive biomarker closely associated with genetic disorders and genetic diseases like cancer. Given the attention bestowed onto DNA MTases in molecular biology and medicine, highly sensitive detection of DNA MTase activity is essential in determining gene regulation, epigenetic modification, clinical diagnosis and therapeutics. Conventional techniques such as isotope labelling are effective, but they often require laborious sample preparation, isotope labelling, sophisticated equipment and large amounts of DNA, rendering them unsuitable for uses at point-of-care. Simple, portable, highly sensitive and low-cost assays are urgently needed for DNA MTase activity screening. In most recent technological advances, many alternative DNA MTase activity assays such as fluorescent, electrochemical, colorimetric and chemiluminescent assays have been proposed. In addition, many of them are coupled with nanomaterials and/or enzymes to significantly enhance their sensitivity. Herein we review the progress in the development of DNA MTase activity assays with an emphasis on assay mechanism and performance with some discussion on challenges and perspectives. It is hoped that this article will provide a broad coverage of DNA MTase activity assays and their latest developments and open new perspectives toward the development of DNA MTase activity assays with much improved performance for uses in molecular biology and clinical practice. PMID:26909112

Do plasma concentrations of apelin predict prognosis in patients with advanced heart failure?

PubMed

Dalzell, Jonathan R; Jackson, Colette E; Chong, Kwok S; McDonagh, Theresa A; Gardner, Roy S

2014-01-01

Apelin is an endogenous vasodilator and inotrope, plasma concentrations of which are reduced in advanced heart failure (HF). We determined the prognostic significance of plasma concentrations of apelin in advanced HF. Plasma concentrations of apelin were measured in 182 patients with advanced HF secondary to left ventricular systolic dysfunction. The predictive value of apelin for the primary end point of all-cause mortality was assessed over a median follow-up period of 544 (IQR: 196-923) days. In total, 30 patients (17%) reached the primary end point. Of those patients with a plasma apelin concentration above the median, 14 (16%) reached the primary end point compared with 16 (17%) of those with plasma apelin levels below the median (p = NS). NT-proBNP was the most powerful prognostic marker in this population (log rank statistic: 10.37; p = 0.001). Plasma apelin concentrations do not predict medium to long-term prognosis in patients with advanced HF secondary to left ventricular systolic dysfunction.

Predictive factors for overall quality of life in patients with advanced cancer.

PubMed

Cramarossa, Gemma; Chow, Edward; Zhang, Liying; Bedard, Gillian; Zeng, Liang; Sahgal, Arjun; Vassiliou, Vassilios; Satoh, Takefumi; Foro, Palmira; Ma, Brigette B Y; Chie, Wei-Chu; Chen, Emily; Lam, Henry; Bottomley, Andrew

2013-06-01

This study examined which domains/symptoms from the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 15 Palliative (QLQ-C15-PAL), an abbreviated version of the health-related EORTC QLQ-C30 questionnaire designed for palliative cancer patients, were predictive of overall quality of life (QOL) in advanced cancer patients. Patients with advanced cancer from six countries completed the QLQ-C15-PAL at consultation and at one follow-up point. Univariate and multivariate regression analyses were conducted to determine the predictive value of the EORTC QLQ-C15-PAL functional/symptom scores for global QOL (question 15). Three hundred forty-nine patients completed the EORTC QLQ-C15-PAL at baseline. In the total patient sample, worse emotional functioning, pain, and appetite loss were the most significant predictive factors for worse QOL. In the subgroup of patients with bone metastases (n = 240), the domains mentioned above were also the most significant predictors, whereas in patients with brain metastases (n = 109), worse physical and emotional functioning most significantly predicted worse QOL. One-month follow-up in 267 patients revealed that the significant predictors changed somewhat over time. For example, in the total patient sample, physical functioning, fatigue, and appetite loss were significant predictors at the follow-up point. A sub-analysis of predictive factors affecting QOL by primary cancer (lung, breast, and prostate) was also conducted for the total patient sample. Deterioration of certain EORTC QLQ-C15-PAL functional/symptom scores significantly contributes to worse overall QOL. Special attention should be directed to managing factors most influential on overall QOL to ensure optimal management of advanced cancer patients.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR

PREDICT: a diagnostic accuracy study of a tool for predicting mortality within one year: who should have an advance healthcare directive?

PubMed

Richardson, Philip; Greenslade, Jaimi; Shanmugathasan, Sulochana; Doucet, Katherine; Widdicombe, Neil; Chu, Kevin; Brown, Anthony

2015-01-01

CARING is a screening tool developed to identify patients who have a high likelihood of death in 1 year. This study sought to validate a modified CARING tool (termed PREDICT) using a population of patients presenting to the Emergency Department. In total, 1000 patients aged over 55 years who were admitted to hospital via the Emergency Department between January and June 2009 were eligible for inclusion in this study. Data on the six prognostic indicators comprising PREDICT were obtained retrospectively from patient records. One-year mortality data were obtained from the State Death Registry. Weights were applied to each PREDICT criterion, and its final score ranged from 0 to 44. Receiver operator characteristic analyses and diagnostic accuracy statistics were used to assess the accuracy of PREDICT in identifying 1-year mortality. The sample comprised 976 patients with a median (interquartile range) age of 71 years (62-81 years) and a 1-year mortality of 23.4%. In total, 50% had ≥1 PREDICT criteria with a 1-year mortality of 40.4%. Receiver operator characteristic analysis gave an area under the curve of 0.86 (95% confidence interval: 0.83-0.89). Using a cut-off of 13 points, PREDICT had a 95.3% (95% confidence interval: 93.6-96.6) specificity and 53.9% (95% confidence interval: 47.5-60.3) sensitivity for predicting 1-year mortality. PREDICT was simpler than the CARING criteria and identified 158 patients per 1000 admitted who could benefit from advance care planning. PREDICT was successfully applied to the Australian healthcare system with findings similar to the original CARING study conducted in the United States. This tool could improve end-of-life care by identifying who should have advance care planning or an advance healthcare directive. © The Author(s) 2014.

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator.

PubMed

Dubay, Rickey; Hassan, Marwan; Li, Chunying; Charest, Meaghan

2014-09-01

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living.

PubMed

Beltrame, T; Amelard, R; Wong, A; Hughson, R L

2017-04-05

Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured and the prediction bias was 0.2 ml·min -1 ·kg -1 . The MNG calculated based on predicted was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living

PubMed Central

Beltrame, T.; Amelard, R.; Wong, A.; Hughson, R. L.

2017-01-01

Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured and the prediction bias was 0.2 ml·min−1·kg−1. The MNG calculated based on predicted was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health. PMID:28378815

Prediction of Response to Preoperative Chemoradiotherapy in Rectal Cancer by Multiplex Kinase Activity Profiling

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

Folkvord, Sigurd; Flatmark, Kjersti; Department of Cancer and Surgery, Norwegian Radium Hospital, Oslo University Hospital

2010-10-01

Purpose: Tumor response of rectal cancer to preoperative chemoradiotherapy (CRT) varies considerably. In experimental tumor models and clinical radiotherapy, activity of particular subsets of kinase signaling pathways seems to predict radiation response. This study aimed to determine whether tumor kinase activity profiles might predict tumor response to preoperative CRT in locally advanced rectal cancer (LARC). Methods and Materials: Sixty-seven LARC patients were treated with a CRT regimen consisting of radiotherapy, fluorouracil, and, where possible, oxaliplatin. Pretreatment tumor biopsy specimens were analyzed using microarrays with kinase substrates, and the resulting substrate phosphorylation patterns were correlated with tumor response to preoperative treatmentmore » as assessed by histomorphologic tumor regression grade (TRG). A predictive model for TRG scores from phosphosubstrate signatures was obtained by partial-least-squares discriminant analysis. Prediction performance was evaluated by leave-one-out cross-validation and use of an independent test set. Results: In the patient population, 73% and 15% were scored as good responders (TRG 1-2) or intermediate responders (TRG 3), whereas 12% were assessed as poor responders (TRG 4-5). In a subset of 7 poor responders and 12 good responders, treatment outcome was correctly predicted for 95%. Application of the prediction model on the remaining patient samples resulted in correct prediction for 85%. Phosphosubstrate signatures generated by poor-responding tumors indicated high kinase activity, which was inhibited by the kinase inhibitor sunitinib, and several discriminating phosphosubstrates represented proteins derived from signaling pathways implicated in radioresistance. Conclusions: Multiplex kinase activity profiling may identify functional biomarkers predictive of tumor response to preoperative CRT in LARC.« less

Overview on NASA's Advanced Electric Propulsion Concepts Activities

NASA Technical Reports Server (NTRS)

Frisbee, Robert H.

1999-01-01

Advanced electric propulsion research activities are currently underway that seek to addresses feasibility issues of a wide range of advanced concepts, and may result in the development of technologies that will enable exciting new missions within our solar system and beyond. Each research activity is described in terms of the present focus and potential future applications. Topics include micro-electric thrusters, electrodynamic tethers, high power plasma thrusters and related applications in materials processing, variable specific impulse plasma thrusters, pulsed inductive thrusters, computational techniques for thruster modeling, and advanced electric propulsion missions and systems studies.

Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

PubMed

Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

2015-06-30

Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer.

Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea

NASA Astrophysics Data System (ADS)

Serreze, Mark C.; Crawford, Alex D.; Stroeve, Julienne C.; Barrett, Andrew P.; Woodgate, Rebecca A.

2016-10-01

As assessed over the period 1979-2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of -0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ˜ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice-albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

Advanced Placement Economics. Macroeconomics: Student Activities.

ERIC Educational Resources Information Center

Morton, John S.

This book is designed to help advanced placement students better understand macroeconomic concepts through various activities. The book contains 6 units with 64 activities, sample multiple-choice questions, sample short essay questions, and sample long essay questions. The units are entitled: (1) "Basic Economic Concepts"; (2) "Measuring Economic…

A community computational challenge to predict the activity of pairs of compounds.

PubMed

Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P; Costello, James C; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea

2014-12-01

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Predicted changes in advanced turboprop noise with shaft angle of attack

NASA Technical Reports Server (NTRS)

Padula, S. L.; Block, P. J. W.

1984-01-01

Advanced turboprop blade designs and new propeller installation schemes motivated an effort to include unsteady loading effects in existing propeller noise prediction computer programs. The present work validates the prediction capability while studing the effects of shaft inclination on the radiated sound field. Classical methods of propeller performance analysis supply the time-dependent blade loading needed to calculate noise. Polar plots of the sound pressure level (SPL) of the first four harmonics and overall SPL are indicative of the change in directivity pattern as a function of propeller angle of attack. Noise predictions are compared with newly available wind tunnel data and the accuracy and applicability of the prediction method are discussed. It is concluded that unsteady blade loading caused by inclining the propeller with respect to the flow changes the directionality and the intensity of the radiated noise. These changes are well modeled by the present quasi-steady prediction method.

Solar-terrestrial predictions proceedings. Volume 4: Prediction of terrestrial effects of solar activity

NASA Technical Reports Server (NTRS)

Donnelly, R. E. (Editor)

1980-01-01

Papers about prediction of ionospheric and radio propagation conditions based primarily on empirical or statistical relations is discussed. Predictions of sporadic E, spread F, and scintillations generally involve statistical or empirical predictions. The correlation between solar-activity and terrestrial seismic activity and the possible relation between solar activity and biological effects is discussed.

Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

NASA Technical Reports Server (NTRS)

Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

2014-01-01

This paper presents recent thermal model results of the Advanced Stirling Radioisotope Generator (ASRG). The three-dimensional (3D) ASRG thermal power model was built using the Thermal Desktop(trademark) thermal analyzer. The model was correlated with ASRG engineering unit test data and ASRG flight unit predictions from Lockheed Martin's (LM's) I-deas(trademark) TMG thermal model. The auxiliary cooling system (ACS) of the ASRG is also included in the ASRG thermal model. The ACS is designed to remove waste heat from the ASRG so that it can be used to heat spacecraft components. The performance of the ACS is reported under nominal conditions and during a Venus flyby scenario. The results for the nominal case are validated with data from Lockheed Martin. Transient thermal analysis results of ASRG for a Venus flyby with a representative trajectory are also presented. In addition, model results of an ASRG mounted on a Cassini-like spacecraft with a sunshade are presented to show a way to mitigate the high temperatures of a Venus flyby. It was predicted that the sunshade can lower the temperature of the ASRG alternator by 20 C for the representative Venus flyby trajectory. The 3D model also was modified to predict generator performance after a single Advanced Stirling Convertor failure. The geometry of the Microtherm HT insulation block on the outboard side was modified to match deformation and shrinkage observed during testing of a prototypic ASRG test fixture by LM. Test conditions and test data were used to correlate the model by adjusting the thermal conductivity of the deformed insulation to match the post-heat-dump steady state temperatures. Results for these conditions showed that the performance of the still-functioning inboard ACS was unaffected.

Refining Low Physical Activity Measurement Improves Frailty Assessment in Advanced Lung Disease and Survivors of Critical Illness.

PubMed

Baldwin, Matthew R; Singer, Jonathan P; Huang, Debbie; Sell, Jessica; Gonzalez, Wendy C; Pollack, Lauren R; Maurer, Mathew S; D'Ovidio, Frank F; Bacchetta, Matthew; Sonett, Joshua R; Arcasoy, Selim M; Shah, Lori; Robbins, Hilary; Hays, Steven R; Kukreja, Jasleen; Greenland, John R; Shah, Rupal J; Leard, Lorriana; Morrell, Matthew; Gries, Cynthia; Katz, Patricia P; Christie, Jason D; Diamond, Joshua M; Lederer, David J

2017-08-01

The frail phenotype has gained popularity as a clinically relevant measure in adults with advanced lung disease and in critical illness survivors. Because respiratory disease and chronic illness can greatly limit physical activity, the measurement of participation in traditional leisure time activities as a frailty component may lead to substantial misclassification of frailty in pulmonary and critical care patients. To test and validate substituting the Duke Activity Status Index (DASI), a simple 12-item questionnaire, for the Minnesota Leisure Time Physical Activity (MLTA) questionnaire, a detailed questionnaire covering 18 leisure time activities, as the measure of low activity in the Fried frailty phenotype (FFP) instrument. In separate multicenter prospective cohort studies of adults with advanced lung disease who were candidates for lung transplant and older survivors of acute respiratory failure, we assessed the FFP using either the MLTA or the DASI. For both the DASI and MLTA, we evaluated content validity by testing floor effects and construct validity through comparisons with conceptually related factors. We tested the predictive validity of substituting the DASI for the MLTA in the FFP assessment using Cox models to estimate associations between the FFP and delisting/death before transplant in those with advanced lung disease and 6-month mortality in older intensive care unit (ICU) survivors. Among 618 adults with advanced lung disease and 130 older ICU survivors, the MLTA had a substantially greater floor effect than the DASI (42% vs. 1%, and 49% vs. 12%, respectively). The DASI correlated more strongly with strength and function measures than did the MLTA in both cohorts. In models adjusting for age, sex, comorbidities, and illness severity, substitution of the DASI for the MLTA led to stronger associations of the FFP with delisting/death in lung transplant candidates (FFP-MLTA hazard ratio [HR], 1.42; 95% confidence interval [CI], 0.55-3.65; FFP

Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

PubMed

Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

2014-05-01

Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed Central

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-01-01

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC. PMID:26420960

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-09-28

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC.

Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

PubMed

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

Dynamo theory prediction of solar activity

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H.

1988-01-01

The dynamo theory technique to predict decadal time scale solar activity variations is introduced. The technique was developed following puzzling correlations involved with geomagnetic precursors of solar activity. Based upon this, a dynamo theory method was developed to predict solar activity. The method was used successfully in solar cycle 21 by Schatten, Scherrer, Svalgaard, and Wilcox, after testing with 8 prior solar cycles. Schatten and Sofia used the technique to predict an exceptionally large cycle, peaking early (in 1990) with a sunspot value near 170, likely the second largest on record. Sunspot numbers are increasing, suggesting that: (1) a large cycle is developing, and (2) that the cycle may even surpass the largest cycle (19). A Sporer Butterfly method shows that the cycle can now be expected to peak in the latter half of 1989, consistent with an amplitude comparable to the value predicted near the last solar minimum.

Measured and predicted rotor performance for the SERI advanced wind turbine blades

NASA Astrophysics Data System (ADS)

Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

1992-02-01

Measured and predicted rotor performance for the Solar Energy Research Institute (SERI) advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction.

Modified CLIP with objective liver reserve assessment retains prognosis prediction for patients with advanced hepatocellular carcinoma.

PubMed

Shao, Yu-Yun; Liu, Tsung-Hao; Lee, Ying-Hui; Hsu, Chih-Hung; Cheng, Ann-Lii

2016-07-01

The Cancer of the Liver Italian Program (CLIP) score is a commonly used staging system for hepatocellular carcinoma (HCC) helpful with predicting prognosis of advanced HCC. CLIP uses the Child-Turcotte-Pugh (CTP) score to evaluate liver reserve. A new scoring system, the albumin-bilirubin (ALBI) grade, has been proposed as they objectively evaluate liver reserve. We examined whether the modification of CLIP with ALBI retained its prognosis prediction for patients with advanced HCC. We included patients who received first-line antiangiogenic therapy for advanced HCC. Liver reserve was assessed using CTP and ALBI scores, which were then incorporated into CLIP and ALBI-CLIP, respectively. To assess their efficacies of prognostic prediction, the Cox's proportional hazard model and concordance indexes were used. A total of 142 patients were included; 137 of them were classified CTP A and 5 patients CTP B. Patients could be divided into four or five groups with different prognosis according to CLIP and ALBI-CLIP, respectively. Higher R(2) (0.249 vs 0.216) and lower Akaike information criterion (995.0 vs 1001.1) were observed for ALBI-CLIP than for CLIP in the Cox's model predicting overall survival. ALBI-CLIP remained an independent predictor for overall survival when CLIP and ALBI-CLIP were simultaneously incorporated in Cox's models allowing variable selection with adjustment for hepatitis etiology, treatment, and performance status. The concordance index was also higher for ALBI-CLIP than for CLIP (0.724 vs 0.703). Modification of CLIP scoring with ALBI, which objectively assesses liver reserve, retains and might have improved prognosis prediction for advanced HCC. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Predicting risky choices from brain activity patterns

PubMed Central

Helfinstein, Sarah M.; Schonberg, Tom; Congdon, Eliza; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Cannon, Tyrone D.; London, Edythe D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights. PMID:24550270

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

PubMed

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

PubMed Central

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Life Prediction of Fretting Fatigue with Advanced Surface Treatments (Preprint)

DTIC Science & Technology

2006-05-01

surfaces and not the fretting pads. The chosen coatings included DLC, Ni-B, Molybdenum, and Nitride. These 4 coatings, their application to the titanium ...Article Preprint 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 4 . TITLE AND SUBTITLE LIFE PREDICTION OF FRETTING FATIGUE WITH ADVANCED SURFACE...TREATMENTS (PREPRINT) 5c. PROGRAM ELEMENT NUMBER N/A 5d. PROJECT NUMBER M02R 5e. TASK NUMBER 30 6 . AUTHOR(S) Patrick J. Golden and Michael

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

"Bridging Activities," New Media Literacies, and Advanced Foreign Language Proficiency

ERIC Educational Resources Information Center

Thorne, Steven L.; Reinhardt, Jonathon

2008-01-01

In this article we propose the pedagogical model "bridging activities" to address advanced foreign language proficiency in the context of existing and emerging internet communication and information tools and communities. The article begins by establishing the need for language and genre-focused activities at the advanced level that attend to the…

A Serum Protein Profile Predictive of the Resistance to Neoadjuvant Chemotherapy in Advanced Breast Cancers*

PubMed Central

Hyung, Seok-Won; Lee, Min Young; Yu, Jong-Han; Shin, Byunghee; Jung, Hee-Jung; Park, Jong-Moon; Han, Wonshik; Lee, Kyung-Min; Moon, Hyeong-Gon; Zhang, Hui; Aebersold, Ruedi; Hwang, Daehee; Lee, Sang-Won; Yu, Myeong-Hee; Noh, Dong-Young

2011-01-01

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies. PMID:21799047

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

Li, Kang; Fu, Yun

2014-08-01

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

Advances in the assessment and prediction of interpersonal violence.

PubMed

Mills, Jeremy F

2005-02-01

This article underscores the weakness of clinical judgment as a mechanism for prediction with examples from other areas in the psychological literature. Clinical judgment has as its Achilles'heel the reliance on a person to incorporate multiple pieces of information while overcoming human judgment errors--a feat insurmountable thus far. The actuarial approach to risk assessment has overcome many of the weaknesses of clinical judgment and has been shown to be a much superior method. Nonetheless, the static/historical nature of the risk factors associated with most actuarial approaches is limiting. Advances in risk prediction will be found in part in the development of dynamic actuarial instruments that will measure both static/historical and changeable risk factors. The dynamic risk factors can be reevaluated on an ongoing basis, and it is proposed that the level of change in dynamic factors necessary to represent a significant change in overall risk will be an interactive function with static risk factors.

Activity of Nivolumab and Utility of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non-Small-Cell Lung Cancer: A Prospective Observational Study.

PubMed

Fukui, Tomoya; Okuma, Yuriko; Nakahara, Yoshiro; Otani, Sakiko; Igawa, Satoshi; Katagiri, Masato; Mitsufuji, Hisashi; Kubota, Masaru; Hiyoshi, Yasuhiro; Ishihara, Mikiko; Kasajima, Masashi; Sasaki, Jiichiro; Naoki, Katsuhiko

2018-05-05

The immune checkpoint inhibitor nivolumab is entering routine oncologic practice. We investigated the safety and efficacy of nivolumab in the real world and alternative predictive factors for survival in patients with advanced non-small-cell lung cancer (NSCLC). We performed a prospective observational study to evaluate the activity of nivolumab treatment for chemotherapy-refractory NSCLC. Patients were treated with nivolumab once every 2 weeks, and the efficacy was assessed every 8 ± 2 weeks. Fifty-two patients were enrolled after nivolumab approval in Japan. These patients received a median of 4 (range, 1-43) cycles of nivolumab. Overall objective response was observed in 12 patients (23.1%). Median progression-free survival was 2.1 (95% confidence interval, 1.0-3.2) months, and 1-year overall survival rate was 59.9%. A total of 23 immune-related adverse events occurred in 20 patients, as follows: 7 cases of pneumonitis, 6 of oral mucositis, 5 of hypothyroidism, 2 of colitis, 2 of liver dysfunction, and 1 of arthritis. All patients recovered after appropriate management. A pretreatment neutrophil-to-lymphocyte ratio (NLR) of ≥ 5 was significantly associated with poor prognosis compared to NLR < 5 (hazard ratio, 4.52; 95% confidence interval, 1.84-11.14; P = .013), independently. Nivolumab showed promising activity with a manageable safety profile in clinical practice, consistent with effects of previous clinical trials. This drug could affect a specific population of patients with advanced NSCLC, and pretreatment NLR was a candidate for surrogate markers for survival benefit of patients with NSCLC treated with nivolumab. Copyright © 2018 Elsevier Inc. All rights reserved.

Using Recent Planetary Science Data to Develop Advanced Undergraduate Physics and Astronomy Activities

NASA Astrophysics Data System (ADS)

Steckloff, Jordan; Lindell, Rebecca

2016-10-01

Teaching science by having students manipulate real data is a popular trend in astronomy and planetary science education. However, many existing activities simply couple this data with traditional "cookbook" style verification labs. As with most topics within science, this instructional technique does not enhance the average students' understanding of the phenomena being studied. Here we present a methodology for developing "science by doing" activities that incorporate the latest discoveries in planetary science with up-to-date constructivist pedagogy to teach advanced concepts in Physics and Astronomy. In our methodology, students are first guided to understand, analyze, and plot real raw scientific data; develop and test physical and computational models to understand and interpret the data; finally use their models to make predictions about the topic being studied and test it with real data.To date, two activities have been developed according to this methodology: Understanding Asteroids through their Light Curves (hereafter "Asteroid Activity"), and Understanding Exoplanetary Systems through Simple Harmonic Motion (hereafter "Exoplanet Activity"). The Asteroid Activity allows students to explore light curves available on the Asteroid Light Curve Database (ALCDB) to discover general properties of asteroids, including their internal structure, strength, and mechanism of asteroid moon formation. The Exoplanet Activity allows students to investigate the masses and semi-major axes of exoplanets in a system by comparing the radial velocity motion of their host star to that of a coupled simple harmonic oscillator. Students then explore how noncircular orbits lead to deviations from simple harmonic motion. These activities will be field tested during the Fall 2016 semester in an advanced undergraduate mechanics and astronomy courses at a large Midwestern STEM-focused university. We will present the development methodologies for these activities, description of the

Long-Range Solar Activity Predictions: A Reprieve from Cycle #24's Activity

NASA Technical Reports Server (NTRS)

Richon, K.; Schatten, K.

2003-01-01

We discuss the field of long-range solar activity predictions and provide an outlook into future solar activity. Orbital predictions for satellites in Low Earth Orbit (LEO) depend strongly on exospheric densities. Solar activity forecasting is important in this regard, as the solar ultra-violet (UV) and extreme ultraviolet (EUV) radiations inflate the upper atmospheric layers of the Earth, forming the exosphere in which satellites orbit. Rather than concentrate on statistical, or numerical methods, we utilize a class of techniques (precursor methods) which is founded in physical theory. The geomagnetic precursor method was originally developed by the Russian geophysicist, Ohl, using geomagnetic observations to predict future solar activity. It was later extended to solar observations, and placed within the context of physical theory, namely the workings of the Sun s Babcock dynamo. We later expanded the prediction methods with a SOlar Dynamo Amplitude (SODA) index. The SODA index is a measure of the buried solar magnetic flux, using toroidal and poloidal field components. It allows one to predict future solar activity during any phase of the solar cycle, whereas previously, one was restricted to making predictions only at solar minimum. We are encouraged that solar cycle #23's behavior fell closely along our predicted curve, peaking near 192, comparable to the Schatten, Myers and Sofia (1996) forecast of 182+/-30. Cycle #23 extends from 1996 through approximately 2006 or 2007, with cycle #24 starting thereafter. We discuss the current forecast of solar cycle #24, (2006-2016), with a predicted smoothed F10.7 radio flux of 142+/-28 (1-sigma errors). This, we believe, represents a reprieve, in terms of reduced fuel costs, etc., for new satellites to be launched or old satellites (requiring reboosting) which have been placed in LEO. By monitoring the Sun s most deeply rooted magnetic fields; long-range solar activity can be predicted. Although a degree of uncertainty

Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

Cancer.gov

Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

Validating a Predictive Model of Acute Advanced Imaging Biomarkers in Ischemic Stroke.

PubMed

Bivard, Andrew; Levi, Christopher; Lin, Longting; Cheng, Xin; Aviv, Richard; Spratt, Neil J; Lou, Min; Kleinig, Tim; O'Brien, Billy; Butcher, Kenneth; Zhang, Jingfen; Jannes, Jim; Dong, Qiang; Parsons, Mark

2017-03-01

Advanced imaging to identify tissue pathophysiology may provide more accurate prognostication than the clinical measures used currently in stroke. This study aimed to derive and validate a predictive model for functional outcome based on acute clinical and advanced imaging measures. A database of prospectively collected sub-4.5 hour patients with ischemic stroke being assessed for thrombolysis from 5 centers who had computed tomographic perfusion and computed tomographic angiography before a treatment decision was assessed. Individual variable cut points were derived from a classification and regression tree analysis. The optimal cut points for each assessment variable were then used in a backward logic regression to predict modified Rankin scale (mRS) score of 0 to 1 and 5 to 6. The variables remaining in the models were then assessed using a receiver operating characteristic curve analysis. Overall, 1519 patients were included in the study, 635 in the derivation cohort and 884 in the validation cohort. The model was highly accurate at predicting mRS score of 0 to 1 in all patients considered for thrombolysis therapy (area under the curve [AUC] 0.91), those who were treated (AUC 0.88) and those with recanalization (AUC 0.89). Next, the model was highly accurate at predicting mRS score of 5 to 6 in all patients considered for thrombolysis therapy (AUC 0.91), those who were treated (0.89) and those with recanalization (AUC 0.91). The odds ratio of thrombolysed patients who met the model criteria achieving mRS score of 0 to 1 was 17.89 (4.59-36.35, P <0.001) and for mRS score of 5 to 6 was 8.23 (2.57-26.97, P <0.001). This study has derived and validated a highly accurate model at predicting patient outcome after ischemic stroke. © 2017 American Heart Association, Inc.

Predicted Biological Activity of Purchasable Chemical Space

PubMed Central

2017-01-01

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL’s bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org. PMID:29193970

Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

Speaking Activities for the Advanced College-Bound Student.

ERIC Educational Resources Information Center

Henderson, Don

Three activities for developing speaking skills of advanced English as second language students are presented. Impromptu speaking, extemporaneous speaking, and debate activities are designed to train students to organize concepts, develop spontaneous oral skills, and enhance confidence and clarity of thought. Impromptu speaking develops…

Development of advanced structural analysis methodologies for predicting widespread fatigue damage in aircraft structures

NASA Technical Reports Server (NTRS)

Harris, Charles E.; Starnes, James H., Jr.; Newman, James C., Jr.

1995-01-01

NASA is developing a 'tool box' that includes a number of advanced structural analysis computer codes which, taken together, represent the comprehensive fracture mechanics capability required to predict the onset of widespread fatigue damage. These structural analysis tools have complementary and specialized capabilities ranging from a finite-element-based stress-analysis code for two- and three-dimensional built-up structures with cracks to a fatigue and fracture analysis code that uses stress-intensity factors and material-property data found in 'look-up' tables or from equations. NASA is conducting critical experiments necessary to verify the predictive capabilities of the codes, and these tests represent a first step in the technology-validation and industry-acceptance processes. NASA has established cooperative programs with aircraft manufacturers to facilitate the comprehensive transfer of this technology by making these advanced structural analysis codes available to industry.

Predictive models for customizing chemotherapy in advanced non-small cell lung cancer (NSCLC).

PubMed

Bonanno, Laura

2013-06-01

The backbone of first-line treatment for Epidermal Growth Factor (EGFR) wild-type (wt) advanced Non-small cell lung cancer (NSCLC) patients is the use of a platinum-based chemotherapy combination. The treatment is characterized by great inter-individual variability in outcome. Molecular predictive markers are extremely needed in order to identify patients most likely to benefit from platinum-based treatment and resistant ones, thus optimizing chemotherapy approach in NSCLC. Several components of DNA repair response (DRR) have been investigated as potential predictive markers. Among them, high levels of expression of ERCC1, both at protein and mRNA levels, have been associated with resistance to cisplatin in NSCLC. In addition, low levels of expression of RRM1, a target for gemcitabine, have been associated with improved OS in advanced NSCLC patients treated with cisplatin and gemcitabine. Preclinical data and retrospective analyses showed that BRCA1 is able to induce resistance to cisplatin and sensitivity to antimicrotubule agents. In addition, the mRNA levels of expression of RAP80, encoding for a protein cooperating with BRCA1 in homologous recombination (HR), have demonstrated to further sub-classify low BRCA1 NSCLC tumors, improving the predictive model. On the basis of biological knowledge on DNA repair pathway and recent controversial results from clinical validation of potential molecular markers, integrated analysis of multiple DNA repair components could improve predictive information and pave the way to a new approach to customized chemotherapy clinical trials.

A mathematical model for predicting the adult height of girls with advanced puberty after spontaneous growth.

PubMed

Lemaire, Pierre; Pierre, Delphine; Bertrand, Jean-Baptiste; Brauner, Raja

2014-07-03

Advanced puberty in girls is defined as the onset of puberty between the ages of 8 yr and 10 yr. The objective was to predict adult height (AH) at initial evaluation and to characterize patients with an actual AH below -2 SD (152 cm) and/or lower than their target height (TH) by > one SD (5.6 cm). Data analysis using multiple linear regression models was performed in 50 girls with advanced puberty who reached their AH after spontaneous puberty. The actual AH (159.0 ± 6.1 cm) was similar to the TH (161.2 ± 4.6 cm) and to the AH predicted at the initial evaluation (160.8 ± 6.0 cm), and the actual AH correlated positively with both (R = 0.76, P = 0.0003; R = 0.71, P = 0.008, respectively).The AH was below 152 cm in 7 girls, of whom 3 were characterized by paternal transmission of the advanced puberty. The AH was lower than the TH by >5.6 cm in 8 girls.The AH (cm) could be calculated at the initial evaluation: 1.8822 age + 3.3510 height (SD) - 0.7465 bone age - 1.7993 pubic hair stage + 2.8409 TH (SD) + 150.32.The formula is available online at http://www.kamick.org/lemaire/med/girls-advpub.html.The calculated AH (159.0 ± 5.7 cm) and the actual AH were highly correlated (R = 0.93). The actual AH was lower than the calculated AH by > 0.5 SD in only one case (4.35 cm). We established a formula that can be used at an initial evaluation to predict the AH, and then to assess the risk of reduced AH as a result of advanced puberty. According to this formula, the actual AH was lower than the calculated AH by more than 2.8 cm (0.5 SD) in only one girl. The AHs of the untreated girls with advanced puberty did not differ from those predicted at the initial evaluation by the Bayley and Pinneau table or from the THs. However, this study provides a useful and ready-to-use formula that can be an additional assessment of girls with advanced puberty.

Characterization of low active ghrelin ratio in patients with advanced pancreatic cancer.

PubMed

Miura, Tomofumi; Mitsunaga, Shuichi; Ikeda, Masafumi; Ohno, Izumi; Takahashi, Hideaki; Suzuki, Hidetaka; Irisawa, Ai; Kuwata, Takeshi; Ochiai, Atsushi

2018-05-18

Acyl ghrelin is an orexigenic peptide. Active ghrelin ratio, the ratio of acyl ghrelin to total ghrelin, has an important role in physiological functions and gastrointestinal symptoms. However, low active ghrelin ratio-related characteristics, gastrointestinal symptoms, and chemotherapy-induced gastrointestinal toxicity in patients with advanced pancreatic cancer have not been previously evaluated. The goal of this study was to identify low active ghrelin ratio-related factors in treatment-naïve advanced pancreatic cancer patients. Patients with treatment-naïve advanced pancreatic cancer were eligible for inclusion in this study. Active ghrelin ratio and clinical parameters of patients were prospectively recorded. Factors correlated with low active ghrelin ratio and survival were analyzed. In total, 92 patients were analyzed. Low active ghrelin ratio-related factors were advanced age (P < 0.01), severe appetite loss (P < 0.01), and decreased cholinesterase (P < 0.01). The adverse events of grade 2 or higher anorexia tended to increase in patients with low active ghrelin ratio. However, no differences were found in survival and body composition between low and high active ghrelin ratio groups. Low active ghrelin ratio was related to lack of appetite and low cholinesterase and tended to be related to anorexia grade 2 or higher in patients with treatment-naïve advanced pancreatic cancer.

CERAPP: Collaborative Estrogen Receptor Activity Prediction ...

EPA Pesticide Factsheets

Humans potentially are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Many of these chemicals never have been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for assessment in costly in vivo tests, for instance, within the EPA Endocrine Disruptor Screening Program. Here, we describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating the efficacy of using predictive computational models on high-throughput screening data to screen thousands of chemicals against the ER. CERAPP combined multiple models developed in collaboration among 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1677 compounds provided by EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were tested using an evaluation set of 7522 chemicals collected from the literature. To overcome the limitations of single models, a consensus was built weighting models using a scoring function (0 to 1) based on their accuracies. Individual model scores ranged from 0.69 to 0.85, showing

Overview of Advanced Space Propulsion Activities in the Space Environmental Effects Team at MSFC

NASA Technical Reports Server (NTRS)

Edwards, David; Carruth, Ralph; Vaughn, Jason; Schneider, Todd; Kamenetzky, Rachel; Gray, Perry

2000-01-01

Exploration of our solar system, and beyond, requires spacecraft velocities beyond our current technological level. Technologies addressing this limitation are numerous. The Space Environmental Effects (SEE) Team at the Marshall Space Flight Center (MSFC) is focused on three discipline areas of advanced propulsion; Tethers, Beamed Energy, and Plasma. This presentation will give an overview of advanced propulsion related activities in the Space Environmental Effects Team at MSFC. Advancements in the application of tethers for spacecraft propulsion were made while developing the Propulsive Small Expendable Deployer System (ProSEDS). New tether materials were developed to meet the specifications of the ProSEDS mission and new techniques had to be developed to test and characterize these tethers. Plasma contactors were developed, tested and modified to meet new requirements. Follow-on activities in tether propulsion include the Air-SEDS activity. Beamed energy activities initiated with an experimental investigation to quantify the momentum transfer subsequent to high power, 5J, ablative laser interaction with materials. The next step with this experimental investigation is to quantify non-ablative photon momentum transfer. This step was started last year and will be used to characterize the efficiency of solar sail materials before and after exposure to Space Environmental Effects (SEE). Our focus with plasma, for propulsion, concentrates on optimizing energy deposition into a magnetically confined plasma and integration of measurement techniques for determining plasma parameters. Plasma confinement is accomplished with the Marshall Magnetic Mirror (M3) device. Initial energy coupling experiments will consist of injecting a 50 amp electron beam into a target plasma. Measurements of plasma temperature and density will be used to determine the effect of changes in magnetic field structure, beam current, and gas species. Experimental observations will be compared to

Classification of TP53 Mutations and HPV Predict Survival in Advanced Larynx Cancer

PubMed Central

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B.; Walline, Heather M.; Prince, Mark E.; Urba, Susan; Wolf, Gregory T.; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E.; Bradford, Carol

2016-01-01

OBJECTIVE Assess TP53 functional mutations in the context of other biomarkers in advanced larynx cancer. STUDY DESIGN Prospective analysis of pretreatment tumor TP53, HPV, Bcl-xL and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. METHODS TP53 exons 4-9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl and cyclin D1 expression. RESULTS TP53 Mutations were found in 22/58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13/58 (22.4%) patients, nonsense mutations in 4/58 (6.9%), and deletions in 5/58 (8.6%). High risk HPV was found in 20/52 (38.5%) tumors. A classification based on crystal Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low risk mutations (p=0.0315). A model including this TP53 classification, HPV status, cyclin D1 and Bcl-xL staining significantly predicts survival (p=0.0017). CONCLUSION EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. PMID:27345657

Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients.

PubMed

Wu, Xiaoying; Ren, Jianwei; Gao, Zulu; Xu, Yun; Xie, Huiqun; Li, Tingfang; Cheng, Yanhua; Hu, Fei; Liu, Hongyun; Gong, Zhihong; Liang, Jinyi; Shen, Jia; Liu, Zhen; Wu, Feng; Sun, Xi; Niu, Zhongzheng; Ning, An

2017-04-01

China still has more than 30,000 patients of advanced schistosomiasis while new cases being reported consistently. D-dimer is a fibrin degradation product. As ascites being the dominating symptom in advanced schistosomiasis, the present study aimed to explore a prediction model of ascites with D-dimer and other clinical easy-achievable indicators. A case-control study nested in a prospective cohort was conducted in schistosomiasis-endemic area of southern China. A total of 291 patients of advanced schistosomiasis were first investigated in 2013 and further followed in 2014. Information on clinical history, physical examination, and abdominal ultrasonography, including the symptom of ascites was repeatedly collected. Result showed 44 patients having ascites. Most of the patients' ascites were confined in the kidney area with median area of 20 mm 2 . The level of plasma D-dimer and pertinent liver function indicators were measured at the initial investigation in 2013. Compared with those without ascites, cases with ascites had significantly higher levels of D-dimer (0.71±2.44 μg/L vs 0.48±2.12 μg/L, P =0.005), as well ALB (44.5 vs 46.2, g/L) and Type IV collagen (50.04 vs 44.50 μg/L). Receiver operating characteristic curve analyses indicated a moderate predictive value of D-dimer by its own area under curve (AUC) of 0.64 (95% CI: 0.54-0.73) and the cutoff value as 0.81 μg/L. Dichotomized by the cutoff level, D-dimer along with other categorical variables generated a prediction model with AUC of 0.76 (95% CI: 0.68-0.89). Risks of patients with specific characteristics in the prediction model were summarized. Our study suggests that the plasma D-dimer level is a reliable predictor for incident ascites in advanced schistosomiasis japonica patients.

Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients

PubMed Central

Wu, Xiaoying; Ren, Jianwei; Gao, Zulu; Xu, Yun; Xie, Huiqun; Li, Tingfang; Cheng, Yanhua; Hu, Fei; Liu, Hongyun; Gong, Zhihong; Liang, Jinyi; Shen, Jia; Liu, Zhen; Wu, Feng; Sun, Xi; Niu, Zhongzheng; Ning, An

2017-01-01

China still has more than 30,000 patients of advanced schistosomiasis while new cases being reported consistently. D-dimer is a fibrin degradation product. As ascites being the dominating symptom in advanced schistosomiasis, the present study aimed to explore a prediction model of ascites with D-dimer and other clinical easy-achievable indicators. A case-control study nested in a prospective cohort was conducted in schistosomiasis-endemic area of southern China. A total of 291 patients of advanced schistosomiasis were first investigated in 2013 and further followed in 2014. Information on clinical history, physical examination, and abdominal ultrasonography, including the symptom of ascites was repeatedly collected. Result showed 44 patients having ascites. Most of the patients’ ascites were confined in the kidney area with median area of 20 mm2. The level of plasma D-dimer and pertinent liver function indicators were measured at the initial investigation in 2013. Compared with those without ascites, cases with ascites had significantly higher levels of D-dimer (0.71±2.44 μg/L vs 0.48±2.12 μg/L, P=0.005), as well ALB (44.5 vs 46.2, g/L) and Type IV collagen (50.04 vs 44.50 μg/L). Receiver operating characteristic curve analyses indicated a moderate predictive value of D-dimer by its own area under curve (AUC) of 0.64 (95% CI: 0.54–0.73) and the cutoff value as 0.81 μg/L. Dichotomized by the cutoff level, D-dimer along with other categorical variables generated a prediction model with AUC of 0.76 (95% CI: 0.68–0.89). Risks of patients with specific characteristics in the prediction model were summarized. Our study suggests that the plasma D-dimer level is a reliable predictor for incident ascites in advanced schistosomiasis japonica patients. PMID:28506039

Acoustic prediction methods for the NASA generalized advanced propeller analysis system (GAPAS)

NASA Technical Reports Server (NTRS)

Padula, S. L.; Block, P. J. W.

1984-01-01

Classical methods of propeller performance analysis are coupled with state-of-the-art Aircraft Noise Prediction Program (ANOPP:) techniques to yield a versatile design tool, the NASA Generalized Advanced Propeller Analysis System (GAPAS) for the novel quiet and efficient propellers. ANOPP is a collection of modular specialized programs. GAPAS as a whole addresses blade geometry and aerodynamics, rotor performance and loading, and subsonic propeller noise.

Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

PubMed

Ryan, J E; Warrier, S K; Lynch, A C; Ramsay, R G; Phillips, W A; Heriot, A G

2016-03-01

Approximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. A systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: 'rectal neoplasm', 'response', 'neoadjuvant', 'preoperative chemoradiation', 'tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. Clear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. At present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Activated alumina preparation and characterization: The review on recent advancement

NASA Astrophysics Data System (ADS)

Rabia, A. R.; Ibrahim, A. H.; Zulkepli, N. N.

2018-03-01

Aluminum and aluminum based material are significant industrial materials synthesis because of their abandonment, low weight and high-quality corrosion resistance. The most advances in aluminum processing are the ability to synthesize it's under suitable chemical composition and conditions, a porous structure can be formed on the surface. Activated alumina particles (AAP) synthesized by the electrochemically process from aluminum have gained serious attention, inexpensive material that can be employed for water filtration due to its active surface. Thus, the paper present a review study based on recent progress and advances in synthesizing activated alumina, various techniques currently being used in preparing activated alumina and its characteristics are studied and summarized

Neutrophil-lymphocyte ratio predicts survival in patients with advanced cholangiocarcinoma on chemotherapy.

PubMed

Lee, Ban Seok; Lee, Sang Hyub; Son, Jun Hyuk; Jang, Dong Kee; Chung, Kwang Hyun; Lee, Yoon Suk; Paik, Woo Hyun; Ryu, Ji Kon; Kim, Yong-Tae

2016-02-01

The blood neutrophil-to-lymphocyte ratio (NLR) is reported to be a prognostic marker in several cancers. However, the prognostic role of NLR in patients with advanced cholangiocarcinoma on chemotherapy is unknown. A total of 221 patients with pathologically confirmed locally advanced or metastatic cholangiocarcinoma receiving first-line palliative chemotherapy were enrolled. Associations between baseline clinical and laboratory variables including NLR and survival were investigated. Patients were classified into two groups according to the NLR level (≤ 5 vs. >5). Median overall survival (OS) and time to progression (TTP) in patients with NLR ≤ 5 were 10.9 and 6.7 months, respectively, and 6.8 and 4.1 months in patients with NLR > 5 (P < 0.001, P = 0.002, respectively). In multivariate analysis, number of cycles of chemotherapy was a significant predictor of longer OS (HR 0.86, P < 0.001), whereas adverse prognostic factors for OS were CA 19-9 > 300 (HR 1.43, P = 0.025), CEA > 5 (HR 1.44, P = 0.029), higher stage (HR 1.69, P = 0.004), and NLR > 5 (HR 1.87, P < 0.001). NLR > 5 was also associated with reduced TTP (HR 1.66, P = 0.007). Among 50 patients with initial NLR > 5, 33 patients had NLR ≤ 5 after two cycles of chemotherapy and they had significantly better survival than the others (HR 0.48, P = 0.015). NLR independently predicts survival in patients with advanced cholangiocarcinoma undergoing chemotherapy. Considering cost-effectiveness and easy availability, NLR may be a useful biomarker for prognosis prediction.

Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Reid, Terry V.; Schifer, Nicholas A.; Briggs, Maxwell H.

2012-01-01

The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two high-efficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. Microporous bulk insulation is used in the ground support test hardware to minimize the loss of thermal energy from the electric heat source to the environment. The insulation package is characterized before operation to predict how much heat will be absorbed by the convertor and how much will be lost to the environment during operation. In an effort to validate these predictions, numerous tasks have been performed, which provided a more accurate value for net heat input into the ASCs. This test and modeling effort included: (a) making thermophysical property measurements of test setup materials to provide inputs to the numerical models, (b) acquiring additional test data that was collected during convertor tests to provide numerical models with temperature profiles of the test setup via thermocouple and infrared measurements, (c) using multidimensional numerical models (computational fluid dynamics code) to predict net heat input of an operating convertor, and (d) using validation test hardware to provide direct comparison of numerical results and validate the multidimensional numerical models used to predict convertor net heat input. This effort produced high fidelity ASC net heat input predictions, which were successfully validated using

Physical activity in advanced cancer patients: a systematic review protocol.

PubMed

Lowe, Sonya S; Tan, Maria; Faily, Joan; Watanabe, Sharon M; Courneya, Kerry S

2016-03-11

Progressive, incurable cancer is associated with increased fatigue, increased muscle weakness, and reduced physical functioning, all of which negatively impact quality of life. Physical activity has demonstrated benefits on cancer-related fatigue and physical functioning in early-stage cancer patients; however, its impact on these outcomes in end-stage cancer has not been established. The aim of this systematic review is to determine the potential benefits, harms, and effects of physical activity interventions on quality of life outcomes in advanced cancer patients. A systematic review of peer-reviewed literature on physical activity in advanced cancer patients will be undertaken. Empirical quantitative studies will be considered for inclusion if they present interventional or observational data on physical activity in advanced cancer patients. Searches will be conducted in the following electronic databases: CINAHL; CIRRIE Database of International Rehabilitation Research; Cochrane Database of Systematic Reviews (CDSR); Database of Abstracts of Reviews of Effects (DARE); Cochrane Central Register of Controlled Trials (CENTRAL); EMBASE; MEDLINE; PEDro: the Physiotherapy Evidence Database; PQDT; PsycInfo; PubMed; REHABDATA; Scopus; SPORTDiscus; and Web of Science, to identify relevant studies of interest. Additional strategies to identify relevant studies will include citation searches and evaluation of reference lists of included articles. Titles, abstracts, and keywords of identified studies from the search strategies will be screened for inclusion criteria. Two independent reviewers will conduct quality appraisal using the Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies (EPHPP) and the Cochrane risk of bias tool. A descriptive summary of included studies will describe the study designs, participant and activity characteristics, and objective and patient-reported outcomes. This systematic review will summarize the current

Compound activity prediction using models of binding pockets or ligand properties in 3D

PubMed Central

Kufareva, Irina; Chen, Yu-Chen; Ilatovskiy, Andrey V.; Abagyan, Ruben

2014-01-01

Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocket-based and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges. PMID:23116466

Advanced Computational Modeling Approaches for Shock Response Prediction

NASA Technical Reports Server (NTRS)

Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

2015-01-01

Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

Improved NASA-ANOPP Noise Prediction Computer Code for Advanced Subsonic Propulsion Systems

NASA Technical Reports Server (NTRS)

Kontos, K. B.; Janardan, B. A.; Gliebe, P. R.

1996-01-01

Recent experience using ANOPP to predict turbofan engine flyover noise suggests that it over-predicts overall EPNL by a significant amount. An improvement in this prediction method is desired for system optimization and assessment studies of advanced UHB engines. An assessment of the ANOPP fan inlet, fan exhaust, jet, combustor, and turbine noise prediction methods is made using static engine component noise data from the CF6-8OC2, E(3), and QCSEE turbofan engines. It is shown that the ANOPP prediction results are generally higher than the measured GE data, and that the inlet noise prediction method (Heidmann method) is the most significant source of this overprediction. Fan noise spectral comparisons show that improvements to the fan tone, broadband, and combination tone noise models are required to yield results that more closely simulate the GE data. Suggested changes that yield improved fan noise predictions but preserve the Heidmann model structure are identified and described. These changes are based on the sets of engine data mentioned, as well as some CFM56 engine data that was used to expand the combination tone noise database. It should be noted that the recommended changes are based on an analysis of engines that are limited to single stage fans with design tip relative Mach numbers greater than one.

Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System

USGS Publications Warehouse

Wilkin, John L.; Rosenfeld, Leslie; Allen, Arthur; Baltes, Rebecca; Baptista, Antonio; He, Ruoying; Hogan, Patrick; Kurapov, Alexander; Mehra, Avichal; Quintrell, Josie; Schwab, David; Signell, Richard; Smith, Jane

2017-01-01

This paper outlines strategies that would advance coastal ocean modelling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the US Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of US-based researchers with a cross-section of coastal oceanography and ocean modelling expertise and community representation drawn from Regional and US Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation, and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modelling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3–8-year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, US Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal–academic partnerships benefiting IOOS stakeholders and end users.

Advances in Predictive Toxicology for Discovery Safety through High Content Screening.

PubMed

Persson, Mikael; Hornberg, Jorrit J

2016-12-19

High content screening enables parallel acquisition of multiple molecular and cellular readouts. In particular the predictive toxicology field has progressed from the advances in high content screening, as more refined end points that report on cellular health can be studied in combination, at the single cell level, and in relatively high throughput. Here, we discuss how high content screening has become an essential tool for Discovery Safety, the discipline that integrates safety and toxicology in the drug discovery process to identify and mitigate safety concerns with the aim to design drug candidates with a superior safety profile. In addition to customized mechanistic assays to evaluate target safety, routine screening assays can be applied to identify risk factors for frequently occurring organ toxicities. We discuss the current state of high content screening assays for hepatotoxicity, cardiotoxicity, neurotoxicity, nephrotoxicity, and genotoxicity, including recent developments and current advances.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

PubMed Central

Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra; Roncaglioni, Alessandra; Tropsha, Alexander; Varnek, Alexandre; Zakharov, Alexey; Worth, Andrew; Richard, Ann M.; Grulke, Christopher M.; Trisciuzzi, Daniela; Fourches, Denis; Horvath, Dragos; Benfenati, Emilio; Muratov, Eugene; Wedebye, Eva Bay; Grisoni, Francesca; Mangiatordi, Giuseppe F.; Incisivo, Giuseppina M.; Hong, Huixiao; Ng, Hui W.; Tetko, Igor V.; Balabin, Ilya; Kancherla, Jayaram; Shen, Jie; Burton, Julien; Nicklaus, Marc; Cassotti, Matteo; Nikolov, Nikolai G.; Nicolotti, Orazio; Andersson, Patrik L.; Zang, Qingda; Politi, Regina; Beger, Richard D.; Todeschini, Roberto; Huang, Ruili; Farag, Sherif; Rosenberg, Sine A.; Slavov, Svetoslav; Hu, Xin; Judson, Richard S.

2016-01-01

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other

Computational methods in the prediction of advanced subsonic and supersonic propeller induced noise: ASSPIN users' manual

NASA Technical Reports Server (NTRS)

Dunn, M. H.; Tarkenton, G. M.

1992-01-01

This document describes the computational aspects of propeller noise prediction in the time domain and the use of high speed propeller noise prediction program ASSPIN (Advanced Subsonic and Supersonic Propeller Induced Noise). These formulations are valid in both the near and far fields. Two formulations are utilized by ASSPIN: (1) one is used for subsonic portions of the propeller blade; and (2) the second is used for transonic and supersonic regions on the blade. Switching between the two formulations is done automatically. ASSPIN incorporates advanced blade geometry and surface pressure modelling, adaptive observer time grid strategies, and contains enhanced numerical algorithms that result in reduced computational time. In addition, the ability to treat the nonaxial inflow case has been included.

Unsteady blade-surface pressures on a large-scale advanced propeller: Prediction and data

NASA Technical Reports Server (NTRS)

Nallasamy, M.; Groeneweg, J. F.

1990-01-01

An unsteady 3-D Euler analysis technique is employed to compute the flow field of an advanced propeller operating at an angle of attack. The predicted blade pressure waveforms are compared with wind tunnel data at two Mach numbers, 0.5 and 0.2. The inflow angle is three degrees. For an inflow Mach number of 0.5, the predicted pressure response is in fair agreement with data: the predicted phases of the waveforms are in close agreement with data while the magnitudes are underpredicted. At the low Mach number of 0.2 (takeoff), the numerical solution shows the formation of a leading edge vortex which is in qualitative agreement with measurements. However, the highly nonlinear pressure response measured on the blade suction surface is not captured in the present inviscid analysis.

Unsteady blade surface pressures on a large-scale advanced propeller - Prediction and data

NASA Technical Reports Server (NTRS)

Nallasamy, M.; Groeneweg, J. F.

1990-01-01

An unsteady three dimensional Euler analysis technique is employed to compute the flowfield of an advanced propeller operating at an angle of attack. The predicted blade pressure waveforms are compared with wind tunnel data at two Mach numbers, 0.5 and 0.2. The inflow angle is three degrees. For an inflow Mach number of 0.5, the predicted pressure response is in fair agreement with data: the predicted phases of the waveforms are in close agreement with data while the magnitudes are underpredicted. At the low Mach number of 0.2 (take-off) the numerical solution shows the formation of a leading edge vortex which is in qualitative agreement with measurements. However, the highly nonlinear pressure response measured on the blade suction surface is not captured in the present inviscid analysis.

Classification of TP53 mutations and HPV predict survival in advanced larynx cancer.

PubMed

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B; Walline, Heather M; Prince, Mark E; Urba, Susan; Wolf, Gregory T; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E; Bradford, Carol

2016-09-01

Assess tumor suppressor p53 (TP53) functional mutations in the context of other biomarkers in advanced larynx cancer. Prospective analysis of pretreatment tumor TP53, human papillomavirus (HPV), Bcl-xL, and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. TP53 exons 4 through 9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl, and cyclin D1 expression. TP53 mutations were found in 22 of 58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13 of 58 (22.4%) patients, nonsense mutations in four of 58 (6.9%), and deletions in five of 58 (8.6%). High-risk HPV was found in 20 of 52 (38.5%) tumors. A classification based on Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low-risk mutations (P = 0.0315). A model including this TP53 classification, HPV status, cyclin D1, and Bcl-xL staining significantly predicts survival (P = 0.0017). EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. NA. Laryngoscope, 126:E292-E299, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Sexual selection predicts advancement of avian spring migration in response to climate change.

PubMed

Spottiswoode, Claire N; Tøttrup, Anders P; Coppack, Timothy

2006-12-22

Global warming has led to earlier spring arrival of migratory birds, but the extent of this advancement varies greatly among species, and it remains uncertain to what degree these changes are phenotypically plastic responses or microevolutionary adaptations to changing environmental conditions. We suggest that sexual selection could help to understand this variation, since early spring arrival of males is favoured by female choice. Climate change could weaken the strength of natural selection opposing sexual selection for early migration, which would predict greatest advancement in species with stronger female choice. We test this hypothesis comparatively by investigating the degree of long-term change in spring passage at two ringing stations in northern Europe in relation to a synthetic estimate of the strength of female choice, composed of degree of extra-pair paternity, relative testes size and degree of sexually dichromatic plumage colouration. We found that species with a stronger index of sexual selection have indeed advanced their date of spring passage to a greater extent. This relationship was stronger for the changes in the median passage date of the whole population than for changes in the timing of first-arriving individuals, suggesting that selection has not only acted on protandrous males. These results suggest that sexual selection may have an impact on the responses of organisms to climate change, and knowledge of a species' mating system might help to inform attempts at predicting these.

Prediction of 30-day morbidity after primary cytoreductive surgery for advanced stage ovarian cancer.

PubMed

Gerestein, C G; Nieuwenhuyzen-de Boer, G M; Eijkemans, M J; Kooi, G S; Burger, C W

2010-01-01

Treatment in advanced stage epithelial ovarian cancer (EOC) is based on primary cytoreductive surgery followed by platinum-based chemotherapy. Successful cytoreduction to minimal residual tumour burden is the most important determinant of prognosis. However, extensive surgical procedures to achieve maximal debulking are inevitably associated with postoperative morbidity and mortality. The objective of this study is to determine predictors of 30-day morbidity after primary cytoreductive surgery for advanced stage EOC. All patients in the South Western part of the Netherlands who underwent primary cytoreductive surgery for advanced stage EOC between January 2004 and December 2007 were identified from the Rotterdam Cancer Registry database. All peri- and postoperative complications within 30 days after surgery were registered and classified according to the definitions of the National Surgical Quality Improvement Programme (NSQIP). To investigate independent predictors of 30-day morbidity, a Cox proportional hazards model with backward stepwise elimination was utilised. The identified predictors were entered into a nomogram. Two hundred and ninety-three patients entered the study protocol. Optimal cytoreduction was achieved in 136 (46%) patients. 30-day morbidity was seen in 99 (34%) patients. Postoperative morbidity could be predicted by age (P=0.007; odds ratio [OR] 1.034), WHO performance status (P=0.046; OR 1.757), extent of surgery (P=0.1308; OR=2.101), and operative time (P=0.017; OR 1.007) with an optimism corrected c-statistic of 0.68. 30-day morbidity could be predicted by age, WHO performance status, operative time and extent of surgery. The generated nomogram could be valuable for predicting operative risk in the individual patient.

Predicting Activity Energy Expenditure Using the Actical[R] Activity Monitor

ERIC Educational Resources Information Center

Heil, Daniel P.

2006-01-01

This study developed algorithms for predicting activity energy expenditure (AEE) in children (n = 24) and adults (n = 24) from the Actical[R] activity monitor. Each participant performed 10 activities (supine resting, three sitting, three house cleaning, and three locomotion) while wearing monitors on the ankle, hip, and wrist; AEE was computed…

The predictive value of MRI in detecting thyroid gland invasion in patients with advanced laryngeal or hypopharyngeal carcinoma.

PubMed

Lin, Peiliang; Huang, Xiaoming; Zheng, Chushan; Cai, Qian; Guan, Zhong; Liang, Faya; Zheng, Yiqing

2017-01-01

The aim of this study was to evaluate the predictive value of magnetic resonance imaging (MRI) in detecting thyroid gland invasion (TGI) in patients with advanced laryngeal or hypopharyngeal carcinoma. In a retrospective chart review, 41 patients with advanced laryngeal or hypopharyngeal carcinoma underwent MRI scan before total laryngectomy and ipsilateral or bilateral thyroidectomy during the past 5 years. The MRI findings were compared with the postoperative pathological results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Among the 41 patients, 3 had thyroid gland invasion in postoperative pathological results. MRI correctly predicted the absence of TGI in 37 of 38 patients and TGI in all 3 patients. The sensitivity, specificity, PPV, and NPV of MRI were 100.0, 97.4, 75.0, and 100 %, respectively, with the diagnostic accuracy of 97.6 %. In consideration of the high negative predictive value of MRI, it may help surgeons selectively preserve thyroid gland in total laryngectomy and reduce the incidence of hypothyroidism and hypoparathyroidism postoperatively.

Bazedoxifene exhibits antiestrogenic activity in animal models of tamoxifen-resistant breast cancer: implications for treatment of advanced disease.

PubMed

Wardell, Suzanne E; Nelson, Erik R; Chao, Christina A; McDonnell, Donald P

2013-05-01

There is compelling evidence to suggest that drugs that function as pure estrogen receptor (ER-α) antagonists, or that downregulate the expression of ER-α, would have clinical use in the treatment of advanced tamoxifen- and aromatase-resistant breast cancer. Although such compounds are currently in development, we reasoned, based on our understanding of ER-α pharmacology, that there may already exist among the most recently developed selective estrogen receptor modulators (SERM) compounds that would have usage as breast cancer therapeutics. Thus, our objective was to identify among available SERMs those with unique pharmacologic activities and to evaluate their potential clinical use with predictive models of advanced breast cancer. A validated molecular profiling technology was used to classify clinically relevant SERMs based on their impact on ER-α conformation. The functional consequences of these observed mechanistic differences on (i) gene expression, (ii) receptor stability, and (iii) activity in cellular and animal models of advanced endocrine-resistant breast cancer were assessed. The high-affinity SERM bazedoxifene was shown to function as a pure ER-α antagonist in cellular models of breast cancer and effectively inhibited the growth of both tamoxifen-sensitive and -resistant breast tumor xenografts. Interestingly, bazedoxifene induced a unique conformational change in ER-α that resulted in its proteasomal degradation, although the latter activity was dispensable for its antagonist efficacy. Bazedoxifene was recently approved for use in the European Union for the treatment of osteoporosis and thus may represent a near-term therapeutic option for patients with advanced breast cancer. ©2013 AACR.

Impact of postgraduate education on advanced practice nurse activity - a national survey.

PubMed

Wilkinson, J; Carryer, J; Budge, C

2018-03-22

There is a wealth of international evidence concerning the contribution post-registration master's level education makes to advancing the discipline of nursing. There are approximately 277 nurse practitioners registered in NZ, but they account for only a small portion of nurses who have undertaken master's level education. The additional contribution these nurses make to the work environment through advanced practice activities has not, hitherto, been documented. To report the extent of advanced practice nurse activity associated with various levels of nursing education in a sample of nurses working in clinical practice in New Zealand. A replication of recent Australian research was done via a national cross-sectional survey of 3255 registered nurses and nurse practitioners in New Zealand using an online questionnaire to collect responses to the amended Advanced Practice Delineation survey tool. In addition, demographic data were collected including position titles and levels of postgraduate education. A positive association was found between postgraduate education at any level and more time spent in advanced practice activities. Independent of level of postgraduate education, the role a nurse holds also effects the extent of involvement in advanced practice activities. There is an additional contribution made to the work environment by nurses with master's level education which occurs even when they are not employed in an advanced practice role. These findings are of significance to workforce policy and planning across the globe as countries work to sustain health services by increasing nursing capacity effectively within available resources. © 2018 International Council of Nurses.

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting Solar Activity Using Machine-Learning Methods

NASA Astrophysics Data System (ADS)

Bobra, M.

2017-12-01

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Guven, Erhan; Moniz, Linda; Babin, Steven M.; Chretien, Jean-Paul

2016-01-01

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article. PMID:27127415

A novel 2-step approach combining the NAFLD fibrosis score and liver stiffness measurement for predicting advanced fibrosis.

PubMed

Chan, Wah-Kheong; Nik Mustapha, Nik Raihan; Mahadeva, Sanjiv

2015-10-01

The non-alcoholic fatty liver disease (NAFLD) fibrosis score (NFS) is indeterminate in a proportion of NAFLD patients. Combining the NFS with liver stiffness measurement (LSM) may improve prediction of advanced fibrosis. We aim to evaluate the NFS and LSM in predicting advanced fibrosis in NAFLD patients. The NFS was calculated and LSM obtained for consecutive adult NAFLD patients scheduled for liver biopsy. The accuracy of predicting advanced fibrosis using either modality and in combination were assessed. An algorithm combining the NFS and LSM was developed from a training cohort and subsequently tested in a validation cohort. There were 101 and 46 patients in the training and validation cohort, respectively. In the training cohort, the percentages of misclassifications using the NFS alone, LSM alone, LSM alone (with grey zone), both tests for all patients and a 2-step approach using LSM only for patients with indeterminate and high NFS were 5.0, 28.7, 2.0, 2.0 and 4.0 %, respectively. The percentages of patients requiring liver biopsy were 30.7, 0, 36.6, 36.6 and 18.8 %, respectively. In the validation cohort, the percentages of misclassifications were 8.7, 28.3, 2.2, 2.2 and 8.7 %, respectively. The percentages of patients requiring liver biopsy were 28.3, 0, 41.3, 43.5 and 19.6 %, respectively. The novel 2-step approach further reduced the number of patients requiring a liver biopsy whilst maintaining the accuracy to predict advanced fibrosis. The combination of NFS and LSM for all patients provided no apparent advantage over using either of the tests alone.

Advance directives in patients with advanced cancer receiving active treatment: attitudes, prevalence, and barriers.

PubMed

McDonald, Julie C; du Manoir, Jeanne M; Kevork, Nanor; Le, Lisa W; Zimmermann, Camilla

2017-02-01

The purposes of the study were to assess awareness and prevalence of advance directives (ADs) among patients with advanced cancer undergoing active outpatient care and to determine factors associated with AD completion before and after the diagnosis of cancer. Patients with advanced solid tumor malignancy receiving treatment at the Chemotherapy Day Unit were approached for recruitment. They completed an onsite questionnaire about completion and timing of ADs, demographic information, and perceived health; a review of their medical records was conducted to document their cancer care and co-morbidities. Multinomial logistic regression analysis identified factors associated with the timing of AD completion (pre-cancer, post-cancer, or not at all). Two hundred patients were enrolled, with 193 surveys available for analysis. ADs were completed in 55 % (106/193) of patients, including a living will in 33 % (63/193), a power of attorney in 49 % (95/193), and a do-not-resuscitate (DNR) designation in 18 % (35/193). Most patients (53 %) had completed an AD before being diagnosed with cancer. Higher income (p = 0.02) and age (p = 0.004) were associated with AD completion pre-cancer diagnosis; discussion of end-of-life care (p = 0.02) and palliative care referral (p < 0.0001) were associated with AD completion post-cancer diagnosis. This study demonstrates that different factors may influence the completion of ADs before and after a diagnosis of cancer and highlights the potential for early palliative care to impact the completion of ADs in patients with advanced cancer who are undergoing active cancer treatment.

Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design

PubMed Central

2017-01-01

Background Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic “big data” from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. Objective The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. Methods An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. Results The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used

25 CFR 170.615 - Can a tribe receive advance payments for non-construction activities?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 1 2010-04-01 2010-04-01 false Can a tribe receive advance payments for non-construction... Agreements Under Isdeaa § 170.615 Can a tribe receive advance payments for non-construction activities? Yes. BIA must make advance payments to a tribe for non-construction activities under 25 U.S.C. 450l for...

25 CFR 170.615 - Can a tribe receive advance payments for non-construction activities?

Code of Federal Regulations, 2011 CFR

2011-04-01

... 25 Indians 1 2011-04-01 2011-04-01 false Can a tribe receive advance payments for non-construction... Agreements Under Isdeaa § 170.615 Can a tribe receive advance payments for non-construction activities? Yes. BIA must make advance payments to a tribe for non-construction activities under 25 U.S.C. 450l for...

Intrinsic resistance to EGFR tyrosine kinase inhibitors in advanced non-small-cell lung cancer with activating EGFR mutations

PubMed Central

Wang, Jun; Wang, Baocheng; Chu, Huili; Yao, Yunfeng

2016-01-01

Identifying activating EGFR mutations is a useful predictive strategy that helps select a population of advanced non-small-cell lung cancer (NSCLC) patients for treatment with EGFR tyrosine kinase inhibitors (TKIs). Patients with sensitizing EGFR mutations (predominantly an in-frame deletion in exon 19 and an L858R substitution) are highly responsive to first-generation EGFR TKIs, such as gefitinib and erlotinib, and show improved progression-free survival without serious side effects. However, all patients with activating EGFR mutations who are initially responsive to EGFR TKIs eventually develop acquired resistance after a median progression-free survival of 10–16 months, followed by disease progression. Moreover, ~20%–30% of NSCLC patients have no objective tumor regression on initial EGFR TKI treatment, although they harbor an activating EGFR mutation. These patients represent an NSCLC subgroup that is defined as having intrinsic or primary resistance to EGFR TKIs. Different mechanisms of acquired EGFR TKI resistance have been identified, and several novel compounds have been developed to reverse acquired resistance, but little is known about EGFR TKI intrinsic resistance. In this review, we summarize the latest findings involving mechanisms of intrinsic resistance to EGFR TKIs in advanced NSCLC with activating EGFR mutations and present possible therapeutic strategies to overcome this resistance. PMID:27382309

ADVANCIS Score Predicts Acute Kidney Injury After Percutaneous Coronary Intervention for Acute Coronary Syndrome.

PubMed

Fan, Pei-Chun; Chen, Tien-Hsing; Lee, Cheng-Chia; Tsai, Tsung-Yu; Chen, Yung-Chang; Chang, Chih-Hsiang

2018-01-01

Acute kidney injury (AKI), a common and crucial complication of acute coronary syndrome (ACS) after receiving percutaneous coronary intervention (PCI), is associated with increased mortality and adverse outcomes. This study aimed to develop and validate a risk prediction model for incident AKI after PCI for ACS. We included 82,186 patients admitted for ACS and receiving PCI between 1997 and 2011 from the Taiwan National Health Insurance Research Database and randomly divided them into a training cohort (n = 57,630) and validation cohort (n = 24,656) for risk model development and validation, respectively. Risk factor analysis revealed that age, diabetes mellitus, ventilator use, prior AKI, number of intervened vessels, chronic kidney disease (CKD), intra-aortic balloon pump (IABP) use, cardiogenic shock, female sex, prior stroke, peripheral arterial disease, hypertension, and heart failure were significant risk factors for incident AKI after PCI for ACS. The reduced model, ADVANCIS, comprised 8 clinical parameters (age, diabetes mellitus, ventilator use, prior AKI, number of intervened vessels, CKD, IABP use, cardiogenic shock), with a score scale ranging from 0 to 22, and performed comparably with the full model (area under the receiver operating characteristic curve, 87.4% vs 87.9%). An ADVANCIS score of ≥6 was associated with higher in-hospital mortality risk. In conclusion, the ADVANCIS score is a novel, simple, robust tool for predicting the risk of incident AKI after PCI for ACS, and it can aid in risk stratification to monitor patient care.

The prediction of progression-free and overall survival in women with an advanced stage of epithelial ovarian carcinoma.

PubMed

Gerestein, C G; Eijkemans, M J C; de Jong, D; van der Burg, M E L; Dykgraaf, R H M; Kooi, G S; Baalbergen, A; Burger, C W; Ansink, A C

2009-02-01

Prognosis in women with ovarian cancer mainly depends on International Federation of Gynecology and Obstetrics stage and the ability to perform optimal cytoreductive surgery. Since ovarian cancer has a heterogeneous presentation and clinical course, predicting progression-free survival (PFS) and overall survival (OS) in the individual patient is difficult. The objective of this study was to determine predictors of PFS and OS in women with advanced stage epithelial ovarian cancer (EOC) after primary cytoreductive surgery and first-line platinum-based chemotherapy. Retrospective observational study. Two teaching hospitals and one university hospital in the south-western part of the Netherlands. Women with advanced stage EOC. All women who underwent primary cytoreductive surgery for advanced stage EOC followed by first-line platinum-based chemotherapy between January 1998 and October 2004 were identified. To investigate independent predictors of PFS and OS, a Cox' proportional hazard model was used. Nomograms were generated with the identified predictive parameters. The primary outcome measure was OS and the secondary outcome measures were response and PFS. A total of 118 women entered the study protocol. Median PFS and OS were 15 and 44 months, respectively. Preoperative platelet count (P = 0.007), and residual disease <1 cm (P = 0.004) predicted PFS with a optimism corrected c-statistic of 0.63. Predictive parameters for OS were preoperative haemoglobin serum concentration (P = 0.012), preoperative platelet counts (P = 0.031) and residual disease <1 cm (P = 0.028) with a optimism corrected c-statistic of 0.67. PFS could be predicted by postoperative residual disease and preoperative platelet counts, whereas residual disease, preoperative platelet counts and preoperative haemoglobin serum concentration were predictive for OS. The proposed nomograms need to be externally validated.

Advanced extravehicular activity systems requirements definition study. Phase 2: Extravehicular activity at a lunar base

NASA Technical Reports Server (NTRS)

Neal, Valerie; Shields, Nicholas, Jr.; Carr, Gerald P.; Pogue, William; Schmitt, Harrison H.; Schulze, Arthur E.

1988-01-01

The focus is on Extravehicular Activity (EVA) systems requirements definition for an advanced space mission: remote-from-main base EVA on the Moon. The lunar environment, biomedical considerations, appropriate hardware design criteria, hardware and interface requirements, and key technical issues for advanced lunar EVA were examined. Six remote EVA scenarios (three nominal operations and three contingency situations) were developed in considerable detail.

Data Mining and Predictive Modeling in Institutional Advancement: How Ten Schools Found Success. Technical Report

ERIC Educational Resources Information Center

Luperchio, Dan

2009-01-01

This technical report, produced in partnership by the Council for Advancement and Support of Education (CASE) and SPSS Inc., explores the promise of data mining alumni records at educational institutions. Working with individual alumni records from The Johns Hopkins Zanvyl Krieger School of Arts and Sciences, a predictive regression model is…

[The third dimension tomography versus cranial X-ray cephalometry to predict maxilla advance by distraction in hypoplastic maxilla].

PubMed

Rosas-Muñoz, Arturo; Soriano-Padilla, Fernando; Rendón-Macías, Mario Enrique

2010-01-01

the osteogenic distraction is the treatment for the correction of the hypoplastic maxilla secondary to the repair of a cleft lip-palate. Its planning is based on articulated models. Our objective was to describe the accuracy of three-dimensional Cephalometry (CT3D) for projecting jaw displacement. three patients with hypoplastic maxilla. Interventions estimation of the advance required of lateral maxilla through Cephalometry of skull (CLC), CT3D and an articulated model (gold standard). Two months after distraction finalized the advance predicted was compared. the error of the advance projection in each patient was smaller with the CT3D versus CLC (+1, +1 and +1 mm versus -10, -14 and -9mm). Corrections post-distraction were of +25 %, +26 % and +38.4 % on the programmed one. CT3D predicted better the correction (+19 %, +10.8 %, +33.4 % versus CLC: -50 %; -60.8 % and -34.6 %). Chewing alterations were not seen in any patient. the planning of the necessary advance for distraction in patients with hypoplastic maxilla by CT3D can shorten the time of studies and should be consider as next to the projection of articulated model.

LiverTox: Advanced QSAR and Toxicogeomic Software for Hepatotoxicity Prediction

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

Lu, P-Y.; Yuracko, K.

2011-02-25

YAHSGS LLC and Oak Ridge National Laboratory (ORNL) established a CRADA in an attempt to develop a predictive system using a pre-existing ORNL computational neural network and wavelets format. This was in the interest of addressing national needs for toxicity prediction system to help overcome the significant drain of resources (money and time) being directed toward developing chemical agents for commerce. The research project has been supported through an STTR mechanism and funded by the National Institute of Environmental Health Sciences beginning Phase I in 2004 (CRADA No. ORNL-04-0688) and extending Phase II through 2007 (ORNL NFE-06-00020). To attempt themore » research objectives and aims outlined under this CRADA, state-of-the-art computational neural network and wavelet methods were used in an effort to design a predictive toxicity system that used two independent areas on which to base the system’s predictions. These two areas were quantitative structure-activity relationships and gene-expression data obtained from microarrays. A third area, using the new Massively Parallel Signature Sequencing (MPSS) technology to assess gene expression, also was attempted but had to be dropped because the company holding the rights to this promising MPSS technology went out of business. A research-scale predictive toxicity database system called Multi-Intelligent System for Toxicogenomic Applications (MISTA) was developed and its feasibility for use as a predictor of toxicological activity was tested. The fundamental focus of the CRADA was an attempt and effort to operate the MISTA database using the ORNL neural network. This effort indicated the potential that such a fully developed system might be used to assist in predicting such biological endpoints as hepatotoxcity and neurotoxicity. The MISTA/LiverTox approach if eventually fully developed might also be useful for automatic processing of microarray data to predict modes of action. A technical paper describing

Advancing atmospheric river forecasts into subseasonal-to-seasonal time scales

NASA Astrophysics Data System (ADS)

Baggett, Cory F.; Barnes, Elizabeth A.; Maloney, Eric D.; Mundhenk, Bryan D.

2017-07-01

Atmospheric rivers are elongated plumes of intense moisture transport that are capable of producing extreme and impactful weather. Along the West Coast of North America, they occasionally cause considerable mayhem—delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal time scales (3 to 5 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into subseasonal-to-seasonal time scales through knowledge of two of the atmosphere's most prominent oscillations, the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). Strong MJO and QBO activity modulates the frequency at which atmospheric rivers strike—offering an opportunity to improve subseasonal-to-seasonal forecast models and thereby skillfully predict atmospheric river activity up to 5 weeks in advance.

Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Volpi, Michele; Copa, Loris

2010-05-01

The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of

[The use of complex interval models for predicting activity of non-nucleoside reverse transcriptase activity].

PubMed

Burliaeva, E V; Tarkhov, A E; Burliaev, V V; Iurkevich, A M; Shvets, V I

2002-01-01

Searching of new anti-HIV agents is still crucial now. In general, researches are looking for inhibitors of certain HIV's vital enzymes, especially for reverse transcriptase (RT) inhibitors. Modern generation of anti-HIV agents represents non-nucleoside reverse transcriptase inhibitors (NNRTIs). They are much less toxic than nucleoside analogues and more chemically stable, thus being slower metabolized and emitted from the human body. Thus, search of new NNRTIs is actual today. Synthesis and study of new anti-HIV drugs is very expensive. So employment of the activity prediction techniques for such a search is very beneficial. This technique allows predicting the activities for newly proposed structures. It is based on the property model built by investigation of a series of known compounds with measured activity. This paper presents an approach of activity prediction based on "structure-activity" models designed to form a hypothesis about probably activity interval estimate. This hypothesis formed is based on structure descriptor domains, calculated for all energetically allowed conformers for each compound in the studied sef. Tetrahydroimidazobenzodiazipenone (TIBO) derivatives and phenylethyltiazolyltiourea (PETT) derivatives illustrated the predictive power of this method. The results are consistent with experimental data and allow to predict inhibitory activity of compounds, which were not included into the training set.

Advanced Extravehicular Activity Breakout Group Summary

NASA Technical Reports Server (NTRS)

Kosmo, Joseph J.; Perka, Alan; Walz, Carl; Cobb, Sharon; Hanford, Anthony; Eppler, Dean

2005-01-01

This viewgraph document summarizes the workings of the Advanced Extravehicular Activity (AEVA) Breakout group in a Martian environment. The group was tasked with: identifying potential contaminants and pathways for AEVA systems with respect to forward and backward contamination; identifying plausible mitigation alternatives and obstacles for pertinent missions; identifying topics that require further research and technology development and discuss development strategies with uncertain Planetary Protection (PP) requirements; Identifying PP requirements that impose the greatest mission/development costs; Identifying PP requirements/topics that require further definition;

Predicting reading and mathematics from neural activity for feedback learning.

PubMed

Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Research Area 3 - Mathematical Sciences: Multiscale Modeling of the Mechanics of Advanced Energetic Materials Relevant to Detonation Prediction

DTIC Science & Technology

2015-08-24

new energetic materials with enhanced energy release rates and reduced sensitivity to unintentional detonation . The following results have been...Mechanics of Advanced Energetic Materials Relevant to Detonation Prediction The views, opinions and/or findings contained in this report are those of the...modeling, molecular simulations, detonation prediction REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S

Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design.

PubMed

Spreco, Armin; Eriksson, Olle; Dahlström, Örjan; Cowling, Benjamin John; Timpka, Toomas

2017-06-15

Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic "big data" from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning

Prediction of Geomagnetic Activity and Key Parameters in High-latitude Ionosphere

NASA Technical Reports Server (NTRS)

Khazanov, George V.; Lyatsky, Wladislaw; Tan, Arjun; Ridley, Aaron

2007-01-01

Prediction of geomagnetic activity and related events in the Earth's magnetosphere and ionosphere are important tasks of US Space Weather Program. Prediction reliability is dependent on the prediction method, and elements included in the prediction scheme. Two of the main elements of such prediction scheme are: an appropriate geomagnetic activity index, and an appropriate coupling function (the combination of solar wind parameters providing the best correlation between upstream solar wind data and geomagnetic activity). We have developed a new index of geomagnetic activity, the Polar Magnetic (PM) index and an improved version of solar wind coupling function. PM index is similar to the existing polar cap PC index but it shows much better correlation with upstream solar wind/IMF data and other events in the magnetosphere and ionosphere. We investigate the correlation of PM index with upstream solar wind/IMF data for 10 years (1995-2004) that include both low and high solar activity. We also have introduced a new prediction function for the predicting of cross-polar-cap voltage and Joule heating based on using both PM index and upstream solar wind/IMF data. As we show such prediction function significantly increase the reliability of prediction of these important parameters. The correlation coefficients between the actual and predicted values of these parameters are approx. 0.9 and higher.

Solar Activity Forecasting for use in Orbit Prediction

NASA Technical Reports Server (NTRS)

Schatten, Kenneth

2001-01-01

Orbital prediction for satellites in low Earth orbit (LEO) or low planetary orbit depends strongly on exospheric densities. Solar activity forecasting is important in orbital prediction, as the solar UV and EUV inflate the upper atmospheric layers of the Earth and planets, forming the exosphere in which satellites orbit. Geomagnetic effects also relate to solar activity. Because of the complex and ephemeral nature of solar activity, with different cycles varying in strength by more than 100%, many different forecasting techniques have been utilized. The methods range from purely numerical techniques (essentially curve fitting) to numerous oddball schemes, as well as a small subset, called 'Precursor techniques.' The situation can be puzzling, owing to the numerous methodologies involved, somewhat akin to the numerous ether theories near the turn of the last century. Nevertheless, the Precursor techniques alone have a physical basis, namely dynamo theory, which provides a physical explanation for why this subset seems to work. I discuss this solar cycle's predictions, as well as the Sun's observed activity. I also discuss the SODA (Solar Dynamo Amplitude) index, which provides the user with the ability to track the Sun's hidden, interior dynamo magnetic fields. As a result, one may then update solar activity predictions continuously, by monitoring the solar magnetic fields as they change throughout the solar cycle. This paper ends by providing a glimpse into what the next solar cycle (#24) portends.

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Advanced techniques for determining long term compatibility of materials with propellants

NASA Technical Reports Server (NTRS)

Green, R. L.; Stebbins, J. P.; Smith, A. W.; Pullen, K. E.

1973-01-01

A method for the prediction of propellant-material compatibility for periods of time up to ten years is presented. Advanced sensitive measurement techniques used in the prediction method are described. These include: neutron activation analysis, radioactive tracer technique, and atomic absorption spectroscopy with a graphite tube furnace sampler. The results of laboratory tests performed to verify the prediction method are presented.

Broad Detection of Alterations Predicted to Confer Lack of Benefit From EGFR Antibodies or Sensitivity to Targeted Therapy in Advanced Colorectal Cancer.

PubMed

Rankin, Andrew; Klempner, Samuel J; Erlich, Rachel; Sun, James X; Grothey, Axel; Fakih, Marwan; George, Thomas J; Lee, Jeeyun; Ross, Jeffrey S; Stephens, Philip J; Miller, Vincent A; Ali, Siraj M; Schrock, Alexa B

2016-09-28

A KRAS mutation represented the first genomic biomarker to predict lack of benefit from anti-epidermal growth factor receptor (EGFR) antibody therapy in advanced colorectal cancer (CRC). Expanded RAS testing has further refined the treatment approach, but understanding of genomic alterations underlying primary and acquired resistance is limited and further study is needed. We prospectively analyzed 4,422 clinical samples from patients with advanced CRC, using hybrid-capture based comprehensive genomic profiling (CGP) at the request of the individual treating physicians. Comparison with prior molecular testing results, when available, was performed to assess concordance. We identified a RAS/RAF pathway mutation or amplification in 62% of cases, including samples harboring KRAS mutations outside of the codon 12/13 hotspot region in 6.4% of cases. Among cases with KRAS non-codon 12/13 alterations for which prior test results were available, 79 of 90 (88%) were not identified by focused testing. Of 1,644 RAS/RAF wild-type cases analyzed by CGP, 31% harbored a genomic alteration (GA) associated with resistance to anti-EGFR therapy in advanced CRC including mutations in PIK3CA, PTEN, EGFR, and ERBB2. We also identified other targetable GA, including novel kinase fusions, receptor tyrosine kinase amplification, activating point mutations, as well as microsatellite instability. Extended genomic profiling reliably detects alterations associated with lack of benefit to anti-EGFR therapy in advanced CRC, while simultaneously identifying alterations potentially important in guiding treatment. The use of CGP during the course of clinical care allows for the refined selection of appropriate targeted therapies and clinical trials, increasing the chance of clinical benefit and avoiding therapeutic futility. Comprehensive genomic profiling (CGP) detects diverse genomic alterations associated with lack of benefit to anti-epidermal growth factor receptor therapy in advanced

Advanced composite elevator for Boeing 727 aircraft

NASA Technical Reports Server (NTRS)

1979-01-01

Detail design activities are reported for a program to develop an advanced composites elevator for the Boeing 727 commercial transport. Design activities include discussion of the full scale ground test and flight test activities, the ancillary test programs, sustaining efforts, weight status, and the production status. Prior to flight testing of the advanced composites elevator, ground, flight flutter, and stability and control test plans were reviewed and approved by the FAA. Both the ground test and the flight test were conducted according to the approved plan, and were witnessed by the FAA. Three and one half shipsets have now been fabricated without any significant difficulty being encountered. Two elevator system shipsets were weighed, and results validated the 26% predicted weight reduction. The program is on schedule.

Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

PubMed

Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

2014-01-01

Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

Increasing potential predictability of Indian Summer monsoon active and break spells

NASA Astrophysics Data System (ADS)

Mani, N. J.; Goswami, B.

2009-12-01

An understanding of the limit on potential predictability is crucial for developing appropriate tools for extended range prediction of active/break spells of Indian summer monsoon (ISM). The global low frequency changes in climate modulate the annual cycle of the ISM and can influence the intrinsic predictability limit of the ISM intraseasonal oscillations (ISOs). Using 104 year (1901-2004) long daily rainfall data, the change in potential predictability of active and break spells are estimated by an empirical method. Using an ISO index based on 10-90 day filtered precipitation, Goswami and Xavier (2003)showed that the monsoon breaks are intrinsically more predictable (20-25 days) than the active conditions (10-15 days. In the present study, employing the same method in 15 year sliding windows, we found that the potential predictability of both active and break spells have undergone a rapid increase during the recent three decades. The potential predictability of active spells has shown an increase from 1 week to 2 weeks while that for break spells increased from 2 weeks to 3 weeks. This result is interesting and intriguing in the backdrop of recent finding that the potential predictability of monsoon weather has decreased substantially over the same period compared to earlier decades due to increased potential instability of the atmosphere. The possible role of internal dynamics and external forcing in producing this change has been explored. The variance among peak active/break conditions shows a steady decrease over the years, indicating a lesser event to event variability in the magnitude of ISO peak phases in recent years. The ISO predictability may be closely linked to the error energy cascading from the synoptic scales and the interaction between these scales. Computation of nonlinear kinetic energy exchange between synoptic and ISO scales in frequency domain, also support the notion of ineffectual influence of synoptic scale errors on the ISO scale

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

Prediction and selection of vocabulary for two leisure activities.

PubMed

Dark, Leigha; Balandin, Susan

2007-01-01

People who use augmentative or alternative communication (AAC) need access to a relevant, socially valid vocabulary if they are to communicate successfully in a variety of contexts. Many people with complex communication needs who utilize some form of high technology or low technology AAC rely on others to predict and select vocabulary for them. In this study the ability of one speech pathologist, nine leisure support workers, and six people with cerebral palsy to accurately predict context-specific vocabulary was explored. Participants predicted vocabulary for two leisure activities - sailing session and Internet café - using the blank page method of vocabulary selection to identify the vocabulary items they considered important for each activity. This predicted vocabulary was then compared with the actual vocabulary used in each of the activities. A total of 187 (68%) of the words predicted for the sailing session were used during recorded conversations, with 88 words (32%) not appearing in the recorded samples. During the visit to the Internet café only 104 (47%) of the words predicted occurred in the recorded samples, with 117 words (53%) not occurring at all. These results support the need to socially validate any vocabulary in order to ensure that it is relevant and useful for the person using the AAC system.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

EPA Pesticide Factsheets

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor.This dataset is associated with the following publication:Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

Developing a musculoskeletal model of the primate skull: predicting muscle activations, bite force, and joint reaction forces using multibody dynamics analysis and advanced optimisation methods.

PubMed

Shi, Junfen; Curtis, Neil; Fitton, Laura C; O'Higgins, Paul; Fagan, Michael J

2012-10-07

An accurate, dynamic, functional model of the skull that can be used to predict muscle forces, bite forces, and joint reaction forces would have many uses across a broad range of disciplines. One major issue however with musculoskeletal analyses is that of muscle activation pattern indeterminacy. A very large number of possible muscle force combinations will satisfy a particular functional task. This makes predicting physiological muscle recruitment patterns difficult. Here we describe in detail the process of development of a complex multibody computer model of a primate skull (Macaca fascicularis), that aims to predict muscle recruitment patterns during biting. Using optimisation criteria based on minimisation of muscle stress we predict working to balancing side muscle force ratios, peak bite forces, and joint reaction forces during unilateral biting. Validation of such models is problematic; however we have shown comparable working to balancing muscle activity and TMJ reaction ratios during biting to those observed in vivo and that peak predicted bite forces compare well to published experimental data. To our knowledge the complexity of the musculoskeletal model is greater than any previously reported for a primate. This complexity, when compared to more simple representations provides more nuanced insights into the functioning of masticatory muscles. Thus, we have shown muscle activity to vary throughout individual muscle groups, which enables them to function optimally during specific masticatory tasks. This model will be utilised in future studies into the functioning of the masticatory apparatus. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting Active Users' Personality Based on Micro-Blogging Behaviors

PubMed Central

Hao, Bibo; Guan, Zengda; Zhu, Tingshao

2014-01-01

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors. PMID:24465462

Predicting eruptions from precursory activity using remote sensing data hybridization

NASA Astrophysics Data System (ADS)

Reath, K. A.; Ramsey, M. S.; Dehn, J.; Webley, P. W.

2016-07-01

Many volcanoes produce some level of precursory activity prior to an eruption. This activity may or may not be detected depending on the available monitoring technology. In certain cases, precursors such as thermal output can be interpreted to make forecasts about the time and magnitude of the impending eruption. Kamchatka (Russia) provides an ideal natural laboratory to study a wide variety of eruption styles and precursory activity prior to an eruption. At Bezymianny volcano for example, a clear increase in thermal activity commonly occurs before an eruption, which has allowed predictions to be made months ahead of time. Conversely, the eruption of Tolbachik volcano in 2012 produced no discernable thermal precursors before the large scale effusive eruption. However, most volcanoes fall between the extremes of consistently behaved and completely undetectable, which is the case with neighboring Kliuchevskoi volcano. This study tests the effectiveness of using thermal infrared (TIR) remote sensing to track volcanic thermal precursors using data from both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) sensors. It focuses on three large eruptions that produced different levels and durations of effusive and explosive behavior at Kliuchevskoi. Before each of these eruptions, TIR spaceborne sensors detected thermal anomalies (i.e., pixels with brightness temperatures > 2 °C above the background temperature). High-temporal, low-spatial resolution (i.e., hours and 1 km) AVHRR data are ideal for detecting large thermal events occurring over shorter time scales, such as the hot material ejected following strombolian eruptions. In contrast, high-spatial, low-temporal resolution (i.e., days to weeks and 90 m) ASTER data enables the detection of much lower thermal activity; however, activity with a shorter duration will commonly be missed. ASTER and AVHRR data are combined to track low

Is gardening a stimulating activity for people with advanced Huntington's disease?

PubMed

Spring, Josephine A; Viera, Marc; Bowen, Ceri; Marsh, Nicola

2014-11-01

This study evaluated adapted gardening as an activity for people with advanced Huntington's disease (HD) and explored its therapeutic aspects. Visitors and staff completed a questionnaire and participated in structured interviews to capture further information, whereas a pictorial questionnaire was designed for residents with communication difficulties. Staff reported that gardening was a constructive, outdoor activity that promoted social interaction, physical activity including functional movement and posed cognitive challenges. Half the staff thought the activity was problem free and a third used the garden for therapy. Visitors used the garden to meet with residents socially. Despite their disabilities, HD clients enjoyed growing flourishing flowers and vegetables, labelling plants, being outside in the sun and the quiet of the garden. The garden is valued by all three groups. The study demonstrates the adapted method of gardening is a stimulating and enjoyable activity for people with advanced HD. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Prediction of helicopter rotor discrete frequency noise: A computer program incorporating realistic blade motions and advanced acoustic formulation

NASA Technical Reports Server (NTRS)

Brentner, K. S.

1986-01-01

A computer program has been developed at the Langley Research Center to predict the discrete frequency noise of conventional and advanced helicopter rotors. The program, called WOPWOP, uses the most advanced subsonic formulation of Farassat that is less sensitive to errors and is valid for nearly all helicopter rotor geometries and flight conditions. A brief derivation of the acoustic formulation is presented along with a discussion of the numerical implementation of the formulation. The computer program uses realistic helicopter blade motion and aerodynamic loadings, input by the user, for noise calculation in the time domain. A detailed definition of all the input variables, default values, and output data is included. A comparison with experimental data shows good agreement between prediction and experiment; however, accurate aerodynamic loading is needed.

The discriminatory capability of existing scores to predict advanced colorectal neoplasia: a prospective colonoscopy study of 5,899 screening participants.

PubMed

Wong, Martin C S; Ching, Jessica Y L; Ng, Simpson; Lam, Thomas Y T; Luk, Arthur K C; Wong, Sunny H; Ng, Siew C; Ng, Simon S M; Wu, Justin C Y; Chan, Francis K L; Sung, Joseph J Y

2016-02-03

We evaluated the performance of seven existing risk scoring systems in predicting advanced colorectal neoplasia in an asymptomatic Chinese cohort. We prospectively recruited 5,899 Chinese subjects aged 50-70 years in a colonoscopy screening programme(2008-2014). Scoring systems under evaluation included two scoring tools from the US; one each from Spain, Germany, and Poland; the Korean Colorectal Screening(KCS) scores; and the modified Asia Pacific Colorectal Screening(APCS) scores. The c-statistics, sensitivity, specificity, positive predictive values(PPVs), and negative predictive values(NPVs) of these systems were evaluated. The resources required were estimated based on the Number Needed to Screen(NNS) and the Number Needed to Refer for colonoscopy(NNR). Advanced neoplasia was detected in 364 (6.2%) subjects. The German system referred the least proportion of subjects (11.2%) for colonoscopy, whilst the KCS scoring system referred the highest (27.4%). The c-statistics of all systems ranged from 0.56-0.65, with sensitivities ranging from 0.04-0.44 and specificities from 0.74-0.99. The modified APCS scoring system had the highest c-statistics (0.65, 95% C.I. 0.58-0.72). The NNS (12-19) and NNR (5-10) were similar among the scoring systems. The existing scoring systems have variable capability to predict advanced neoplasia among asymptomatic Chinese subjects, and further external validation should be performed.

BH3-only protein Bim predicts advanced stage of cutaneous melanoma.

PubMed

Gambichler, T; Rooms, I; Scholl, L; Stockfleth, E; Stücker, M; Sand, M

2016-11-01

Bim having strong pro-apoptotic effects belongs to the BH3-only proteins of the Bcl-2 protein family and contributes to survival pathways in cancer cells. We aimed to investigate Bim protein expression in cutaneous melanoma (CM). Bim protein expression was assessed by immunohistochemistry in primary and metastatic melanomas and correlated with clinical and histopathological features. The Bim immunoreactivity score of the primary melanomas investigated (4.6 ± 1.5) was significantly (P < 0.0001) higher than that observed in metastases (2.8 ± 1.1). Low Bim expression was significantly associated with primary nodular melanoma type (P = 0.005). Moreover, Bim expression was significantly inversely correlated with tumour thickness (r = -0.36; P = 0.0035), advanced stage of disease (stage III and IV; r = -0.60; P < 0.0001), disease relapse (r = -0.18; P = 0.034) and disease-related death (r = -0.19; P = 0.026). Advanced stage of disease was independently predicted by low Bim expression (P = 0.0010, odds ratio: 0.22, 95% CI: 0.10-0.56) on multivariate analysis; however, Bim was not shown to be an independent predictor for disease relapse (P = 0.40) and disease-related death (P = 0.77). Our data demonstrate that Bim protein expression is significantly inversely correlated with melanoma features that are associated with worse prognosis. We have shown that Bim protein expression in CM is an independent predictor for advanced disease confirming that this pro-apoptotic BH3-only protein might be a potent biomarker and promising therapeutic target. © 2016 European Academy of Dermatology and Venereology.

Advanced Active Thermal Control Systems Architecture Study

NASA Technical Reports Server (NTRS)

Hanford, Anthony J.; Ewert, Michael K.

1996-01-01

The Johnson Space Center (JSC) initiated a dynamic study to determine possible improvements available through advanced technologies (not used on previous or current human vehicles), identify promising development initiatives for advanced active thermal control systems (ATCS's), and help prioritize funding and personnel distribution among many research projects by providing a common basis to compare several diverse technologies. Some technologies included were two-phase thermal control systems, light-weight radiators, phase-change thermal storage, rotary fluid coupler, and heat pumps. JSC designed the study to estimate potential benefits from these various proposed and under-development thermal control technologies for five possible human missions early in the next century. The study compared all the technologies to a baseline mission using mass as a basis. Each baseline mission assumed an internal thermal control system; an external thermal control system; and aluminum, flow-through radiators. Solar vapor compression heat pumps and light-weight radiators showed the greatest promise as general advanced thermal technologies which can be applied across a range of missions. This initial study identified several other promising ATCS technologies which offer mass savings and other savings compared to traditional thermal control systems. Because the study format compares various architectures with a commonly defined baseline, it is versatile and expandable, and is expected to be updated as needed.

Advancing research on animal-transported subsidies by integrating animal movement and ecosystem modelling.

PubMed

Earl, Julia E; Zollner, Patrick A

2017-09-01

Connections between ecosystems via animals (active subsidies) support ecosystem services and contribute to numerous ecological effects. Thus, the ability to predict the spatial distribution of active subsidies would be useful for ecology and conservation. Previous work modelling active subsidies focused on implicit space or static distributions, which treat passive and active subsidies similarly. Active subsidies are fundamentally different from passive subsidies, because animals can respond to the process of subsidy deposition and ecosystem changes caused by subsidy deposition. We propose addressing this disparity by integrating animal movement and ecosystem ecology to advance active subsidy investigations, make more accurate predictions of subsidy spatial distributions, and enable a mechanistic understanding of subsidy spatial distributions. We review selected quantitative techniques that could be used to accomplish integration and lead to novel insights. The ultimate objective for these types of studies is predictions of subsidy spatial distributions from characteristics of the subsidy and the movement strategy employed by animals that transport subsidies. These advances will be critical in informing the management of ecosystem services, species conservation and ecosystem degradation related to active subsidies. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Substrate Deformation Predicts Neuronal Growth Cone Advance

PubMed Central

Athamneh, Ahmad I.M.; Cartagena-Rivera, Alexander X.; Raman, Arvind; Suter, Daniel M.

2015-01-01

Although pulling forces have been observed in axonal growth for several decades, their underlying mechanisms, absolute magnitudes, and exact roles are not well understood. In this study, using two different experimental approaches, we quantified retrograde traction force in Aplysia californica neuronal growth cones as they develop over time in response to a new adhesion substrate. In the first approach, we developed a novel method, to our knowledge, for measuring traction forces using an atomic force microscope (AFM) with a cantilever that was modified with an Aplysia cell adhesion molecule (apCAM)-coated microbead. In the second approach, we used force-calibrated glass microneedles coated with apCAM ligands to guide growth cone advance. The traction force exerted by the growth cone was measured by monitoring the microneedle deflection using an optical microscope. Both approaches showed that Aplysia growth cones can develop traction forces in the 100–102 nN range during adhesion-mediated advance. Moreover, our results suggest that the level of traction force is directly correlated to the stiffness of the microneedle, which is consistent with a reinforcement mechanism previously observed in other cell types. Interestingly, the absolute level of traction force did not correlate with growth cone advance toward the adhesion site, but the amount of microneedle deflection did. In cases of adhesion-mediated growth cone advance, the mean needle deflection was 1.05 ± 0.07 μm. By contrast, the mean deflection was significantly lower (0.48 ± 0.06 μm) when the growth cones did not advance. Our data support a hypothesis that adhesion complexes, which can undergo micron-scale elastic deformation, regulate the coupling between the retrogradely flowing actin cytoskeleton and apCAM substrates, stimulating growth cone advance if sufficiently abundant. PMID:26445437

Which domains of childhood physical activity predict physical activity in adulthood? A 20-year prospective tracking study.

PubMed

Cleland, Verity; Dwyer, Terence; Venn, Alison

2012-06-01

It is important to examine how childhood physical activity is related to adult physical activity in order to best tailor physical activity-promotion strategies. The time- and resource-intensive nature of studies spanning childhood into adulthood means the understanding of physical activity trajectories over this time span is limited. This study aimed to determine whether childhood domain-specific physical activities predict domain-specific physical activity 20 years later in adulthood, and whether age and sex play a role in these trajectories. In 1985, 6412 children of age 9-15 years self-reported frequency and duration of discretionary sport and exercise (leisure activity), transport activity, school sport and physical education (PE) in the past week and number of sports played in the past year. In 2004-2006, 2201 of these participants (aged 26-36 years) completed the long International Physical Activity Questionnaire and/or wore a Yamax pedometer. Analyses included partial correlation coefficients and log-binomial regression. Childhood and adult activity were weakly correlated (r=-0.08-0.14). Total weekly physical activity in childhood did not predict adult activity. School PE predicted adult total weekly physical activity and daily steps (older females), while school sport demonstrated inconsistent associations. Leisure and transport activity in childhood predicted adult leisure activity among younger males and older females, respectively. Childhood past year sport participation positively predicted adult physical activity (younger males and older females). Despite modest associations between childhood and adult physical activity that varied by domain, age and sex, promoting a range of physical activities to children of all ages is warranted.

The status of perineural invasion predicts the outcomes of postoperative radiotherapy in locally advanced esophageal squamous cell carcinoma.

PubMed

Ning, Zhong-Hua; Zhao, Wei; Li, Xiao-Dong; Chen, Lu-Jun; Xu, Bin; Gu, Wen-Dong; Shao, Ying-Jie; Xu, Yun; Huang, Jin; Pei, Hong-Lei; Jiang, Jing-Ting

2015-01-01

Prognosis of locally advanced esophageal squamous cell carcinoma (ESCC) remains dismal even after curative resection and adjuvant radiotherapy. New biomarkers for predicting prognosis and treatment outcomes are needed for improved treatment stratification of patients with locally advanced ESCC. The prognostic and treatment predictive significance of perineural invasion (PNI) in the locally advanced ESCC remains unclear. This study aimed to examine the effect of PNI on the outcomes of locally advanced ESCC patients after curative resection with or without postoperative radiotherapy (PORT). We retrospectively reviewed 262 consecutive locally advanced ESCC patients who underwent curative resection. Tumors sections were re-evaluated for PNI by an independent pathologist blinded to the patients' outcomes. Overall survival (OS) and disease-free survival (DFS) were determined using the Kaplan-Meier method; univariate log-rank test and multivariate Cox proportional hazard model were used to evaluate the prognostic value of PNI. Finally, 243 patients were analyzed and enrolled into this study, of which 132 received PORT. PNI was identified in 22.2% (54/243) of the pathologic sections. The 5-year DFS was favorable for PNI-negative patients versus PNI-positive patients (21.3% vs. 36.7%, respectively; P = 0.005). The 5-year OS was 40.3% for PNI-negative patients versus 21.7% for PNI-positive patients (P < 0.001). On multivariate analysis, PNI was an independent prognostic factor. In a subset analysis for patients received PORT, PNI was evaluated as a prognostic predictor as well (P < 0.05). In contrast to patients without PORT, PORT couldn't improve the disease recurrence and survival in locally advanced ESCC patients with PNI-positive (P > 0.05). PNI could serve as an independent prognostic factor and prognosticate treatment outcomes in locally advanced ESCC patients. The PNI status should be considered when stratifying high-risk locally advanced ESCC patients for adjuvant

EarthCube Activities: Community Engagement Advancing Geoscience Research

NASA Astrophysics Data System (ADS)

Kinkade, D.

2015-12-01

Our ability to advance scientific research in order to better understand complex Earth systems, address emerging geoscience problems, and meet societal challenges is increasingly dependent upon the concept of Open Science and Data. Although these terms are relatively new to the world of research, Open Science and Data in this context may be described as transparency in the scientific process. This includes the discoverability, public accessibility and reusability of scientific data, as well as accessibility and transparency of scientific communication (www.openscience.org). Scientists and the US government alike are realizing the critical need for easy discovery and access to multidisciplinary data to advance research in the geosciences. The NSF-supported EarthCube project was created to meet this need. EarthCube is developing a community-driven common cyberinfrastructure for the purpose of accessing, integrating, analyzing, sharing and visualizing all forms of data and related resources through advanced technological and computational capabilities. Engaging the geoscience community in EarthCube's development is crucial to its success, and EarthCube is providing several opportunities for geoscience involvement. This presentation will provide an overview of the activities EarthCube is employing to entrain the community in the development process, from governance development and strategic planning, to technical needs gathering. Particular focus will be given to the collection of science-driven use cases as a means of capturing scientific and technical requirements. Such activities inform the development of key technical and computational components that collectively will form a cyberinfrastructure to meet the research needs of the geoscience community.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma.

PubMed

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-03-30

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration-time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters ( ABCB1 and ABCG2 ), UGT1A , and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate ( P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC ( P < 0.0001), and correctly predicted objective response rate ( P = 0.0044) as well as adverse events ( P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma

PubMed Central

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-01-01

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration–time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters (ABCB1 and ABCG2), UGT1A, and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate (P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC (P < 0.0001), and correctly predicted objective response rate (P = 0.0044) as well as adverse events (P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment (P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC. PMID:29682213

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.

PubMed

Vishnepolsky, Boris; Gabrielian, Andrei; Rosenthal, Alex; Hurt, Darrell E; Tartakovsky, Michael; Managadze, Grigol; Grigolava, Maya; Makhatadze, George I; Pirtskhalava, Malak

2018-05-29

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

A hybrid numerical technique for predicting the aerodynamic and acoustic fields of advanced turboprops

NASA Technical Reports Server (NTRS)

Homicz, G. F.; Moselle, J. R.

1985-01-01

A hybrid numerical procedure is presented for the prediction of the aerodynamic and acoustic performance of advanced turboprops. A hybrid scheme is proposed which in principle leads to a consistent simultaneous prediction of both fields. In the inner flow a finite difference method, the Approximate-Factorization Alternating-Direction-Implicit (ADI) scheme, is used to solve the nonlinear Euler equations. In the outer flow the linearized acoustic equations are solved via a Boundary-Integral Equation (BIE) method. The two solutions are iteratively matched across a fictitious interface in the flow so as to maintain continuity. At convergence the resulting aerodynamic load prediction will automatically satisfy the appropriate free-field boundary conditions at the edge of the finite difference grid, while the acoustic predictions will reflect the back-reaction of the radiated field on the magnitude of the loading source terms, as well as refractive effects in the inner flow. The equations and logic needed to match the two solutions are developed and the computer program implementing the procedure is described. Unfortunately, no converged solutions were obtained, due to unexpectedly large running times. The reasons for this are discussed and several means to alleviate the situation are suggested.

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.

PubMed

Oguz, Cihan; Sen, Shurjo K; Davis, Adam R; Fu, Yi-Ping; O'Donnell, Christopher J; Gibbons, Gary H

2017-10-26

One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89 th -99 th CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC). RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and "vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs. We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease

Predicting forest insect flight activity: A Bayesian network approach

PubMed Central

Pawson, Stephen M.; Marcot, Bruce G.; Woodberry, Owen G.

2017-01-01

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways. PMID:28953904

Improvement of Advanced Storm-scale Analysis and Prediction System (ASAPS) on Seoul Metropolitan Area, Korea

NASA Astrophysics Data System (ADS)

Park, Jeong-Gyun; Jee, Joon-Bum

2017-04-01

Dangerous weather such as severe rain, heavy snow, drought and heat wave caused by climate change make more damage in the urban area that dense populated and industry areas. Urban areas, unlike the rural area, have big population and transportation, dense the buildings and fuel consumption. Anthropogenic factors such as road energy balance, the flow of air in the urban is unique meteorological phenomena. However several researches are in process about prediction of urban meteorology. ASAPS (Advanced Storm-scale Analysis and Prediction System) predicts a severe weather with very short range (prediction with 6 hour) and high resolution (every hour with time and 1 km with space) on Seoul metropolitan area based on KLAPS (Korea Local Analysis and Prediction System) from KMA (Korea Meteorological Administration). This system configured three parts that make a background field (SUF5), analysis field (SU01) with observation and forecast field with high resolution (SUF1). In this study, we improve a high-resolution ASAPS model and perform a sensitivity test for the rainfall case. The improvement of ASAPS include model domain configuration, high resolution topographic data and data assimilation with WISE observation data.

Prediction Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the

Prestimulus brain activity predicts primacy in list learning

PubMed Central

Galli, Giulia; Choy, Tsee Leng; Otten, Leun J.

2012-01-01

Brain activity immediately before an event can predict whether the event will later be remembered. This indicates that memory formation is influenced by anticipatory mechanisms engaged ahead of stimulus presentation. Here, we asked whether anticipatory processes affect the learning of short word lists, and whether such activity varies as a function of serial position. Participants memorized lists of intermixed visual and auditory words with either an elaborative or rote rehearsal strategy. At the end of each list, a distraction task was performed followed by free recall. Recall performance was better for words in initial list positions and following elaborative rehearsal. Electrical brain activity before auditory words predicted later recall in the elaborative rehearsal condition. Crucially, anticipatory activity only affected recall when words occurred in initial list positions. This indicates that anticipatory processes, possibly related to general semantic preparation, contribute to primacy effects. PMID:22888370

Advances in the treatment of polyarticular juvenile idiopathic arthritis

PubMed Central

Webb, Kate; Wedderburn, Lucy R.

2015-01-01

Purpose of review To review recent advances in the management strategies of polyarticular course juvenile idiopathic arthritis (JIA) and identify unanswered questions and avenues for further research. Recent findings There is evidence for an early, aggressive, treat-to-target approach for polyarticular JIA. Clinical disease activity criteria have been recently defined and validated, including criteria for inactive disease and the juvenile arthritis disease activity score (JADAS). There is a need for evidence-based, defined disease targets and biomarkers for prediction of response, including targets for remission induction, and guidelines on drug withdrawal. Recent treatment consensus plans and guidelines are discussed and compared, including the 2015 NHS England clinical policy statement, the 2014 Childhood Arthritis and Rheumatology Research Alliance (CARRA) treatment plans and the 2011 American College of Rheumatology (ACR) guidelines. Evidence for new agents such as tocilizumab, rituximab, golimumab, ustekinumab, certolizumab and tofacitinib is promising: the recent clinical trials are summarized here. Stratification of individual patient treatment remains a goal, and predictive biomarkers have been shown to predict success in the withdrawal of methotrexate therapy. Summary There are promising advances in the treatment approaches, disease activity criteria, clinical guidelines, pharmaceutical choices and individually stratified therapy choices for polyarticular JIA. PMID:26147756

Integrated Application of Active Controls (IAAC) technology to an advanced subsonic transport project: Current and advanced act control system definition study

NASA Technical Reports Server (NTRS)

1982-01-01

The Current and Advanced Technology ACT control system definition tasks of the Integrated Application of Active Controls (IAAC) Technology project within the Energy Efficient Transport Program are summarized. The systems mechanize six active control functions: (1) pitch augmented stability; (2) angle of attack limiting; (3) lateral/directional augmented stability; (4) gust load alleviation; (5) maneuver load control; and (6) flutter mode control. The redundant digital control systems meet all function requirements with required reliability and declining weight and cost as advanced technology is introduced.

Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

PubMed

Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

2017-07-01

Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p < .001). Elevated caudate activation was associated with robust improvement for methylphenidate and little improvement for atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p < .001). Enhanced caudate activation for response inhibition may be a candidate biomarker of superior response to methylphenidate over atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American

[The survival prediction model of advanced gallbladder cancer based on Bayesian network: a multi-institutional study].

PubMed

Tang, Z H; Geng, Z M; Chen, C; Si, S B; Cai, Z Q; Song, T Q; Gong, P; Jiang, L; Qiu, Y H; He, Y; Zhai, W L; Li, S P; Zhang, Y C; Yang, Y

2018-05-01

Objective: To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery. Methods: The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test. Results: A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8

Advances and Challenges In Uncertainty Quantification with Application to Climate Prediction, ICF design and Science Stockpile Stewardship

NASA Astrophysics Data System (ADS)

Klein, R.; Woodward, C. S.; Johannesson, G.; Domyancic, D.; Covey, C. C.; Lucas, D. D.

2012-12-01

Uncertainty Quantification (UQ) is a critical field within 21st century simulation science that resides at the very center of the web of emerging predictive capabilities. The science of UQ holds the promise of giving much greater meaning to the results of complex large-scale simulations, allowing for quantifying and bounding uncertainties. This powerful capability will yield new insights into scientific predictions (e.g. Climate) of great impact on both national and international arenas, allow informed decisions on the design of critical experiments (e.g. ICF capsule design, MFE, NE) in many scientific fields, and assign confidence bounds to scientifically predictable outcomes (e.g. nuclear weapons design). In this talk I will discuss a major new strategic initiative (SI) we have developed at Lawrence Livermore National Laboratory to advance the science of Uncertainty Quantification at LLNL focusing in particular on (a) the research and development of new algorithms and methodologies of UQ as applied to multi-physics multi-scale codes, (b) incorporation of these advancements into a global UQ Pipeline (i.e. a computational superstructure) that will simplify user access to sophisticated tools for UQ studies as well as act as a self-guided, self-adapting UQ engine for UQ studies on extreme computing platforms and (c) use laboratory applications as a test bed for new algorithms and methodologies. The initial SI focus has been on applications for the quantification of uncertainty associated with Climate prediction, but the validated UQ methodologies we have developed are now being fed back into Science Based Stockpile Stewardship (SSS) and ICF UQ efforts. To make advancements in several of these UQ grand challenges, I will focus in talk on the following three research areas in our Strategic Initiative: Error Estimation in multi-physics and multi-scale codes ; Tackling the "Curse of High Dimensionality"; and development of an advanced UQ Computational Pipeline to enable

Autonomous Motivation Predicts 7-Day Physical Activity in Hong Kong Students.

PubMed

Ha, Amy S; Ng, Johan Y Y

2015-07-01

Autonomous motivation predicts positive health behaviors such as physical activity. However, few studies have examined the relation between motivational regulations and objectively measured physical activity and sedentary behaviors. Thus, we investigated whether different motivational regulations (autonomous motivation, controlled motivation, and amotivation) predicted 7-day physical activity, sedentary behaviors, and health-related quality of life (HRQoL) of students. A total of 115 students (mean age = 11.6 years, 55.7% female) self-reported their motivational regulations and health-related quality of life. Physical activity and sedentary behaviors were measured using accelerometers for seven days. Using multilevel modeling, we found that autonomous motivation predicted higher levels of moderate-to-vigorous physical activity, less sedentary behaviors, and better HRQoL. Controlled motivation and amotivation each only negatively predicted one facet of HRQoL. Results suggested that autonomous motivation could be an important predictor of physical activity behaviors in Hong Kong students. Promotion of this form of motivational regulation may also increase HRQoL. © 2015 The International Association of Applied Psychology.

Safety and activity of PD-1 blockade-activated DC-CIK cells in patients with advanced solid tumors.

PubMed

Chen, Chang-Long; Pan, Qiu-Zhong; Weng, De-Sheng; Xie, Chuan-Miao; Zhao, Jing-Jing; Chen, Min-Shan; Peng, Rui-Qing; Li, Dan-Dan; Wang, Ying; Tang, Yan; Wang, Qi-Jing; Zhang, Zhi-Ling; Zhang, Xiao-Fei; Jiang, Li-Juan; Zhou, Zi-Qi; Zhu, Qian; He, Jia; Liu, Yuan; Zhou, Fang-Jian; Xia, Jian-Chuan

2018-01-01

Cytokine-induced killer (CIK) cells that are stimulated using mature dendritic cells (DCs), referred to as (DC-CIK cells) exhibit superior anti-tumor potency. Anti-programmed death-1 (PD-1) antibodies reinvigorate T cell-mediated antitumor immunity. This phase I study aimed to assess the safety and clinical activity of immunotherapy with PD-1 blockade (pembrolizumab)-activated autologous DC-CIK cells in patients with advanced solid tumors. Patients with selected types of advanced solid tumors received a single intravenous infusion of activated autologous DC-CIK cells weekly for the first month and every 2 weeks thereafter. The primary end points were safety and adverse event (AE) profiles. Antitumor responses, overall survival (OS), progression-free survival (PFS) and cytolytic activity were secondary end points. Treatment-related AEs occurred in 20/31 patients. Grade 3 or 4 toxicities, including fever and chills, were observed in two patients. All treatment-related AEs were reversible or controllable. The cytotoxicity of DC-CIK cells induced up-regulation of PD-L1 expression on autologous tumor cells. When activated using pembrolizumab ex vivo , DC-CIK cells exerted superior antitumor properties and elevated IFN-γ secretion. Objective responses (complete or partial responses) were observed in 7 of the 31patients.These responses were durable, with 6 of 7 responses lasting more than 5 months. The overall disease control rate in the patients was 64.5%. At the time of this report, the median OS and PFS were 270 and 162 days, respectively. In conclusions, treatment with pembrolizumab-activated autologous DC-CIK cells was safe and exerted encouraging antitumor activity in advanced solid tumors. A larger phase II trial is warranted.

Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables.

PubMed

Seddon, Johanna M; Reynolds, Robyn; Maller, Julian; Fagerness, Jesen A; Daly, Mark J; Rosner, Bernard

2009-05-01

The joint effects of genetic, ocular, and environmental variables were evaluated and predictive models for prevalence and incidence of AMD were assessed. Participants in the multicenter Age-Related Eye Disease Study (AREDS) were included in a prospective evaluation of 1446 individuals, of which 279 progressed to advanced AMD (geographic atrophy or neovascular disease) and 1167 did not progress during 6.3 years of follow-up. For prevalent AMD, 509 advanced cases were compared with 222 controls. Covariates for the incidence analysis included age, sex, education, smoking, body mass index (BMI), baseline AMD grade, and the AREDS vitamin-mineral treatment assignment. DNA specimens were evaluated for six variants in five genes related to AMD. Unconditional logistic regression analyses were performed for prevalent and incident advanced AMD. An algorithm was developed and receiver operating characteristic curves and C statistics were calculated to assess the predictive ability of risk scores to discriminate progressors from nonprogressors. All genetic polymorphisms were independently related to prevalence of advanced AMD, controlling for genetic factors, smoking, BMI, and AREDS treatment. Multivariate odds ratios (ORs) were 3.5 (95% confidence interval [CI], 1.7-7.1) for CFH Y402H; 3.7 (95% CI, 1.6-8.4) for CFH rs1410996; 25.4 (95% CI, 8.6-75.1) for LOC387715 A69S (ARMS2); 0.3 (95% CI, 0.1-0.7) for C2 E318D; 0.3 (95% CI, 0.1-0.5) for CFB; and 3.6 (95% CI, 1.4-9.4) for C3 R102G, comparing the homozygous risk/protective genotypes to the referent genotypes. For incident AMD, all these variants except CFB were significantly related to progression to advanced AMD, after controlling for baseline AMD grade and other factors, with ORs from 1.8 to 4.0 for presence of two risk alleles and 0.4 for the protective allele. An interaction was seen between CFH402H and treatment, after controlling for all genotypes. Smoking was independently related to AMD, with a multiplicative joint

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

NASA Technical Reports Server (NTRS)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

The Accuracy of Physicians' Clinical Predictions of Survival in Patients With Advanced Cancer.

PubMed

Amano, Koji; Maeda, Isseki; Shimoyama, Satofumi; Shinjo, Takuya; Shirayama, Hiroto; Yamada, Takeshi; Ono, Shigeki; Yamamoto, Ryo; Yamamoto, Naoki; Shishido, Hideki; Shimizu, Mie; Kawahara, Masanori; Aoki, Shigeru; Demizu, Akira; Goshima, Masahiro; Goto, Keiji; Gyoda, Yasuaki; Hashimoto, Kotaro; Otomo, Sen; Sekimoto, Masako; Shibata, Takemi; Sugimoto, Yuka; Morita, Tatsuya

2015-08-01

Accurate prognoses are needed for patients with advanced cancer. To evaluate the accuracy of physicians' clinical predictions of survival (CPS) and assess the relationship between CPS and actual survival (AS) in patients with advanced cancer in palliative care units, hospital palliative care teams, and home palliative care services, as well as those receiving chemotherapy. This was a multicenter prospective cohort study conducted in 58 palliative care service centers in Japan. The palliative care physicians evaluated patients on the first day of admission and followed up all patients to their death or six months after enrollment. We evaluated the accuracy of CPS and assessed the relationship between CPS and AS in the four groups. We obtained a total of 2036 patients: 470, 764, 404, and 398 in hospital palliative care teams, palliative care units, home palliative care services, and chemotherapy, respectively. The proportion of accurate CPS (0.67-1.33 times AS) was 35% (95% CI 33-37%) in the total sample and ranged from 32% to 39% in each setting. While the proportion of patients living longer than CPS (pessimistic CPS) was 20% (95% CI 18-22%) in the total sample, ranging from 15% to 23% in each setting, the proportion of patients living shorter than CPS (optimistic CPS) was 45% (95% CI 43-47%) in the total sample, ranging from 43% to 49% in each setting. Physicians tend to overestimate when predicting survival in all palliative care patients, including those receiving chemotherapy. Copyright © 2015 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Prediction of Geomagnetic Activity and Key Parameters in High-Latitude Ionosphere-Basic Elements

NASA Technical Reports Server (NTRS)

Lyatsky, W.; Khazanov, G. V.

2007-01-01

Prediction of geomagnetic activity and related events in the Earth's magnetosphere and ionosphere is an important task of the Space Weather program. Prediction reliability is dependent on the prediction method and elements included in the prediction scheme. Two main elements are a suitable geomagnetic activity index and coupling function -- the combination of solar wind parameters providing the best correlation between upstream solar wind data and geomagnetic activity. The appropriate choice of these two elements is imperative for any reliable prediction model. The purpose of this work was to elaborate on these two elements -- the appropriate geomagnetic activity index and the coupling function -- and investigate the opportunity to improve the reliability of the prediction of geomagnetic activity and other events in the Earth's magnetosphere. The new polar magnetic index of geomagnetic activity and the new version of the coupling function lead to a significant increase in the reliability of predicting the geomagnetic activity and some key parameters, such as cross-polar cap voltage and total Joule heating in high-latitude ionosphere, which play a very important role in the development of geomagnetic and other activity in the Earth s magnetosphere, and are widely used as key input parameters in modeling magnetospheric, ionospheric, and thermospheric processes.

APPLICATION OF ADVANCED IN VITRO TECHNIQUES TO MEASURE, UNDERSTAND AND PREDICT THE KINETICS AND MECHANISMS OF XENOBIOTIC METABOLISM

EPA Science Inventory

We have developed a research program in metabolism that involves numerous collaborators across EPA as well as other federal and academic labs. A primary goal is to develop and apply advanced in vitro techniques to measure, understand and predict the kinetics and mechanisms of xen...

Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

PubMed

Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

2013-12-01

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

Have We Entered a 21st Century Prolonged Minimum of Solar Activity? Updated Implications of a 1987 Prediction

NASA Astrophysics Data System (ADS)

Shirley, James H.

2009-05-01

Fairbridge and Shirley (1987) predicted that a new prolonged minimum of solar activity would be underway by the year 2013 (Solar Physics 110, 191). While it is much too early to tell if this prediction will be fully realized, recent observations document a striking reduction in the Sun's general level of activity. While other forecasts of reduced future activity levels on decadal time scales have appeared, the Fairbridge-Shirley (FS) prediction is unique in pinpointing the current epoch. We are unaware of any forecast method that shows a better correspondence with the actual behavior of the Sun to this point. The FS prediction was based on the present-day recurrence of two physical indicators that were correlated in time with the occurrence of the Wolf, Sporer, and Maunder Minima. The amplitude of the inertial revolution of the axis of symmetry of the Sun's orbital motion about the solar system barycenter, and the direction in space of that axis, each bear a relationship to the occurrence of the prolonged minima of the historic record. The FS prediction appeared before the importance of solar meridional flows was generally appreciated, and before the existence and role of the tachocline was suspected. We will update and restate some of the physical implications of the FS results, along with those of some more recent investigations, particularly with reference to orbit-spin coupling hypotheses (Shirley, 2006: M.N.R.A.S. 368, 280). New investigations combining and integrating modern dynamo models with physical solutions describing key aspects of the variability of the solar motion may lead to significant advances in our ability to forecast future changes in the Sun. Acknowledgement: This work was supported by the resources of the author. No part of this work was performed at the Jet Propulsion Laboratory under a contract from NASA.

Advancing decadal-scale climate prediction in the North Atlantic sector.

PubMed

Keenlyside, N S; Latif, M; Jungclaus, J; Kornblueh, L; Roeckner, E

2008-05-01

The climate of the North Atlantic region exhibits fluctuations on decadal timescales that have large societal consequences. Prominent examples include hurricane activity in the Atlantic, and surface-temperature and rainfall variations over North America, Europe and northern Africa. Although these multidecadal variations are potentially predictable if the current state of the ocean is known, the lack of subsurface ocean observations that constrain this state has been a limiting factor for realizing the full skill potential of such predictions. Here we apply a simple approach-that uses only sea surface temperature (SST) observations-to partly overcome this difficulty and perform retrospective decadal predictions with a climate model. Skill is improved significantly relative to predictions made with incomplete knowledge of the ocean state, particularly in the North Atlantic and tropical Pacific oceans. Thus these results point towards the possibility of routine decadal climate predictions. Using this method, and by considering both internal natural climate variations and projected future anthropogenic forcing, we make the following forecast: over the next decade, the current Atlantic meridional overturning circulation will weaken to its long-term mean; moreover, North Atlantic SST and European and North American surface temperatures will cool slightly, whereas tropical Pacific SST will remain almost unchanged. Our results suggest that global surface temperature may not increase over the next decade, as natural climate variations in the North Atlantic and tropical Pacific temporarily offset the projected anthropogenic warming.

Predicted reliability of aerospace electronics: Application of two advanced probabilistic concepts

NASA Astrophysics Data System (ADS)

Suhir, E.

Two advanced probabilistic design-for-reliability (PDfR) concepts are addressed and discussed in application to the prediction, quantification and assurance of the aerospace electronics reliability: 1) Boltzmann-Arrhenius-Zhurkov (BAZ) model, which is an extension of the currently widely used Arrhenius model and, in combination with the exponential law of reliability, enables one to obtain a simple, easy-to-use and physically meaningful formula for the evaluation of the probability of failure (PoF) of a material or a device after the given time in operation at the given temperature and under the given stress (not necessarily mechanical), and 2) Extreme Value Distribution (EVD) technique that can be used to assess the number of repetitive loadings that result in the material/device degradation and eventually lead to its failure by closing, in a step-wise fashion, the gap between the bearing capacity (stress-free activation energy) of the material or the device and the demand (loading). It is shown that the material degradation (aging, damage accumulation, flaw propagation, etc.) can be viewed, when BAZ model is considered, as a Markovian process, and that the BAZ model can be obtained as the ultimate steady-state solution to the well-known Fokker-Planck equation in the theory of Markovian processes. It is shown also that the BAZ model addresses the worst, but a reasonably conservative, situation. It is suggested therefore that the transient period preceding the condition addressed by the steady-state BAZ model need not be accounted for in engineering evaluations. However, when there is an interest in understanding the transient degradation process, the obtained solution to the Fokker-Planck equation can be used for this purpose. As to the EVD concept, it attributes the degradation process to the accumulation of damages caused by a train of repetitive high-level loadings, while loadings of levels that are considerably lower than their extreme values do not contribute

In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

PubMed

Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S

2015-01-01

The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

Predictive value of diminutive colonic adenoma trial: the PREDICT trial.

PubMed

Schoenfeld, Philip; Shad, Javaid; Ormseth, Eric; Coyle, Walter; Cash, Brooks; Butler, James; Schindler, William; Kikendall, Walter J; Furlong, Christopher; Sobin, Leslie H; Hobbs, Christine M; Cruess, David; Rex, Douglas

2003-05-01

Diminutive adenomas (1-9 mm in diameter) are frequently found during colon cancer screening with flexible sigmoidoscopy (FS). This trial assessed the predictive value of these diminutive adenomas for advanced adenomas in the proximal colon. In a multicenter, prospective cohort trial, we matched 200 patients with normal FS and 200 patients with diminutive adenomas on FS for age and gender. All patients underwent colonoscopy. The presence of advanced adenomas (adenoma >or= 10 mm in diameter, villous adenoma, adenoma with high grade dysplasia, and colon cancer) and adenomas (any size) was recorded. Before colonoscopy, patients completed questionnaires about risk factors for adenomas. The prevalence of advanced adenomas in the proximal colon was similar in patients with diminutive adenomas and patients with normal FS (6% vs. 5.5%, respectively) (relative risk, 1.1; 95% confidence interval [CI], 0.5-2.6). Diminutive adenomas on FS did not accurately predict advanced adenomas in the proximal colon: sensitivity, 52% (95% CI, 32%-72%); specificity, 50% (95% CI, 49%-51%); positive predictive value, 6% (95% CI, 4%-8%); and negative predictive value, 95% (95% CI, 92%-97%). Male gender (odds ratio, 1.63; 95% CI, 1.01-2.61) was associated with an increased risk of proximal colon adenomas. Diminutive adenomas on sigmoidoscopy may not accurately predict advanced adenomas in the proximal colon.

A DNA Sequence Element That Advances Replication Origin Activation Time in Saccharomyces cerevisiae

PubMed Central

Pohl, Thomas J.; Kolor, Katherine; Fangman, Walton L.; Brewer, Bonita J.; Raghuraman, M. K.

2013-01-01

Eukaryotic origins of DNA replication undergo activation at various times in S-phase, allowing the genome to be duplicated in a temporally staggered fashion. In the budding yeast Saccharomyces cerevisiae, the activation times of individual origins are not intrinsic to those origins but are instead governed by surrounding sequences. Currently, there are two examples of DNA sequences that are known to advance origin activation time, centromeres and forkhead transcription factor binding sites. By combining deletion and linker scanning mutational analysis with two-dimensional gel electrophoresis to measure fork direction in the context of a two-origin plasmid, we have identified and characterized a 19- to 23-bp and a larger 584-bp DNA sequence that are capable of advancing origin activation time. PMID:24022751

A DNA sequence element that advances replication origin activation time in Saccharomyces cerevisiae.

PubMed

Pohl, Thomas J; Kolor, Katherine; Fangman, Walton L; Brewer, Bonita J; Raghuraman, M K

2013-11-06

Eukaryotic origins of DNA replication undergo activation at various times in S-phase, allowing the genome to be duplicated in a temporally staggered fashion. In the budding yeast Saccharomyces cerevisiae, the activation times of individual origins are not intrinsic to those origins but are instead governed by surrounding sequences. Currently, there are two examples of DNA sequences that are known to advance origin activation time, centromeres and forkhead transcription factor binding sites. By combining deletion and linker scanning mutational analysis with two-dimensional gel electrophoresis to measure fork direction in the context of a two-origin plasmid, we have identified and characterized a 19- to 23-bp and a larger 584-bp DNA sequence that are capable of advancing origin activation time.

Advancement in Watershed Modelling Using Dynamic Lateral and Longitudinal Sediment (Dis)connectivity Prediction

NASA Astrophysics Data System (ADS)

Mahoney, D. T.; al Aamery, N. M. H.; Fox, J.

2017-12-01

The authors find that sediment (dis)connectivity has seldom taken precedence within watershed models, and the present study advances this modeling framework and applies the modeling within a bedrock-controlled system. Sediment (dis)connectivity, defined as the detachment and transport of sediment from source to sink between geomorphic zones, is a major control on sediment transport. Given the availability of high resolution geospatial data, coupling sediment connectivity concepts within sediment prediction models offers an approach to simulate sediment sources and pathways within a watershed's sediment cascade. Bedrock controlled catchments are potentially unique due to the presence of rock outcrops causing longitudinal impedance to sediment transport pathways in turn impacting the longitudinal distribution of the energy gradient responsible for conveying sediment. Therefore, the authors were motivated by the need to formulate a sediment transport model that couples sediment (dis)connectivity knowledge to predict sediment flux for bedrock controlled catchments. A watershed-scale sediment transport model was formulated that incorporates sediment (dis)connectivity knowledge collected via field reconnaissance and predicts sediment flux through coupling with the Partheniades equation and sediment continuity model. Sediment (dis)connectivity was formulated by coupling probabilistic upland lateral connectivity prediction with instream longitudinal connectivity assessments via discretization of fluid and sediment pathways. Flux predictions from the upland lateral connectivity model served as an input to the instream longitudinal connectivity model. Disconnectivity in the instream model was simulated via the discretization of stream reaches due to barriers such as bedrock outcroppings and man-made check dams. The model was tested for a bedrock controlled catchment in Kentucky, USA for which extensive historic water and sediment flux data was available. Predicted sediment

Predicting Physical Activity in Arab American School Children

ERIC Educational Resources Information Center

Martin, Jeffrey J.; McCaughtry, Nate; Shen, Bo

2008-01-01

Theoretically grounded research on the determinants of Arab American children's physical activity is virtually nonexistent. Thus, the purpose of our investigation was to evaluate the ability of the theory of planned behavior (TPB) and social cognitive theory (SCT) to predict Arab American children's moderate-to-vigorous physical activity (MVPA).…

Active Vibration Reduction of the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Metscher, Jonathan F.; Schifer, Nicholas A.

2016-01-01

Stirling Radioisotope Power Systems (RPS) are being developed as an option to provide power on future space science missions where robotic spacecraft will orbit, flyby, land or rove. A Stirling Radioisotope Generator (SRG) could offer space missions a more efficient power system that uses one fourth of the nuclear fuel and decreases the thermal footprint compared to the current state of the art. The Stirling Cycle Technology Development (SCTD) Project is funded by the RPS Program to developing Stirling-based subsystems, including convertors and controller maturation efforts that have resulted in high fidelity hardware like the Advanced Stirling Radioisotope Generator (ASRG), Advanced Stirling Convertor (ASC), and ASC Controller Unit (ACU). The SCTD Project also performs research to develop less mature technologies with a wide variety of objectives, including increasing temperature capability to enable new environments, improving system reliability or fault tolerance, reducing mass or size, and developing advanced concepts that are mission enabling. Active vibration reduction systems (AVRS), or "balancers", have historically been developed and characterized to provide fault tolerance for generator designs that incorporate dual-opposed Stirling convertors or enable single convertor, or small RPS, missions. Balancers reduce the dynamic disturbance forces created by the power piston and displacer internal moving components of a single operating convertor to meet spacecraft requirements for induced disturbance force. To improve fault tolerance for dual-opposed configurations and enable single convertor configurations, a breadboard AVRS was implemented on the Advanced Stirling Convertor (ASC). The AVRS included a linear motor, a motor mount, and a closed-loop controller able to balance out the transmitted peak dynamic disturbance using acceleration feedback. Test objectives included quantifying power and mass penalty and reduction in transmitted force over a range of ASC

Active Vibration Reduction of the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Metscher, Jonathan F.; Schifer, Nicholas A.

2016-01-01

Stirling Radioisotope Power Systems (RPS) are being developed as an option to provide power on future space science missions where robotic spacecraft will orbit, flyby, land or rove. A Stirling Radioisotope Generator (SRG) could offer space missions a more efficient power system that uses one fourth of the nuclear fuel and decreases the thermal footprint compared to the current state of the art. The Stirling Cycle Technology Development (SCTD) Project is funded by the RPS Program to developing Stirling-based subsystems, including convertors and controller maturation efforts that have resulted in high fidelity hardware like the Advanced Stirling Radioisotope Generator (ASRG), Advanced Stirling Convertor (ASC), and ASC Controller Unit (ACU). The SCTD Project also performs research to develop less mature technologies with a wide variety of objectives, including increasing temperature capability to enable new environments, improving system reliability or fault tolerance, reducing mass or size, and developing advanced concepts that are mission enabling. Active vibration reduction systems (AVRS), or "balancers", have historically been developed and characterized to provide fault tolerance for generator designs that incorporate dual-opposed Stirling convertors or enable single convertor, or small RPS, missions. Balancers reduce the dynamic disturbance forces created by the power piston and displacer internal moving components of a single operating convertor to meet spacecraft requirements for induced disturbance force. To improve fault tolerance for dual-opposed configurations and enable single convertor configurations, a breadboard AVRS was implemented on the Advanced Stirling Convertor (ASC). The AVRS included a linear motor, a motor mount, and a closed-loop controller able to balance out the transmitted peak dynamic disturbance using acceleration feedback. Test objectives included quantifying power and mass penalty and reduction in transmitted force over a range of ASC

Evaluation of Instrumental Activities of Daily Living in Greek Patients with Advanced Cancer

ERIC Educational Resources Information Center

Mystakidou, Kyriaki; Parpa, Efi; Tsilika, Eleni; Panagiotoua, Irene; Roumeliotou, Anna; Symeonidi, Matina; Galanos, Antonis; Kouvaris, Ioannis

2013-01-01

Translation of the instrumental activities of daily living (IADL) was carried out and its psychometric properties were assessed in a Greek sample of patients with advanced cancer. The scale was translated with the forward-backward procedure into the Greek language. It was initially administered to 136 advanced cancer patients. To assess…

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

PubMed

James, Matthew T; Pannu, Neesh; Hemmelgarn, Brenda R; Austin, Peter C; Tan, Zhi; McArthur, Eric; Manns, Braden J; Tonelli, Marcello; Wald, Ron; Quinn, Robert R; Ravani, Pietro; Garg, Amit X

2017-11-14

Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Demographic, laboratory, and comorbidity variables measured prior to discharge. Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher

Predicting vigorous physical activity using social cognitive theory.

PubMed

Petosa, R Lingyak; Suminski, Rick; Hortz, Brian

2003-01-01

To test Social Cognitive Theory (SCT) in predicting future vigorous physical activity among college students. College students (n=350) completed a set of instruments measuring SCT constructs. Their vigorous physical activity was tracked for 4 weeks. Exercise role identity, self-regulation, outcome expectancy value, social support, self-efficacy, and positive exercise experience accounted for 27% of the variance in days of vigorous physical activity. The results supported the use of SCT in understanding factors associated with vigorous physical activity rates among college students.

Online communication predicts Belgian adolescents' initiation of romantic and sexual activity.

PubMed

Vandenbosch, Laura; Beyens, Ine; Vangeel, Laurens; Eggermont, Steven

2016-04-01

Online communication is associated with offline romantic and sexual activity among college students. Yet, it is unknown whether online communication is associated with the initiation of romantic and sexual activity among adolescents. This two-wave panel study investigated whether chatting, visiting dating websites, and visiting erotic contact websites predicted adolescents' initiation of romantic and sexual activity. We analyzed two-wave panel data from 1163 Belgian adolescents who participated in the MORES Study. We investigated the longitudinal impact of online communication on the initiation of romantic relationships and sexual intercourse using logistic regression analyses. The odds ratios of initiating a romantic relationship among romantically inexperienced adolescents who frequently used chat rooms, dating websites, or erotic contact websites were two to three times larger than those of non-users. Among sexually inexperienced adolescents who frequently used chat rooms, dating websites, or erotic contact websites, the odds ratios of initiating sexual intercourse were two to five times larger than that among non-users, even after a number of other relevant factors were introduced. This is the first study to demonstrate that online communication predicts the initiation of offline sexual and romantic activity as early as adolescence. Practitioners and parents need to consider the role of online communication in adolescents' developing sexuality. • Adolescents increasingly communicate online with peers. • Online communication predicts romantic and sexual activity among college students. What is New: • Online communication predicts adolescents' offline romantic activity over time. • Online communication predicts adolescents' offline sexual activity over time.

Caregiver Activation and Home Hospice Nurse Communication in Advanced Cancer Care.

PubMed

Dingley, Catherine E; Clayton, Margaret; Lai, Djin; Doyon, Katherine; Reblin, Maija; Ellington, Lee

Activated patients have the skills, knowledge, and confidence to manage their care, resulting in positive outcomes such as lower hospital readmission and fewer adverse consequences due to poor communication with providers. Despite extensive evidence on patient activation, little is known about activation in the home hospice setting, when family caregivers assume more responsibility in care management. We examined caregiver and nurse communication behaviors associated with caregiver activation during home hospice visits of patients with advanced cancer using a prospective observational design. We adapted Street's Activation Verbal Coding tool to caregiver communication and used qualitative thematic analysis to develop codes for nurse communications that preceded and followed each activation statement in 60 audio-recorded home hospice visits. Caregiver communication that reflected activation included demonstrating knowledge regarding the patient/care, describing care strategies, expressing opinions regarding care, requesting explanations of care, expressing concern about the patient, and redirecting the conversation toward the patient. Nurses responded by providing education, reassessing the patient/care environment, validating communications, clarifying care issues, updating/revising care, and making recommendations for future care. Nurses prompted caregiver activation through focused care-specific questions, open-ended questions/statements, and personal questions. Few studies have investigated nurse/caregiver communication in home hospice, and, to our knowledge, no other studies focused on caregiver activation. The current study provides a foundation to develop a framework of caregiver activation through enhanced communication with nurses. Activated caregivers may facilitate patient-centered care through communication with nurses in home hospice, thus resulting in enhanced outcomes for patients with advanced cancer.

Pharmacological mechanism-based drug safety assessment and prediction.

PubMed

Abernethy, D R; Woodcock, J; Lesko, L J

2011-06-01

Advances in cheminformatics, bioinformatics, and pharmacology in the context of biological systems are now at a point that these tools can be applied to mechanism-based drug safety assessment and prediction. The development of such predictive tools at the US Food and Drug Administration (FDA) will complement ongoing efforts in drug safety that are focused on spontaneous adverse event reporting and active surveillance to monitor drug safety. This effort will require the active collaboration of scientists in the pharmaceutical industry, academe, and the National Institutes of Health, as well as those at the FDA, to reach its full potential. Here, we describe the approaches and goals for the mechanism-based drug safety assessment and prediction program.

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2013-03-15

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2013-03-06

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Weather Prediction Improvement Using Advanced Satellite Technology

NASA Technical Reports Server (NTRS)

Einaudi, Franco; Uccellini, L.; Purdom, J.; Rogers, D.; Gelaro, R.; Dodge, J.; Atlas, R.; Lord, S.

2001-01-01

We discuss in this paper some of the problems that exist today in the fall utilization of satellite data to improve weather forecasts and we propose specific recommendations to solve them. This discussion can be viewed as an aspect of the general debate on how best to organize the transition from research to operational satellites and how to evaluate the impact of a research instrument on numerical weather predictions. A method for providing this transition is offered by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). This mission will bridge the time between the present NOAA and Department of Defense (DOD) polar orbiting missions and the initiation of the converged NPOESS series and will evaluate some of the Earth Observing System (EOS) instruments as appropriate for operational missions. Thus, this mission can be viewed as an effort to meet the operational requirements of NOAA and DOD and the research requirements of NASA. More generally, however, it can be said that the process of going from the conception of new, more advanced instruments to their operational implementation and full utilization by the weather forecast communities is not optimal. Instruments developed for research purposes may have insufficient funding to explore their potential operational capabilities. Furthermore, instrument development programs designed for operational satellites typically have insufficient funding for assimilation algorithms needed to transform the satellite observations into data that can be used by sophisticated global weather forecast models. As a result, years often go by before satellite data are efficiently used for operational forecasts. NASA and NOAA each have unique expertise in the design of satellite instruments, their use for basic and applied research and their utilization in weather and climate research. At a time of limited resources, the two agencies must combine their efforts to work toward common

Gamma-band activation predicts both associative memory and cortical plasticity

PubMed Central

Headley, Drew B.; Weinberger, Norman M.

2011-01-01

Gamma-band oscillations are a ubiquitous phenomenon in the nervous system and have been implicated in multiple aspects of cognition. In particular, the strength of gamma oscillations at the time a stimulus is encoded predicts its subsequent retrieval, suggesting that gamma may reflect enhanced mnemonic processing. Likewise, activity in the gamma-band can modulate plasticity in vitro. However, it is unclear whether experience-dependent plasticity in vivo is also related to gamma-band activation. The aim of the present study is to determine whether gamma activation in primary auditory cortex modulates both the associative memory for an auditory stimulus during classical conditioning and its accompanying specific receptive field plasticity. Rats received multiple daily sessions of single tone/shock trace and two-tone discrimination conditioning, during which local field potentials and multiunit discharges were recorded from chronically implanted electrodes. We found that the strength of tone-induced gamma predicted the acquisition of associative memory 24 h later, and ceased to predict subsequent performance once asymptote was reached. Gamma activation also predicted receptive field plasticity that specifically enhanced representation of the signal tone. This concordance provides a long-sought link between gamma oscillations, cortical plasticity and the formation of new memories. PMID:21900554

Prediction of adolescents doing physical activity after completing secondary education.

PubMed

Moreno-Murcia, Juan Antonio; Huéscar, Elisa; Cervelló, Eduardo

2012-03-01

The purpose of this study, based on the self-determination theory (Ryan & Deci, 2000) was to test the prediction power of student's responsibility, psychological mediators, intrinsic motivation and the importance attached to physical education in the intention to continue to practice some form of physical activity and/or sport, and the possible relationships that exist between these variables. We used a sample of 482 adolescent students in physical education classes, with a mean age of 14.3 years, which were measured for responsibility, psychological mediators, sports motivation, the importance of physical education and intention to be physically active. We completed an analysis of structural equations modelling. The results showed that the responsibility positively predicted psychological mediators, and this predicted intrinsic motivation, which positively predicted the importance students attach to physical education, and this, finally, positively predicted the intention of the student to continue doing sport. Results are discussed in relation to the promotion of student's responsibility towards a greater commitment to the practice of physical exercise.

Angiotensin converting enzyme 2 activity and human atrial fibrillation: increased plasma angiotensin converting enzyme 2 activity is associated with atrial fibrillation and more advanced left atrial structural remodelling.

PubMed

Walters, Tomos E; Kalman, Jonathan M; Patel, Sheila K; Mearns, Megan; Velkoska, Elena; Burrell, Louise M

2017-08-01

Angiotensin converting enzyme 2 (ACE2) is an integral membrane protein whose main action is to degrade angiotensin II. Plasma ACE2 activity is increased in various cardiovascular diseases. We aimed to determine the relationship between plasma ACE2 activity and human atrial fibrillation (AF), and in particular its relationship to left atrial (LA) structural remodelling. One hundred and three participants from a tertiary arrhythmia centre, including 58 with paroxysmal AF (PAF), 20 with persistent AF (PersAF), and 25 controls, underwent clinical evaluation, echocardiographic analysis, and measurement of plasma ACE2 activity. A subgroup of 20 participants underwent invasive LA electroanatomic mapping. Plasma ACE2 activity levels were increased in AF [control 13.3 (9.5-22.3) pmol/min/mL; PAF 16.9 (9.7-27.3) pmol/min/mL; PersAF 22.8 (13.7-33.4) pmol/min/mL, P = 0.006]. Elevated plasma ACE2 was associated with older age, male gender, hypertension and vascular disease, elevated left ventricular (LV) mass, impaired LV diastolic function and advanced atrial disease (P < 0.05 for all). Independent predictors of elevated plasma ACE2 activity were AF (P = 0.04) and vascular disease (P < 0.01). There was a significant relationship between elevated ACE2 activity and low mean LA bipolar voltage (adjusted R2 = 0.22, P = 0.03), a high proportion of complex fractionated electrograms (R2 = 0.32, P = 0.009) and a long LA activation time (R2 = 0.20, P = 0.04). Plasma ACE2 activity is elevated in human AF. Both AF and vascular disease predict elevated plasma ACE2 activity, and elevated plasma ACE2 is significantly associated with more advanced LA structural remodelling. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

Physical activity in patients with advanced-stage cancer: a systematic review of the literature.

PubMed

Albrecht, Tara A; Taylor, Ann Gill

2012-06-01

The importance of physical activity for chronic disease prevention and management has become generally well accepted. The number of research interventions and publications examining the benefits of physical activity for patients with cancer has been rising steadily. However, much of that research has focused on the impact of physical activity either prior to or early in the cancer diagnosis, treatment, and survivorship process. Research focusing on the effects of physical activity, specifically for patients with advanced-stage cancer and poorer prognostic outcomes, has been addressed only recently. The purpose of this article is to examine the state of the science for physical activity in the advanced-stage disease subset of the cancer population. Exercise in a variety of intensities and forms, including yoga, walking, biking, and swimming, has many health benefits for people, including those diagnosed with cancer. Research has shown that, for people with cancer (including advanced-stage cancer), exercise can decrease anxiety, stress, and depression while improving levels of pain, fatigue, shortness of breath, constipation, and insomnia. People diagnosed with cancer should discuss with their oncologist safe, easy ways they can incorporate exercise into their daily lives.

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Accurate prediction of energy expenditure using a shoe-based activity monitor.

PubMed

Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

2011-07-01

The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

Advanced error-prediction LDPC with temperature compensation for highly reliable SSDs

NASA Astrophysics Data System (ADS)

Tokutomi, Tsukasa; Tanakamaru, Shuhei; Iwasaki, Tomoko Ogura; Takeuchi, Ken

2015-09-01

To improve the reliability of NAND Flash memory based solid-state drives (SSDs), error-prediction LDPC (EP-LDPC) has been proposed for multi-level-cell (MLC) NAND Flash memory (Tanakamaru et al., 2012, 2013), which is effective for long retention times. However, EP-LDPC is not as effective for triple-level cell (TLC) NAND Flash memory, because TLC NAND Flash has higher error rates and is more sensitive to program-disturb error. Therefore, advanced error-prediction LDPC (AEP-LDPC) has been proposed for TLC NAND Flash memory (Tokutomi et al., 2014). AEP-LDPC can correct errors more accurately by precisely describing the error phenomena. In this paper, the effects of AEP-LDPC are investigated in a 2×nm TLC NAND Flash memory with temperature characterization. Compared with LDPC-with-BER-only, the SSD's data-retention time is increased by 3.4× and 9.5× at room-temperature (RT) and 85 °C, respectively. Similarly, the acceptable BER is increased by 1.8× and 2.3×, respectively. Moreover, AEP-LDPC can correct errors with pre-determined tables made at higher temperatures to shorten the measurement time before shipping. Furthermore, it is found that one table can cover behavior over a range of temperatures in AEP-LDPC. As a result, the total table size can be reduced to 777 kBytes, which makes this approach more practical.

Seasonal Extratropical Storm Activity Potential Predictability and its Origins during the Cold Seasons

NASA Astrophysics Data System (ADS)

Pingree-Shippee, K. A.; Zwiers, F. W.; Atkinson, D. E.

2016-12-01

Extratropical cyclones (ETCs) often produce extreme hazardous weather conditions, such as high winds, blizzard conditions, heavy precipitation, and flooding, all of which can have detrimental socio-economic impacts. The North American east and west coastal regions are both strongly influenced by ETCs and, subsequently, land-based, coastal, and maritime economic sectors in Canada and the USA all experience strong adverse impacts from extratropical storm activity from time to time. Society would benefit if risks associated with ETCs and storm activity variability could be reliably predicted for the upcoming season. Skillful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to storm activity variability and the associated risks and impacts. In this study, the potential predictability of seasonal variations in extratropical storm activity is investigated using analysis of variance to provide quantitative and geographical observational evidence indicative of whether it may be possible to predict storm activity on the seasonal timescale. This investigation will also identify origins of the potential predictability using composite analysis and large-scale teleconnections (Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation), providing the basis upon which seasonal predictions can be developed. Seasonal potential predictability and its origins are investigated for the cold seasons (OND, NDJ, DJF, JFM) during the 1979-2015 time period using daily mean sea level pressure, absolute pressure tendency, and 10-m wind speed from the ECMWF ERA-Interim reanalysis as proxies for extratropical storm activity. Results indicate potential predictability of seasonal variations in storm activity in areas strongly influenced by ETCs and with origins in the investigated teleconnections. For instance, the North Pacific storm track has considerable potential predictability and with notable origins in the SO and PDO.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

77 FR 74861 - Agency Information Collection Activities: Application for Advance Permission To Enter as...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-18

...-0017] Agency Information Collection Activities: Application for Advance Permission To Enter as...: Extension, Without Change, of a Currently Approved Collection. (2) Title of the Form/Collection: Application for Advance Permission to Enter as Nonimmigrant [Pursuant to Section 212(d)(3)(A(ii) of the INA]. (3...

Laboratory experiments on active suppression of advanced turboprop noise

NASA Technical Reports Server (NTRS)

Dittmar, J. H.

1985-01-01

The noise generated by supersonic tip speed propellers may be a cabin environment problem for future propeller-driven airplanes. Active suppression from speakers inside the airplane cabin has been proposed for canceling out this noise. The potential of active suppression of advanced turboprop noise was tested by using speakers in a rectangular duct. Experiments were first performed with sine wave signals. The results compared well with the ideal cancellation curve of noise as a function of phase angle. Recorded noise signals from subsonic and supersonic tip speed propellers were than used in the duct to deterthe potential for canceling their noise. The subsonic propeller data showed significant cancellations but less than those obtained with the sine wave. The blade-passing-tone cancellation curve for the supersonic propeller was very similar to the subsonic curve, indicating that it is potentially just as easy to cancel supersonic as subsonic propeller blade-passing-tone noise. Propeller duct data from a recorded propeller source and spatial data taken on a propeller-drive airplane showed generally good agreement when compared versus phase angle. This agreement, combined with the similarity of the subsonic and supersonic duct propeller data, indicates that the area of cancellation for advanced supersonic propellers will be similar to that measured on the airplane. Since the area of cancellation on the airplane was small, a method for improving the active noise suppression by using outside speakers is discussed.

The Transformation of Learning: Advances in Cultural-Historical Activity Theory

ERIC Educational Resources Information Center

van Oers, Bert, Ed.; Wardekker, Wim, Ed.; Elbers, Ed, Ed.; van der Veer, Rene, Ed.

2010-01-01

Learning is a changing phenomenon, depending on the advances in theory and research. This book presents a relatively new approach to learning, based on meaningful human activities in cultural practices and in collaboration with others. It draws extensively from the ideas of Lev Vygotsky and his recent followers. The book presents ideas that…

Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

NASA Technical Reports Server (NTRS)

Kranbuehl, D.; Kingsley, P.; Hart, S.; Loos, A.; Hasko, G.; Dexter, B.

1992-01-01

In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

Sleep Quality Prediction From Wearable Data Using Deep Learning

PubMed Central

Sathyanarayana, Aarti; Joty, Shafiq; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-01-01

Background The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN

Advanced fire-resistant forms of activated carbon and methods of adsorbing and separating gases using same

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

Xiong, Yongliang; Wang, Yifeng

Advanced, fire-resistant activated carbon compositions useful in adsorbing gases; and having vastly improved fire resistance are provided, and methods for synthesizing the compositions are also provided. The advanced compositions have high gas adsorption capacities and rapid adsorption kinetics (comparable to commercially-available activated carbon), without having any intrinsic fire hazard. They also have superior performance to Mordenites in both adsorption capacities and kinetics. In addition, the advanced compositions do not pose the fibrous inhalation hazard that exists with use of Mordenites. The fire-resistant compositions combine activated carbon mixed with one or more hydrated and/or carbonate-containing minerals that release H.sub.2O and/or CO.sub.2more » when heated. This effect raises the spontaneous ignition temperature to over 500.degree. C. in most examples, and over 800.degree. C. in some examples. Also provided are methods for removing and/or separating target gases, such as Krypton or Argon, from a gas stream by using such advanced activated carbons.« less

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury

PubMed Central

Pannu, Neesh; Hemmelgarn, Brenda R.; Austin, Peter C.; Tan, Zhi; McArthur, Eric; Manns, Braden J.; Tonelli, Marcello; Wald, Ron; Quinn, Robert R.; Ravani, Pietro; Garg, Amit X.

2017-01-01

Importance Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. Objective To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Design, Setting, and Participants Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Exposures Demographic, laboratory, and comorbidity variables measured prior to discharge. Main Outcomes and Measures Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. Results The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher

Breathlessness during daily activity: The psychometric properties of the London Chest Activity of Daily Living Scale in patients with advanced disease and refractory breathlessness.

PubMed

Reilly, Charles C; Bausewein, Claudia; Garrod, Rachel; Jolley, Caroline J; Moxham, John; Higginson, Irene J

2017-10-01

The London Chest Activities of Daily Living Scale measures the impact of breathlessness on both activity and social functioning. However, the London Chest Activities of Daily Living Scale is not routinely used in patients with advanced disease. To assess the psychometric properties of the London Chest Activities of Daily Living Scale in patients with refractory breathlessness due to advanced disease. A cross-sectional secondary analysis of data from a randomised controlled parallel-group, pragmatic, single-blind fast-track trial (randomised controlled trial) investigating the effectiveness of an integrated palliative and respiratory care service for patients with advanced disease and refractory breathlessness, known as the Breathlessness Support Service (NCT01165034). All patients completed the following questionnaires: the London Chest Activities of Daily Living Scale, Chronic Respiratory Questionnaire, the Palliative care Outcome Scale, Palliative care Outcome Scale-symptoms, the Hospital Anxiety and Depression Scale and breathlessness measured on a numerical rating scale. Data quality, scaling assumptions, acceptability, internal consistency and construct validity of the London Chest Activities of Daily Living Scale were determined using standard psychometric approaches. Breathless patients with advanced malignant and non-malignant disease. A total of 88 patients were studied, primary diagnosis included; chronic obstructive pulmonary disease = 53, interstitial lung disease = 17, cancer = 18. Median (range) London Chest Activities of Daily Living Scale total score was 46.5 (14-67). No floor or ceiling effect was observed for the London Chest Activities of Daily Living Scale total score. Internal consistency was good, and Cronbach's alpha for the London Chest Activities of Daily Living Scale total score was 0.90. Construct validity was good with 13 out of 15 a priori hypotheses met. Psychometric analyses suggest that the London Chest Activities of Daily Living

Breathlessness during daily activity: The psychometric properties of the London Chest Activity of Daily Living Scale in patients with advanced disease and refractory breathlessness

PubMed Central

Reilly, Charles C; Bausewein, Claudia; Garrod, Rachel; Jolley, Caroline J; Moxham, John; Higginson, Irene J

2016-01-01

Background: The London Chest Activities of Daily Living Scale measures the impact of breathlessness on both activity and social functioning. However, the London Chest Activities of Daily Living Scale is not routinely used in patients with advanced disease. Aim: To assess the psychometric properties of the London Chest Activities of Daily Living Scale in patients with refractory breathlessness due to advanced disease. Design: A cross-sectional secondary analysis of data from a randomised controlled parallel-group, pragmatic, single-blind fast-track trial (randomised controlled trial) investigating the effectiveness of an integrated palliative and respiratory care service for patients with advanced disease and refractory breathlessness, known as the Breathlessness Support Service (NCT01165034). All patients completed the following questionnaires: the London Chest Activities of Daily Living Scale, Chronic Respiratory Questionnaire, the Palliative care Outcome Scale, Palliative care Outcome Scale–symptoms, the Hospital Anxiety and Depression Scale and breathlessness measured on a numerical rating scale. Data quality, scaling assumptions, acceptability, internal consistency and construct validity of the London Chest Activities of Daily Living Scale were determined using standard psychometric approaches. Setting/participants: Breathless patients with advanced malignant and non-malignant disease. Results: A total of 88 patients were studied, primary diagnosis included; chronic obstructive pulmonary disease = 53, interstitial lung disease = 17, cancer = 18. Median (range) London Chest Activities of Daily Living Scale total score was 46.5 (14–67). No floor or ceiling effect was observed for the London Chest Activities of Daily Living Scale total score. Internal consistency was good, and Cronbach’s alpha for the London Chest Activities of Daily Living Scale total score was 0.90. Construct validity was good with 13 out of 15 a priori hypotheses met. Conclusion

Advanced fire-resistant forms of activated carbon and methods of adsorbing and separating gases using same

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

Xiong, Yongliang; Wang, Yifeng

A method of removing a target gas from a gas stream is disclosed. The method uses advanced, fire-resistant activated carbon compositions having vastly improved fire resistance. Methods for synthesizing the compositions are also provided. The advanced compositions have high gas adsorption capacities and rapid adsorption kinetics (comparable to commercially-available activated carbon), without having any intrinsic fire hazard.

Welcome to Lotus 1-2-3 Advanced. Learning Activity Packets.

ERIC Educational Resources Information Center

Mills, Steven; And Others

This learning activity packet (LAP) contains five self-paced study lessons that allow students to study advanced concepts of Lotus 1-2-3 at their own pace. The lessons used in the LAP are organized in the following way: lesson name, lesson number, objectives, completion standard, performance standard, required materials, unit test, and exercises.…

A risk score for the prediction of advanced age-related macular degeneration: Development and validation in 2 prospective cohorts

USDA-ARS?s Scientific Manuscript database

We aimed to develop an eye specific model which used readily available information to predict risk for advanced age-related macular degeneration (AMD). We used the Age-Related Eye Disease Study (AREDS) as our training dataset, which consisted of the 4,507 participants (contributing 1,185 affected v...

Activity of thalidomide and capecitabine in patients with advanced hepatocellular carcinoma.

PubMed

Ang, Soo-Fan; Tan, Sze-Huey; Toh, Han-Chong; Poon, Donald Y H; Ong, Simon Y K; Foo, Kian-Fong; Choo, Su-Pin

2012-06-01

Thalidomide has shown modest activity in advanced hepatocellular carcinomas (HCCs). Single-agent capecitabine has also been used in patients with HCC, with objective responses being reported. In our study, we review the use of thalidomide and capecitabine combination in advanced HCC. From November 2003 and September 2008, 42 patients with advanced HCC who were not eligible for clinical trial or conventional chemotherapy were treated with oral capecitabine (2000 mg/m/d) for 14 days every 3 weeks and oral thalidomide at the doses of 50 to 200 mg/d. Almost 50% of patients had Child-Pugh B or C liver cirrhosis and a history of regional or systemic therapy. Three patients achieved complete responses lasting more than 52 weeks, including 1 patient who achieved pathological complete response and underwent curative resection. There were 3 patients with partial responses and 13 with stable disease. Median overall survival of all 42 patients was 9.9 months. The median progression-free survival was 5.1 months. The presence of ascites, portal vein thrombosis, and poorer Child-Pugh liver cirrhosis status also resulted in significantly poorer survival outcome. Treatment was well tolerated. Fatigue was the most common side effect occurring in 16 (38%) patients, but only 1 patient had grade 3 toxicity and had to stop treatment. Two other patients developed grade 3 palmar-plantar erythrodysesthesia from capecitabine. The combination of thalidomide and capecitabine has activity in advanced HCC and can result in complete pathological response. Treatment is well tolerated even in less-fit patients who have been pretreated and deserve further study.

An Injury Severity-, Time Sensitivity-, and Predictability-Based Advanced Automatic Crash Notification Algorithm Improves Motor Vehicle Crash Occupant Triage.

PubMed

Stitzel, Joel D; Weaver, Ashley A; Talton, Jennifer W; Barnard, Ryan T; Schoell, Samantha L; Doud, Andrea N; Martin, R Shayn; Meredith, J Wayne

2016-06-01

Advanced Automatic Crash Notification algorithms use vehicle telemetry measurements to predict risk of serious motor vehicle crash injury. The objective of the study was to develop an Advanced Automatic Crash Notification algorithm to reduce response time, increase triage efficiency, and improve patient outcomes by minimizing undertriage (<5%) and overtriage (<50%), as recommended by the American College of Surgeons. A list of injuries associated with a patient's need for Level I/II trauma center treatment known as the Target Injury List was determined using an approach based on 3 facets of injury: severity, time sensitivity, and predictability. Multivariable logistic regression was used to predict an occupant's risk of sustaining an injury on the Target Injury List based on crash severity and restraint factors for occupants in the National Automotive Sampling System - Crashworthiness Data System 2000-2011. The Advanced Automatic Crash Notification algorithm was optimized and evaluated to minimize triage rates, per American College of Surgeons recommendations. The following rates were achieved: <50% overtriage and <5% undertriage in side impacts and 6% to 16% undertriage in other crash modes. Nationwide implementation of our algorithm is estimated to improve triage decisions for 44% of undertriaged and 38% of overtriaged occupants. Annually, this translates to more appropriate care for >2,700 seriously injured occupants and reduces unnecessary use of trauma center resources for >162,000 minimally injured occupants. The algorithm could be incorporated into vehicles to inform emergency personnel of recommended motor vehicle crash triage decisions. Lower under- and overtriage was achieved, and nationwide implementation of the algorithm would yield improved triage decision making for an estimated 165,000 occupants annually. Copyright © 2016. Published by Elsevier Inc.

Advanced interatrial block predicts new-onset atrial fibrillation and ischemic stroke in patients with heart failure: The "Bayes' Syndrome-HF" study.

PubMed

Escobar-Robledo, Luis Alberto; Bayés-de-Luna, Antoni; Lupón, Josep; Baranchuk, Adrian; Moliner, Pedro; Martínez-Sellés, Manuel; Zamora, Elisabet; de Antonio, Marta; Domingo, Mar; Cediel, Germán; Núñez, Julio; Santiago-Vacas, Evelyn; Bayés-Genís, Antoni

2018-05-18

Advanced interatrial block (IAB) is characterized by a prolonged (≥120 ms) and bimodal P wave in the inferior leads. The association between advanced IAB and atrial fibrillation (AF) is known as "Bayes' Syndrome", and there is scarce information about it in heart failure (HF). We examined the prevalence of IAB and whether advanced IAB could predict new-onset AF and/or stroke in HF patients. The prospective observational "Bayes' Syndrome-HF" study included consecutive outpatients with chronic HF. The primary endpoints were new-onset AF, ischemic stroke, and the composite of both. A secondary endpoint included all-cause death alone or in combination with the primary endpoint. Comprehensive multivariable Cox regression analyses were performed. Among 1050 consecutive patients, 536 (51.0%) were in sinus rhythm, 464 with a measurable P wave are the focus of this study. Two-hundred and sixty patients (56.0%) had normal atrial conduction, 95 (20.5%) partial IAB, and 109 (23.5%) advanced IAB. During a mean follow-up of 4.5 ± 2.1 years, 235 patients experienced all-cause death, new-onset AF, or stroke. In multivariable comprehensive Cox regression analyses, advanced IAB was associated with new-onset AF (HR 2.71 [1.61-4.56], P < 0.001), ischemic stroke (HR 3.02 [1.07-8.53], P = 0.04), and the composite of both (HR 2.42 [1.41-4.15], P < 0.001). In patients with HF advanced IAB predicts new-onset AF and ischemic stroke. Future studies must assess whether anticoagulant treatment in Bayes' Syndrome leads to better outcomes in HF. Copyright © 2017 Elsevier B.V. All rights reserved.

Tumor-infiltrating lymphocytes predict response to chemotherapy in patients with advance non-small cell lung cancer.

PubMed

Liu, Hui; Zhang, Tiantuo; Ye, Jin; Li, Hongtao; Huang, Jing; Li, Xiaodong; Wu, Benquan; Huang, Xubing; Hou, Jinghui

2012-10-01

Accumulating preclinical evidence suggests that anticancer immune responses contribute to the success of chemotherapy. The predictive significance of tumor-infiltrating lymphocytes (TILs) for response to neoadjuvant chemotherapy in non-small cell lung cancer (NSCLC) remains unknown. The aim of this study was to investigate the prognostic and predictive value of TIL subtypes in patients with advanced NSCLC treated with platinum-based chemotherapy. In total, 159 patients with stage III and IV NSCLC were retrospectively enrolled. The prevalence of CD3(+), CD4(+), CD8(+) and Foxp3(+) TILs was assessed by immunohistochemistry in tumor tissue obtained before chemotherapy. The density of TILs subgroups was treated as dichotomous variables using the median values as cutoff. Survival curves were estimated by the Kaplan-Meier method, and differences in overall survival between groups were determined using the Log-rank test. Prognostic effects of TIL subsets density were evaluated by Cox regression analysis. The presence of CD3(+), CD4(+), CD8(+), and FOXP3(+) TILs was not correlated with any clinicopathological features. Neither the prevalence of TILs nor combined analysis displayed obvious prognostic performances for overall survival in Cox regression model. Instead, higher FOXP3(+)/CD8(+) ratio in tumor sites was an independent factor for poor response to platinum-based chemotherapy in overall cohort. These findings suggest that immunological CD8(+) and FOXP3(+)Tregs cell infiltrate within tumor environment is predictive of response to platinum-based neoadjuvant chemotherapy in advanced NSCLC patients. The understanding of the clinical relevance of the microenvironmental immunological milieu might provide an important clue for the design of novel strategies in cancer immunotherapy.

Prediction of advanced endovascular stent graft rotation and its associated morbidity and mortality.

PubMed

Crawford, Sean A; Sanford, Ryan M; Doyle, Matthew G; Wheatcroft, Mark; Amon, Cristina H; Forbes, Thomas L

2018-01-29

Advanced endovascular aneurysm repair (EVAR) with fenestrated and branched stent grafts is increasingly being used to repair complex aortic aneurysms; however, these devices can rotate unpredictably during deployment, leading to device misalignment. The objectives of this study were to quantify the short-term clinical outcomes in patients with intraoperative stent graft rotation and to identify quantitative anatomic markers of the arterial geometry that can predict stent graft rotation preoperatively. A prospective study evaluating all patients undergoing advanced EVAR was conducted at two university-affiliated hospitals between November 2015 and December 2016. Stent graft rotation (defined as ≥10 degrees) was measured on intraoperative fluoroscopic video of the deployment sequence. Standard preoperative computed tomography angiography imaging was used to calculate the geometric properties of the arterial anatomy. Any in-hospital and 30-day complications were prospectively documented, and a composite outcome of any end-organ ischemia or death was used as the primary end point. Thirty-nine patients undergoing advanced EVAR were enrolled in the study with a mean age of 75 years (interquartile range [IQR], 71-80 years) and a mean aneurysm diameter of 64 mm (IQR, 59-65 mm). The incidence of stent graft rotation was 37% (n = 14), with a mean rotation of 25 degrees (IQR, 21-28 degrees). A nominal logistic regression model identified iliac artery torsion, volume of iliac artery calcification, and stent graft length as the primary predictive factors. The total net torsion and the total volume of calcific plaque were higher in patients with stent graft rotation, 8.9 ± 0.8 mm -1 vs 4.1 ± 0.5 mm -1 (P < .0001) and 1054 ± 144 mm 3 vs 525 ± 83 mm 3 (P < .01), respectively. The length of the implanted stent grafts was also higher in patients with intraoperative rotation, 172 ± 9 mm vs 156 ± 8 mm (P < .01). The composite outcome of any end

THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY ...

EPA Pesticide Factsheets

A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. These approaches are less well-suited, however, to the challenges of global toxicity prediction, i.e., to predicting the potential toxicity of structurally diverse chemicals across a wide range of end points of regulatory and pharmaceutical concern. New approaches that have the potential to significantly improve capabilities in predictive toxicology are elaborating the “activity” portion of the SAR paradigm. Recent advances in two areas of endeavor are particularly promising. Toxicity data informatics relies on standardized data schema, developed for particular areas of toxicological study, to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation. Bioassay profiling refers to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical “properties” to the biological activity domain. The effective capture and representation of legacy and new toxicity data into mineable form and the large-scale generation of new bioassay data in relation to chemical toxicity, both employing chemical stru

Frequency of reporting and predictive factors for anxiety and depression in patients with advanced cancer.

PubMed

Salvo, N; Zeng, L; Zhang, L; Leung, M; Khan, L; Presutti, R; Nguyen, J; Holden, L; Culleton, S; Chow, E

2012-03-01

The prevalence of anxiety and depression in patients with advanced cancer has been reported to be on average 25% and to significantly affect patients' quality of life. Despite high prevalence rates, these disorders remain underdiagnosed and undertreated. The purpose of our study was to examine the self-report rates of anxiety and depression with the Edmonton Symptom Assessment System (ESAS) and to assess the predictive factors for these reports in cancer patients with metastatic disease. Consecutive patients who attended the Rapid Response Radiotherapy Program (RRRP) completed the ESAS as well as baseline demographic information. Ordinal logistic regression analysis was used to determine factors that significantly predicted anxiety and/or depression. Pearson χ(2) was used to test goodness-of-fit for categorical variables and established whether or not an observed frequency distribution differed from a predicted frequency distribution. A univariate analysis was conducted first and those variables with a P value<0.100 were included in a multivariate analysis. A score test was used to test the proportional odds assumption. In total, 1439 patients seen in the RRRP between January 1999 and October 2009 completed ESAS questionnaires. Fifty-five per cent of patients reported at least mild symptoms of depression and 65% reported at least mild anxiety. In the univariate analysis, patients who were female, who had a lower performance status score, or primary lung cancer were more likely to report depressed and anxious feelings. Primary prostate cancer patients were significantly less likely to report depression and anxiety. Patients referred for spinal cord compression were significantly less depressed. The multivariate models showed that younger patients were significantly more anxious than older patients and females reported more anxiety than males. Patients who reported higher feelings of nausea, tiredness, drowsiness, dyspnoea, and worse appetite and overall well

Predicting activity approach based on new atoms similarity kernel function.

PubMed

Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

2015-07-01

Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Improved neutron activation prediction code system development

NASA Technical Reports Server (NTRS)

Saqui, R. M.

1971-01-01

Two integrated neutron activation prediction code systems have been developed by modifying and integrating existing computer programs to perform the necessary computations to determine neutron induced activation gamma ray doses and dose rates in complex geometries. Each of the two systems is comprised of three computational modules. The first program module computes the spatial and energy distribution of the neutron flux from an input source and prepares input data for the second program which performs the reaction rate, decay chain and activation gamma source calculations. A third module then accepts input prepared by the second program to compute the cumulative gamma doses and/or dose rates at specified detector locations in complex, three-dimensional geometries.

Stock price change rate prediction by utilizing social network activities.

PubMed

Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

Stock Price Change Rate Prediction by Utilizing Social Network Activities

PubMed Central

Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

The role of different sampling methods in improving biological activity prediction using deep belief network.

PubMed

Ghasemi, Fahimeh; Fassihi, Afshin; Pérez-Sánchez, Horacio; Mehri Dehnavi, Alireza

2017-02-05

Thousands of molecules and descriptors are available for a medicinal chemist thanks to the technological advancements in different branches of chemistry. This fact as well as the correlation between them has raised new problems in quantitative structure activity relationship studies. Proper parameter initialization in statistical modeling has merged as another challenge in recent years. Random selection of parameters leads to poor performance of deep neural network (DNN). In this research, deep belief network (DBN) was applied to initialize DNNs. DBN is composed of some stacks of restricted Boltzmann machine, an energy-based method that requires computing log likelihood gradient for all samples. Three different sampling approaches were suggested to solve this gradient. In this respect, the impact of DBN was applied based on the different sampling approaches mentioned above to initialize the DNN architecture in predicting biological activity of all fifteen Kaggle targets that contain more than 70k molecules. The same as other fields of processing research, the outputs of these models demonstrated significant superiority to that of DNN with random parameters. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Advanced extravehicular activity systems requirements definition study

NASA Technical Reports Server (NTRS)

1988-01-01

A study to define the requirements for advanced extravehicular activities (AEVA) was conducted. The purpose of the study was to develop an understanding of the EVA technology requirements and to map a pathway from existing or developing technologies to an AEVA system capable of supporting long-duration missions on the lunar surface. The parameters of an AEVA system which must sustain the crewmembers and permit productive work for long periods in the lunar environment were examined. A design reference mission (DRM) was formulated and used as a tool to develop and analyze the EVA systems technology aspects. Many operational and infrastructure design issues which have a significant influence on the EVA system are identified.

Prediction of activity type in preschool children using machine learning techniques.

PubMed

Hagenbuchner, Markus; Cliff, Dylan P; Trost, Stewart G; Van Tuc, Nguyen; Peoples, Gregory E

2015-07-01

Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Eleven children aged 3-6 years (mean age=4.8±0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children. Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Advances in Inner Magnetosphere Passive and Active Wave Research

NASA Technical Reports Server (NTRS)

Green, James L.; Fung, Shing F.

2004-01-01

This review identifies a number of the principal research advancements that have occurred over the last five years in the study of electromagnetic (EM) waves in the Earth's inner magnetosphere. The observations used in this study are from the plasma wave instruments and radio sounders on Cluster, IMAGE, Geotail, Wind, Polar, Interball, and others. The data from passive plasma wave instruments have led to a number of advances such as: determining the origin and importance of whistler mode waves in the plasmasphere, discovery of the source of kilometric continuum radiation, mapping AKR source regions with "pinpoint" accuracy, and correlating the AKR source location with dipole tilt angle. Active magnetospheric wave experiments have shown that long range ducted and direct echoes can be used to obtain the density distribution of electrons in the polar cap and along plasmaspheric field lines, providing key information on plasmaspheric filling rates and polar cap outflows.

Protein Kinase C Activation Promotes Microtubule Advance in Neuronal Growth Cones by Increasing Average Microtubule Growth Lifetimes

PubMed Central

Kabir, Nurul; Schaefer, Andrew W.; Nakhost, Arash; Sossin, Wayne S.; Forscher, Paul

2001-01-01

We describe a novel mechanism for protein kinase C regulation of axonal microtubule invasion of growth cones. Activation of PKC by phorbol esters resulted in a rapid, robust advance of distal microtubules (MTs) into the F-actin rich peripheral domain of growth cones, where they are normally excluded. In contrast, inhibition of PKC activity by bisindolylmaleimide and related compounds had no perceptible effect on growth cone motility, but completely blocked phorbol ester effects. Significantly, MT advance occurred despite continued retrograde F-actin flow—a process that normally inhibits MT advance. Polymer assembly was necessary for PKC-mediated MT advance since it was highly sensitive to a range of antagonists at concentrations that specifically interfere with microtubule dynamics. Biochemical evidence is presented that PKC activation promotes formation of a highly dynamic MT pool. Direct assessment of microtubule dynamics and translocation using the fluorescent speckle microscopy microtubule marking technique indicates PKC activation results in a nearly twofold increase in the typical lifetime of a MT growth episode, accompanied by a 1.7-fold increase and twofold decrease in rescue and catastrophe frequencies, respectively. No significant effects on instantaneous microtubule growth, shortening, or sliding rates (in either anterograde or retrograde directions) were observed. MTs also spent a greater percentage of time undergoing retrograde transport after PKC activation, despite overall MT advance. These results suggest that regulation of MT assembly by PKC may be an important factor in determining neurite outgrowth and regrowth rates and may play a role in other cellular processes dependent on directed MT advance. PMID:11238458

A new mathematical solution for predicting char activation reactions

USGS Publications Warehouse

Rafsanjani, H.H.; Jamshidi, E.; Rostam-Abadi, M.

2002-01-01

The differential conservation equations that describe typical gas-solid reactions, such as activation of coal chars, yield a set of coupled second-order partial differential equations. The solution of these coupled equations by exact analytical methods is impossible. In addition, an approximate or exact solution only provides predictions for either reaction- or diffusion-controlling cases. A new mathematical solution, the quantize method (QM), was applied to predict the gasification rates of coal char when both chemical reaction and diffusion through the porous char are present. Carbon conversion rates predicted by the QM were in closer agreement with the experimental data than those predicted by the random pore model and the simple particle model. ?? 2002 Elsevier Science Ltd. All rights reserved.

Predictability of action sub-steps modulates motor system activation during the observation of goal-directed actions.

PubMed

Braukmann, Ricarda; Bekkering, Harold; Hidding, Margreeth; Poljac, Edita; Buitelaar, Jan K; Hunnius, Sabine

2017-08-01

Action perception and execution are linked in the human motor system, and researchers have proposed that this action-observation matching system underlies our ability to predict observed behavior. If the motor system is indeed involved in the generation of action predictions, activation should be modulated by the degree of predictability of an observed action. This study used EEG and eye-tracking to investigate whether and how predictability of an observed action modulates motor system activation as well as behavioral predictions in the form of anticipatory eye-movements. Participants were presented with object-directed actions (e.g., making a cup of tea) consisting of three action steps which increased in their predictability. While the goal of the first step was ambiguous (e.g., when making tea, one can first grab the teabag or the cup), the goals of the following steps became predictable over the course of the action. Motor system activation was assessed by measuring attenuation of sensorimotor mu- and beta-oscillations. We found that mu- and beta-power were attenuated during observation, indicating general activation of the motor system. Importantly, predictive motor system activation, indexed by beta-band attenuation, increased for each action step, showing strongest activation prior to the final (i.e. most predictable) step. Sensorimotor activity was related to participants' predictive eye-movements which also showed a modulation by action step. Our results demonstrate that motor system activity and behavioral predictions become stronger for more predictable action steps. The functional roles of sensorimotor oscillations in predicting other's actions are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Physical activity behavior predicts endogenous pain modulation in older adults.

PubMed

Naugle, Kelly M; Ohlman, Thomas; Naugle, Keith E; Riley, Zachary A; Keith, NiCole R

2017-03-01

Older adults compared with younger adults are characterized by greater endogenous pain facilitation and a reduced capacity to endogenously inhibit pain, potentially placing them at a greater risk for chronic pain. Previous research suggests that higher levels of self-reported physical activity are associated with more effective pain inhibition and less pain facilitation on quantitative sensory tests in healthy adults. However, no studies have directly tested the relationship between physical activity behavior and pain modulatory function in older adults. This study examined whether objective measures of physical activity behavior cross-sectionally predicted pain inhibitory function on the conditioned pain modulation (CPM) test and pain facilitation on the temporal summation (TS) test in healthy older adults. Fifty-one older adults wore an accelerometer on the hip for 7 days and completed the CPM and TS tests. Measures of sedentary time, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) were obtained from the accelerometer. Hierarchical linear regressions were conducted to determine the relationship of TS and CPM with levels of physical activity, while controlling for demographic, psychological, and test variables. The results indicated that sedentary time and LPA significantly predicted pain inhibitory function on the CPM test, with less sedentary time and greater LPA per day associated with greater pain inhibitory capacity. Additionally, MVPA predicted pain facilitation on the TS test, with greater MVPA associated with less TS of pain. These results suggest that different types of physical activity behavior may differentially impact pain inhibitory and facilitatory processes in older adults.

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%.

The atmospheric boundary layer — advances in knowledge and application

NASA Astrophysics Data System (ADS)

Garratt, J. R.; Hess, G. D.; Physick, W. L.; Bougeault, P.

1996-02-01

We summarise major activities and advances in boundary-layer knowledge in the 25 years since 1970, with emphasis on the application of this knowledge to surface and boundary-layer parametrisation schemes in numerical models of the atmosphere. Progress in three areas is discussed: (i) the mesoscale modelling of selected phenomena; (ii) numerical weather prediction; and (iii) climate simulations. Future trends are identified, including the incorporation into models of advanced cloud schemes and interactive canopy schemes, and the nesting of high resolution boundary-layer schemes in global climate models.

Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

USGS Publications Warehouse

Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

1999-01-01

Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier

Flutter prediction for a wing with active aileron control

NASA Technical Reports Server (NTRS)

Penning, K.; Sandlin, D. R.

1983-01-01

A method for predicting the vibrational stability of an aircraft with an analog active aileron flutter suppression system (FSS) is expained. Active aileron refers to the use of an active control system connected to the aileron to damp vibrations. Wing vibrations are sensed by accelerometers and the information is used to deflect the aileron. Aerodynamic force caused by the aileron deflection oppose wing vibrations and effectively add additional damping to the system.

Predictive Suppression of Cortical Excitability and Its Deficit in Schizophrenia

PubMed Central

Schroeder, Charles E.; Leitman, David I.

2013-01-01

Recent neuroscience advances suggest that when interacting with our environment, along with previous experience, we use contextual cues and regularities to form predictions that guide our perceptions and actions. The goal of such active “predictive sensing” is to selectively enhance the processing and representation of behaviorally relevant information in an efficient manner. Since a hallmark of schizophrenia is impaired information selection, we tested whether this deficiency stems from dysfunctional predictive sensing by measuring the degree to which neuronal activity predicts relevant events. In healthy subjects, we established that these mechanisms are engaged in an effort-dependent manner and that, based on a correspondence between human scalp and intracranial nonhuman primate recordings, their main role is a predictive suppression of excitability in task-irrelevant regions. In contrast, schizophrenia patients displayed a reduced alignment of neuronal activity to attended stimuli, which correlated with their behavioral performance deficits and clinical symptoms. These results support the relevance of predictive sensing for normal and aberrant brain function, and highlight the importance of neuronal mechanisms that mold internal ongoing neuronal activity to model key features of the external environment. PMID:23843536

Factors predicting non-alcoholic steatohepatitis (NASH) and advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD).

PubMed

Tasneem, Abbas Ali; Luck, Nasir Hassan; Majid, Zain

2018-04-01

Introduction To determine the factors predicting non-alcoholic steatohepatitis (NASH) and advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). Methodology All patients aged >18 years and having a fatty liver on abdominal ultrasound (US), presenting from January 2011 to January 2017, were included. A liver biopsy was performed on all the patients. Results Of 96 patients undergoing liver biopsy for non-alcoholic fatty liver disease (NAFLD), 76 (79.2%) were men. On liver US, diffuse fatty liver (DFL) was noted in 68 (70.8%) patients. Liver biopsy showed non-alcoholic steatohepatitis (NASH) in 78 (81.3%) patients. Factors associated with NASH were male gender, body mass index (BMI) > 27 kg/m 2 , DFL and raised alanine aminotransferase (ALT). A GULAB score (based on gender, US liver findings, lipid (fasting) levels, ALT level and BMI) of ≥5 predicted NASH with 82.05% sensitivity. Factors associated with advanced fibrosis in NAFLD were age >40 years, diabetes mellitus, AST/ALT ratio > 1 and raised GGT. Conclusion NASH is common in patients with male gender, high BMI, DFL on liver US, raised ALT and GULAB score ≥5.

Polymeric drugs: Advances in the development of pharmacologically active polymers

PubMed Central

Li, Jing; Yu, Fei; Chen, Yi; Oupický, David

2015-01-01

Synthetic polymers play a critical role in pharmaceutical discovery and development. Current research and applications of pharmaceutical polymers are mainly focused on their functions as excipients and inert carriers of other pharmacologically active agents. This review article surveys recent advances in alternative pharmaceutical use of polymers as pharmacologically active agents known as polymeric drugs. Emphasis is placed on the benefits of polymeric drugs that are associated with their macromolecular character and their ability to explore biologically relevant multivalency processes. We discuss the main therapeutic uses of polymeric drugs as sequestrants, antimicrobials, antivirals, and anticancer and anti-inflammatory agents. PMID:26410809

Diesel engine emissions and combustion predictions using advanced mixing models applicable to fuel sprays

NASA Astrophysics Data System (ADS)

Abani, Neerav; Reitz, Rolf D.

2010-09-01

An advanced mixing model was applied to study engine emissions and combustion with different injection strategies ranging from multiple injections, early injection and grouped-hole nozzle injection in light and heavy duty diesel engines. The model was implemented in the KIVA-CHEMKIN engine combustion code and simulations were conducted at different mesh resolutions. The model was compared with the standard KIVA spray model that uses the Lagrangian-Drop and Eulerian-Fluid (LDEF) approach, and a Gas Jet spray model that improves predictions of liquid sprays. A Vapor Particle Method (VPM) is introduced that accounts for sub-grid scale mixing of fuel vapor and more accurately and predicts the mixing of fuel-vapor over a range of mesh resolutions. The fuel vapor is transported as particles until a certain distance from nozzle is reached where the local jet half-width is adequately resolved by the local mesh scale. Within this distance the vapor particle is transported while releasing fuel vapor locally, as determined by a weighting factor. The VPM model more accurately predicts fuel-vapor penetrations for early cycle injections and flame lift-off lengths for late cycle injections. Engine combustion computations show that as compared to the standard KIVA and Gas Jet spray models, the VPM spray model improves predictions of in-cylinder pressure, heat released rate and engine emissions of NOx, CO and soot with coarse mesh resolutions. The VPM spray model is thus a good tool for efficiently investigating diesel engine combustion with practical mesh resolutions, thereby saving computer time.

Prediction of response to preoperative chemoradiotherapy and establishment of individualized therapy in advanced rectal cancer.

PubMed

Nakao, Toshihiro; Iwata, Takashi; Hotchi, Masanori; Yoshikawa, Kozo; Higashijima, Jun; Nishi, Masaaki; Takasu, Chie; Eto, Shohei; Teraoku, Hiroki; Shimada, Mitsuo

2015-10-01

Preoperative chemoradiotherapy (CRT) has become the standard treatment for patients with locally advanced rectal cancer. However, no specific biomarker has been identified to predict a response to preoperative CRT. The aim of the present study was to assess the gene expression patterns of patients with advanced rectal cancer to predict their responses to preoperative CRT. Fifty-nine rectal cancer patients were subjected to preoperative CRT. Patients were randomly assigned to receive CRT with tegafur/gimeracil/oteracil (S-1 group, n=30) or tegafur-uracil (UFT group, n=29). Gene expression changes were studied with cDNA and miRNA microarray. The association between gene expression and response to CRT was evaluated. cDNA microarray showed that 184 genes were significantly differentially expressed between the responders and the non‑responders in the S-1 group. Comparatively, 193 genes were significantly differentially expressed in the responders in the UFT group. TBX18 upregulation was common to both groups whereas BTNL8, LOC375010, ADH1B, HRASLS2, LOC284232, GCNT3 and ALDH1A2 were significantly differentially lower in both groups when compared with the non-responders. Using miRNA microarray, we found that 7 and 16 genes were significantly differentially expressed between the responders and non-responders in the S-1 and UFT groups, respectively. miR-223 was significantly higher in the responders in the S-1 group and tended to be higher in the responders in the UFT group. The present study identified several genes likely to be useful for establishing individualized therapies for patients with rectal cancer.

Advancing Atmospheric River Forecasts into Subseasonal-to-Seasonal Timescales

NASA Astrophysics Data System (ADS)

Barnes, E. A.; Baggett, C.; Mundhenk, B. D.; Nardi, K.; Maloney, E. D.

2017-12-01

Atmospheric rivers can cause considerable mayhem along the west coast of North America - delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal (S2S) timescales ( 2 to 6 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into S2S timescales through knowledge of two of the atmosphere's most prominent oscillations; the Madden-Julian oscillation (MJO) and the Quasi-biennial oscillation (QBO). The dynamical relationship between atmospheric rivers, the MJO and the QBO is hypothesized to occur through modulation of North Pacific blocking. We present an empirical prediction scheme for anomalous atmospheric river activity based solely on the MJO and QBO and demonstrate skillful subseasonal "forecasts of opportunity" 5+ weeks ahead. We conclude with a discussion of the ability of state-of-the-art NWP models to predict atmospheric river characteristics on S2S timescales. With the wide-ranging impacts associated with landfalling atmospheric rivers, even modest gains in the subseasonal prediction of anomalous atmospheric river activity may support early action decision making and benefit numerous sectors of society.

A Brief Examination of Institutional Advancement Activities at Hispanic Serving Institutions.

ERIC Educational Resources Information Center

Mulnix, Michael William; Bowden, Randall G.; Lopez, Esther Elena

2002-01-01

Examined what level of importance university presidents of Hispanic serving institutions place on institutional advancement. Found that they believe strongly in the importance of such activities but most believe their efforts in areas such as fund raising, marketing, and public relations are not very satisfactory. Also found that many do not…

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

PubMed

Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

2016-03-14

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Rocket-Based Combined Cycle Activities in the Advanced Space Transportation Program Office

NASA Technical Reports Server (NTRS)

Hueter, Uwe; Turner, James

1999-01-01

NASA's Office of Aero-Space Technology (OAST) has established three major goals, referred to as, "The Three Pillars for Success". The Advanced Space Transportation Program Office (ASTP) at the NASA's Marshall Space Flight Center (MSFC) in Huntsville, Ala. focuses on future space transportation technologies Under the "Access to Space" pillar. The Core Technologies Project, part of ASTP, focuses on the reusable technologies beyond those being pursued by X-33. One of the main activities over the past two and a half years has been on advancing the rocket-based combined cycle (RBCC) technologies. In June of last year, activities for reusable launch vehicle (RLV) airframe and propulsion technologies were initiated. These activities focus primarily on those technologies that support the decision to determine the path this country will take for Space Shuttle and RLV. This year, additional technology efforts in the reusable technologies will be awarded. The RBCC effort that was completed early this year was the initial step leading to flight demonstrations of the technology for space launch vehicle propulsion.

Predicting Physical Activity Promotion in Health Care Settings.

ERIC Educational Resources Information Center

Faulkner, Guy; Biddle, Stuart

2001-01-01

Tested the theory of planned behavior's (TPB) ability to predict stage of change for physical activity promotion among health professionals. Researchers measured attitudes, subjective norms, intentions, perceived behavioral control, and stage of change, then later reassessed stage of change. TPB variables of attitude, subjective norms, perceived…

Sleep Quality Prediction From Wearable Data Using Deep Learning.

PubMed

Sathyanarayana, Aarti; Joty, Shafiq; Fernandez-Luque, Luis; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

2016-11-04

The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and

Cortical activity predicts good variation in human motor output.

PubMed

Babikian, Sarine; Kanso, Eva; Kutch, Jason J

2017-04-01

Human movement patterns have been shown to be particularly variable if many combinations of activity in different muscles all achieve the same task goal (i.e., are goal-equivalent). The nervous system appears to automatically vary its output among goal-equivalent combinations of muscle activity to minimize muscle fatigue or distribute tissue loading, but the neural mechanism of this "good" variation is unknown. Here we use a bimanual finger task, electroencephalography (EEG), and machine learning to determine if cortical signals can predict goal-equivalent variation in finger force output. 18 healthy participants applied left and right index finger forces to repeatedly perform a task that involved matching a total (sum of right and left) finger force. As in previous studies, we observed significantly more variability in goal-equivalent muscle activity across task repetitions compared to variability in muscle activity that would not achieve the goal: participants achieved the task in some repetitions with more right finger force and less left finger force (right > left) and in other repetitions with less right finger force and more left finger force (left > right). We found that EEG signals from the 500 milliseconds (ms) prior to each task repetition could make a significant prediction of which repetitions would have right > left and which would have left > right. We also found that cortical maps of sites contributing to the prediction contain both motor and pre-motor representation in the appropriate hemisphere. Thus, goal-equivalent variation in motor output may be implemented at a cortical level.

Functional brain imaging predicts public health campaign success.

PubMed

Falk, Emily B; O'Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-02-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a 'self-localizer' defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400,000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R(2) up to 0.65) and (ii) this relationship depends on message content-self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Functional brain imaging predicts public health campaign success

PubMed Central

O’Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-01-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a ‘self-localizer’ defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400 000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R2 up to 0.65) and (ii) this relationship depends on message content—self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. PMID:26400858

Impulsive Approach Tendencies towards Physical Activity and Sedentary Behaviors, but Not Reflective Intentions, Prospectively Predict Non-Exercise Activity Thermogenesis

PubMed Central

Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

2014-01-01

Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined. PMID:25526596

Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

PubMed

Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

2014-01-01

Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

Advanced space power PEM fuel cell systems

NASA Technical Reports Server (NTRS)

Vanderborgh, N. E.; Hedstrom, J.; Huff, J. R.

1989-01-01

A model showing mass and heat transfer in proton exchange membrane (PEM) single cells is presented. For space applications, stack operation requiring combined water and thermal management is needed. Advanced hardware designs able to combine these two techniques are available. Test results are shown for membrane materials which can operate with sufficiently fast diffusive water transport to sustain current densities of 300 ma per square centimeter. Higher power density levels are predicted to require active water removal.

The costs and benefits of temporal predictability: impaired inhibition of prepotent responses accompanies increased activation of task-relevant responses.

PubMed

Korolczuk, Inga; Burle, Boris; Coull, Jennifer T

2018-06-20

While the benefit of temporal predictability on sensorimotor processing is well established, it is still unknown whether this is due to efficient execution of an appropriate response and/or inhibition of an inappropriate one. To answer this question, we examined the effects of temporal predictability in tasks that required selective (Simon task) or global (Stop-signal task) inhibitory control of prepotent responses. We manipulated temporal expectation by presenting cues that either predicted (temporal cues) or not (neutral cues) when the target would appear. In the Simon task, performance was better when target location (left/right) was compatible with the hand of response and performance was improved further still if targets were temporally cued. However, Conditional Accuracy Functions revealed that temporal predictability selectively increased the number of fast, impulsive errors. Temporal cueing had no effect on selective response inhibition, as measured by the dynamics of the interference effect (delta plots) in the Simon task. By contrast, in the Stop-signal task, Stop-signal reaction time, a covert measure of a more global form of response inhibition, was significantly longer in temporally predictive trials. Therefore, when the time of target onset could be predicted in advance, it was harder to stop the impulse to respond to the target. Collectively, our results indicate that temporal cueing compounded the interfering effects of a prepotent response on task performance. We suggest that although temporal predictability enhances activation of task-relevant responses, it impairs inhibition of prepotent responses. Copyright © 2018 Elsevier B.V. All rights reserved.

Activation of a camptothecin prodrug by specific carboxylesterases as predicted by quantitative structure-activity relationship and molecular docking studies.

PubMed

Yoon, Kyoung Jin P; Krull, Erik J; Morton, Christopher L; Bornmann, William G; Lee, Richard E; Potter, Philip M; Danks, Mary K

2003-11-01

7-Ethyl-10-[4-(1-piperidino)-1-piperidino]carbonyloxycamptothecin (irinotecan, CPT-11) is a camptothecin prodrug that is metabolized by carboxylesterases (CE) to the active metabolite 7-ethyl-10-hydroxycamptothecin (SN-38), a topoisomerase I inhibitor. CPT-11 has shown encouraging antitumor activity against a broad spectrum of tumor types in early clinical trials, but hematopoietic and gastrointestinal toxicity limit its administration. To increase the therapeutic index of CPT-11 and to develop other prodrug analogues for enzyme/prodrug gene therapy applications, our laboratories propose to develop camptothecin prodrugs that will be activated by specific CEs. Specific analogues might then be predicted to be activated, for example, predominantly by human liver CE(hCE1), by human intestinal CE (hiCE), or in gene therapy approaches using a rabbit liver CE (rCE). This study describes a molecular modeling approach to relate the structure of rCE-activated camptothecin prodrugs with their biological activation. Comparative molecular field analysis, comparative molecular similarity index analysis, and docking studies were used to predict the biological activity of a 4-benzylpiperazine derivative of CPT-11 [7-ethyl-10-[4-(1-benzyl)-1-piperazino]carbonyloxycamptothecin (BP-CPT)] in U373MG glioma cell lines transfected with plasmids encoding rCE or hiCE. BP-CPT has been reported to be activated more efficiently than CPT-11 by a rat serum esterase activity; however, three-dimensional quantitative structure-activity relationship studies predicted that rCE would activate BP-CPT less efficiently than CPT-11. This was confirmed by both growth inhibition experiments and kinetic studies. The method is being used to design camptothecin prodrugs predicted to be activated by specific CEs.

Role-play games, experiments, workshops, blog posts: how community activities in HEPEX contribute to advance hydrologic ensemble prediction

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Wetterhall, Fredrik; Wood, Andy; Wang, Qj; Pappenberger, Florian; Verkade, Jan

2017-04-01

Since 2004, HEPEX (Hydrologic Ensemble Prediction Experiment) has been fostering a community of researchers and practitioners around the world. Through the years, it has contributed to establish a more integrative view of hydrological forecasting, where data assimilation, hydro-meteorological modelling chains, post-processing techniques, expert knowledge, and decision support systems are connected to enhance operational systems and water management applications. Here we present the community activities in HEPEX that have contributed to strengthening this unfunded/volunteer effort for more than a decade. It includes the organization of workshops, conference sessions, testbeds and inter-comparison experiments. More recently, HEPEX has also prompted the development of several publicly available role-play games and, since 2013, it has been running a blog portal (www.hepex.org), which is used as an intersection point for members. Through this website, members can continuously share their research, make announcements, report on workshops, projects and meetings, and hear about related research and operational challenges. It also creates a platform for early career scientists to become increasingly involved in hydrological forecasting science and applications.

Advancing from offline to online activity recognition with wearable sensors.

PubMed

Ermes, Miikka; Parkka, Juha; Cluitmans, Luc

2008-01-01

Activity recognition with wearable sensors could motivate people to perform a variety of different sports and other physical exercises. We have earlier developed algorithms for offline analysis of activity data collected with wearable sensors. In this paper, we present our current progress in advancing the platform for the existing algorithms to an online version, onto a PDA. Acceleration data are obtained from wireless motion bands which send the 3D raw acceleration signals via a Bluetooth link to the PDA which then performs the data collection, feature extraction and activity classification. As a proof-of-concept, the online activity system was tested with three subjects. All of them performed at least 5 minutes of each of the following activities: lying, sitting, standing, walking, running and cycling with an exercise bike. The average second-by-second classification accuracies for the subjects were 99%, 97%, and 82 %. These results suggest that earlier developed offline analysis methods for the acceleration data obtained from wearable sensors can be successfully implemented in an online activity recognition application.

Prediction of muscle activation for an eye movement with finite element modeling.

PubMed

Karami, Abbas; Eghtesad, Mohammad; Haghpanah, Seyyed Arash

2017-10-01

In this paper, a 3D finite element (FE) modeling is employed in order to predict extraocular muscles' activation and investigate force coordination in various motions of the eye orbit. A continuum constitutive hyperelastic model is employed for material description in dynamic modeling of the extraocular muscles (EOMs). Two significant features of this model are accurate mass modeling with FE method and stimulating EOMs for motion through muscle activation parameter. In order to validate the eye model, a forward dynamics simulation of the eye motion is carried out by variation of the muscle activation. Furthermore, to realize muscle activation prediction in various eye motions, two different tracking-based inverse controllers are proposed. The performance of these two inverse controllers is investigated according to their resulted muscle force magnitude and muscle force coordination. The simulation results are compared with the available experimental data and the well-known existing neurological laws. The comparison authenticates both the validation and the prediction results. Copyright © 2017 Elsevier Ltd. All rights reserved.

The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks

NASA Astrophysics Data System (ADS)

Lu, Z. L.; Li, D. C.; Lu, B. H.; Zhang, A. F.; Zhu, G. X.; Pi, G.

2010-05-01

Laser Engineered Net Shaping (LENS) is an advanced manufacturing technology, but it is difficult to control the depositing height (DH) of the prototype because there are many technology parameters influencing the forming process. The effect of main parameters (laser power, scanning speed and powder feeding rate) on the DH of single track is firstly analyzed, and then it shows that there is the complex nonlinear intrinsic relationship between them. In order to predict the DH, the back propagation (BP) based network improved with Adaptive learning rate and Momentum coefficient (AM) algorithm, and the least square support vector machine (LS-SVM) network are both adopted. The mapping relationship between above parameters and the DH is constructed according to training samples collected by LENS experiments, and then their generalization ability, function-approximating ability and real-time are contrastively investigated. The results show that although the predicted result by the BP-AM approximates the experimental result, above performance index of the LS-SVM are better than those of the BP-AM. Finally, high-definition thin-walled parts of AISI316L are successfully fabricated. Hence, the LS-SVM network is more suitable for the prediction of the DH.

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

PubMed

Garnotel, M; Bastian, T; Romero-Ugalde, H M; Maire, A; Dugas, J; Zahariev, A; Doron, M; Jallon, P; Charpentier, G; Franc, S; Blanc, S; Bonnet, S; Simon, C

2018-03-01

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated

Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils.

PubMed

Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M

2015-01-01

Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils

PubMed Central

Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M.

2015-01-01

Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM. PMID:26457111

Consciousness levels one week after admission to a palliative care unit improve survival prediction in advanced cancer patients.

PubMed

Tsai, Jaw-Shiun; Chen, Chao-Hsien; Wu, Chih-Hsun; Chiu, Tai-Yuan; Morita, Tatsuya; Chang, Chin-Hao; Hung, Shou-Hung; Lee, Ya-Ping; Chen, Ching-Yu

2015-02-01

Consciousness is an important factor of survival prediction in advanced cancer patients. However, effects on survival of changes over time in consciousness in advanced cancer patients have not been fully explored. This study evaluated changes in consciousness after admission to a palliative care unit and their correlation with prognosis in terminal cancer patients. This is a prospective observational study. From a palliative care unit in Taiwan, 531 cancer patients (51.8% male) were recruited. Consciousness status was assessed at admission and one week afterwards and recorded as normal or impaired. The mean age was 65.28±13.59 years, and the average survival time was 23.41±37.69 days. Patients with normal consciousness at admission (n=317) had better survival than those with impaired consciousness at admission (n=214): (17.0 days versus 6.0 days, p<0.001). In the analysis on survival within one week after admission, those with normal consciousness at admission had a higher percentage of survival than the impaired (78.9% versus 44.3%, p<0.001). Patients were further classified into four groups according to consciousness levels: (1) normal at admission and one week afterwards, (2) impaired at admission but normal one week afterwards, (3) normal at admission but impaired one week afterwards, and (4) impaired both at admission and one week afterwards. The former two groups had significantly better survival than the latter two groups: (median survival counted from day 7 after admission), 25.5, 27.0, 7.0, and 7.0 days, respectively. Consciousness levels one week after admission should be integrated into survival prediction in advanced cancer patients.

Do Urinary Cystine Parameters Predict Clinical Stone Activity?

PubMed

Friedlander, Justin I; Antonelli, Jodi A; Canvasser, Noah E; Morgan, Monica S C; Mollengarden, Daniel; Best, Sara; Pearle, Margaret S

2018-02-01

An accurate urinary predictor of stone recurrence would be clinically advantageous for patients with cystinuria. A proprietary assay (Litholink, Chicago, Illinois) measures cystine capacity as a potentially more reliable estimate of stone forming propensity. The recommended capacity level to prevent stone formation, which is greater than 150 mg/l, has not been directly correlated with clinical stone activity. We investigated the relationship between urinary cystine parameters and clinical stone activity. We prospectively followed 48 patients with cystinuria using 24-hour urine collections and serial imaging, and recorded stone activity. We compared cystine urinary parameters at times of stone activity with those obtained during periods of stone quiescence. We then performed correlation and ROC analysis to evaluate the performance of cystine parameters to predict stone activity. During a median followup of 70.6 months (range 2.2 to 274.6) 85 stone events occurred which could be linked to a recent urine collection. Cystine capacity was significantly greater for quiescent urine than for stone event urine (mean ± SD 48 ± 107 vs -38 ± 163 mg/l, p <0.001). Cystine capacity significantly correlated inversely with stone activity (r = -0.29, p <0.001). Capacity also correlated highly negatively with supersaturation (r = -0.88, p <0.001) and concentration (r = -0.87, p <0.001). Using the suggested cutoff of greater than 150 mg/l had only 8.0% sensitivity to predict stone quiescence. Decreasing the cutoff to 90 mg/l or greater improved sensitivity to 25.2% while maintaining specificity at 90.9%. Our results suggest that the target for capacity should be lower than previously advised. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Predicting Persuasion-Induced Behavior Change from the Brain

PubMed Central

Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

2011-01-01

Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

Using Laboratory Test Results at Hospital Admission to Predict Short-term Survival in Critically Ill Patients With Metastatic or Advanced Cancer.

PubMed

Cheng, Lee; DeJesus, Alma Y; Rodriguez, Maria A

2017-04-01

Accurately estimating the life expectancy of critically ill patients with metastatic or advanced cancer is a crucial step in planning appropriate palliative or supportive care. We evaluated the results of laboratory tests performed within two days of hospital admission to predict the likelihood of death within 14 days. We retrospectively selected patients 18 years or older with metastatic or advanced cancer who were admitted to intensive care units or palliative and supportive care services in our hospital. We evaluated whether the following are independent predictors in a logistic regression model: age, sex, comorbidities, and the results of seven commonly available laboratory tests. The end point was death within 14 days in or out of the hospital. Of 901 patients in the development cohort and 45% died within 14 days. The risk of death within 14 days after admission increased with increasing age, lactate dehydrogenase levels, and white blood cell counts and decreasing albumin levels and platelet counts (P < 0.01). The model predictions were confirmed using a separate validation cohort. The areas under the receiver operating characteristic curves were 0.74 and 0.70 for the development and validation cohorts, respectively, indicating good discriminatory ability for the model. Our results suggest that laboratory test results performed within two days of admission are valuable in predicting death within 14 days for patients with metastatic or advanced cancer. Such results may provide an objective assessment tool for physicians and help them initiate conversations with patients and families about end-of-life care. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Neural activity to a partner's facial expression predicts self-regulation after conflict.

PubMed

Hooker, Christine I; Gyurak, Anett; Verosky, Sara C; Miyakawa, Asako; Ayduk, Ozlem

2010-03-01

Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to emotion regulation in response to laboratory-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk factor for mood and behavior problems after an interpersonal conflict. However, it remains unclear whether LPFC activity to a laboratory-based affective challenge predicts self-regulation in real life. We investigated whether LPFC activity to a laboratory-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During a functional magnetic resonance imaging scan, healthy, adult participants in committed relationships (n = 27) viewed positive, negative, and neutral facial expressions of their partners. In a three-week online daily diary, participants reported conflict occurrence, level of negative mood, rumination, and substance use. LPFC activity in response to the laboratory-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance use. Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Neural activity to a partner's facial expression predicts self-regulation after conflict

PubMed Central

Hooker, Christine I.; Gyurak, Anett; Verosky, Sara; Miyakawa, Asako; Ayduk, Özlem

2009-01-01

Introduction Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to the regulation of emotional experience in response to lab-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk-factor for mood and behavior problems after an interpersonal stressor. However, it remains unclear whether LPFC activity to a lab-based affective challenge predicts self-regulation in real-life. Method We investigated whether LPFC activity to a lab-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During an fMRI scan, healthy, adult participants in committed, dating relationships (N = 27) viewed positive, negative, and neutral facial expressions of their partners. In an online daily-diary, participants reported conflict occurrence, level of negative mood, rumination, and substance-use. Results LPFC activity in response to the lab-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to the change in mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted the change in mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance-use. Conclusions Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. PMID:20004365

Prediction of energy expenditure and physical activity in preschoolers

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) ...

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

NASA Astrophysics Data System (ADS)

Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; de Zeeuw, Chris I.

2016-11-01

Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity.

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

PubMed Central

Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; De Zeeuw, Chris I.

2016-01-01

Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity. PMID:27805050

Predictions for Swift Follow-up Observations of Advanced LIGO/Virgo Gravitational Wave Sources

NASA Astrophysics Data System (ADS)

Racusin, Judith; Evans, Phil; Connaughton, Valerie

2015-04-01

The likely detection of gravitational waves associated with the inspiral of neutron star binaries by the upcoming advanced LIGO/Virgo observatories will be complemented by searches for electromagnetic counterparts over large areas of the sky by Swift and other observatories. As short gamma-ray bursts (GRB) are the most likely electromagnetic counterpart candidates to these sources, we can make predictions based upon the last decade of GRB observations by Swift and Fermi. Swift is uniquely capable of accurately localizing new transients rapidly over large areas of the sky in single and tiled pointings, enabling ground-based follow-up. We describe simulations of the detectability of short GRB afterglows by Swift given existing and hypothetical tiling schemes with realistic observing conditions and delays, which guide the optimal observing strategy and improvements provided by coincident detection with observatories such as Fermi-GBM.

Physics-based enzyme design: predicting binding affinity and catalytic activity.

PubMed

Sirin, Sarah; Pearlman, David A; Sherman, Woody

2014-12-01

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications. © 2014 Wiley Periodicals, Inc.

The potential predictive value of circulating immune cell ratio and tumor marker in atezolizumab treated advanced non-small cell lung cancer patients.

PubMed

Zhuo, Minglei; Chen, Hanxiao; Zhang, Tianzhuo; Yang, Xue; Zhong, Jia; Wang, Yuyan; An, Tongtong; Wu, Meina; Wang, Ziping; Huang, Jing; Zhao, Jun

2018-05-04

The PD-L1 antibody atezolizumab has shown promising efficacy in patients with advanced non-small cell lung cancer. But the predictive marker of clinical benefit has not been identified. This study aimed to search for potential predictive factors in circulating blood of patients receiving atezolizumab. Ten patients diagnosed with advanced non-small cell lung cancer were enrolled in this open-label observing study. Circulating immune cells and plasma tumor markers were examined in peripheral blood from these patients before and after atezolizumab treatment respectively. Relation between changes in circulating factors and anti-tumor efficacy were analyzed. Blood routine test showed that atezolizumab therapy induced slightly elevation of white blood cells count generally. The lymphocyte ratio was increased slightly in disease controlled patients but decreased prominently in disease progressed patients in response to atezolizumab therapy. Flow cytometric analysis revealed changes in percentage of various immune cell types, including CD4+ T cell, CD8+ T cell, myeloid-derived suppressor cell, regulatory T cell and PD-1 expressing T cell after atezolizumab. Levels of plasma tumor marker CEA, CA125 and CA199 were also altered after anti-PD-L1 therapy. In comparison with baseline, the disease progressed patients showed sharp increase in tumor marker levels, while those disease controlled patients were seen with decreased regulatory T cell and myeloid-derived suppressor cell ratios. The circulating immune cell ratios and plasma tumor marker levels were related with clinical efficacy of atezolizumab therapy. These factors could be potential predictive marker for anti-PD-L1 therapy in advanced non-small cell lung cancer.

Forecasts and predictions of eruptive activity at Mount St. Helens, USA: 1975-1984

USGS Publications Warehouse

Swanson, D.A.; Casadevall, T.J.; Dzurisin, D.; Holcomb, R.T.; Newhall, C.G.; Malone, S.D.; Weaver, C.S.

1985-01-01

Public statements about volcanic activity at Mount St. Helens include factual statements, forecasts, and predictions. A factual statement describes current conditions but does not anticipate future events. A forecast is a comparatively imprecise statement of the time, place, and nature of expected activity. A prediction is a comparatively precise statement of the time, place, and ideally, the nature and size of impending activity. A prediction usually covers a shorter time period than a forecast and is generally based dominantly on interpretations and measurements of ongoing processes and secondarily on a projection of past history. The three types of statements grade from one to another, and distinctions are sometimes arbitrary. Forecasts and predictions at Mount St. Helens became increasingly precise from 1975 to 1982. Stratigraphic studies led to a long-range forecast in 1975 of renewed eruptive activity at Mount St. Helens, possibly before the end of the century. On the basis of seismic, geodetic and geologic data, general forecasts for a landslide and eruption were issued in April 1980, before the catastrophic blast and landslide on 18 May 1980. All extrusions except two from June 1980 to the end of 1984 were predicted on the basis of integrated geophysical, geochemical, and geologic monitoring. The two extrusions that were not predicted were preceded by explosions that removed a substantial part of the dome, reducing confining pressure and essentially short-circuiting the normal precursors. ?? 1985.

Simple Response Surface Methodology: Investigation on Advance Photocatalytic Oxidation of 4-Chlorophenoxyacetic Acid Using UV-Active ZnO Photocatalyst.

PubMed

Lee, Kian Mun; Hamid, Sharifah Bee Abd

2015-01-19

The performance of advance photocatalytic degradation of 4-chlorophenoxyacetic acid (4-CPA) strongly depends on photocatalyst dosage, initial concentration and initial pH. In the present study, a simple response surface methodology (RSM) was applied to investigate the interaction between these three independent factors. Thus, the photocatalytic degradation of 4-CPA in aqueous medium assisted by ultraviolet-active ZnO photocatalyst was systematically investigated. This study aims to determine the optimum processing parameters to maximize 4-CPA degradation. Based on the results obtained, it was found that a maximum of 91% of 4-CPA was successfully degraded under optimal conditions (0.02 g ZnO dosage, 20.00 mg/L of 4-CPA and pH 7.71). All the experimental data showed good agreement with the predicted results obtained from statistical analysis.

Recent advances in the development and use of molecular tests to predict antimicrobial resistance in Neisseria gonorrhoeae.

PubMed

Donà, Valentina; Low, Nicola; Golparian, Daniel; Unemo, Magnus

2017-09-01

The number of genetic tests, mostly real-time PCRs, to detect antimicrobial resistance (AMR) determinants and predict AMR in Neisseria gonorrhoeae is increasing. Several of these assays are promising, but there are important shortcomings and few assays have been adequately validated and quality assured. Areas covered: Recent advances, focusing on publications since 2012, in the development and use of molecular tests to predict gonococcal AMR for surveillance and for clinical use, advantages and disadvantages of these tests and of molecular AMR prediction compared with phenotypic AMR testing, and future perspectives for effective use of molecular AMR tests for different purposes. Expert commentary: Several challenges for direct testing of clinical, especially extra-genital, specimens remain. The choice of molecular assay needs to consider the assay target, quality controls, sample types, limitations intrinsic to molecular technologies, and specific to the chosen methodology, and the intended use of the test. Improved molecular- and particularly genome-sequencing-based methods will supplement AMR testing for surveillance purposes, and translate into point-of-care tests that will lead to personalized treatments, while sparing the last available empiric treatment option (ceftriaxone). However, genetic AMR prediction will never completely replace phenotypic AMR testing, which detects also AMR due to unknown AMR determinants.

Advance cueing produces enhanced action-boundary patterns of spike activity in the sensorimotor striatum

PubMed Central

Barnes, Terra D.; Mao, Jian-Bin; Hu, Dan; Kubota, Yasuo; Dreyer, Anna A.; Stamoulis, Catherine; Brown, Emery N.

2011-01-01

One of the most characteristic features of habitual behaviors is that they can be evoked by a single cue. In the experiments reported here, we tested for the effects of such advance cueing on the firing patterns of striatal neurons in the sensorimotor striatum. Rats ran in a T-maze with instruction cues about the location of reward given at the start of the runs. This advance cueing about reward produced a highly augmented task-bracketing pattern of activity at the beginning and end of procedural task performance relative to the patterns found previously with midtask cueing. Remarkably, the largest increase in activity early during the T-maze runs was not associated with the instruction cues themselves, the earliest predictors of reward; instead, the highest peak of early activity was associated with the beginning of the motor period of the task. We suggest that the advance cueing, reducing midrun demands for decision making but adding a working-memory load, facilitated chunking of the maze runs as executable scripts anchored to sensorimotor aspects of the task and unencumbered by midtask decision-making demands. Our findings suggest that the acquisition of stronger task-bracketing patterns of striatal activity in the sensorimotor striatum could reflect this enhancement of behavioral chunking. Deficits in such representations of learned sequential behaviors could contribute to motor and cognitive problems in a range of neurological disorders affecting the basal ganglia, including Parkinson's disease. PMID:21307317

Posture and activity recognition and energy expenditure prediction in a wearable platform.

PubMed

Sazonova, Nadezhda; Browning, Raymond; Melanson, Edward; Sazonov, Edward

2014-01-01

The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time prediction and display of time spent in various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we introduce new algorithms that require substantially less computational power. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a four-hour stay in a room calorimeter. Use of Multinomial Logistic Discrimination (MLD) for posture and activity classification resulted in an accuracy comparable to that of Support Vector Machines (SVM) (90% vs. 95%-98%) while reducing the running time by a factor of 190 and reducing the memory requirement by a factor of 104. Per minute EE estimation using activity-specific models resulted in an accurate EE prediction (RMSE of 0.53 METs vs. RMSE of 0.69 METs using previously reported SVM-branched models). These results demonstrate successful implementation of real-time physical activity monitoring and EE prediction system on a wearable platform.

Predictive and preventive strategies to advance the treatments of cardiovascular and cerebrovascular diseases: the Ukrainian context

PubMed Central

2012-01-01

Despite great efforts in treatments of cardiovascular diseases, the field requires innovative strategies because of high rates of morbidity, mortality and disability, indicating evident deficits in predictive vascular diagnosis and individualized treatment approaches. Talking about the vascular system, currently, physicians are not provided with integrated medical approaches to diagnose and treat vascular diseases. Only an individual global approach to the analysis of all segments in the vascular system of a patient allows finding the optimal way for vascular disease treatment. As for the existing methodology, there is a dominance of static methods such as X-ray contrast angiography and magnetic resonance imaging in angiomode. Taking into account the world experience, this article deals with innovative strategies, aiming at predictive diagnosis in vascular system, personalization of the biomedical treatment approaches, and targeted prevention of individual patient cohorts. Clinical examples illustrate the advances in corresponding healthcare sectors. Recommendations are provided to promote the field. PMID:23083430

Predicting flow at work: investigating the activities and job characteristics that predict flow states at work.

PubMed

Nielsen, Karina; Cleal, Bryan

2010-04-01

Flow (a state of consciousness where people become totally immersed in an activity and enjoy it intensely) has been identified as a desirable state with positive effects for employee well-being and innovation at work. Flow has been studied using both questionnaires and Experience Sampling Method (ESM). In this study, we used a newly developed 9-item flow scale in an ESM study combined with a questionnaire to examine the predictors of flow at two levels: the activities (brainstorming, planning, problem solving and evaluation) associated with transient flow states and the more stable job characteristics (role clarity, influence and cognitive demands). Participants were 58 line managers from two companies in Denmark; a private accountancy firm and a public elder care organization. We found that line managers in elder care experienced flow more often than accountancy line managers, and activities such as planning, problem solving, and evaluation predicted transient flow states. The more stable job characteristics included in this study were not, however, found to predict flow at work. Copyright 2010 APA, all rights reserved.

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

PubMed Central

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S.

2017-01-01

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a=(u,v) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages. PMID:28771201

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

PubMed

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Receptor for advanced glycation end-products and ARDS prediction: a multicentre observational study.

PubMed

Jabaudon, Matthieu; Berthelin, Pauline; Pranal, Thibaut; Roszyk, Laurence; Godet, Thomas; Faure, Jean-Sébastien; Chabanne, Russell; Eisenmann, Nathanael; Lautrette, Alexandre; Belville, Corinne; Blondonnet, Raiko; Cayot, Sophie; Gillart, Thierry; Pascal, Julien; Skrzypczak, Yvan; Souweine, Bertrand; Blanchon, Loic; Sapin, Vincent; Pereira, Bruno; Constantin, Jean-Michel

2018-02-08

Acute respiratory distress syndrome (ARDS) prediction remains challenging despite available clinical scores. To assess soluble receptor for advanced glycation end-products (sRAGE), a marker of lung epithelial injury, as a predictor of ARDS in a high-risk population, adult patients with at least one ARDS risk factor upon admission to participating intensive care units (ICUs) were enrolled in a multicentre, prospective study between June 2014 and January 2015. Plasma sRAGE and endogenous secretory RAGE (esRAGE) were measured at baseline (ICU admission) and 24 hours later (day one). Four AGER candidate single nucleotide polymorphisms (SNPs) were also assayed because of previous reports of functionality (rs1800625, rs1800624, rs3134940, and rs2070600). The primary outcome was ARDS development within seven days. Of 500 patients enrolled, 464 patients were analysed, and 59 developed ARDS by day seven. Higher baseline and day one plasma sRAGE, but not esRAGE, were independently associated with increased ARDS risk. AGER SNP rs2070600 (Ser/Ser) was associated with increased ARDS risk and higher plasma sRAGE in this cohort, although confirmatory studies are needed to assess the role of AGER SNPs in ARDS prediction. These findings suggest that among at-risk ICU patients, higher plasma sRAGE may identify those who are more likely to develop ARDS.

In Vitro and In Vivo Activities of Antimicrobial Peptides Developed Using an Amino Acid-Based Activity Prediction Method

PubMed Central

Wu, Xiaozhe; Wang, Zhenling; Li, Xiaolu; Fan, Yingzi; He, Gu; Wan, Yang; Yu, Chaoheng; Tang, Jianying; Li, Meng; Zhang, Xian; Zhang, Hailong; Xiang, Rong; Pan, Ying; Liu, Yan; Lu, Lian

2014-01-01

To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen for further analysis. Compared to the parent peptide HH2, this novel AMP showed broad-spectrum, improved antimicrobial activity, and in a cytotoxicity assay it showed lower toxicity against human cells. The in vivo antimicrobial activity of DP7 was tested in a Staphylococcus aureus infection murine model. When inoculated and treated via intraperitoneal injection, DP7 reduced the bacterial load in the peritoneal lavage solution. Electron microscope imaging and the results indicated disruption of the S. aureus outer membrane by DP7. Our new prediction method can therefore be employed to identify AMPs possessing minor amino acid differences with improved antimicrobial activities, potentially increasing the therapeutic agents available to combat multidrug-resistant infections. PMID:24982064

Disrupted Prediction Error Links Excessive Amygdala Activation to Excessive Fear.

PubMed

Sengupta, Auntora; Winters, Bryony; Bagley, Elena E; McNally, Gavan P

2016-01-13

Basolateral amygdala (BLA) is critical for fear learning, and its heightened activation is widely thought to underpin a variety of anxiety disorders. Here we used chemogenetic techniques in rats to study the consequences of heightened BLA activation for fear learning and memory, and to specifically identify a mechanism linking increased activity of BLA glutamatergic neurons to aberrant fear. We expressed the excitatory hM3Dq DREADD in rat BLA glutamatergic neurons and showed that CNO acted selectively to increase their activity, depolarizing these neurons and increasing their firing rates. This chemogenetic excitation of BLA glutamatergic neurons had no effect on the acquisition of simple fear learning, regardless of whether this learning led to a weak or strong fear memory. However, in an associative blocking task, chemogenetic excitation of BLA glutamatergic neurons yielded significant learning to a blocked conditioned stimulus, which otherwise should not have been learned about. Moreover, in an overexpectation task, chemogenetic manipulation of BLA glutamatergic neurons prevented use of negative prediction error to reduce fear learning, leading to significant impairments in fear inhibition. These effects were not attributable to the chemogenetic manipulation enhancing arousal, increasing asymptotic levels of fear learning or fear memory consolidation. Instead, chemogenetic excitation of BLA glutamatergic neurons disrupted use of prediction error to regulate fear learning. Several neuropsychiatric disorders are characterized by heightened activation of the amygdala. This heightened activation has been hypothesized to underlie increased emotional reactivity, fear over generalization, and deficits in fear inhibition. Yet the mechanisms linking heightened amygdala activation to heightened emotional learning are elusive. Here we combined chemogenetic excitation of rat basolateral amygdala glutamatergic neurons with a variety of behavioral approaches to show that

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks

DOE PAGES

Zhao, Suwen; Sakai, Ayano; Zhang, Xinshuai; ...

2014-06-30

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins inmore » 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.« less

Macromolecular target prediction by self-organizing feature maps.

PubMed

Schneider, Gisbert; Schneider, Petra

2017-03-01

Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

Earth Observing System/Advanced Microwave SoundingUnit-A (EOS/AMSU-A): Acquisition activities plan

NASA Technical Reports Server (NTRS)

Schwantje, Robert

1994-01-01

This is the acquisition activities plan for the software to be used in the Earth Observing System (EOS) Advanced Microwave Sounding Unit-A (AMSU-A) system. This document is submitted in response to Contract NAS5-323 14 as CDRL 508. The procurement activities required to acquire software for the EOS/AMSU-A program are defined.

Predicting the Activity Coefficients of Free-Solvent for Concentrated Globular Protein Solutions Using Independently Determined Physical Parameters

PubMed Central

McBride, Devin W.; Rodgers, Victor G. J.

2013-01-01

The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations. PMID:24324733

Advance Preparation in Task-Switching: Converging Evidence from Behavioral, Brain Activation, and Model-Based Approaches

PubMed Central

Karayanidis, Frini; Jamadar, Sharna; Ruge, Hannes; Phillips, Natalie; Heathcote, Andrew; Forstmann, Birte U.

2010-01-01

Recent research has taken advantage of the temporal and spatial resolution of event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI) to identify the time course and neural circuitry of preparatory processes required to switch between different tasks. Here we overview some key findings contributing to understanding strategic processes in advance preparation. Findings from these methodologies are compatible with advance preparation conceptualized as a set of processes activated for both switch and repeat trials, but with substantial variability as a function of individual differences and task requirements. We then highlight new approaches that attempt to capitalize on this variability to link behavior and brain activation patterns. One approach examines correlations among behavioral, ERP and fMRI measures. A second “model-based” approach accounts for differences in preparatory processes by estimating quantitative model parameters that reflect latent psychological processes. We argue that integration of behavioral and neuroscientific methodologies is key to understanding the complex nature of advance preparation in task-switching. PMID:21833196

SOD activity of carboxyfullerenes predicts their neuroprotective efficacy: A structure-activity study

PubMed Central

Ali, Sameh Saad; Hardt, Joshua I.; Dugan, Laura L.

2008-01-01

Superoxide radical anion is a biologically important oxidant that has been linked to tissue injury and inflammation in several diseases. Here we carried out a structure-activity study on 6 different carboxyfullerene superoxide dismutase (SOD) mimetics with distinct electronic and biophysical characteristics. Neurotoxicity via NMDA receptors, which involves intracellular superoxide, was used as a model to evaluate structure-activity relationships between reactivity towards superoxide and neuronal rescue by these drugs. A significant correlation between neuroprotection by carboxyfullerenes and their ki towards superoxide radical was observed. Computer-assistant molecular modeling demonstrated that the reactivity towards superoxide is sensitive to changes in dipole moment which are dictated not only by the number of carboxyl groups, but also by their distribution on the fullerene ball. These results indicate that the SOD activity of these cell-permeable compounds predicts neuroprotection, and establishes a structure-activity relationship to aid in future studies on the biology of superoxide across disciplines. PMID:18656425

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

PubMed Central

Wager, Tor D.; Atlas, Lauren Y.; Leotti, Lauren A.; Rilling, James K.

2012-01-01

Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provided both unbiased estimates of predictive accuracy and interpretable maps of which regions are most important for prediction. Increased anticipatory activity in a frontoparietal network and decreases in a posterior insular/temporal network predicted placebo analgesia. Patterns of anticipatory activity across the cortex predicted a moderate amount of variance in the placebo response (~12% overall, ~40% for study 2 alone), which is substantial considering the multiple likely contributing factors. The most predictive regions were those associated with emotional appraisal, rather than cognitive control or pain processing. During pain, decreases in limbic and paralimbic regions most strongly predicted placebo analgesia. Responses within canonical pain-processing regions explained significant variance in placebo analgesia, but the pattern of effects was inconsistent with widespread decreases in nociceptive processing. Together, the findings suggest that engagement of emotional appraisal circuits drives individual variation in placebo analgesia, rather than early suppression of nociceptive processing. This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models. PMID:21228154

[Activities of Research Institute for Advanced Computer Science

NASA Technical Reports Server (NTRS)

Gross, Anthony R. (Technical Monitor); Leiner, Barry M.

2001-01-01

The Research Institute for Advanced Computer Science (RIACS) carries out basic research and technology development in computer science, in support of the National Aeronautics and Space Administrations missions. RIACS is located at the NASA Ames Research Center, Moffett Field, California. RIACS research focuses on the three cornerstones of IT research necessary to meet the future challenges of NASA missions: 1. Automated Reasoning for Autonomous Systems Techniques are being developed enabling spacecraft that will be self-guiding and self-correcting to the extent that they will require little or no human intervention. Such craft will be equipped to independently solve problems as they arise, and fulfill their missions with minimum direction from Earth. 2. Human-Centered Computing Many NASA missions require synergy between humans and computers, with sophisticated computational aids amplifying human cognitive and perceptual abilities. 3. High Performance Computing and Networking Advances in the performance of computing and networking continue to have major impact on a variety of NASA endeavors, ranging from modeling and simulation to analysis of large scientific datasets to collaborative engineering, planning and execution. In addition, RIACS collaborates with NASA scientists to apply IT research to a variety of NASA application domains. RIACS also engages in other activities, such as workshops, seminars, visiting scientist programs and student summer programs, designed to encourage and facilitate collaboration between the university and NASA IT research communities.

Advanced Marketing 8130. Instructional Areas. Duties and Tasks. Learning Activities. Referenced Resources.

ERIC Educational Resources Information Center

Virginia State Dept. of Education, Richmond.

This resource handbook, which is designed for use by instructors of courses in advanced marketing, consists of a duty/task list with referenced resources, a duty/task list with learning activities, and a list of resources. Included in each list are materials dealing with the following topics: communication in marketing, economics in marketing,…

Fan Noise Prediction: Status and Needs

NASA Technical Reports Server (NTRS)

Huff, Dennis L.

1997-01-01

The prediction of fan noise is an important part to the prediction of overall turbofan engine noise. Advances in computers and better understanding of the flow physics have allowed researchers to compute sound generation from first principles and rely less on empirical correlations. While progress has been made, there are still many aspects of the problem that need to be explored. This paper presents some recent advances in fan noise prediction and suggests areas that still need further development. Fan noise predictions that support the recommendations are taken from existing publications.

A cluster expansion model for predicting activation barrier of atomic processes

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

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit, E-mail: achatter@iitk.ac.in

2013-06-15

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEBmore » results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog.« less

Advanced biological activated carbon filter for removing pharmaceutically active compounds from treated wastewater.

PubMed

Sbardella, Luca; Comas, Joaquim; Fenu, Alessio; Rodriguez-Roda, Ignasi; Weemaes, Marjoleine

2018-04-28

Through their release of effluents, conventional wastewater treatment plants (WWTPs) represent a major pollution point sources for pharmaceutically active compounds (PhACs) in water bodies. The combination of a biological activated carbon (BAC) filter coupled with an ultrafiltration (UF) unit was evaluated as an advanced treatment for PhACs removal at pilot scale. The BAC-UF pilot plant was monitored for one year. The biological activity of the biofilm that developed on the granular activated carbon (GAC) particles and the contribution of this biofilm to the overall removal of PhACs were evaluated. Two different phases were observed during the long-term monitoring of PhACs removal. During the first 9200 bed volumes (BV; i.e., before GAC saturation), 89, 78, 83 and 79% of beta-blockers, psychiatric drugs, antibiotics and a mix of other therapeutic groups were removed, respectively. The second phase was characterized by deterioration of the overall performances during the period between 9200 and 13,800 BV. To quantify the respective contribution of adsorption and biodegradation, a lab-scale setup was operated for four months and highlighted the essential role played by GAC in biofiltration units. Physical adsorption was indeed the main removal mechanism. Nevertheless, a significant contribution due to biological activity was detected for some PhACs. The biofilm contributed to the removal of 22, 25, 30, 32 and 35% of ciprofloxacin, bezafibrate, ofloxacin, azithromycin and sulfamethoxazole, respectively. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting active school travel: the role of planned behavior and habit strength.

PubMed

Murtagh, Shemane; Rowe, David A; Elliott, Mark A; McMinn, David; Nelson, Norah M

2012-05-30

Despite strong support for predictive validity of the theory of planned behavior (TPB) substantial variance in both intention and behavior is unaccounted for by the model's predictors. The present study tested the extent to which habit strength augments the predictive validity of the TPB in relation to a currently under-researched behavior that has important health implications, namely children's active school travel. Participants (N = 126 children aged 8-9 years; 59 % males) were sampled from five elementary schools in the west of Scotland and completed questionnaire measures of all TPB constructs in relation to walking to school and both walking and car/bus use habit. Over the subsequent week, commuting steps on school journeys were measured objectively using an accelerometer. Hierarchical multiple regressions were used to test the predictive utility of the TPB and habit strength in relation to both intention and subsequent behavior. The TPB accounted for 41 % and 10 % of the variance in intention and objectively measured behavior, respectively. Together, walking habit and car/bus habit significantly increased the proportion of explained variance in both intention and behavior by 6 %. Perceived behavioral control and both walking and car/bus habit independently predicted intention. Intention and car/bus habit independently predicted behavior. The TPB significantly predicts children's active school travel. However, habit strength augments the predictive validity of the model. The results indicate that school travel is controlled by both intentional and habitual processes. In practice, interventions could usefully decrease the habitual use of motorized transport for travel to school and increase children's intention to walk (via increases in perceived behavioral control and walking habit, and decreases in car/bus habit). Further research is needed to identify effective strategies for changing these antecedents of children's active school travel.

Predicting active school travel: The role of planned behavior and habit strength

PubMed Central

2012-01-01

Background Despite strong support for predictive validity of the theory of planned behavior (TPB) substantial variance in both intention and behavior is unaccounted for by the model’s predictors. The present study tested the extent to which habit strength augments the predictive validity of the TPB in relation to a currently under-researched behavior that has important health implications, namely children’s active school travel. Method Participants (N = 126 children aged 8–9 years; 59 % males) were sampled from five elementary schools in the west of Scotland and completed questionnaire measures of all TPB constructs in relation to walking to school and both walking and car/bus use habit. Over the subsequent week, commuting steps on school journeys were measured objectively using an accelerometer. Hierarchical multiple regressions were used to test the predictive utility of the TPB and habit strength in relation to both intention and subsequent behavior. Results The TPB accounted for 41 % and 10 % of the variance in intention and objectively measured behavior, respectively. Together, walking habit and car/bus habit significantly increased the proportion of explained variance in both intention and behavior by 6 %. Perceived behavioral control and both walking and car/bus habit independently predicted intention. Intention and car/bus habit independently predicted behavior. Conclusions The TPB significantly predicts children’s active school travel. However, habit strength augments the predictive validity of the model. The results indicate that school travel is controlled by both intentional and habitual processes. In practice, interventions could usefully decrease the habitual use of motorized transport for travel to school and increase children’s intention to walk (via increases in perceived behavioral control and walking habit, and decreases in car/bus habit). Further research is needed to identify effective strategies for changing these

Development of Computational Capabilities to Predict the Corrosion Wastage of Boiler Tubes in Advanced Combustion Systems

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

Kung, Steven; Rapp, Robert

reheaters in coal-fired boilers resulting from the coexistence of sulfur and chlorine in the fuel. A new corrosion mechanism, i.e., “Active Sulfidation Corrosion Mechanism,” has been proposed to account for the accelerated corrosion wastage observed on the furnace walls of utility boilers burning coals containing sulfur and chlorine. In addition, a second corrosion mechanism, i.e., “Active Sulfide-to-Oxide Corrosion Mechanism,” has been identified to account for the rapid corrosion attack on superheaters and reheaters. Both of the newly discovered corrosion mechanisms involve the formation of iron chloride (FeCl2) vapor from iron sulfide (FeS) and HCl, followed by the decomposition of FeCl2 via self-sustaining cycling reactions. For higher alloys containing sufficient chromium, the attack on superheaters and reheaters is dominated by Hot Corrosion in the presence of a fused salt. Furthermore, two stages of the hot corrosion mechanism have been identified and characterized in detail. The initiation of hot corrosion attack induced by molten sulfate leads to Stage 1 “acidic” fluxing and re-precipitation of the protective scale formed initially on the deposit-covered alloy surfaces. Once the protective scale is penetrated, Stage 2 Hot Corrosion is initiated, which is dominated by “basic” fluxing and re-precipitation of the scale in the fused salt. Based on the extensive corrosion information generated from this project, corrosion modeling was performed using non-linear regression analysis. As a result of the modeling efforts, two predictive equations have been formulated, one for furnace walls and the other for superheaters and reheaters. These first-of-the-kind equations can be used to estimate the corrosion rates of boiler tubes based on coal chemistry, alloy compositions, and boiler operating conditions for advanced boiler systems.« less

High solar activity predictions through an artificial neural network

NASA Astrophysics Data System (ADS)

Orozco-Del-Castillo, M. G.; Ortiz-Alemán, J. C.; Couder-Castañeda, C.; Hernández-Gómez, J. J.; Solís-Santomé, A.

The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24-26, which we found to occur before 2040.

MGMT expression levels predict disease stabilisation, progression-free and overall survival in patients with advanced melanomas treated with DTIC.

PubMed

Busch, Christian; Geisler, Jürgen; Lillehaug, Johan R; Lønning, Per Eystein

2010-07-01

Metastatic melanoma responds poorly to systemic treatment. We report the results of a prospective single institution study evaluating O(6)-methylguanine-DNA methyltransferase (MGMT) status as a potential predictive and/or prognostic marker among patients treated with dacarbazine (DTIC) 800-1000 mg/m(2) monotherapy administered as a 3-weekly schedule for advanced malignant melanomas. The study was approved by the Regional Ethical Committee. Surgical biopsies from metastatic or loco-regional deposits obtained prior to DTIC treatment were snap-frozen immediately upon removal and stored in liquid nitrogen up to processing. Median time from enrolment to end of follow-up was 67 months. MGMT expression levels evaluated by qRT-PCR correlated significantly to DTIC benefit (CR/PR/SD; p=0.005), time to progression (TTP) (p=0.005) and overall survival (OS) (p=0.003). MGMT expression also correlated to Breslow thickness in the primary tumour (p=0.014). While MGMT promoter hypermethylation correlated to MGMT expression, MGMT promoter hypermethylation did not correlate to treatment benefit, TTP or OS, suggesting that other factors may be critical in determining MGMT expression levels in melanomas. In a Cox proportional regression analysis, serum lactate dehydrogenase (LDH, p<0.001), MGMT expression (p=0.022) and p16(INK4a) expression (p=0.037) independently predicted OS, while TTP correlated to DTIC benefit after 6 weeks only (p=0.001). Our data reveal MGMT expression levels to be associated with disease stabilisation and prognosis in patients receiving DTIC monotherapy for advanced melanoma. The role of MGMT expression as a predictor to DTIC sensitivity versus a general prognostic factor in advanced melanomas warrants further evaluation. Copyright 2010 Elsevier Ltd. All rights reserved.

Biomarkers in inflammatory bowel disease: current practices and recent advances.

PubMed

Iskandar, Heba N; Ciorba, Matthew A

2012-04-01

Crohn's disease and ulcerative colitis represent the two main forms of the idiopathic chronic inflammatory bowel diseases (IBD). Currently available blood and stool based biomarkers provide reproducible, quantitative tools that can complement clinical assessment to aid clinicians in IBD diagnosis and management. C-reactive protein and fecal based leukocyte markers can help the clinician distinguish IBD from noninflammatory diarrhea and assess disease activity. The ability to differentiate between forms of IBD and predict risk for disease complications is specific to serologic tests including antibodies against Saccharomyces cerevisiae and perinuclear antineutrophil cytoplasmic proteins. Advances in genomic, proteomic, and metabolomic array based technologies are facilitating the development of new biomarkers for IBD. The discovery of novel biomarkers, which can correlate with mucosal healing or predict long-term disease course has the potential to significantly improve patient care. This article reviews the uses and limitations of currently available biomarkers and highlights recent advances in IBD biomarker discovery. Copyright © 2012 Mosby, Inc. All rights reserved.

Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

NASA Astrophysics Data System (ADS)

Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

2017-12-01

Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

Prefrontal Brain Activity Predicts Temporally Extended Decision-Making Behavior

ERIC Educational Resources Information Center

Yarkoni, Tal; Braver, Todd S.; Gray, Jeremy R.; Green, Leonard

2005-01-01

Although functional neuroimaging studies of human decision-making processes are increasingly common, most of the research in this area has relied on passive tasks that generate little individual variability. Relatively little attention has been paid to the ability of brain activity to predict overt behavior. Using functional magnetic resonance…

Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.

PubMed

Gao, Yu-Fei; Li, Bi-Qing; Cai, Yu-Dong; Feng, Kai-Yan; Li, Zhan-Dong; Jiang, Yang

2013-01-27

Identification of catalytic residues plays a key role in understanding how enzymes work. Although numerous computational methods have been developed to predict catalytic residues and active sites, the prediction accuracy remains relatively low with high false positives. In this work, we developed a novel predictor based on the Random Forest algorithm (RF) aided by the maximum relevance minimum redundancy (mRMR) method and incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility to predict active sites of enzymes and achieved an overall accuracy of 0.885687 and MCC of 0.689226 on an independent test dataset. Feature analysis showed that every category of the features except disorder contributed to the identification of active sites. It was also shown via the site-specific feature analysis that the features derived from the active site itself contributed most to the active site determination. Our prediction method may become a useful tool for identifying the active sites and the key features identified by the paper may provide valuable insights into the mechanism of catalysis.

A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

PubMed Central

Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

2013-01-01

In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

PubMed

Naik, P K; Singh, T; Singh, H

2009-07-01

Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

Propulsion/ASME Rocket-Based Combined Cycle Activities in the Advanced Space Transportation Program Office

NASA Technical Reports Server (NTRS)

Hueter, Uwe; Turner, James

1998-01-01

NASA's Office Of Aeronautics and Space Transportation Technology (OASTT) has establish three major coals. "The Three Pillars for Success". The Advanced Space Transportation Program Office (ASTP) at the NASA's Marshall Space Flight Center in Huntsville,Ala. focuses on future space transportation technologies under the "Access to Space" pillar. The Advanced Reusable Technologies (ART) Project, part of ASTP, focuses on the reusable technologies beyond those being pursued by X-33. The main activity over the past two and a half years has been on advancing the rocket-based combined cycle (RBCC) technologies. In June of last year, activities for reusable launch vehicle (RLV) airframe and propulsion technologies were initiated. These activities focus primarily on those technologies that support the year 2000 decision to determine the path this country will take for Space Shuttle and RLV. In February of this year, additional technology efforts in the reusable technologies were awarded. The RBCC effort that was completed early this year was the initial step leading to flight demonstrations of the technology for space launch vehicle propulsion. Aerojet, Boeing-Rocketdyne and Pratt & Whitney were selected for a two-year period to design, build and ground test their RBCC engine concepts. In addition, ASTROX, Pennsylvania State University (PSU) and University of Alabama in Huntsville also conducted supporting activities. The activity included ground testing of components (e.g., injectors, thrusters, ejectors and inlets) and integrated flowpaths. An area that has caused a large amount of difficulty in the testing efforts is the means of initiating the rocket combustion process. All three of the prime contractors above were using silane (SiH4) for ignition of the thrusters. This follows from the successful use of silane in the NASP program for scramjet ignition. However, difficulties were immediately encountered when silane (an 80/20 mixture of hydrogen/silane) was used for rocket

Anthropometry and physical activity level in the prediction of metabolic syndrome in children.

PubMed

Andaki, Alynne Christian Ribeiro; Tinôco, Adelson Luiz Araújo; Mendes, Edmar Lacerda; Andaki Júnior, Roberto; Hills, Andrew P; Amorim, Paulo Roberto S

2014-10-01

To evaluate the effectiveness of anthropometric measures and physical activity level in the prediction of metabolic syndrome (MetS) in children. Cross-sectional study with children from public and private schools. Children underwent an anthropometric assessment, blood pressure measurement and biochemical evaluation of serum for determination of TAG, HDL-cholesterol and glucose. Physical activity level was calculated and number of steps per day obtained using a pedometer for seven consecutive days. Viçosa, south-eastern Brazil. Boys and girls (n 187), mean age 9·90 (SD 0·7) years. Conicity index, sum of four skinfolds, physical activity level and number of steps per day were accurate in predicting MetS in boys. Anthropometric indicators were accurate in predicting MetS for girls, specifically BMI, waist circumference measured at the narrowest point and at the level of the umbilicus, four skinfold thickness measures evaluated separately, the sum of subscapular and triceps skinfold thickness, the sum of four skinfolds and body fat percentage. The sum of four skinfolds was the most accurate method in predicting MetS in both genders.

Spontaneous brain activity predicts learning ability of foreign sounds.

PubMed

Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

2013-05-29

Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

Further advances in predicting species distributions

Treesearch

Gretchen G. Moisen; Thomas C. Edwards; Patrick E. Osborne

2006-01-01

In 2001, a workshop focused on the use of generalized linear models (GLM: McCullagh and Nelder, 1989) and generalized additive models (GAM: Hastie and Tibshirani, 1986, 1990) for predicting species distributions was held in Riederalp, Switzerland. This topic led to the publication of special issues in Ecological Modelling (Guisan et al., 2002) and Biodiversity and...

Addressing fundamental architectural challenges of an activity-based intelligence and advanced analytics (ABIAA) system

NASA Astrophysics Data System (ADS)

Yager, Kevin; Albert, Thomas; Brower, Bernard V.; Pellechia, Matthew F.

2015-06-01

The domain of Geospatial Intelligence Analysis is rapidly shifting toward a new paradigm of Activity Based Intelligence (ABI) and information-based Tipping and Cueing. General requirements for an advanced ABIAA system present significant challenges in architectural design, computing resources, data volumes, workflow efficiency, data mining and analysis algorithms, and database structures. These sophisticated ABI software systems must include advanced algorithms that automatically flag activities of interest in less time and within larger data volumes than can be processed by human analysts. In doing this, they must also maintain the geospatial accuracy necessary for cross-correlation of multi-intelligence data sources. Historically, serial architectural workflows have been employed in ABIAA system design for tasking, collection, processing, exploitation, and dissemination. These simpler architectures may produce implementations that solve short term requirements; however, they have serious limitations that preclude them from being used effectively in an automated ABIAA system with multiple data sources. This paper discusses modern ABIAA architectural considerations providing an overview of an advanced ABIAA system and comparisons to legacy systems. It concludes with a recommended strategy and incremental approach to the research, development, and construction of a fully automated ABIAA system.

Actively Encouraging Learning and Degree Persistence in Advanced Astrophysics Courses

NASA Astrophysics Data System (ADS)

McIntosh, Daniel H.

2018-01-01

The need to grow and diversify the STEM workforce remains a critical national challenge. Less than 40% of college students interested in STEM achieve a bachelor's degree. These numbers are even more dire for women and URMs, underscoring a serious concern about the country's ability to remain competitive in science and tech. A major factor is persistent performance gaps in rigorous 'gateway' and advanced STEM courses for majors from diverse backgrounds leading to discouragement, a sense of exclusion, and high dropout rates. Education research has clearly demonstrated that interactive-engagement (`active learning') strategies increase performance, boost confidence, and help build positive 'identity' in STEM. Likewise, the evidence shows that traditional science education practices do not help most students gain a genuine understanding of concepts nor the necessary skill set to succeed in their disciplines. Yet, lecture-heavy courses continue to dominate the higher-ed curriculum, thus, reinforcing the tired notion that only a small percentage of 'special' students have the inherent ability to achieve a STEM degree. In short, very capable students with less experience and confidence in science, who belong to groups that traditionally are less identified with STEM careers, are effectively and efficiently 'weeded out' by traditional education practices. I will share specific examples for how I successfully incorporate active learning in advanced astrophysics courses to encourage students from all backgrounds to synthesize complex ideas, build bedrock conceptual frameworks, gain technical communication skills, and achieve mastery learning outcomes all necessary to successfully complete rigorous degrees like astrophysics. By creating an inclusive and active learning experience in junior-level extragalactic and stellar interiors/atmospheres courses, I am helping students gain fluency in their chosen major and the ability to 'think like a scientist', both critical to

STRUCTURE-ACTIVITY RELATIONSHIP STUIDES AND THEIR ROLE IN PREDICTING AND INVESTIGATING CHEMICAL TOXICITY

EPA Science Inventory

Structure-Activity Relationship Studies and their Role in Predicting and Investigating Chemical Toxicity

Structure-activity relationships (SAR) represent attempts to generalize chemical information relative to biological activity for the twin purposes of generating insigh...

Gene expression profiling reveals activation of the FA/BRCA pathway in advanced squamous cervical cancer with intrinsic resistance and therapy failure.

PubMed

Balacescu, Ovidiu; Balacescu, Loredana; Tudoran, Oana; Todor, Nicolae; Rus, Meda; Buiga, Rares; Susman, Sergiu; Fetica, Bogdan; Pop, Laura; Maja, Laura; Visan, Simona; Ordeanu, Claudia; Berindan-Neagoe, Ioana; Nagy, Viorica

2014-04-08

Advanced squamous cervical cancer, one of the most commonly diagnosed cancers in women, still remains a major problem in oncology due to treatment failure and distant metastasis. Antitumor therapy failure is due to both intrinsic and acquired resistance; intrinsic resistance is often decisive for treatment response. In this study, we investigated the specific pathways and molecules responsible for baseline therapy failure in locally advanced squamous cervical cancer. Twenty-one patients with locally advanced squamous cell carcinoma were enrolled in this study. Primary biopsies harvested prior to therapy were analyzed for whole human gene expression (Agilent) based on the patient's 6 months clinical response. Ingenuity Pathway Analysis was used to investigate the altered molecular function and canonical pathways between the responding and non-responding patients. The microarray results were validated by qRT-PCR and immunohistochemistry. An additional set of 24 formalin-fixed paraffin-embedded cervical cancer samples was used for independent validation of the proteins of interest. A 2859-gene signature was identified to distinguish between responder and non-responder patients. 'DNA Replication, Recombination and Repair' represented one of the most important mechanisms activated in non-responsive cervical tumors, and the 'Role of BRCA1 in DNA Damage Response' was predicted to be the most significantly altered canonical pathway involved in intrinsic resistance (p = 1.86E-04, ratio = 0.262). Immunohistological staining confirmed increased expression of BRCA1, BRIP1, FANCD2 and RAD51 in non-responsive compared with responsive advanced squamous cervical cancer, both in the initial set of 21 cervical cancer samples and the second set of 24 samples. Our findings suggest that FA/BRCA pathway plays an important role in treatment failure in advanced cervical cancer. The assessment of FANCD2, RAD51, BRCA1 and BRIP1 nuclear proteins could provide important information about the

Office of River Protection Advanced Low-Activity Waste Glass Research and Development Plan

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

Kruger, A. A.; Peeler, D. K.; Kim, D. S.

2015-11-23

The U.S. Department of Energy Office of River Protection (ORP) has initiated and leads an integrated Advanced Waste Glass (AWG) program to increase the loading of Hanford tank wastes in glass while meeting melter lifetime expectancies and process, regulatory, and product performance requirements. The integrated ORP program is focused on providing a technical, science-based foundation for making key decisions regarding the successful operation of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) facilities in the context of an optimized River Protection Project (RPP) flowsheet. The fundamental data stemming from this program will support development of advanced glass formulations, keymore » product performance and process control models, and tactical processing strategies to ensure safe and successful operations for both the low-activity waste (LAW) and high-level waste vitrification facilities. These activities will be conducted with the objective of improving the overall RPP mission by enhancing flexibility and reducing cost and schedule.« less

TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

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

Liu, J; Wu, Q.J.; Yin, F

2014-06-15

Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into fivemore » groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH/NCI under

Discriminative structural approaches for enzyme active-site prediction.

PubMed

Kato, Tsuyoshi; Nagano, Nozomi

2011-02-15

Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.

Artificial neural networks to predict activity type and energy expenditure in youth.

PubMed

Trost, Stewart G; Wong, Weng-Keen; Pfeiffer, Karen A; Zheng, Yonglei

2012-09-01

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous-intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip, and VO2 was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE). As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Office of River Protection Advanced Low-Activity Waste Glass Research and Development Plan

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

Peeler, David K.; Kim, Dong-Sang; Vienna, John D.

2015-11-01

The U.S. Department of Energy Office of River Protection (ORP) has initiated and leads an integrated Advanced Waste Glass (AWG) program to increase the loading of Hanford tank wastes in glass while meeting melter lifetime expectancies and process, regulatory, and product performance requirements. The integrated ORP program is focused on providing a technical, science-based foundation for making key decisions regarding the successful operation of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) facilities in the context of an optimized River Protection Project (RPP) flowsheet. The fundamental data stemming from this program will support development of advanced glass formulations, keymore » product performance and process control models, and tactical processing strategies to ensure safe and successful operations for both the low-activity waste (LAW) and high-level waste vitrification facilities. These activities will be conducted with the objective of improving the overall RPP mission by enhancing flexibility and reducing cost and schedule. The purpose of this advanced LAW glass research and development plan is to identify the near-term, mid-term, and longer-term research and development activities required to develop and validate advanced LAW glasses, property-composition models and their uncertainties, and an advanced glass algorithm to support WTP facility operations, including both Direct Feed LAW and full pretreatment flowsheets. Data are needed to develop, validate, and implement 1) new glass property-composition models and 2) a new glass formulation algorithm. Hence, this plan integrates specific studies associated with increasing the Na2O and SO3/halide concentrations in glass, because these components will ultimately dictate waste loadings for LAW vitrification. Of equal importance is the development of an efficient and economic strategy for 99Tc management. Specific and detailed studies are being implemented to understand the fate of Tc

UV254 absorbance as real-time monitoring and control parameter for micropollutant removal in advanced wastewater treatment with powdered activated carbon.

PubMed

Altmann, Johannes; Massa, Lukas; Sperlich, Alexander; Gnirss, Regina; Jekel, Martin

2016-05-01

This study investigates the applicability of UV absorbance measurements at 254 nm (UVA254) to serve as a simple and reliable surrogate parameter to monitor and control the removal of organic micropollutants (OMPs) in advanced wastewater treatment applying powdered activated carbon (PAC). Correlations between OMP removal and corresponding UVA254 reduction were determined in lab-scale adsorption batch tests and successfully applied to a pilot-scale PAC treatment stage to predict OMP removals in aggregate samples with good accuracy. Real-time UVA254 measurements were utilized to evaluate adapted PAC dosing strategies and proved to be effective for online monitoring of OMP removal. Furthermore, active PAC dosing control according to differential UVA254 measurements was implemented and tested. While precise removal predictions based on real-time measurements were not accurate for all OMPs, UVA254-controlled dynamic PAC dosing was capable of achieving stable OMP removals. UVA254 can serve as an effective surrogate parameter for OMP removal in technical PAC applications. Even though the applicability as control parameter to adjust PAC dosing to water quality changes might be limited to applications with fast response between PAC adjustment and adsorptive removal (e.g. direct filtration), UVA254 measurements can also be used to monitor the adsorption efficiency in more complex PAC applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Uncertainty in Predicting CCN Activity of Aged and Primary Aerosols

NASA Astrophysics Data System (ADS)

Zhang, Fang; Wang, Yuying; Peng, Jianfei; Ren, Jingye; Collins, Don; Zhang, Renyi; Sun, Yele; Yang, Xin; Li, Zhanqing

2017-11-01

Understanding particle CCN activity in diverse atmospheres is crucial when evaluating aerosol indirect effects. Here aerosols measured at three sites in China were categorized as different types for attributing uncertainties in CCN prediction in terms of a comprehensive data set including size-resolved CCN activity, size-resolved hygroscopic growth factor, and chemical composition. We show that CCN activity for aged aerosols is unexpectedly underestimated 22% at a supersaturation (S) of 0.2% when using κ-Kohler theory with an assumption of an internal mixture with measured bulk composition that has typically resulted in an overestimate of the CCN activity in previous studies. We conclude that the underestimation stems from neglect of the effect of aging/coating on particle hygroscopicity, which is not considered properly in most current models. This effect enhanced the hygroscopicity parameter (κ) by between 11% (polluted conditions) and 30% (clean days), as indicated in diurnal cycles of κ based on measurements by different instruments. In the urban Beijing atmosphere heavily influenced by fresh emissions, the CCN activity was overestimated by 45% at S = 0.2%, likely because of inaccurate assumptions of particle mixing state and because of variability of chemical composition over the particle size range. For both fresh and aged aerosols, CCN prediction exhibits very limited sensitivity to κSOA, implying a critical role of other factors like mixing of aerosol components within and between particles in regulating CCN activity. Our findings could help improving CCN parameterization in climate models.

Finite element predictions of active buckling control of stiffened panels

NASA Astrophysics Data System (ADS)

Thompson, Danniella M.; Griffin, O. H., Jr.

1993-04-01

Materials systems and structures that can respond 'intelligently' to their environment are currently being proposed and investigated. A series of finite element analyses was performed to investigate the potential for active buckling control of two different stiffened panels by embedded shape memory alloy (SMA) rods. Changes in the predicted buckling load increased with the magnitude of the actuation level for a given structural concept. Increasing the number of actuators for a given concept yielded greater predicted increases in buckling load. Considerable control authority was generated with a small number of actuators, with greater authority demonstrated for those structural concepts where the activated SMA rods could develop greater forces and moments on the structure. Relatively simple and inexpensive analyses were performed with standard finite elements to determine such information, indicating the viability of these types of models for design purposes.

Predicting the Inflow Distortion Tone Noise of the NASA Glenn Advanced Noise Control Fan with a Combined Quadrupole-Dipole Model

NASA Technical Reports Server (NTRS)

Koch, L. Danielle

2012-01-01

A combined quadrupole-dipole model of fan inflow distortion tone noise has been extended to calculate tone sound power levels generated by obstructions arranged in circumferentially asymmetric locations upstream of a rotor. Trends in calculated sound power level agreed well with measurements from tests conducted in 2007 in the NASA Glenn Advanced Noise Control Fan. Calculated values of sound power levels radiated upstream were demonstrated to be sensitive to the accuracy of the modeled wakes from the cylindrical rods that were placed upstream of the fan to distort the inflow. Results indicate a continued need to obtain accurate aerodynamic predictions and measurements at the fan inlet plane as engineers work towards developing fan inflow distortion tone noise prediction tools.

Benefits of advanced space suits for supporting routine extravehicular activity

NASA Technical Reports Server (NTRS)

Alton, L. R.; Bauer, E. H.; Patrick, J. W.

1975-01-01

Technology is available to produce space suits providing a quick-reaction, safe, much more mobile extravehicular activity (EVA) capability than before. Such a capability may be needed during the shuttle era because the great variety of missions and payloads complicates the development of totally automated methods of conducting operations and maintenance and resolving contingencies. Routine EVA now promises to become a cost-effective tool as less complex, serviceable, lower-cost payload designs utilizing this capability become feasible. Adoption of certain advanced space suit technologies is encouraged for reasons of economics as well as performance.

How to acquire new biological activities in old compounds by computer prediction

NASA Astrophysics Data System (ADS)

Poroikov, V. V.; Filimonov, D. A.

2002-11-01

Due to the directed way of testing chemical compounds' in drug research and development many projects fail because serious adverse effects and toxicity are discovered too late, and many existing prospective activities remain unstudied. Evaluation of the general biological potential of molecules is possible using a computer program PASS that predicts more than 780 pharmacological effects, mechanisms of action, mutagenicity, carcinogenicity, etc. on the basis of structural formulae of compounds, with average accuracy ˜85%. PASS applications to both databases of available samples included hundreds of thousands compounds, and small collections of compounds synthesized by separate medicinal chemists are described. It is shown that 880 compounds from Prestwick chemical library represent a very diverse pharmacological space. New activities can be found in existing compounds by prediction. Therefore, on this basis, the selection of compounds with required and without unwanted properties is possible. Even when PASS cannot predict very new activities, it may recognize some unwanted actions at the early stage of R&D, providing the medicinal chemist with the means to increase the efficiency of projects.

Advanced planetary studies

NASA Technical Reports Server (NTRS)

1977-01-01

Results of planetary advanced studies and planning support are summarized. The scope of analyses includes cost estimation research, planetary mission performance, penetrator advanced studies, Mercury mission transport requirements, definition of super solar electric propulsion/solar sail mission discriminators, and advanced planning activities.

Early prediction of movie box office success based on Wikipedia activity big data.

PubMed

Mestyán, Márton; Yasseri, Taha; Kertész, János

2013-01-01

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response.

PubMed

Cho, Seung Hyun; Kim, Gab Chul; Jang, Yun-Jin; Ryeom, Hunkyu; Kim, Hye Jung; Shin, Kyung-Min; Park, Jun Seok; Choi, Gyu-Seog; Kim, See Hyung

2015-09-01

The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC. © The Foundation Acta Radiologica 2014.

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations

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

Marre, O.; El Boustani, S.; Fregnac, Y.

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogatesmore » that reproduce the spatial and temporal correlations of a given data set.« less

Neural Activity During Health Messaging Predicts Reductions in Smoking Above and Beyond Self-Report

PubMed Central

Falk, Emily B.; Berkman, Elliot T.; Whalen, Danielle; Lieberman, Matthew D.

2011-01-01

Objective The current study tested whether neural activity in response to messages designed to help smokers quit could predict smoking reduction, above and beyond self-report. Design Using neural activity in an a priori region of interest (a subregion of medial prefrontal cortex [MPFC]), in response to ads designed to help smokers quit smoking, we prospectively predicted reductions in smoking in a community sample of smokers (N = 28) who were attempting to quit smoking. Smoking was assessed via expired carbon monoxide (CO; a biological measure of recent smoking) at baseline and 1 month following exposure to professionally developed quitting ads. Results A positive relationship was observed between activity in the MPFC region of interest and successful quitting (increased activity in MPFC was associated with a greater decrease in expired CO). The addition of neural activity to a model predicting changes in CO from self-reported intentions, self-efficacy, and ability to relate to the messages significantly improved model fit, doubling the variance explained ( Rself−report2=.15,Rself−report+neuralactivity2=.35,Rchange2=.20). Conclusion: Neural activity is a useful complement to existing self-report measures. In this investigation, we extend prior work predicting behavior change based on neural activity in response to persuasive media to an important health domain and discuss potential psychological interpretations of the brain–behavior link. Our results support a novel use of neuroimaging technology for understanding the psychology of behavior change and facilitating health promotion. PMID:21261410

NASA's Advanced Propulsion Technology Activities for Third Generation Fully Reusable Launch Vehicle Applications

NASA Technical Reports Server (NTRS)

Hueter, Uwe

2000-01-01

NASA's Office of Aeronautics and Space Transportation Technology (OASTT) established the following three major goals, referred to as "The Three Pillars for Success": Global Civil Aviation, Revolutionary Technology Leaps, and Access to Space. The Advanced Space Transportation Program Office (ASTP) at the NASA's Marshall Space Flight Center in Huntsville, Ala. focuses on future space transportation technologies under the "Access to Space" pillar. The Propulsion Projects within ASTP under the investment area of Spaceliner100, focus on the earth-to-orbit (ETO) third generation reusable launch vehicle technologies. The goals of Spaceliner 100 is to reduce cost by a factor of 100 and improve safety by a factor of 10,000 over current conditions. The ETO Propulsion Projects in ASTP, are actively developing combination/combined-cycle propulsion technologies that utilized airbreathing propulsion during a major portion of the trajectory. System integration, components, materials and advanced rocket technologies are also being pursued. Over the last several years, one of the main thrusts has been to develop rocket-based combined cycle (RBCC) technologies. The focus has been on conducting ground tests of several engine designs to establish the RBCC flowpaths performance. Flowpath testing of three different RBCC engine designs is progressing. Additionally, vehicle system studies are being conducted to assess potential operational space access vehicles utilizing combined-cycle propulsion systems. The design, manufacturing, and ground testing of a scale flight-type engine are planned. The first flight demonstration of an airbreathing combined cycle propulsion system is envisioned around 2005. The paper will describe the advanced propulsion technologies that are being being developed under the ETO activities in the ASTP program. Progress, findings, and future activities for the propulsion technologies will be discussed.

Active-learning implementation in an advanced elective course on infectious diseases.

PubMed

Hidayat, Levita; Patel, Shreya; Veltri, Keith

2012-06-18

To describe the development, implementation, and assessment of an advanced elective course on infectious diseases using active-learning strategies. Pedagogy for active learning was incorporated by means of mini-lecture, journal club, and debate with follow-up discussion. Forty-eight students were enrolled in this 4-week elective course, in which 30% of course time was allocated for active-learning exercises. All activities were fundamentally designed as a stepwise approach in complementing each active-learning exercise. Achievement of the course learning objectives was assessed using a 5-point Likert scale survey instrument. Students' awareness of the significance of antimicrobial resistance was improved (p ≤ 0.05). Students' ability to critically evaluate the infectious-disease literature and its application in informed clinical judgments was also enhanced through these active-learning exercises (p ≤ 0.05). Students agreed that active learning should be part of the pharmacy curriculum and that active-learning exercises improved their critical-thinking, literature-evaluation, and self-learning skills. An elective course using active-learning strategies allowed students to combine information gained from the evaluation of infectious-disease literature, critical thinking, and informed clinical judgment. This blended approach ultimately resulted in an increased knowledge and awareness of infectious diseases.

Active-Learning Implementation in an Advanced Elective Course on Infectious Diseases

PubMed Central

Patel, Shreya; Veltri, Keith

2012-01-01

Objectives. To describe the development, implementation, and assessment of an advanced elective course on infectious diseases using active-learning strategies. Design. Pedagogy for active learning was incorporated by means of mini-lecture, journal club, and debate with follow-up discussion. Forty-eight students were enrolled in this 4-week elective course, in which 30% of course time was allocated for active-learning exercises. All activities were fundamentally designed as a stepwise approach in complementing each active-learning exercise. Assessment. Achievement of the course learning objectives was assessed using a 5-point Likert scale survey instrument. Students’ awareness of the significance of antimicrobial resistance was improved (p ≤ 0.05). Students’ ability to critically evaluate the infectious-disease literature and its application in informed clinical judgments was also enhanced through these active-learning exercises (p ≤ 0.05). Students agreed that active learning should be part of the pharmacy curriculum and that active-learning exercises improved their critical-thinking, literature-evaluation, and self-learning skills. Conclusion. An elective course using active-learning strategies allowed students to combine information gained from the evaluation of infectious-disease literature, critical thinking, and informed clinical judgment. This blended approach ultimately resulted in an increased knowledge and awareness of infectious diseases. PMID:22761528

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation

PubMed Central

Carlson, Jordan A.; Liu, Bo; Sallis, James F.; Kerr, Jacqueline; Papa, Amy; Dean, Kelsey; Vasconcelos, Nuno M.

2017-01-01

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82–0.98). Total MET-minutes were slightly underestimated by 9.3–17.1% and the ICCs were good (0.68–0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings. PMID:29194358

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

PubMed

Carlson, Jordan A; Liu, Bo; Sallis, James F; Kerr, Jacqueline; Hipp, J Aaron; Staggs, Vincent S; Papa, Amy; Dean, Kelsey; Vasconcelos, Nuno M

2017-12-01

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings.

Prediction of Active-Region CME Productivity from Magnetograms

NASA Technical Reports Server (NTRS)

Falconer, D. A.; Moore, R. L.; Gary, G. A.

2004-01-01

We report results of an expanded evaluation of whole-active-region magnetic measures as predictors of active-region coronal mass ejection (CME) productivity. Previously, in a sample of 17 vector magnetograms of 12 bipolar active regions observed by the Marshall Space Flight Center (MSFC) vector magnetograph, from each magnetogram we extracted a measure of the size of the active region (the active region s total magnetic flux a) and four measures of the nonpotentiality of the active region: the strong-shear length L(sub SS), the strong-gradient length L(sub SG), the net vertical electric current I(sub N), and the net-current magnetic twist parameter alpha (sub IN). This sample size allowed us to show that each of the four nonpotentiality measures was statistically significantly correlated with active-region CME productivity in time windows of a few days centered on the day of the magnetogram. We have now added a fifth measure of active-region nonpotentiality (the best-constant-alpha magnetic twist parameter (alpha sub BC)), and have expanded the sample to 36 MSFC vector magnetograms of 31 bipolar active regions. This larger sample allows us to demonstrate statistically significant correlations of each of the five nonpotentiality measures with future CME productivity, in time windows of a few days starting from the day of the magnetogram. The two magnetic twist parameters (alpha (sub 1N) and alpha (sub BC)) are normalized measures of an active region s nonpotentially in that they do not depend directly on the size of the active region, while the other three nonpotentiality measures (L(sub SS), L(sub SG), and I(sub N)) are non-normalized measures in that they do depend directly on active-region size. We find (1) Each of the five nonpotentiality measures is statistically significantly correlated (correlation confidence level greater than 95%) with future CME productivity and has a CME prediction success rate of approximately 80%. (2) None of the nonpotentiality

Fluorescence-based visualization of autophagic activity predicts mouse embryo viability

NASA Astrophysics Data System (ADS)

Tsukamoto, Satoshi; Hara, Taichi; Yamamoto, Atsushi; Kito, Seiji; Minami, Naojiro; Kubota, Toshiro; Sato, Ken; Kokubo, Toshiaki

2014-03-01

Embryo quality is a critical parameter in assisted reproductive technologies. Although embryo quality can be evaluated morphologically, embryo morphology does not correlate perfectly with embryo viability. To improve this, it is important to understand which molecular mechanisms are involved in embryo quality control. Autophagy is an evolutionarily conserved catabolic process in which cytoplasmic materials sequestered by autophagosomes are degraded in lysosomes. We previously demonstrated that autophagy is highly activated after fertilization and is essential for further embryonic development. Here, we developed a simple fluorescence-based method for visualizing autophagic activity in live mouse embryos. Our method is based on imaging of the fluorescence intensity of GFP-LC3, a versatile marker for autophagy, which is microinjected into the embryos. Using this method, we show that embryonic autophagic activity declines with advancing maternal age, probably due to a decline in the activity of lysosomal hydrolases. We also demonstrate that embryonic autophagic activity is associated with the developmental viability of the embryo. Our results suggest that embryonic autophagic activity can be utilized as a novel indicator of embryo quality.

Recent Advances in Free-Living Physical Activity Monitoring: A Review

PubMed Central

Andre, David; Wolf, Donna L.

2007-01-01

It has become clear recently that the epidemic of type 2 diabetes sweeping the globe is associated with decreased levels of physical activity and an increase in obesity. Incorporating appropriate and sufficient physical activity into one's life is an essential component of achieving and maintaining a healthy weight and overall health, especially for those with type II diabetes mellitus. Regular physical activity can have a positive impact by lowering blood glucose, helping the body to be more efficient at using insulin. There are other substantial benefits for patients with diabetes, including prevention of cardiovascular disease, hyperlipidemia, hypertension, and obesity. Several complications of utilizing a self-care treatment methodology involving exercise include (1) patients may not know how much activity that they engage in and (2) health-care providers do not have objective measurements of how much activity their patients perform. However, several technological advances have brought a variety of activity monitoring devices to the market that can address these concerns. Ranging from simple pedometers to multisensor devices, the different technologies offer varying levels of accuracy, comfort, and reliability. The key notion is that by providing feedback to the patient, motivation can be increased and targets can be set and aimed toward. Although these devices are not specific to the treatment of diabetes, the importance of physical activity in treating the disease makes an understanding of these devices important. This article reviews these physical activity monitors and describes the advantages and disadvantages of each. PMID:19885145

Subduction and Slab Advance at Orogen Syntaxes: Predicting Exhumation Rates and Thermochronometric Ages with Numerical Modeling

NASA Astrophysics Data System (ADS)

Nettesheim, Matthias; Ehlers, Todd A.; Whipp, David M.

2017-04-01

The change in plate boundary orientation and subducting plate geometry along orogen syntaxes may have major control on the subduction and exhumation dynamics at these locations. Previous work documents that the curvature of subducting plates in 3D at orogen syntaxes forces a buckling and flexural stiffening of the downgoing plate. The geometry of this stiffened plate region, also called indenter, can be observed in various subduction zones around the world (e.g. St. Elias Range, Alaska; Cascadia, USA; Andean syntaxis, South America). The development of a subducting, flexurally stiffened indenter beneath orogen syntaxes influences deformation in the overriding plate and can lead to accelerated and focused rock uplift above its apex. Moreover, the style of deformation in the overriding plate is influenced by the amount of trench or slab advance, which is the amount of overall shortening not accommodated by underthrusting. While many subduction zones exhibit little to no slab advance, the Nazca-South America subduction and especially the early stages of the India-Eurasia collision provide end-member examples. Here, we use a transient, lithospheric-scale, thermomechanical 3D model of an orogen syntaxis to investigate the effects of subducting a flexurally stiffened plate geometry and slab advance on upper plate deformation. A visco-plastic upper-plate rheology is used, along with a buckled, rigid subducting plate. The free surface of the thermomechanical model is coupled to a landscape evolution model that accounts for erosion by fluvial and hillslope processes. The cooling histories of exhumed rocks are used to predict the evolution of low-temperature thermochronometer ages on the surface. With a constant overall shortening for all simulations, the magnitude of slab advance is varied stepwise from no advance, with all shortening accommodated by underthrusting, to full slab advance, i.e. no motion on the megathrust. We show that in models where most shortening is

Neural activity predicts attitude change in cognitive dissonance.

PubMed

van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

2009-11-01

When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

Thymidilate synthase and p53 primary tumour expression as predictive factors for advanced colorectal cancer patients.

PubMed

Paradiso, A; Simone, G; Petroni, S; Leone, B; Vallejo, C; Lacava, J; Romero, A; Machiavelli, M; De Lena, M; Allegra, C J; Johnston, P G

2000-02-01

The purpose of this work was to analyse the ability of p53 and thymidilate synthase (TS) primary tumour expression to retrospectively predict clinical response to chemotherapy and long-term prognosis in patients with advanced colorectal cancers homogeneously treated by methotrexate (MTX)-modulated-5-fluorouracil (5-FU-FA). A total of 108 advanced colorectal cancer patients entered the present retrospective study. Immunohistochemical p53 (pAb 1801 mAb) and TS (TS106 mAb) expression on formalin-fixed paraffin-embedded primary tumour specimens was related to probability of clinical response to chemotherapy, time to progression and overall survival. p53 was expressed in 53/108 (49%) tumours, while 54/108 (50%) showed TS immunostaining. No relationship was demonstrated between p53 positivity and clinical response to chemotherapy (objective response (OR): 20% vs 23%, in p53+ and p53- cases respectively) or overall survival. Percent of OR was significantly higher in TS-negative with respect to TS-positive tumours (30% vs 15% respectively; P < 0.04); simultaneous analysis of TS and p53 indicated 7% OR for p53-positive/TS-positive tumours vs 46% for p53-positive/TS-negative tumours (P < 0.03). Logistic regression analysis confirmed a significant association between TS tumour status and clinical response to chemotherapy (hazard ratio (HR): 2.91; 95% confidence interval (CI) 8.34-1.01; two-sided P < 0.05). A multivariate analysis of overall survival showed that only a small number of metastatic sites was statistically relevant (HR 1.89; 95% CI 2.85-1.26; two-sided P < 0.03). Our study suggests that immunohistochemical expression of p53 and TS could assist the clinician in predicting response of colorectal cancer patients to modulated MTX-5-FU therapy.

Thymidilate synthase and p53 primary tumour expression as predictive factors for advanced colorectal cancer patients

PubMed Central

Paradiso, A; Simone, G; Petroni, S; Leone, B; Vallejo, C; Lacava, J; Romero, A; Machiavelli, M; Lena, M De; Allegra, C J; Johnston, P G

2000-01-01

The purpose of this work was to analyse the ability of p53 and thymidilate synthase (TS) primary tumour expression to retrospectively predict clinical response to chemotherapy and long-term prognosis in patients with advanced colorectal cancers homogeneously treated by methotrexate (MTX)-modulated–5-fluorouracil (5-FU-FA). A total of 108 advanced colorectal cancer patients entered the present retrospective study. Immunohistochemical p53 (pAb 1801 mAb) and TS (TS106 mAb) expression on formalin-fixed paraffin-embedded primary tumour specimens was related to probability of clinical response to chemotherapy, time to progression and overall survival. p53 was expressed in 53/108 (49%) tumours, while 54/108 (50%) showed TS immunostaining. No relationship was demonstrated between p53 positivity and clinical response to chemotherapy (objective response (OR): 20% vs 23%, in p53+ and p53– cases respectively) or overall survival. Percent of OR was significantly higher in TS-negative with respect to TS-positive tumours (30% vs 15% respectively;P< 0.04); simultaneous analysis of TS and p53 indicated 7% OR for p53-positive/TS-positive tumours vs 46% for p53-positive/TS-negative tumours (P< 0.03). Logistic regression analysis confirmed a significant association between TS tumour status and clinical response to chemotherapy (hazard ratio (HR): 2.91; 95% confidence interval (CI) 8.34–1.01; two-sided P< 0.05). A multivariate analysis of overall survival showed that only a small number of metastatic sites was statistically relevant (HR 1.89; 95% CI 2.85–1.26; two-sided P< 0.03). Our study suggests that immunohistochemical expression of p53 and TS could assist the clinician in predicting response of colorectal cancer patients to modulated MTX-5-FU therapy. © 2000 Cancer Research Campaign PMID:10682666

Motor Skill Competence and Perceived Motor Competence: Which Best Predicts Physical Activity among Girls?

PubMed

Khodaverdi, Zeinab; Bahram, Abbas; Khalaji, Hassan; Kazemnejad, Anoshirvan

2013-10-01

The main purpose of this study was to determine which correlate, perceived motor competence or motor skill competence, best predicts girls' physical activity behavior. A sample of 352 girls (mean age=8.7, SD=0.3 yr) participated in this study. To assess motor skill competence and perceived motor competence, each child completed the Test of Gross Motor Development-2 and Physical Ability sub-scale of Marsh's Self-Description Questionnaire. Children's physical activity was assessed by the Physical Activity Questionnaire for Older Children. Multiple linear regression model was used to determine whether perceived motor competence or motor skill competence best predicts moderate-to-vigorous self-report physical activity. Multiple regression analysis indicated that motor skill competence and perceived motor competence predicted 21% variance in physical activity (R(2)=0.21, F=48.9, P=0.001), and motor skill competence (R(2)=0.15, ᵝ=0.33, P= 0.001) resulted in more variance than perceived motor competence (R(2)=0.06, ᵝ=0.25, P=0.001) in physical activity. Results revealed motor skill competence had more influence in comparison with perceived motor competence on physical activity level. We suggest interventional programs based on motor skill competence and perceived motor competence should be administered or implemented to promote physical activity in young girls.

Predicting consumer behavior: using novel mind-reading approaches.

PubMed

Calvert, Gemma A; Brammer, Michael J

2012-01-01

Advances in machine learning as applied to functional magnetic resonance imaging (fMRI) data offer the possibility of pretesting and classifying marketing communications using unbiased pattern recognition algorithms. By using these algorithms to analyze brain responses to brands, products, or existing marketing communications that either failed or succeeded in the marketplace and identifying the patterns of brain activity that characterize success or failure, future planned campaigns or new products can now be pretested to determine how well the resulting brain responses match the desired (successful) pattern of brain activity without the need for verbal feedback. This major advance in signal processing is poised to revolutionize the application of these brain-imaging techniques in the marketing sector by offering greater accuracy of prediction in terms of consumer acceptance of new brands, products, and campaigns at a speed that makes them accessible as routine pretesting tools that will clearly demonstrate return on investment.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions.

PubMed

Weilnhammer, Veith A; Stuke, Heiner; Sterzer, Philipp; Schmack, Katharina

2018-05-23

target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain. Copyright © 2018 the authors 0270-6474/18/385008-14$15.00/0.

National Centers for Environmental Prediction

Science.gov Websites

advance prediction skills for monsoon variability, improved understanding of Indian Ocean-Atmosphere variability and predictability Coordination on research to improve: understanding of ocean processes in the

NASA GRC Fatigue Crack Initiation Life Prediction Models

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Halford, Gary R.

2002-01-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

NASA GRC Fatigue Crack Initiation Life Prediction Models

NASA Astrophysics Data System (ADS)

Arya, Vinod K.; Halford, Gary R.

2002-10-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

Institutional Advancement Activities at Select Hispanic-Serving Institutions: The Politics of Raising Funds

ERIC Educational Resources Information Center

Mulnix, Michael William; Bowden, Randall G.; Lopez, Esther Elena

2004-01-01

This article analyzes the current state of institutional advancement activities at Hispanic-serving institutions (HSIs) of higher education. Since the 1980s, a core group of colleges and universities in the United States with significant enrollments of Hispanic students has come to be recognized as primary providers of education to the burgeoning…

The role of advanced reactive surface area characterization in improving predictions of mineral reaction rates

NASA Astrophysics Data System (ADS)

Beckingham, L. E.; Zhang, S.; Mitnick, E.; Cole, D. R.; Yang, L.; Anovitz, L. M.; Sheets, J.; Swift, A.; Kneafsey, T. J.; Landrot, G.; Mito, S.; Xue, Z.; Steefel, C. I.; DePaolo, D. J.; Ajo Franklin, J. B.

2014-12-01

Geologic sequestration of CO2 in deep sedimentary formations is a promising means of mitigating carbon emissions from coal-fired power plants but the long-term fate of injected CO2 is challenging to predict. Reactive transport models are used to gain insight over long times but rely on laboratory determined mineral reaction rates that have been difficult to extrapolate to field systems. This, in part, is due to a lack of understanding of mineral reactive surface area. Many models use an arbitrary approximation of reactive surface area, applying orders of magnitude scaling factors to measured BET or geometric surface areas. Recently, a few more sophisticated approaches have used 2D and 3D image analyses to determine mineral-specific reactive surface areas that account for the accessibility of minerals. However, the ability of these advanced surface area estimates to improve predictions of mineral reaction rates has yet to be determined. In this study, we fuse X-ray microCT, SEM QEMSCAN, XRD, SANS, and SEM-FIB analysis to determine mineral-specific accessible reactive surface areas for a core sample from the Nagaoka pilot CO2 injection site (Japan). This sample is primarily quartz, plagioclase, smectite, K-feldspar, and pyroxene. SEM imaging shows abundant smectite cement and grain coatings that decrease the fluid accessibility of other minerals. However, analysis of FIB-SEM images reveals that smectite nano-pores are well connected such that access to underlying minerals is not occluded by smectite coatings. Mineral-specific accessible surfaces are determined, accounting for the connectivity of the pore space with and without connected smectite nano-pores. The large-scale impact of variations in accessibility and dissolution rates are then determined through continuum scale modeling using grid-cell specific information on accessible surface areas. This approach will be compared with a traditional continuum scale model using mineral abundances and common surface area

Spontaneous sensorimotor cortical activity is suppressed by deep brain stimulation in patients with advanced Parkinson's disease.

PubMed

Luoma, Jarkko; Pekkonen, Eero; Airaksinen, Katja; Helle, Liisa; Nurminen, Jussi; Taulu, Samu; Mäkelä, Jyrki P

2018-06-22

Advanced Parkinson's disease (PD) is characterized by an excessive oscillatory beta band activity in the subthalamic nucleus (STN). Deep brain stimulation (DBS) of STN alleviates motor symptoms in PD and suppresses the STN beta band activity. The effect of DBS on cortical sensorimotor activity is more ambiguous; both increases and decreases of beta band activity have been reported. Non-invasive studies with simultaneous DBS are problematic due to DBS-induced artifacts. We recorded magnetoencephalography (MEG) from 16 advanced PD patients with and without STN DBS during rest and wrist extension. The strong magnetic artifacts related to stimulation were removed by temporal signal space separation. MEG oscillatory activity at 5-25 Hz was suppressed during DBS in a widespread frontoparietal region, including the sensorimotor cortex identified by the cortico-muscular coherence. The strength of suppression did not correlate with clinical improvement. Our results indicate that alpha and beta band oscillations are suppressed at the frontoparietal cortex by STN DBS in PD. Copyright © 2018. Published by Elsevier B.V.

Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

DOE PAGES

Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

2016-01-19

A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Kohler theory to predict themore » effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. Furthermore, the model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.« less

Advanced flight control system study

NASA Technical Reports Server (NTRS)

Hartmann, G. L.; Wall, J. E., Jr.; Rang, E. R.; Lee, H. P.; Schulte, R. W.; Ng, W. K.

1982-01-01

A fly by wire flight control system architecture designed for high reliability includes spare sensor and computer elements to permit safe dispatch with failed elements, thereby reducing unscheduled maintenance. A methodology capable of demonstrating that the architecture does achieve the predicted performance characteristics consists of a hierarchy of activities ranging from analytical calculations of system reliability and formal methods of software verification to iron bird testing followed by flight evaluation. Interfacing this architecture to the Lockheed S-3A aircraft for flight test is discussed. This testbed vehicle can be expanded to support flight experiments in advanced aerodynamics, electromechanical actuators, secondary power systems, flight management, new displays, and air traffic control concepts.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

Predicting Relapse among Young Adults: Psychometric Validation of the Advanced Warning of Relapse (AWARE) Scale

PubMed Central

Kelly, John F.; Hoeppner, Bettina B.; Urbanoski, Karen A.; Slaymaker, Valerie

2011-01-01

Objective Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure - the Advance WArning of RElapse scale (AWARE) scale (Miller and Harris, 2000) in an understudied but clinically important sample of young adults. Method Inpatient youth (N=303; Age 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Results Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. Conclusions The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. PMID:21700396

Predicting relapse among young adults: psychometric validation of the Advanced WArning of RElapse (AWARE) scale.

PubMed

Kelly, John F; Hoeppner, Bettina B; Urbanoski, Karen A; Slaymaker, Valerie

2011-10-01

Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure-the Advance WArning of RElapse (AWARE) scale (Miller & Harris, 2000) in an understudied but clinically important sample of young adults. Inpatient youth (N=303; Ages 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting Work Activities with Divergent Thinking Tests: A Longitudinal Study

ERIC Educational Resources Information Center

Clapham, Maria M.; Cowdery, Edwina M.; King, Kelly E.; Montang, Melissa A.

2005-01-01

This study examined whether divergent thinking test scores obtained from engineering students during college predicted creative work activities fifteen years later. Results showed that a subscore of the "Owens Creativity Test", which assesses divergent thinking about mechanical objects, correlated significantly with self-ratings of…

ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

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

Visel, Axel; Blow, Matthew J.; Li, Zirong

2009-02-01

A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. Wemore » tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.« less

The sequential structure of brain activation predicts skill.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

PubMed Central

Myint, Kyaw Z.; Xie, Xiang-Qun

2015-01-01

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

Predictive factors of esophageal stenosis associated with tumor regression in radiation therapy for locally advanced esophageal cancer.

PubMed

Atsumi, Kazushige; Shioyama, Yoshiyuki; Nakamura, Katsumasa; Nomoto, Satoshi; Ohga, Saiji; Yoshitake, Tadamasa; Nonoshita, Takeshi; Ueda, Masanobu; Hirata, Hideki; Honda, Hiroshi

2010-01-01

The purpose of this retrospective study was to clarify the predictive factors correlated with esophageal stenosis within three months after radiation therapy for locally advanced esophageal cancer. We enrolled 47 patients with advanced esophageal cancer with T2-4 and stage II-III who were treated with definitive radiation therapy and achieving complete response of primary lesion at Kyushu University Hospital between January 1998 and December 2005. Esophagography was performed for all patients before treatment and within three months after completion of the radiation therapy, the esophageal stenotic ratio was evaluated. The stenotic ratio was used to define four levels of stenosis: stenosis level 1, stenotic ratio of 0-25%; 2, 25-50%; 3,50-75%; 4,75-100%. We then estimated the correlation between the esophageal stenosis level after radiation therapy and each of numerous factors. The numbers and total percentages of patients at each stenosis level were as follows: level 1: n = 14 (30%); level 2: 8 (17%); level 3: 14 (30%); and level 4: 11 (23%). Esophageal stenosis in the case of full circumference involvement tended to be more severe and more frequent. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. The extent of involved circumference and wall thickness of tumor region were significantly correlated with esophageal stenosis associated with tumor regression in radiation therapy (p = 0.0006, p = 0.005). For predicting the possibility of esophageal stenosis with tumor regression within three months in radiation therapy, the extent of involved circumference and esophageal wall thickness of the tumor region may be useful.

Implementing the Constructed Scaffold Model: Hands-On Activity Units for Advanced Placement Calculus

ERIC Educational Resources Information Center

Scott, Susan

2017-01-01

The purpose of the present action research study is to describe a hands-on activity model, named the Constructed Scaffold Model (CSM), used in an Advanced Placement Calculus class in a southeastern United States suburban high school. Data were collected over an 8-week period during the spring 2017 semester. The teacher-researcher developed a…

Accuracy of Dolphin visual treatment objective (VTO) prediction software on class III patients treated with maxillary advancement and mandibular setback.

PubMed

Peterman, Robert J; Jiang, Shuying; Johe, Rene; Mukherjee, Padma M

2016-12-01

Dolphin® visual treatment objective (VTO) prediction software is routinely utilized by orthodontists during the treatment planning of orthognathic cases to help predict post-surgical soft tissue changes. Although surgical soft tissue prediction is considered to be a vital tool, its accuracy is not well understood in tow-jaw surgical procedures. The objective of this study was to quantify the accuracy of Dolphin Imaging's VTO soft tissue prediction software on class III patients treated with maxillary advancement and mandibular setback and to validate the efficacy of the software in such complex cases. This retrospective study analyzed the records of 14 patients treated with comprehensive orthodontics in conjunction with two-jaw orthognathic surgery. Pre- and post-treatment radiographs were traced and superimposed to determine the actual skeletal movements achieved in surgery. This information was then used to simulate surgery in the software and generate a final soft tissue patient profile prediction. Prediction images were then compared to the actual post-treatment profile photos to determine differences. Dolphin Imaging's software was determined to be accurate within an error range of +/- 2 mm in the X-axis at most landmarks. The lower lip predictions were most inaccurate. Clinically, the observed error suggests that the VTO may be used for demonstration and communication with a patient or consulting practitioner. However, Dolphin should not be useful for precise treatment planning of surgical movements. This program should be used with caution to prevent unrealistic patient expectations and dissatisfaction.

firestar--advances in the prediction of functionally important residues.

PubMed

Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L

2011-07-01

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php.

firestar—advances in the prediction of functionally important residues

PubMed Central

Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L.

2011-01-01

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php. PMID:21672959

The role of predicted solar activity in TOPEX/Poseidon orbit maintenance maneuver design

NASA Technical Reports Server (NTRS)

Frauenholz, Raymond B.; Shapiro, Bruce E.

1992-01-01

Following launch in June 1992, the TOPEX/Poseidon satellite will be placed in a near-circular frozen orbit at an altitude of about 1336 km. Orbit maintenance maneuvers are planned to assure all nodes of the 127-orbit 10-day repeat ground track remain within a 2 km equatorial longitude bandwidth. Orbit determination, maneuver execution, and atmospheric drag prediction errors limit overall targeting performance. This paper focuses on the effects of drag modeling errors, with primary emphasis on the role of SESC solar activity predictions, especially the 27-day outlook of the 10.7 cm solar flux and geomagnetic index used by a simplified version of the Jacchia-Roberts density model developed for this TOPEX/Poseidon application. For data evaluated from 1983-90, the SESC outlook performed better than a simpler persistence strategy, especially during the first 7-10 days. A targeting example illustrates the use of ground track biasing to compensate for expected orbit predictions errors, emphasizing the role of solar activity prediction errors.

Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

NASA Astrophysics Data System (ADS)

Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

2010-12-01

Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on

Developing a Degree-Day Model to Predict Billbug (Coleoptera: Curculionidae) Seasonal Activity in Utah and Idaho Turfgrass.

PubMed

Dupuy, Madeleine M; Powell, James A; Ramirez, Ricardo A

2017-10-01

Billbugs are native pests of turfgrass throughout North America, primarily managed with preventive, calendar-based insecticide applications. An existing degree-day model (lower development threshold of 10°C, biofix 1 March) developed in the eastern United States for bluegrass billbug, Sphenophorus parvulus (Gyllenhal; Coleoptera: Curculionidae), may not accurately predict adult billbug activity in the western United States, where billbugs occur as a species complex. The objectives of this study were 1) to track billbug phenology and species composition in managed Utah and Idaho turfgrass and 2) to evaluate model parameters that best predict billbug activity, including those of the existing bluegrass billbug model. Tracking billbugs with linear pitfall traps at two sites each in Utah and Idaho, we confirmed a complex of three univoltine species damaging turfgrass consisting of (in descending order of abundance) bluegrass billbug, hunting billbug (Sphenophorus venatus vestitus Chittenden; Coleoptera: Curculionidae), and Rocky Mountain billbug (Sphenophorus cicatristriatus Fabraeus; Coleoptera: Curculionidae). This complex was active from February through mid-October, with peak activity in mid-June. Based on linear regression analysis, we found that the existing bluegrass billbug model was not robust in predicting billbug activity in Utah and Idaho. Instead, the model that best predicts adult activity of the billbug complex accumulates degree-days above 3°C after 13 January. This model predicts adult activity levels important for management within 11 d of observed activity at 77% of sites. In conjunction with outreach and cooperative networking, this predictive degree-day model may assist end users to better time monitoring efforts and insecticide applications against billbug pests in Utah and Idaho by predicting adult activity. © The Author 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For

Advanced Active-Magnetic-Bearing Thrust-Measurement System

NASA Technical Reports Server (NTRS)

Imlach, Joseph; Kasarda, Mary; Blumber, Eric

2008-01-01

An advanced thrust-measurement system utilizes active magnetic bearings to both (1) levitate a floating frame in all six degrees of freedom and (2) measure the levitation forces between the floating frame and a grounded frame. This system was developed for original use in measuring the thrust exerted by a rocket engine mounted on the floating frame, but can just as well be used in other force-measurement applications. This system offers several advantages over prior thrust-measurement systems based on mechanical support by flexures and/or load cells: The system includes multiple active magnetic bearings for each degree of freedom, so that by selective use of one, some, or all of these bearings, it is possible to test a given article over a wide force range in the same fixture, eliminating the need to transfer the article to different test fixtures to obtain the benefit of full-scale accuracy of different force-measurement devices for different force ranges. Like other active magnetic bearings, the active magnetic bearings of this system include closed-loop control subsystems, through which the stiffness and damping characteristics of the magnetic bearings can be modified electronically. The design of the system minimizes or eliminates cross-axis force-measurement errors. The active magnetic bearings are configured to provide support against movement along all three orthogonal Cartesian axes, and such that the support along a given axis does not produce force along any other axis. Moreover, by eliminating the need for such mechanical connections as flexures used in prior thrust-measurement systems, magnetic levitation of the floating frame eliminates what would otherwise be major sources of cross-axis forces and the associated measurement errors. Overall, relative to prior mechanical-support thrust-measurement systems, this system offers greater versatility for adaptation to a variety of test conditions and requirements. The basic idea of most prior active

Modified Advanced Crew Escape Suit Intravehicular Activity Suit for Extravehicular Activity Mobility Evaluations

NASA Technical Reports Server (NTRS)

Watson, Richard D.

2014-01-01

The use of an intravehicular activity (IVA) suit for a spacewalk or extravehicular activity (EVA) was evaluated for mobility and usability in the Neutral Buoyancy Laboratory (NBL) environment at the Sonny Carter Training Facility near NASA Johnson Space Center in Houston, Texas. The Space Shuttle Advanced Crew Escape Suit was modified to integrate with the Orion spacecraft. The first several missions of the Orion Multi-Purpose Crew Vehicle will not have mass available to carry an EVA-specific suit; therefore, any EVA required will have to be performed by the Modified Advanced Crew Escape Suit (MACES). Since the MACES was not designed with EVA in mind, it was unknown what mobility the suit would be able to provide for an EVA or whether a person could perform useful tasks for an extended time inside the pressurized suit. The suit was evaluated in multiple NBL runs by a variety of subjects, including crewmembers with significant EVA experience. Various functional mobility tasks performed included: translation, body positioning, tool carrying, body stabilization, equipment handling, and tool usage. Hardware configurations included with and without Thermal Micrometeoroid Garment, suit with IVA gloves and suit with EVA gloves. Most tasks were completed on International Space Station mock-ups with existing EVA tools. Some limited tasks were completed with prototype tools on a simulated rocky surface. Major findings include: demonstrating the ability to weigh-out the suit, understanding the need to have subjects perform multiple runs prior to getting feedback, determining critical sizing factors, and need for adjusting suit work envelope. Early testing demonstrated the feasibility of EVA's limited duration and limited scope. Further testing is required with more flight-like tasking and constraints to validate these early results. If the suit is used for EVA, it will require mission-specific modifications for umbilical management or Primary Life Support System integration

Motor Skill Competence and Perceived Motor Competence: Which Best Predicts Physical Activity among Girls?

PubMed Central

Khodaverdi, Zeinab; Bahram, Abbas; Khalaji, Hassan; Kazemnejad, Anoshirvan

2013-01-01

Abstract Background The main purpose of this study was to determine which correlate, perceived motor competence or motor skill competence, best predicts girls’ physical activity behavior. Methods A sample of 352 girls (mean age=8.7, SD=0.3 yr) participated in this study. To assess motor skill competence and perceived motor competence, each child completed the Test of Gross Motor Development-2 and Physical Ability sub-scale of Marsh’s Self-Description Questionnaire. Children’s physical activity was assessed by the Physical Activity Questionnaire for Older Children. Multiple linear regression model was used to determine whether perceived motor competence or motor skill competence best predicts moderate-to-vigorous self-report physical activity. Results Multiple regression analysis indicated that motor skill competence and perceived motor competence predicted 21% variance in physical activity (R2=0.21, F=48.9, P=0.001), and motor skill competence (R2=0.15, ᵝ=0.33, P= 0.001) resulted in more variance than perceived motor competence (R2=0.06, ᵝ=0.25, P=0.001) in physical activity. Conclusion Results revealed motor skill competence had more influence in comparison with perceived motor competence on physical activity level. We suggest interventional programs based on motor skill competence and perceived motor competence should be administered or implemented to promote physical activity in young girls. PMID:26060623

An Advanced Buffet Load Alleviation System

NASA Technical Reports Server (NTRS)

Burnham, Jay K.; Pitt, Dale M.; White, Edward V.; Henderson, Douglas A.; Moses, Robert W.

2001-01-01

This paper describes the development of an advanced buffet load alleviation (BLA) system that utilizes distributed piezoelectric actuators in conjunction with an active rudder to reduce the structural dynamic response of the F/A-18 aircraft vertical tails to buffet loads. The BLA system was defined analytically with a detailed finite-element-model of the tail structure and piezoelectric actuators. Oscillatory aerodynamics were included along with a buffet forcing function to complete the aeroservoelastic model of the tail with rudder control surface. Two single-input-single-output (SISO) controllers were designed, one for the active rudder and one for the active piezoelectric actuators. The results from the analytical open and closed loop simulations were used to predict the system performance. The objective of this BLA system is to extend the life of vertical tail structures and decrease their life-cycle costs. This system can be applied to other aircraft designs to address suppression of structural vibrations on military and commercial aircraft.

Active lifestyles in older adults: an integrated predictive model of physical activity and exercise

PubMed Central

Galli, Federica; Chirico, Andrea; Mallia, Luca; Girelli, Laura; De Laurentiis, Michelino; Lucidi, Fabio; Giordano, Antonio; Botti, Gerardo

2018-01-01

Physical activity and exercise have been identified as behaviors to preserve physical and mental health in older adults. The aim of the present study was to test the Integrated Behavior Change model in exercise and physical activity behaviors. The study evaluated two different samples of older adults: the first engaged in exercise class, the second doing spontaneous physical activity. The key analyses relied on Variance-Based Structural Modeling, which were performed by means of WARP PLS 6.0 statistical software. The analyses estimated the Integrated Behavior Change model in predicting exercise and physical activity, in a longitudinal design across two months of assessment. The tested models exhibited a good fit with the observed data derived from the model focusing on exercise, as well as with those derived from the model focusing on physical activity. Results showed, also, some effects and relations specific to each behavioral context. Results may form a starting point for future experimental and intervention research. PMID:29875997

CONSIDERATION OF REACTION INTERMEDIATES IN STRUCTURE-ACTIVITY RELATIONSHIPS: A KEY TO UNDERSTANDING AND PREDICTION

EPA Science Inventory

Consideration of Reaction Intermediates in Structure- Activity Relationships: A Key to Understanding and Prediction

A structure-activity relationship (SAR) represents an empirical means for generalizing chemical information relative to biological activity, and is frequent...

Study on predictive role of AR and EGFR family genes with response to neoadjuvant chemotherapy in locally advanced breast cancer in Indian women.

PubMed

Singh, L C; Chakraborty, Anurupa; Mishra, Ashwani K; Devi, Thoudam Regina; Sugandhi, Nidhi; Chintamani, Chintamani; Bhatnagar, Dinesh; Kapur, Sujala; Saxena, Sunita

2012-06-01

Locally advanced breast cancer (LABC) remains a clinical challenge as the majority of patients with this diagnosis develop distant metastases despite appropriate therapy. We analyzed expression of steroid and growth hormone receptor genes as well as gene associated with metabolism of chemotherapeutic drugs in locally advanced breast cancer before and after neoadjuvant chemotherapy (NACT) to study whether there is a change in gene expression induced by chemotherapy and whether such changes are associated with tumor response or non-response. Fifty patients were included with locally advanced breast cancer treated with cyclophosphamide, adriamycin, 5-fluorouracil (CAF)-based neoadjuvant chemotherapy before surgery. Total RNA was extracted from 50 match samples of pre- and post-NACT tumor tissues. RNA expression levels of epidermal growth factor receptor family genes including EGFR, ERBB2, ERBB3, androgen receptor (AR), and multidrug-resistance gene 1 (MDR1) were determined by quantitative real-time reverse transcriptase-polymerase chain reaction. Responders show significantly high levels of pre-NACT AR gene expression (P = 0.016), which reduces following NACT (P = 0.008), and hence can serve as a useful tool for the prediction of the success of neoadjuvant chemotherapy in individual cancer patients with locally advanced breast carcinoma. Moreover, a significant post-therapeutic increase in the expression levels of EGFR and MDR1 gene in responders (P = 0.026 and P < 0.001) as well as in non-responders (P = 0.055, P = 0.001) suggests that expression of these genes changes during therapy but they do not have any impact on tumor response, whereas a post-therapeutic reduction was observed in AR in responders. This indicates an independent predictive role of AR with response to NACT.

Which nerve conduction parameters can predict spontaneous electromyographic activity in carpal tunnel syndrome?

PubMed

Chang, Chia-Wei; Lee, Wei-Ju; Liao, Yi-Chu; Chang, Ming-Hong

2013-11-01

We investigate electrodiagnostic markers to determine which parameters are the best predictors of spontaneous electromyographic (EMG) activity in carpal tunnel syndrome (CTS). We enrolled 229 patients with clinically proven and nerve conduction study (NCS)-proven CTS, as well as 100 normal control subjects. All subjects were evaluated using electrodiagnostic techniques, including median distal sensory latencies (DSLs), sensory nerve action potentials (SNAPs), distal motor latencies (DMLs), compound muscle action potentials (CMAPs), forearm median nerve conduction velocities (FMCVs) and wrist-palm motor conduction velocities (W-P MCVs). All CTS patients underwent EMG examination of the abductor pollicis brevis (APB) muscle, and the presence or absence of spontaneous EMG activities was recorded. Normal limits were determined by calculating the means ± 2 standard deviations from the control data. Associations between parameters from the NCS and EMG findings were investigated. In patients with clinically diagnosed CTS, abnormal median CMAP amplitudes were the best predictors of spontaneous activity during EMG examination (p<0.001; OR 36.58; 95% CI 15.85-84.43). If the median CMAP amplitude was ≤ 2.1 mV, the rate of occurrence of spontaneous EMG activity was >95% (positive predictive rate >95%). If the median CMAP amplitude was higher than the normal limit (>4.9 mV), the rate of no spontaneous EMG activity was >94% (negative predictive rate >94%). An abnormal SNAP amplitude was the second best predictor of spontaneous EMG activity (p<0.001; OR 4.13; 95% CI 2.16-7.90), and an abnormal FMCV was the third best predictor (p=0.01; OR 2.10; 95% CI 1.20-3.67). No other nerve conduction parameters had significant power to predict spontaneous activity upon EMG examination. The CMAP amplitudes of the APB are the most powerful predictors of the occurrence of spontaneous EMG activity. Low CMAP amplitudes are strongly associated with spontaneous activity, whereas high CMAP

Advanced Technological Education Program: 1995 Awards and Activities.

ERIC Educational Resources Information Center

National Science Foundation, Washington, DC. Directorate for Education and Human Resources.

The Advanced Technological Education (ATE) program promotes exemplary improvement in advanced technological education at the national and regional level through support of curriculum development and program improvement at the undergraduate and secondary school levels, especially for technicians being educated for the high performance workplace of…

Physical Activity Predicts Performance in an Unpracticed Bimanual Coordination Task.

PubMed

Boisgontier, Matthieu P; Serbruyns, Leen; Swinnen, Stephan P

2017-01-01

Practice of a given physical activity is known to improve the motor skills related to this activity. However, whether unrelated skills are also improved is still unclear. To test the impact of physical activity on an unpracticed motor task, 26 young adults completed the international physical activity questionnaire and performed a bimanual coordination task they had never practiced before. Results showed that higher total physical activity predicted higher performance in the bimanual task, controlling for multiple factors such as age, physical inactivity, music practice, and computer games practice. Linear mixed models allowed this effect of physical activity to be generalized to a large population of bimanual coordination conditions. This finding runs counter to the notion that generalized motor abilities do not exist and supports the existence of a "learning to learn" skill that could be improved through physical activity and that impacts performance in tasks that are not necessarily related to the practiced activity.

An integrated biochemical prediction model of all-cause mortality in patients undergoing lower extremity bypass surgery for advanced peripheral artery disease

PubMed Central

Owens, Christopher D.; Kim, Ji Min; Hevelone, Nathanael D.; Gasper, Warren J.; Belkin, Michael; Creager, Mark A.; Conte, Michael S.

2012-01-01

Background Patients with advanced peripheral artery disease (PAD) have a high prevalence of cardiovascular (CV) risk factors and shortened life expectancy. However, CV risk factors poorly predict midterm (<5 years) mortality in this population. This study was designed to test the hypothesis that baseline biochemical parameters would add clinically meaningful predictive information in patients undergoing lower extremity bypass. Methods This was a prospective cohort study of subjects with clinically advanced PAD undergoing lower extremity bypass surgery. The Cox proportional hazard was used to assess the main outcome of all-cause mortality. A clinical model was constructed with known cardiovascular risk factors and the incremental value of the addition of clinical chemistry, lipid, and a panel of 11 inflammatory parameters were investigated using c-statistic, the integrated discrimination improvement (IDI) index and Akaike information criterion (AIC). Results 225 subjects were followed for a median 893 days; IQR 539–1315 days). In this study 50 (22.22%) subjects died during the follow-up period. By life table analysis (expressed as percent surviving ± standard error), survival at 1, 2, 3, 4, and 5 years respectively was 90.5 ± 1.9%, 83.4 ± 2.5%, 77.5 ± 3.1%, 71.0 ± 3.8%, and 65.3 ± 6.5%. Compared with survivors, decedents were older, diabetic, had extant CAD, and were more likely to present with CLI as their indication for bypass surgery, P<.05. After adjustment for the above, clinical chemistry and inflammatory parameters significant for all cause mortality were albumin, HR .43 (95% CI .26–.71); P=.001, estimated glomerular filtration rate (eGFR), HR .98 (95% CI .97–.99), P=.023, high sensitivity C-reactive protein (hsCRP), HR 3.21 (95% CI 1.21–8.55), P=.019, and soluble vascular cell adhesion molecule (sVCAM), HR 1.74 (1.04–2.91), P=.034. Of all inflammatory molecules investigated, hsCRP proved most robust and representative of the integrated

An integrated biochemical prediction model of all-cause mortality in patients undergoing lower extremity bypass surgery for advanced peripheral artery disease.

PubMed

Owens, Christopher D; Kim, Ji Min; Hevelone, Nathanael D; Gasper, Warren J; Belkin, Michael; Creager, Mark A; Conte, Michael S

2012-09-01

Patients with advanced peripheral artery disease (PAD) have a high prevalence of cardiovascular (CV) risk factors and shortened life expectancy. However, CV risk factors poorly predict midterm (<5 years) mortality in this population. This study tested the hypothesis that baseline biochemical parameters would add clinically meaningful predictive information in patients undergoing lower extremity bypass operations. This was a prospective cohort study of patients with clinically advanced PAD undergoing lower extremity bypass surgery. The Cox proportional hazard model was used to assess the main outcome of all-cause mortality. A clinical model was constructed with known CV risk factors, and the incremental value of the addition of clinical chemistry, lipid assessment, and a panel of 11 inflammatory parameters was investigated using the C statistic, the integrated discrimination improvement index, and Akaike information criterion. The study monitored 225 patients for a median of 893 days (interquartile range, 539-1315 days). In this study, 50 patients (22.22%) died during the follow-up period. By life-table analysis (expressed as percent surviving ± standard error), survival at 1, 2, 3, 4, and 5 years, respectively, was 90.5% ± 1.9%, 83.4% ± 2.5%, 77.5% ± 3.1%, 71.0% ± 3.8%, and 65.3% ± 6.5%. Compared with survivors, decedents were older, diabetic, had extant coronary artery disease, and were more likely to present with critical limb ischemia as their indication for bypass surgery (P < .05). After adjustment for the above, clinical chemistry and inflammatory parameters significant (hazard ratio [95% confidence interval]) for all-cause mortality were albumin (0.43 [0.26-0.71]; P = .001), estimated glomerular filtration rate (0.98 [0.97-0.99]; P = .023), high-sensitivity C-reactive protein (hsCRP; 3.21 [1.21-8.55]; P = .019), and soluble vascular cell adhesion molecule (1.74 [1.04-2.91]; P = .034). Of the inflammatory molecules investigated, hsCRP proved most robust

Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

PubMed

Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

2018-05-17

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

Beyond Classical Information Theory: Advancing the Fundamentals for Improved Geophysical Prediction

NASA Astrophysics Data System (ADS)

Perdigão, R. A. P.; Pires, C. L.; Hall, J.; Bloeschl, G.

2016-12-01

Information Theory, in its original and quantum forms, has gradually made its way into various fields of science and engineering. From the very basic concepts of Information Entropy and Mutual Information to Transit Information, Interaction Information and respective partitioning into statistical synergy, redundancy and exclusivity, the overall theoretical foundations have matured as early as the mid XX century. In the Earth Sciences various interesting applications have been devised over the last few decades, such as the design of complex process networks of descriptive and/or inferential nature, wherein earth system processes are "nodes" and statistical relationships between them designed as information-theoretical "interactions". However, most applications still take the very early concepts along with their many caveats, especially in heavily non-Normal, non-linear and structurally changing scenarios. In order to overcome the traditional limitations of information theory and tackle elusive Earth System phenomena, we introduce a new suite of information dynamic methodologies towards a more physically consistent and information comprehensive framework. The methodological developments are then illustrated on a set of practical examples from geophysical fluid dynamics, where high-order nonlinear relationships elusive to the current non-linear information measures are aptly captured. In doing so, these advances increase the predictability of critical events such as the emergence of hyper-chaotic regimes in ocean-atmospheric dynamics and the occurrence of hydro-meteorological extremes.

Advanced composite vertical stabilizer for DC-10 transport aircraft

NASA Technical Reports Server (NTRS)

Stephens, C. O.

1979-01-01

Structural design, tooling, fabrication, and test activities are reported for a program to develop an advanced composite vertical stabilizer (CVS) for the DC 10 Commercial Transport Aircraft. Structural design details are described and the status of structural and weight analyses are reported. A structural weight reduction of 21.7% is currently predicted. Test results are discussed for sine wave stiffened shear webs containing representative of the CVS spar webs and for lightning current transfer and tests on a panel representative of the CVS skins.

Advanced Light Source Activity Report 2002

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

Duque, Theresa; Greiner, Annette; Moxon, Elizabeth

2003-06-12

This annual report of the Advanced Light Source details science highlights and facility improvements during the year. It also offers information on events sponsored by the facility, technical specifications, and staff and publication information.

PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

EPA Science Inventory

Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

Advanced 2-dimensional quantitative coronary angiographic analysis for prediction of fractional flow reserve in intermediate coronary stenoses.

PubMed

Opolski, Maksymilian P; Pregowski, Jerzy; Kruk, Mariusz; Kepka, Cezary; Staruch, Adam D; Witkowski, Adam

2014-07-01

The widespread clinical application of coronary computed tomography angiography (CCTA) has resulted in increased referral patterns of patients with intermediate coronary stenoses to invasive coronary angiography. We evaluated the application of advanced quantitative coronary angiography (A-QCA) for predicting fractional flow reserve (FFR) in intermediate coronary lesions detected on CCTA. Fifty-six patients with 66 single intermediate coronary lesions (≥ 50% to 80% stenosis) on CCTA prospectively underwent coronary angiography and FFR. A-QCA including calculation of the Poiseuille-based index defined as the ratio of lesion length to the fourth power of the minimal lumen diameter (MLD) was performed. Significant stenosis was defined as FFR ≤ 0.80. The mean FFR was 0.86 ± 0.09, and 18 lesions (27%) were functionally significant. FFR correlated with lesion length (R=-0.303, P=0.013), MLD (R=0.527, P<0.001), diameter stenosis (R=-0.404, P=0.001), minimum lumen area (MLA) (R=0.530, P<0.001), lumen stenosis (R=-0.400, P=0.001), and Poiseuille-based index (R=-0.602, P<0.001). The optimal cutoff values for MLD, MLA, diameter stenosis, and lumen stenosis were ≤ 1.3 mm, ≤ 1.5 mm, >44%, and >69%, respectively (maximum negative predictive value of 94% for MLA, maximum positive predictive value of 58% for diameter stenosis). The Poiseuille-based index was the most accurate (C statistic 0.86, sensitivity 100%, specificity 71%, positive predictive value 56%, and negative predictive value 100%) predictor of FFR ≤ 0.80, but showed the lowest interobserver agreement (intraclass correlation coefficient 0.37). A-QCA might be used to rule out significant ischemia in intermediate stenoses detected by CCTA. The diagnostic application of the Poiseuille-based angiographic index is precluded by its high interobserver variability.

Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities

NASA Astrophysics Data System (ADS)

Schemm, J. E.; Long, L.; Baxter, S.

2013-12-01

Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities Jae-Kyung E. Schemm, Lindsey Long and Stephen Baxter Climate Prediction Center, NCEP/NWS/NOAA Predictability of intraseasonal tropical storm (TS) activities is assessed using the 1999-2010 CFSv2 hindcast suite. Weekly TS activities in the CFSv2 45-day forecasts were determined using the TS detection and tracking method devised by Carmago and Zebiak (2002). The forecast periods are divided into weekly intervals for Week 1 through Week 6, and also the 30-day mean. The TS activities in those intervals are compared to the observed activities based on the NHC HURDAT and JTWC Best Track datasets. The CFSv2 45-day hindcast suite is made of forecast runs initialized at 00, 06, 12 and 18Z every day during the 1999 - 2010 period. For predictability evaluation, forecast TS activities are analyzed based on 20-member ensemble forecasts comprised of 45-day runs made during the most recent 5 days prior to the verification period. The forecast TS activities are evaluated in terms of the number of storms, genesis locations and storm tracks during the weekly periods. The CFSv2 forecasts are shown to have a fair level of skill in predicting the number of storms over the Atlantic Basin with the temporal correlation scores ranging from 0.73 for Week 1 forecasts to 0.63 for Week 6, and the average RMS errors ranging from 0.86 to 1.07 during the 1999-2010 hurricane season. Also, the forecast track density distribution and false alarm statistics are compiled using the hindcast analyses. In real-time applications of the intraseasonal TS activity forecasts, the climatological TS forecast statistics will be used to make the model bias corrections in terms of the storm counts, track distribution and removal of false alarms. An operational implementation of the weekly TS activity prediction is planned for early 2014 to provide an objective input for the CPC's Global Tropical Hazards

The use of early summer mosquito surveillance to predict late summer West Nile virus activity

USGS Publications Warehouse

Ginsberg, Howard S.; Rochlin, Ilia; Campbell, Scott R.

2010-01-01

Utility of early-season mosquito surveillance to predict West Nile virus activity in late summer was assessed in Suffolk County, NY. Dry ice-baited CDC miniature light traps paired with gravid traps were set weekly. Maximum-likelihood estimates of WNV positivity, minimum infection rates, and % positive pools were generally well correlated. However, positivity in gravid traps was not correlated with positivity in CDC light traps. The best early-season predictors of WNV activity in late summer (estimated using maximum-likelihood estimates of Culex positivity in August and September) were early date of first positive pool, low numbers of mosquitoes in July, and low numbers of mosquito species in July. These results suggest that early-season entomological samples can be used to predict WNV activity later in the summer, when most human cases are acquired. Additional research is needed to establish which surveillance variables are most predictive and to characterize the reliability of the predictions.

Investigation to advance prediction techniques of the low-speed aerodynamics of V/STOL aircraft

NASA Technical Reports Server (NTRS)

Maskew, B.; Strash, D.; Nathman, J.; Dvorak, F. A.

1985-01-01

A computer program, VSAERO, has been applied to a number of V/STOL configurations with a view to advancing prediction techniques for the low-speed aerodynamic characteristics. The program couples a low-order panel method with surface streamline calculation and integral boundary layer procedures. The panel method--which uses piecewise constant source and doublet panels-includes an iterative procedure for wake shape and models boundary layer displacement effect using the source transpiration technique. Certain improvements to a basic vortex tube jet model were installed in the code prior to evaluation. Very promising results were obtained for surface pressures near a jet issuing at 90 deg from a flat plate. A solid core model was used in the initial part of the jet with a simple entrainment model. Preliminary representation of the downstream separation zone significantly improve the correlation. The program accurately predicted the pressure distribution inside the inlet on the Grumman 698-411 design at a range of flight conditions. Furthermore, coupled viscous/potential flow calculations gave very close correlation with experimentally determined operational boundaries dictated by the onset of separation inside the inlet. Experimentally observed degradation of these operational boundaries between nacelle-alone tests and tests on the full configuration were also indicated by the calculation. Application of the program to the General Dynamics STOL fighter design were equally encouraging. Very close agreement was observed between experiment and calculation for the effects of power on pressure distribution, lift and lift curve slope.

Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function.